ISSN 1991-1696 Africa Research Journal · Editorial Board Chairperson Prof IK Craig ... A slightly...

December 2009Volume 100 No. 4www.saiee.org.za

Africa Research JournalResearch Journal of the South African Institute of Electrical Engineers

Incorporating the SAIEE Transactions

ISSN 1991-1696

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(SAIEE FOUNDED JUNE 1909 INCORPORATED DECEMBER 1909)AN OFFICIAL JOURNAL OF THE INSTITUTE

ISSN 1991-1696

President Immediate Past PresidentMr du Toit Grobler Mr Ian McKechnieVice-Presidents Deputy PresidentMr Andries Tshabalala Dr Angus HayMr Mike Cary Honorary Treasurer Mr Les James

Secretary and Head OfficeMs Gerda GeyerSouth African Institute for Electrical Engineers (SAIEE)PO Box 751253, Gardenview, 2047, South AfricaTel: (27-11) 487-3003Fax: (27-11) 487-3002E-mail: [email protected]

SAIEE AFRICA RESEARCH JOURNAL

Editorial BoardChairperson Prof IK Craig

ARJ Editor-in-Chief Prof BM Lacquet

Additional reviewers are approached as necessary ARTICLES SUBMITTED TO THE SAIEE AFRICA RESEARCH JOURNAL ARE FULLY PEER REVIEWED

PRIOR TO ACCEPTANCE FOR PUBLICATIONThe following organizations have listed SAIEE Africa Research Journal for abstraction purposes:

INSPEC (The Institution of Electrical Engineers, London); “The Engineering Index” (Engineering Information Inc.)

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purposes without reference to the SAIEE.

EDITORS AND REVIEWERSEDITOR-IN-CHIEFProf. B.M. Lacquet, Faculty of Engineering and the Built Environment, University of Witwatersrand, Johannesburg, South Africa [email protected]

MANAGING EDITORDr S. Sinha, Dept. of Electrical, Electronic & Computer Engineering, University of Pretoria, Pretoria, South Africa [email protected]

SPECIALIST EDITORSCommunications, Signal Processing:Prof. L.P. Linde, Dept. of Electrical, Electronic & Computer Engineering, University of Pretoria, Pretoria, South Africa Prof. S. Maharaj, Dept. of Electrical, Electronic & Computer Engineering, University of Pretoria, Pretoria, South Africa Dr O. Holland, Centre for Telecommunications Research, London, United Kingdom Prof. F. Takawira, School of Electrical, Electronic and Computer Engineering, University of KwaZulu-Natal, Durban, South Africa Computer, Information Systems and Software Engineering:Prof. A. van der Merwe, Meraka Institute, CSIR, Pretoria, South Africa Prof. E. Barnard, Meraka Institute, CSIR, Pretoria, South AfricaProf. B. Dwolatzky, Joburg Centre for Software Engineering, University of the Witwatersrand, Johannesburg, South AfricaControl and Automation:Dr B. Yuksel, University of Tokyo, Japan Electromagnetics and Antennas:Prof. J.H. Cloete, Dept. of Electrical and Electronic Engineering, University of Stellenbosch, South Africa Prof. T.J.O. Afullo, School of Electrical, Electronic and Computer Engineering, University of KwaZulu-Natal, Durban, South Africa Dr R. Geschke, Dept. of Electrical and Electronic Engineering, University of Stellenbosch, South Africa Electron Devices and Circuits:Prof. M. du Plessis, Dept. of Electrical, Electronic & Computer Engineering, Pre-toria, University of Pretoria, South AfricaEnergy and Power Systems:Prof. M. Delimar, Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia Engineering and Technology Management:Prof. J-H. Pretorius, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South AfricaProf. L. Pretorius, Graduate School of Technology Management (GSTM), University of Pretoria, Pretoria, South Africa

General Topics / Editors-at-large: Dr P.J. Cilliers, Hermanus Magnetic Observatory, Hermanus, South Africa Prof. M.A. van Wyk, Dept. of Electrical and Information Engineering, University of Witwatersrand, Johannesburg, South Africa

INTERNATIONAL PANEL OF REVIEWERSW. Boeck, Technical University of Munich, Munich, GermanyW.A. Brading, AustraliaProf. G. De Jager, Dept. of Electrical Engineering, University of Cape Town, Cape Town, South AfricaProf. B. Downing, Dept. of Electrical Engineering, University of Cape Town, Cape Town, South AfricaDr W. Drury, Control Techniques Ltd, United KingdomPD Evans, Dept. of Electrical, Electronic & Computer Engineering, The University of Birmingham, Birmingham, UK Prof. J.A. Ferreira, Electrical Power Processing Unit, Delft University of Technol-ogy, Delft, The NetherlandsO. Flower, University of Warwick, UK Prof. H.L. Hartnagel, Dept. of Electrical Engineering and Information Technology, Technical University of Darmstadt, Darmstadt, GermanyC.F. Landy, Engineering Systems Inc., USAD.A. Marshall, ALSTOM T&D, FranceDr M.D. McCulloch, Dept. of Engineering Science, Oxford, United KingdomProf. D.A. McNamara, University of Ottawa, Ottawa, CanadaM. Milner, Hugh MacMillan Rehabilitation Centre, CanadaProf. A. Petroianu, Dept. of Electrical Engineering, University of Cape Town, Cape Town, South AfricaProf. K.F. Poole, Holcombe Dept. of Electrical and Computer Engineering, Clemson University, United States of AmericaProf. J.P. Reynders, Dept. of Electrical & Information Engineering, University of the Witwatersrand, Johannesburg, South AfricaI.S. Shaw, University of Johannesburg, SAH.W. van der Broeck, Phillips Forschungslabor Aachen, GermanyProf. P.W. van der Walt, University of Stellenbosch, Stellenbosch, South AfricaProf. J.D. van Wyk, Dept. of Electrical and Computer Engineering, Virginia Tech, United States of AmericaRT Waters, UKTJ Williams, Purdue University, USA

Additional reviewers are approached as necessary

Published bySAIEE Publications (Pty) Ltd, PO Box 751253, Gardenview, 2047, Tel. (27-11) 487-3003, Fax. (27-11) 487-3002, E-mail: [email protected]

This journal publishes research, survey and expository contributions in the field of electrical, electronics, computer, information and communications engineering. Articles may be of a theoretical or applied nature, must be novel and

must not have been published elsewhere.

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• Papers: Presentation of significant research and development and/or novel applications in electrical, electronic, computer, information or communications engineering.

• Research and Development Notes: Brief technical contributions, technical comments on published papers or on electrical engineering topics.

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The Managing Editor, SAIEE Africa Research Journal, PO Box 751253, Gardenview 2047, South Africa.

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• TITLE in capitals, not underlined.• Author name(s): First name(s) or initials, surname (without academic title or preposition ‘by’)• Abstract, in single spacing, not exceeding 20 lines.• List of references (references to published literature should be cited in the text using Arabic numerals in

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SAIEE AFRICA RESEARCH JOURNAL – NOTES FOR AUTHORS

Vol.100(4) December 2009 SOUTH AFRICAN INSTITUTE OF ELECTRICAL ENGINEERS 89

VOL 100 No 4December 2009

SAIEE Africa Research Journal

SAIEE AFRICA RESEARCH JOURNAL EDITORIAL STAFF ...................... IFC

Object-oriented embedded C

by M. Neser and G. van Schoor ................................................................. 90

Development of a spoken language identification system for

South African languages

by M. Peché, M.H. Davel and E. Barnard ..................................................97

On the parametric curves for the design, performance

optimization and characterization of the LPDA Antenna

by L.I. Onwuegbuna and A.A. Jimoh ........................................................104

NOTES FOR AUTHORS ...................................................................................IBC

Vol.100(4) December 2009SOUTH AFRICAN INSTITUTE OF ELECTRICAL ENGINEERS90

OBJECT-ORIENTED EMBEDDED C

M. Neser* and G. van Schoor*

* School of Electrical, Electronic and Computer Engineering, North-West University, South Africa, Email: [email protected] / [email protected]

Abstract: This paper presents an object-oriented implementation of ANSI-C for embedded systems. It offers practical guidelines for producing generic software libraries and portable applications. While various object-oriented implementations of C is available, the aim is to impress a culture of producing safe, robust embedded software which can easily be shared and integrated amongst developers and systems. Starting from coding standards, a design philosophy is proposed for creating reusable drivers, services, applications and finally, a complete real-time operating system.

Key words: modular design, coding standards, real-time, operating system

1. INTRODUCTION

Effective software engineering requires the ability to rapidly produce and integrate robust and secure code. The key to good software development lies in modular design, testing and code reuse. This is strongly supported by the Unified Modelling Language (UML) design philosophy [1]. By encapsulating functionality with clear interfaces, modules can be identified which can be re-implemented within single applications or across projects. In the object-oriented environment, modules are typically defined as classes consisting of attributes and behaviours in the form of member variables and methods. In embedded systems, object-oriented design is frequently hampered by intensive platform dependent hardware interfacing and real-time performance requirements. In addition, C, which is still the most popular programming language for embedded applications, is not inherently object-oriented. An object-oriented implementation of ANSI-C is proposed in this paper, which follows the UML approach for embedded real-time applications. It promotes code reusability through the development of platform independent modules for high-level functionality, but also allows for object-oriented hardware drivers and interrupt handling. In addition it allows for seamless integration with legacy code and supplied software libraries. Another important aspect of code reusability is maintainability. While design documentation and code comments are of high importance, existing code is often discarded because it is poorly written and incomprehensible. Well-written code should be self-explanatory. When code is shared among developers, code familiarity can be instilled through coding standards and design patterns ranging from commenting styles to driver interfacing protocols. This can drastically improve the understanding and reusability of code.

After a brief discussion of coding standards, the object-oriented implementation of C is presented. This is followed by design patterns for some common functionality in embedded application development. Note that a working knowledge of embedded software development in ANSI-C and object-oriented programming is required.

2. MODULAR DESIGN

In accordance with the UML philosophy, good software follows a hierarchical architectural design [2]. Through hardware abstraction and good interface design, application software will never need to access hardware directly. Furthermore, through the implementation of services and protocol managers, a further layer of functional abstraction can be created, allowing application software to be completely portable between different hardware systems.

Application

Services

Common drivers

Custom drivers

Hardware

Figure 1: Layered architecture.

Drivers are task independent and can be shared among similar hardware with different applications. Common services and protocol managers can also be shared among different systems, independent of hardware and applications. This architecture is illustrated in Fig. 1. Together, the grey sections produce an operating system. Common drivers comprise the software that communicates directly with generic hardware. It includes code for setting and reading specific address registers and handling interrupts. Devices on-board micro-controllers also require drivers. While software libraries for such devices are usually available, it rarely uses a common interface and does not implement any object-oriented approach. Fortunately, this code can easily be wrapped in


classes. The goal is to compile a library including all common drivers and services to be shared among systems. The aim is further to implement a common driver interface for various different devices. This allows for the swapping of hardware and drivers with minimal modification of application software, e.g. replacing EPROM with FLASH or an UART with LAN. In most embedded systems, some hardware is application specific, e.g. discrete hardware and interrupts or custom PLD configurations. Custom drivers are implemented here. In such cases, the value of separate application software and hardware interfaces lies in the ability to upgrade the components independently. The hardware independent, application specific code forms the application layer. Code in this layer should contain no direct interfacing with hardware. However, large parts of application specific software can often be reused. Typically, code used for communicating between processors may be duplicated on the two sides of the link, creating a communication protocol. This common code can be extracted from the application software and included in the library as a common service. The required drivers and services from the library can be combined to form a hardware interface. The hardware will require custom configuration to create a hardware specific operating system. Through proper design, an application independent hardware interface can thus be constructed. By making use of common design patterns, this interface can be made generic. This allows the application software to be hardware independent. Depending on the purpose of the system, the applications will however have some minimum functional hardware requirements from the operating system. Embedded systems are often event driven. Certain tasks are only executed if specific events occur. External interrupts generate the non-deterministic events used to trigger the associated tasks. Interrupts are asynchronous and may originate from an internal or external source, and from hardware or software. These are all handled by the interrupt manager. A scheduler can be used to dispatch deterministic repetitive tasks. With the use of a single timer, the scheduler can generate multiple events at different frequencies, which can be used to trigger synchronous tasks. The notion of foreground vs. background processing is frequently used in embedded software design and distinguishes between the main execution loop and the various event triggered threads. Definition of these terminologies differs amongst users and therefore, this reference will not be used here. While it is possible that all processing can be done within event-triggered threads, this is not good practice. To manage time-critical tasks and task prioritization in systems with asynchronously occurring events, interrupt

triggered threads should be kept as short as possible. Only time-critical processing, such as loading values to and from hardware interfaces, should be done in these routines. The main thread should handle all other non-critical housekeeping. Fig. 2 illustrates how the main thread could execute various tasks and different event triggered threads. This ensures that when the processor gets temporarily heavily loaded with concurrent interrupts, the non-critical tasks will get delayed to when processing is less congested. Furthermore, embedded systems usually have repetitive behaviour. After initialization, the main thread typically enters into an infinite loop. The loop produces the system behaviour, which may be required to change upon meeting certain conditions. These conditions could be detected through continuous polling or through interrupts. In either case, sections of code will need to be included or excluded from the loop. It is generally possible to identify different modes with distinct behaviour. Complex systems may require switching back and forth between modes based on internal or external conditions. In addition, mode changes often require special treatment like the starting and stopping of processes.

Figure 2: Main and event triggered threads.

The implementation of these requirements fits neatly into the modular design philosophy. Every mode can be defined by a class, which contains functions for normal behaviour as well as mode transitions. These functions can then be appropriately invoked as conditions change. In an object-oriented language, it is advantageous to let all the modes inherit from the same generic mode interface. Mode changes can then be managed centrally by a mode controller. Generally, there is a great deal of code that has to be executed independent of the current mode. The mode controller can execute this common code by default. Different instantiated modes can be registered at the mode controller. The main thread enters a loop, which executes mode behaviour and checks for conditions that may require mode changes. Upon detection, it calls the appropriate mode transition code and changes the current mode. Following this, it continues the loop, executing the new mode behaviour. Conditions, which are checked for


mode changes, may also depend on the current mode and should be derived from a mode diagram in the system design. In embedded applications, the main thread typically contains an unconditional loop. Any other loops should be deterministic. Tasks such as algorithm computation can utilise for loops. When processing serial input buffers where data can be processed faster than it can be received, completion is guaranteed and a while-loop may be used. Using open-ended loops are prohibited. When polling for non-deterministic conditions, the possibility exists that the processor can get trapped in the loop. One option is to implement a time-out counter, limiting the repetitions of the loop. Often deterministic idle loops are implemented to pause between consecutive condition testing. This is strongly discouraged and for extended time periods it is prohibited, wasting useful processing time. As an alternative, the start of a waiting state can be time stamped and the conditions (including a possible timeout) can be polled once on every pass of the loop in the main thread. This allows for processing of other tasks while waiting.

3. CODING STANDARDS

An extremely effective way of improving code readability is by setting coding standards. This consists of cosmetic rules, token formats, common function prototypes and standard code structures [3]. Whereas code cosmetics give a familiar look-and-feel to the code, predefined types and variable name assignment formats can help readers anticipate compiler interpretations and processor actions. Finally, common function prototypes and interface rules like: “Size is always defined in bytes”, makes their usage much more intuitive. Table 1 contains a list of potential C coding standards. These rules can be customized to preferences. It is only important that developer alliances will share the same rules.

Table 1: Coding standards.

Issue Standard Reason File headers

predefined templates define header filenames

Ensure inclusion of author, use, date etc.

Marcos highly discouraged Complicates single step debugging

Tabs 4 bank spaces Prevent misalignment when changing editors

Blocks for/if/else/while always followed by in next line; code indented

Prevent assumption of code execution. Readability

Case clause

code indented under case, end with break

Prevent unintended code execution.

Assign-ment

space before & after =, no nested assignments

Readability

The use of common definitions can drastically improve code readability and standardization. However, they should be kept simple and limited in number to ensure that their definitions are well known to their users. Definitions, which are supposed to be shared by all code, are declared in a common header file in Sample 1.This should be included in other files by default.

Sample 1: Generic header predef.h

/* Default predefined types and prototypes */ /* Author: M. Neser Version 1.0 */ #ifndef predef_h #define predef_h #define class typedef struct #define vol volatile unsigned char #define TASK void (*)(void*) #define INTERFACE int (*)(void*, int, int, void*) typedef enum boolean FALSE = 0, TRUE = 1 boolean; #endif

This file demonstrates a simple file header template in its first two lines. This is followed by a conditional block that ends at the last line. This block is included only if the file name is undefined. In this block, the first token defines the file name. This scheme prevents the compiler from attempting to re-include the definition. All header files should implement this scheme. This is followed by a set of common definitions which will be clarified on use in the next section. Finally, a new type ‘boolean’ is defined for general condition assignment. Using this type improves readability of arguments. By following basic naming conventions, tokens can bear additional information. The most widely used standard is Hungarian notation in which variable names are prefixed with key characters signifying their type [4]. A basic list of type keys is given in Table 2. This list can be freely extended to more types.

Table 2: Hungarian notation.

Key Interpretation Description c char 8 bit integer i integer 16 bit integer f float 16 bit real value d double 32 bit real value b boolean (custom) TRUE = 1, FALSE = 0 v volatile (custom) volatile unsigned char u unsigned modifier (prefix) p pointer modifier (prefix) a array modifier (prefix)

This notion can be extended beyond variable names and other token types can be distinguished through similar conventions. Examples of typical C token types and possible naming conventions are listed in Table 3.


Table 3: Token identification.

Type Token Predef values All caps, words separated by underscore Predef types Type appended with _t, _st etc. Class names Capital C followed by name, capitalized

1st letter for every word, no underscore Variables Hungarian notation followed by upper

case 1st letter for every word, no underscore

Methods Capitalized 1st letter for every word, no underscore

Appending predefined types with an underscore and a postfix is in accordance with standard predefined C types like size_t, which is used to create code with target dependent variable types.

4. IMPLEMENTING CLASSES

The importance of object-oriented programming has been stated repeatedly for advanced software design [5], as seen in the UML development paradigm. In embedded software design, object-oriented programming is especially useful for the instantiation of multiple drivers for duplicated devices and data channels [6]. Real-time embedded software, however, holds a unique set of challenges for hardware interfacing and interrupt handling. While standard C is the programming language of choice for embedded systems, it relies on global variables and lacks classes and inheritance [7], which hinders the object-oriented programming philosophy. The remainder of the paper describes how a company named Kreon Technology has successfully implemented the object-oriented philosophy for embedded software development with ANSI-C. As far as possible, newly introduced conventions attempt to emulate the JavaTM syntax and event model [8]. An object consists of data in a set of member variables as well as a set of methods, which is relevant to the data. Objects and classes are not a standard part of C, but they can be emulated with structures. Methods can be implemented with the use of function pointers. C classes are therefore defined as structures with member variables as well as function pointers to all the methods. Normally, a constructor function is used to initialise member variables after instantiation of an object. In this case, the constructor also assigns all other method addresses to the object’s function pointers. In effect, this results in dynamic method binding. The constructor function is named to emulate the JavaTM new operator. Neither C nor C++ allows common definition and declaration of classes in one file as is the case with JavaTM. As a result the :: operator for method declaration in C++ is emulated with an underscore [7].

Class definitions and declarations are done in separate respective header and C files. These files are named according to the class names. It is suggested that the file containing the main function be named ‘main.c’. This file could be made quite generic, instantiating and constructing an application class and calling its execution method. Since the methods of a class instance normally require access to its related member variables, a pointer to the class instance accompanies every function call. This pointer is referred to as the this pointer and can be accessed from within the method as such. By default every method call will pass the this pointer as its first parameter. A device driver provides an interface to hardware through input or output functions. Generally, data can be read from or written to a device. By standardizing this interface, changing hardware e.g. from UART to USB, can be done with minimal code changes. An illustrative device driver class that gives the driver interface definition is presented in Sample 2. The definition starts with the standard file header, default inclusions and header file definition check block. This is followed by the definition of two utility structures, the context and the device structure. Their functions are discussed below. Following these structures is the actual class definition. For every method of the class, a compatible function pointer is added to the class definition. This class contains one function pointer to a sample method and one member variable namely the device structure pointer. In practice, the class will contain various function pointers and member variables. The method shown in the sample follows a predefined prototype. Read and Write are reserved interfacing method names that follow a strict design pattern. It extends the conventional C read and write function prototypes with buffer pointer, data size and data count [7]. The parameter list is prefixed with the this pointer and appended with a context structure pointer. These two pointers’ types are class dependent. The context structure is used to pass any additional information to the methods. If no additional information is required, this is replaced with a void pointer to be consistent with the pattern. Following this design pattern, services can be made independent of the underlying drivers. Driver methods can be dynamically assigned to generic function pointers in the services. By passing the driver methods and context to the service from an external source, the services can interface with the drivers generically. The driver uses the device structure pointer to access the device hardware registers. By initializing it with the devices offset in memory, the structure forms a memory


map to all the devices’ mapped registers. Device structure members are defined as volatile. The class definition is followed by the constructor prototype. This is the only method of the class that is defined outside of the class definition and can be accessed directly. The device offset is passed to the driver as a parameter, making the driver independent of the system memory map. Sample 3 gives an implementation template of the driver. The standard file header is followed by inclusion of the header file. The methods defined in the class definition are then implemented. In this case, the function names emulate the C++ syntax [8]. Finally, the class constructor implements all once-off initialization. Most importantly, the function pointers need to be initialized to the appropriate methods. Any further object initialization is also done here. A parameter, which holds the memory

Sample 2: Header of device.h

/* Demo device driver interface definition */ /* Author: M. Neser Version 1.0 */ #include “predef.h” #ifndef driver_h #define driver_h /* additional info for read/write function calls */ typedef struct unsigned int uiOffset; ctx_st; /* map device with config byte and 8 data bytes */ typedef struct vol vConfig; vol avData[8]; dev_st; /* device driver class definition */ class CDriver /* function pointer prototypes */ size_t (*Write)(struct CDriver *this, void *pBuffer, size_t uiSize, size_t uiCount, ctx_st *stContext); /* device hardware pointer */ dev_st *pstDev; CDriver; /* class constructor prototype */ boolean new_CDriver(CDriver *this, dev_st *pstDev); #endif /* driver_h */

offset of the device, is typically passed. Anything that might be required to be reinitialized should rather be done in a separate setup method. Both internal and external devices are commonly used to generate interrupts, which are used to invoke associated interrupt service routines.

In the object-oriented paradigm, using device driver classes, event handler methods are implemented to handle associated interrupts. These method calls require passing the this pointer. This is especially important to distinguish between multiple driver instances, e.g. for duplicate UARTs, where the same method is called for different devices. To handle this requirement in a modular fashion, an interrupt manager is introduced, which forms part of the set of operating system services.

5. ADVANCED SERVICES

Four services are proposed in this section. The first is the interrupt manager mentioned in the previous paragraph. This service is processor specific and is of great importance for real-time systems. This is followed by two

Sample 3: Implementation of driver.c

/* Demo device driver implementation */ /* Author: M. Neser Version 1.0 */ #include “driver.h” /* Write method implementation */ size_t CDriver_Write( CDriver *this, void *pBuffer, size_t uiSize, size_t uiCount, ctx_st *pstContext) /* implementation... */ return uiCount; /* constructor implementation */ boolean new_CDriver(CDriver *this, dev_st *pstDev) /* method binding */ this->Write = CDriver_write; /* member initialization */ this->pstDev = pstDev; return TRUE;

processor independent services, the scheduler and the mode controller, as mentioned in Section 2. Finally, a thread manager is proposed for creating a cooperative multi-threading operating system. The interrupt manager is responsible for calling the appropriate event handing methods on the occurrence of interrupts. The manager should contain interrupt driven service routines for all the interrupt vectors and must be able to uniquely distinguish between the sources of multiplexed interrupts. Borrowing from the JavaTM event model, event listeners can be registered in association with different interrupt sources [8]. Event listeners consist of pointers to the event handling objects and methods. On identification of an interrupt source, the interrupt manager calls the corresponding registered method. This allows for


multiple instances of the same driver to be declared and used independently for duplicate devices. Services are built on the concept of registering event listeners and tasks. Objects and methods can be generally linked to services, creating an independent functional layer. As is the case with JavaTM, a registration method is used to pass the event listener parameters to the services. Event handling methods have to be cast to a predefined method pointer prototype, which can be used by the interrupt manager. One important function of an operating system is that of task scheduling. Often repetitive tasks such as status message generation and sampling need to be scheduled at fixed intervals. A scheduler can be used to dispatch tasks at different frequencies using a single timer. The scheduler is invoked at the maximum required frequency for scheduled events. Tasks are registered to the scheduler with their respective periods. A registration method is used to pass all the required parameters. Counters are assigned to the tasks and decremented every time the scheduler is invoked. When a counter reaches zero, the scheduler calls the associated task method. A mode controller can be implemented as an independent service in a similar way. By implementing every mode's methods in a separate class, each mode can be registered at the mode controller in a common manner. The mode controller typically enters an unconditional loop where it checks for mode change requests and accordingly, calls the mode’s processing method or the requested mode’s entry method. Incorporating pre-emptive multi-threading in an operating system is an ambitious task. Although it might simplify software design, it is not essential for producing efficient and well-written code. That said, it is possible to realise cooperative multi-threading in the object-oriented paradigm using thread objects. Each thread object contains its own software stack and a method to instigate a context switch. This method suspends the calling thread and resumes another. Threads are registered at a kernel class during construction, which is responsible for context switching. This typically involves intricate processor specific machine code, which is outside the scope of this paper. Pre-emptive multi-threading can be made possible by invoking context switches from scheduled time-slicing events [9]. As depicted in Fig. 1, a final notion is to combine all the implemented services and drivers into a system class to create a complete operating system. The system class is responsible for the interconnected construction and initialization of all these components. Sample 4 demonstrates how a timer and different services are constructed and a task is set up.

Sample 4: Code from system.c

void new_CSystem(CSystem *this) new_CInterrupt(&this->interrupt); new_CScheduler(&this->scheduler); new_CTimer0(&this->aTimer[0], (timer_dev*),TIMER0); this->timer.Setup(&this->aTimer[0],1000); this->interrupt.RegTask(&this->interrupt, &this->scheduler, (TASK)(this->scheduler.Process), TIMER0_INT)

6. CONCLUDING REMARKS

It is good object-oriented practice to restrict access to member variables from outside an object. This is especially true for changing values – only the object itself should be allowed to modify its variables. To update member variables of another object, new values should be passed via access methods of that object, allowing the object to handle any potential consequences of the change. GetVariable and SetVariable are common design pattern names for such access methods. For passing larger structures, the Read and Write design patterns can be utilised [8]. Various other regulations may also be implemented to improve determinism in the execution of software. These may range from enforcing single function exit points for improving traceability, to the prohibiting of dynamic memory allocation; depending on the implied burden on the CPU and RAM (dynamic memory allocation is considered a memory leakage risk and is therefore, not recommended in application specific systems). The paper shows that the object-oriented philosophy can be followed in ANSI-C for all levels of embedded software development. Furthermore, the overheads accompanying this approach involve limited machine instructions, one additional pointer per function call on the stack and one pointer in memory for every object method. The cost of software development for embedded systems often overshadows all other costs [2]. Since object-oriented programming can greatly reduce development time, the cost savings from implementing this approach usually outweighs the cost on resources, even if it implies the utilisation of more expensive hardware.

7. ACKNOWLEDGEMENTS

The authors would like to thank Kreon Technology Ltd. for its support and in particular, Mr. L. van Tonder ([email protected]) for his contribution to the standards proposed in this paper.


8. REFERENCES

[1] B.P. Douglass: “Real Time UML Workshop for Embedded Systems”, Newnes, 2006

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DEVELOPMENT OF A SPOKEN LANGUAGE IDENTIFICATIONSYSTEM FOR SOUTH AFRICAN LANGUAGES

M. Peche∗ , M.H. Davel† and E. Barnard ‡

∗ Department of Electrical, Electronic and Computer Engineering, University of Pretoria. E-mail:[email protected]† HLT Research Group, Meraka Institute, CSIR. E-mail: [email protected]‡ HLT Research Group, Meraka Institute, CSIR. E-mail: [email protected]

Abstract: This article introduces the first Spoken Language Identification system developed todistinguish among all eleven of South Africa’s official languages. The PPR-LM (Parallel PhonemeRecognition followed by Language Modeling) architecture is implemented, and techniques such asphoneme frequency filtering, which aims to utilize the available training data to maximum efficiency, areutilized. The system performs reasonably well, achieving an overall accuracy of 71.72% on test samplesof three to ten seconds in length. This accuracy improves when the predicted results are combined intolanguage families, reaching an overall accuracy of 82.39%

Key words: Spoken Language Identification, Parallel Phoneme Recognition followed by LanguageModeling, South African languages.

1. INTRODUCTION

Spoken Language Identification (S-LID) is a processwhereby the most probable language of a segment of audiospeech is determined. This choice is made from a setof possible target languages, be it a closed set whereall possibilities are known or an open set with unknownlanguages included in the test corpora as well. S-LID isa difficult task since the process to identify and extractmeaningful tokens (from the audio) upon which a decisioncan be made is itself prone to errors.

This article describes the process followed to create anS-LID system that is able to distinguish among all elevenofficial languages of South Africa. The accuracy of thesystem, as well as techniques used to improve on initialbaseline performance are reported. In addition, the effectof clearly defined language families on the accuracy of thesystem is investigated.

The article is structured as follows: Section 2 providesan overview of the background to the S-LID task andthe specific challenges encountered in the South Africanenvironment. A short description of the general systemdesign and data sets used follows in Section 3. Section4 discusses the research approach, Section 5 details theresults obtained on a per language basis and Section 6investigates the role of language families. The article isconcluded in Section 7 with some suggestions for furtheroptimization.

2. BACKGROUND

In this section, the background to the S-LID task isdiscussed in greater detail, specifically focusing on thefollowing areas: Section 2.1 provides a brief overviewof the S-LID task; variables that influence the accuracyof S-LID systems is examined in Section 2.2 with the

formulas to measure the system performance given inSection 2.3; and Section 2.4 takes a more in-depth look atthe Parallel Phoneme Recognition followed by LanguageModeling (PPR-LM) approach to S-LID. Furthermore,background is also provided on the official languages ofSouth Africa (in Section 2.5) and possibilities for a SouthAfrican S-LID system are examined in Section 2.6.

2.1 S-LID task overview

A segment of audio speech has several features that candiffer from language to language. These may be usedin different S-LID system designs, each with varyingcomplexities and results. Prosodic information, whichincludes factors such as rhythm and intonation, wasan early focus of S-LID research [3], as was spectralinformation [11], which characterizes an utterance in termsof its spectral content. Other approaches include referencesounds [5] and other raw waveform features [4]. Thesefeatures represent a low level of linguistic knowledge(limited or no language-specific information is required),and the systems utilizing these features are typically simplein design.

In addition, systems that use language-specific information(such as syntax or semantics) have been developed, andit has been conjectured that there is a correlation withthe level of knowledge presented within the extractedfeatures and the results of the system utilizing thosefeatures [9]. However, systems with a higher linguisticknowledge representation have also proven more difficultto design, requiring a more complex architecture andgreater computational power. This in turn implies thatmore accurate systems require more labeled trainingdata. Therefore most researchers prefer to utilize acousticresources [12], especially phonetic tokens, as is thecase with the PPR-LM approach, which is discussedlater. Variations of the PPR-LM approach provide


state-of-the-art accuracies and require no additionallinguistic knowledge apart from what can be deduced fromlarge labeled audio corpora.

2.2 Variables that influence S-LID accuracy

Related work has identified a number of factors which playan important role in the classification of a complex syntaxthat has been constructed from a set of distinct tokens, suchas natural language. These factors include the following:

• The number of tokens available in a sample used fortesting [12].

• The amount of available training data [8].

• The classification algorithm [7].

• The level of similarity of the target languages [2].

The composition of the target languages is of particularimportance in this article, as it is much harder todistinguish between related languages such as isiZulu andisiXhosa than between two unrelated languages such asisiZulu and English [2]. The number of target languagesalso has a significant influence on overall system accuracy,as researchers have been able to achieve much betterresults using language-pair recognition than trying torecognize between several languages at once [11]. The useof an open test set instead of a closed test set also has anegative effect on system accuracy, and complicates thedesign of the system as a whole.

Current benchmark results are established by the NationalInstitute of Standards and Technology (NIST) LanguageRecognition Evaluation (LRE) [12]. Initially startedin 1996, the next evaluation was in 2003, after whichthe evaluation has been repeated every two years.The NIST-LRE measures system achievements basedon pair-wise language recognition performance. Ascore according to the probability that the systemincorrectly classifies an audio segment is calculated foreach target-non-target language pair. The average ofthese scores (CAVG) then represents the final systemperformance.

Figure 1 expresses the historic scores achieved acrossthree different test sample lengths. Note how the longersegments outperform those of a much shorter length.

2.3 Measuring the system performance

The system developed for this article does not functionon a language-pair basis, and therefore this specificmeasurement will not be used for the remainder of thearticle. Instead, the performance of the system as awhole is measured by evaluating both the front-end andthe back-end separately.

The Automatic Speech Recognition (ASR) systems usedfor phoneme recognition in the front-end are evaluated

Figure 1: Historic scores for the 1996 - 2009 NIST LRE in thegeneral language recognition task. Image has been reproduced

from [12].

using both the phoneme recognition accuracy andphoneme correctness. Accuracy and correctness aredefined as follows:

accuracy =N−D−S− I

N∗100% (1)

correctness =N−D−S

N∗100% (2)

where N is the total number of labels, D is the number ofdeletion errors, S is the number of substitution errors, andI is the number of insertion errors.

As for the back-end classifiers, the overall accuracy ofthe S-LID system, as well as the precision and recall foreach language is reported. The overall accuracy is simplythe percentage of all utterances correctly identified by theclassifier. Precision and recall scores of a specific languagel are defined as follows:

precision =

(

lcorrectlcorrect + lincorrect

)

∗100% (3)

recall =

(

lcorrectlcorrect +Oincorrect

)

∗100% (4)

where lcorrect is the number of utterances correctlyclassified, and lincorrect and Oincorrect represents the numberof false accepts and false rejects respectively.

2.4 The PPR-LM approach to S-LID

PPR-LM is an S-LID system configuration whereby theaudio data is first processed into a phoneme string beforethe actual classification is performed [8]. The firststep (referred to as the front-end) extracts phonotacticinformation in the form of phonemic tokens from the audiosignal. The resulting string of tokens is then passed toa back-end where some form of language modeling (forexample n-grams) is used to determine the most probablelanguage from the set of target languages [6].


Figure 2: Visual representation of the PPR-LM architecture.

The front-end usually consists of the phonemicrecognizer modules of a number of ASR systems,as the above-mentioned tokens are commonlylanguage-dependent phonemes. Although PPR-LMsystems can function with a tokenizer trained in only oneof the target languages [11], tokenizers for several of thetarget languages that process the audio in parallel seemto be the most accurate system configuratio [7]. In sucha case, the typical PPR-LM system usually utilizes onetokenizer for each of the target languages.

Once the audio signal has been processed by the front-end,the resulting token strings are scored by a classifie in theback-end and the language with the highest probabilityscore is returned. Language models are usually employedto distinguish among languages, although the use of aSupport Vector Machine (SVM) [6] has proved to be moresuccessful [7]. The SVM requires that n-gram frequenciesare extracted from the phoneme strings and represented asa point in a high-dimensional vector-space. Languages arethen represented as groups of vectors.

Figure 2 provides a visual representation of the PPR-LMarchitecture. An utterance given as input to the systemis passed to three ASR systems (English, French andPortuguese phoneme recognizers in the image) thattogether form the front-end of the system. These ASRsystems produce phoneme strings which are then passedas a vector of biphone frequencies to the language modelat the back-end (an SVM classifie in the image) whichthen predicts the language spoken in the utterance.

2.5 Languages of South Africa

Since 1994, South Africa has recognized eleven officialanguages. These are listed in Table 1, along with eachlanguage’s international ISO language code, the numberof native speakers (in millions) and the language family towhich it belongs. As can be seen from Table 1, severalof South Africa’s officia languages do not have a largespeaker population, which makes the gathering of audioresources difficult

Of particular importance to this article is the languagefamilies. These families represent languages whichexhibit similarities with regard to grammar, vocabularyand pronunciation. The Germanic languages are ofIndo-European origin, but both the other two major

Table 1: South Africa’s eleven officia languages [16].

Language ISO Native Languagecode speakers family

isiZulu zul 10.7 NguniisiXhosa xho 7.9 NguniAfrikaans afr 6.0 GermanicSepedi nso 4.2 Sotho-TswanaSetswana tsn 3.7 Sotho-TswanaSesotho sot 3.6 Sotho-TswanaSA English eng 3.6 GermanicXitsonga tso 2.0 Tswa-RongaSiswati ssw 1.2 NguniTshivenda ven 1.0 VendaisiNdebele nbl 0.7 Nguni

families, the Nguni and the Sotho-Tswana families,represent two of the major branches of the Southern Bantulanguages which originated in Central to Southern Africa.Tswa-Ronga and Venda are also classifie as being partof the Southern Bantu languages, though they fall intofamilies of their own. [16]

It should be noted that though Afrikaans and English areboth Germanic languages, they are subdivided into LowFranconian and Anglo-Frisian respectively, and will betreated as different families later in the article.

2.6 S-LID for South African languages

Currently, there is no existing S-LID system that candistinguish among all eleven of South Africa’s officialanguages. Prior work has been done in the related fielof Textual Language Identificatio (T-LID) which hasshown promise [2]. However, T-LID has already achievedimpressive results on relative small test sets as early as1994, and is therefore considered to be mostly a solvedproblem [13].

On the other hand, developing a S-LID system todistinguish among all South African languages will proveto be quite a challenge. Referring to Section 2.1, thisenvironment poses the following two main difficulties

• Eleven target languages, and

• Closely related language families.


Another important obstacle which has to be overcome isthe availability of high quality audio data. The linguisticresources available for South African languages (suchas large annotated speech corpora) are extremely limitedin comparison to the resources available for the majorlanguages of the word. Section 3.2 gives more informationon the corpus which is used for the development ofthe current system. Figure 3 should be given particularattention, as the very short lengths of the availableutterances will also impact negatively on the system’sperformance.

3. SYSTEM DESIGN

This section describes the design of the South AfricanS-LID system. A more detailed description of the systemarchitecture follows in Section 3.1 and the corpus used todevelop the ASR recognizers and the S-LID back-end isdescribed in Section 3.2.

3.1 System architecture

The South African S-LID system implements the popularPPR-LM architecture, as described in Section 2.3. Here aswell, the phoneme recognizers of ASR systems are used astokenizers to extract language-dependent phonemes fromthe audio signal.

These phoneme recognizers utilize context-dependentHidden Markov Models (HMMs), which consist of threeemitting states with Gaussian Mixture Models (GMMs)of seven mixtures within each state. The HMMs aretrained using the Hidden Markov Model Toolkit (HTK)[10]. 13 Mel Frequency Cepstral Coefficients (MFCCs),their 13 delta and 13 acceleration values are used asfeatures, resulting in a 39-dimensional feature vector.Cepstral Mean Normalization (CMN) as well as CepstralVariance Normalization (CVN) are used as feature-domainchannel normalization techniques. Semi-tied transformsare applied to the HMMs and a flat phone-based languagemodel is employed for phone recognition [1]. Optimalinsertion penalties are estimated by balancing insertionsand deletions during recognition.

Per training sample, biphone frequencies are extractedfrom the phoneme strings of each phoneme recognizerand formatted into a vector, with each unique biphonerepresenting a term. (Most of these terms would bezero.) The resulting vectors (one per tokenizer) are thenconcatenated one after the other to form a single vectorfor each utterance. This vector is used as an input to theSVM that serves as back-end classifier for the system. Agrid search is used to optimize the main SVM parameters(margin-error trade-off parameter and kernel width) priorto classification.

3.2 Corpus statistics

The South African S-LID system is trained and tested withthe Lwazi corpus, which consists of telephonic audio dataas well as transcriptions, collected in all eleven of South

Table 2: Number of speakers (Spkrs) and utterances (Utts)in the training and test set of each language, as well as the

duration of each set in hours.Language Set Spkrs Utts DurationAfrikaans Train 170 4382 3.32

Test 30 787 0.60SA Train 175 4287 3.25English Test 30 728 0.55isiNdebele Train 170 4878 3.69

Test 30 846 0.64isiZulu Train 171 4852 3.23

Test 29 685 0.52isiXhosa Train 180 4458 3.38

Test 30 669 0.51Sepedi Train 169 3674 2.78

Test 30 664 0.50Sesotho Train 170 4642 3.52

Test 30 815 0.62Setswana Train 176 4257 3.10

Test 30 686 0.54Siswati Train 178 4778 3.62

Test 30 811 0.62Tshivenda Train 171 4414 3.34

Test 30 770 0.58Xitsonga Train 168 4230 3.21

Test 30 755 0.57

Africa’s official languages [1]. For the development of theSouth African S-LID system, all eleven languages in thecorpus are utilized.

Table 2 provides statistics on the available data. Thenumber of speakers per language is given, as well as thecombined number of utterances and the length of the audiodata in hours. Table 2 also provides the number of speakersand associated utterances in the training set, which is usedto train both the ASR systems in the front-end and and theSVM at the back-end. As can be seen, a test set of about15% the total size of a language’s data is kept aside forvalidation purposes. Care has been taken to ensure that aspeaker is not represented in both the train and test sets.

Figure 3 displays the distribution of the different utterancelengths across all eleven languages available in the corpus.Note that a large percentage of the utterances are extremelyshort (less than 6 seconds in duration).

4. GENERAL DISCUSSION OF THE APPROACH

The first step in the development of the proposed systemis to establish baseline performance. This is done bydeveloping a system which implements the PPR-LMarchitecture, as described previously. The baseline systemutilizes phoneme recognizers from all eleven officiallanguages of South Africa. The system results are givenin Section 5.1


Figure 3: Distribution of utterance lengths in the Lwazi corpus.The number of training and test utterances are displayed

separately.

Two improvements are then implemented:

• The Lwazi corpus, as described in Section 3.2,consists of telephonic audio data which is not asclean as audio recorded under laboratory conditions.(Channel conditions, background noise and speakerdisfluencies all affect the quality of the availableaudio.) Problematic utterances can have a negativeinfluence on the overall accuracy of the system;therefore all utterances are automatically filtered inorder to refine the system [14].

• Figure 3 displays the unbalanced nature of theLwazi corpus when the lengths of the utterancesare compared. This may also have a negativeimpact on the system as a whole, especially whenit is considered that the length of the utterances isan important variable which influences the systemaccuracy (referring to Section 2.2). Therefore thetraining data is further refined, and all utterancesshorter than three seconds are removed from thetraining set.

Both the baseline system and the refined system are testedon two test sets, one with all utterances three seconds andshorter, and the other containing only utterances longerthen three seconds. Results are discussed in Section 5.2

5. INDIVIDUAL LANGUAGE CLASSIFICATION

5.1 S-LID baseline results

Section 4 describes the system which provides the baselineresults against which the experiments in the rest of thearticle are compared. Figure 4 shows that an increasingnumber of tokenizers results in an increase in performance,as have previously been shown [15]. Therefore, eventhough it utilizes significant computational resources, the

Figure 4: Overall SVM performance of all three configurationsfor the South African S-LID systems.

best performing system – with eleven tokenizers – servesas the baseline for the experiments in this article.

As predicted in Section 2.2, the longer utterances performbetter than the shorter samples, achieving an overallaccuracy of 69.89%, compared to the 56.60% achievedon the shorter utterances. Figure 5 displays the resultsgraphically, with the system performance achieved for thetest samples shorter and longer than three seconds plottedseparately.

Table 3 summarizes the performance of the South AfricanS-LID system on the test samples longer than three secondsin more detail. The accuracy and correctness of thephoneme recognizers as well as the precision and recall foreach language are also given. Note that, since the lengthof the test sample does not influence the performance ofthe phoneme recognizers, the front-end results presentedin Table 3 were generated on the entire test set.

5.2 Removing problematic utterances

In order to improve the South African S-LID system,the data used to train the SVM is filtered so that onlyutterances that have recorded a phoneme frequency ofhigher than three phonemes per second, and utteranceslonger than three seconds in length are selected. (Thespecific values of these variables were determined duringprior experimentation.) Furthermore, the number oftraining utterances available from each language is limitedto the same number in order to ensure that the SVM isnot biased towards a particular language. A new, refinedsystem is now developed, using this new subset of trainingdata and the same training process as described above.

The data set used for testing is also filtered in a similarfashion as described above, but then divided into twosubsets: one containing utterances less than three secondslong, while the other contains all the utterances longerthan three seconds. The performance of the refined system


Table 3: The performance of the ASR systems in the front-end as well as the SVM classifier in the back-end of the initialSouth African S-LID (baseline) system.

afr eng nbl nso sot tsn ssw ven tso xho zulASR Front-end

% Correctness 70.49 58.72 73.02 68.00 67.76 70.64 74.04 75.76 68.53 68.54 69.81% Accuracy 65.47 52.30 66.61 57.78 57.17 57.08 65.77 67.37 60.92 58.57 63.06

SVM Back-end% Precision 75.90 82.59 64.47 64.09 57.76 66.12 63.15 69.45 68.27 61.42 50.90% Recall 81.86 85.18 70.24 51.67 58.94 66.27 73.17 68.79 54.27 56.41 54.42

Overall system accuracy : 69.89%

Figure 5: The results of both the baseline system and the refinedsystem, with the overall system performance achieved for thetest samples shorter and longer than three seconds plotted on

separate graphs.

described above is then verified individually using both testsets. These new training and test sets are recognized by alleleven phoneme recognizers before the resulting phonemestrings are used to retrain and test the SVM classifier at theback-end.

Both sets of samples used to test the system report anincrease in performance, achieving 67.29% and 71.72%for the shorter and longer segments respectively. Figure5 also displays these results graphically, with the systemperformance achieved for the test samples shorter andlonger than three seconds plotted on separate graphs.

Table 4 describes the performance of the South AfricanS-LID system on the test samples longer than three secondsin more detail. The accuracy and correctness of thephoneme recognizers as well as the precision and recall foreach language are given. The front-end results presented inTable 3 were again generated on the entire test set.

6. INVESTIGATING THE EFFECT OF LANGUAGEFAMILIES

The S-LID system developed in Section 5 distinguishesbetween eleven languages, some of which are closelyrelated. In the light of the discussion in Section 2.2,

we are interested to know how well the system performswhen those distinctions are not attempted. (For practicalapplications, this may be acceptable, since several of thelanguages are mutually intelligible.) This was investigatedby combining the results according to language families,instead of representing the languages individually. Doingthis reduces the number of target groups the recognizerhas to distinguish between, and simplifies the underlyingrelationships. (The languages with their associatedfamilies are listed in Table 1.)

When the ambiguity of closely related target languages isremoved, the systems performance increases even more toan overall accuracy of 71.58% and 82.39% on test samplesshorter and longer than three seconds respectively. Table 5details the performance of the SVM classifier on the longerutterances for both the baseline system of Section 5.1 andthe refined system of Section 5.2.

7. CONCLUSION

The South African context provides a challengingenvironment for Spoken Language Identification, for twomain reasons: the large number of closely related targetlanguages that occur, as well as the restricted availabilityof high quality linguistic resources.

This article has described the successful developmentof an S-LID system which performs reasonably well indifferentiating among all the official languages of SouthAfrica. Through the careful implementation of elevendifferent tokenizers, corpus filtering and corpus balancinga practically usable system was developed, The finalsystem is capable of identifying the language family aswell as the exact language spoken with an accuracy of82% and 72%, respectively, for utterances of three secondsor longer. (With the longer utterances in the order of tenseconds in length.)

Further optimization of the system is currently beingconsidered. Interesting areas of research includeanalyzing the effect of language family-specific tokenizers,experimenting with longer utterances and chunking andrecombining utterances during SVM classification.


Table 4: The performance of the SVM classifier in the back-end of the refined South African S-LID system, when a testset containing only utterances longer than three seconds are used.

afr eng nbl nso sot tsn ssw ven tso xho zul% Precision 73.15 84.42 67.66 63.74 64.37 68.76 62.63 73.28 63.17 67.41 58.58% Recall 85.80 88.40 73.66 66.35 54.45 69.20 73.96 71.22 57.75 59.60 53.37


Table 5: The performance of the SVM classifier in the back-end of both the baseline and refined South African S-LIDsystems, when only language families are considered.

Afrikaans English Nguni Sotho-Tswana Tswa-Ronga VendaBaseline SA S-LID

% Correctness 75.90 82.59 84.57 85.43 68.27 69.45% Accuracy 81.86 85.18 90.38 80.97 54.27 68.79

Overall system accuracy : 81.71%Refined SA S-LID

% Precision 73.15 84.42 85.92 85.81 67.41 73.28% Recall 85.80 88.40 88,06 83.02 59.60 71.22


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[14] Marius Pech´e, Marelie Davel, and Etienne Bar-nard: “Porting a spoken language identification system to a new environment”, Proceedings: An-nual Symposium of the Pattern Recognition Associa-tion of South Africa (PRASA), Cape Town, South Africa, pp. 58-62, December 2008.

[15] M. Peché, M. Davel, and E. Barnard: “Phonotactic spoken language identification with limited train-ing data”, Proceedings: Annual Conference of the International Speech Communication Association, Antwerp, Belgium, pp. 1537-1540, Aug 2007.

[16] Pali Lehohla: “Census 2001”, Census in brief, Statistics South Africa, 2003.


ON THE PARAMETRIC CURVES FOR THE DESIGN, PERFORMANCE OPTIMIZATION AND CHARACTERIZATION OF THE LPDA ANTENNA L. I. Onwuegbuna*, MIEEE and A. A. Jimoh**, MIEEE

* Faculty of Engineering and the Built Environment, Tshwane University of Technology, Private bag X680, Pretoria South Africa E-mail: [email protected] ** Faculty of Engineering and the Built Environment, Tshwane University of Technology, Private bag X680 Pretoria South Africa E-mail: [email protected]

Abstract: This paper attempts to overcome the limitations of the parametric curves that characterize the performance of the Log Periodic Dipole Array Antenna (here after referred to as the LPDA). For instance, the parametric curves in design handbooks e.g. ARRL antenna handbook and other relevant literature e.g. Peixeiro do not contain those giving the relationship between the boom-length 'L' and the number of dipole element 'N' for any given bandwidth, even when it is known that these two parameters are the main cost determinants of a LPDA Antenna. The concept of convergence is introduced to aid cost optimization of the LPDA Antenna in terms of number of dipole element 'N'. Although 'N' is used as the minimization criterion, the criteria for establishing convergence encompass all the main electrical characteristics of the LPDA antenna, such as VSWR, gain and radiation patterns. Key words: LPDA, convergence, sparse, optimization criterion. 1. INTRODUCTION

The log periodic dipole antenna hereafter referred to as LPDA antenna was invented in 1958 by Isbell [1], of the University of Illinois, United States of America. This antenna is a system of driven elements designed to operate over a wide range of frequencies. The LPDA antenna exhibits constant electrical characteristics (i.e. fairly constant gain, pattern and VSWR etc), over the designed bandwidth [2], [3]. The basic concept guiding the operation of the LPDA antenna is that a gradually expanding periodic arrays, radiates most effectively when the array elements (dipoles) are near resonance, so that with frequency variation, the radiating or active region moves along the array. [4, 5]. Structurally speaking, the LPDA antenna consists of a sequence of dipoles with successively increasing lengths outwards from the feed point at apex as figure1 depicts. The feed lines cross over between adjacent elements, so as to give an 180º phase reversal between any two adjacent elements; this ensures that the radiated fields from the resonant elements are in phase at the far field (broadside radiation pattern) [6]. The active or radiating region moves along the structure with changing frequency. The active region is comprised of dipole elements whose wavelengths are /2 at the resonant frequency and most of the antenna currents are concentrated within this region. The remaining elements of the array are then considered as parasitic being comprised of directors and reflectors. Consequent to the actions of the directors and reflectors, the LPDA antenna is highly directional in its radiating and receiving patterns. In addition the phase reversal of currents in

adjacent elements, ultimately results in highly directive broadside beam pattern emerging from the direction of the smallest element in the array.

Figure 1: The LPDA Antenna

The behaviour of the LPDA antenna is fairly well modelled, mathematically speaking. The elements are shortened by a factor (tau) as we move towards the source of excitation, where is known as the scaling factor and is one of the key parameters of the LPDA antenna. The scale factor is defined by ,

= Ln+1/Ln = dn+1/dn = Dn+1/Dn = Xn+1/Xn. . . . (1)

(Where Dn is the diameter of the nth dipole element and Ln is the length of the nth dipole element.) The scaling factor specifies the dimensional relationship between any two adjacent dipole elements. Another important

2

X

N nnn

D

d

L

Z

12

.. ..

L


parameter of the LPDA antenna is the space constant ''(sigma), which is given by

= dn /2Ln = (1- )/4cot . . (2)

Where is the apex angle and dn is the distance of the nth dipole element from the preceding element. The number of dipole elements N, which is another parameter of the LPDA Antenna is specified in terms of both the scaling factor and spacing constant and is given by,

N = 1 + log (L1/LN)/log (1/) ... (3) Zo in Ohms is the impedance of the Antenna boom and must be specified during design. The boom-length L in meters is given as;

L = 2(L1 - LN)/ (1- ) ... (4)

where L1 and LN are the longest and the shortest dipole elements respectively. Carrel [2] published a simple design procedure for designing LPDA antennas. The only draw back to his procedure is that it neglects the effects of the boom-impedance and length to diameter ratio on the gain performance characteristics of the LPDA antennas. This though does not detract from the elegance and simplicity of this design technique. Subsequent works by De vito and Stracca [7], [8], Peixeiro [9] and others have taken advantage of the availability of computers to numerically model the LPDA antenna, thereby introducing more accurate design curves (although still limiting in scope). Despite the attractiveness and popularity of the LPDA antenna, much information is still lacking regarding its performance characteristics, hence the justification for this research. The parametric curves giving the performance characteristics of the LPDA antennas, currently available in design books and other literature are those of Carrel [2], De vito and Stracca [7], [8], and Peixeiro [9]. Each of these researchers though having essentially the same objective, which is the design of a functional LPDA antenna, placed emphasis on different aspects of the design problems. Carrel [2], one of the many pioneer researchers of the LPDA antenna, though recognising the effects of the length to diameter ratio (Ln/Dn) and the boom-impedance (Zo) on the gain performance of the LPDA antenna, took the view that these effects are negligibly small. Carrel's design curves therefore did not incorporate these effects. De vito and Stracca [7], [8], on the other hand, considered Carrel's miscalculation of the gain a major problem and therefore sought to correct it. In looking at the problem, Peixeiro [9] was of the view that not only is carrel's gain prediction overly optimistic, his neglect of the effects of the length to diameter ratio and the boom-impedance on the performance characteristics of the LPDA antenna was

unacceptable. His design curves therefore contained LPDA antenna performance characteristics for values of length to diameter ratio and boom-impedance. Butson and Thompson [10], also considered the incorrect gain values as given by Carrel [2], but differed with Peixeiro [9] as to the origin of the error. These arguments are indications that the performance characteristics of the LPDA antenna are still incomplete. The aim of this paper is to take advantage of the immense computational capability inherent in the hybrid method of moments (mom)/geometric theory of diffraction (GTD) based Super-Nec [11], combined with the graphical capabilities of Matlab, to achieve the following: Extend the range of the parametric curves beyond

those given by Peixeiro [9]. This is necessitated by the fact that the design curves as given by Peixeiro are limiting in scope as it does not cover the range of scale factor ‘’, below 0.7 and space ‘’, beyond the range of values between 0.05 and 0.22 . This limitation of the parametric curves was also investigated by Bantin and Balmain [12]. Their paper concentrated on different aspect of this problem though.

Find a means of minimising the number of element 'N' as a means of cost optimising LPDA antenna. The justification for minimising "N' is propelled by the need to keep the cost down as the number of elements and the boom-length are the main cost determinants of the LPDA antenna, De vito and Stracca [7], [8]. This minimisation criterion is referred to in this paper as convergence and it will enable the Engineer to ascertain the minimum number of elements necessary for a LPDA antenna of given length and operating bandwidth, to yield acceptable performance.

Give cognisance to the effects of boom-length (L in meters) and operating bandwidth (B) on the gain performance characteristics of LPDA antennas by presenting parametric curves in terms of these parameters.

The investigations in this paper were performed in three stages. Firstly, in order to perform computational investigation of the LPDA antenna, measured impedance and VSWR are compared with theoretical predictions of them. Favourable comparison between the two sets of values will serve as a validation of the numerical modelling tool (in this instance Super-Nec) used in obtaining the theoretical results. Secondly, once the numerical model is validated, the generalised performance characteristics of the LPDA antenna for various lengths and operating bandwidths are then presented without cognisance to whether the Antenna is convergent or not. Thirdly, trends in the variation patterns of the average and standard deviation of the gain are used, as the basis for optimising LPDA antennas in terms of the number of element (N). Thereafter, the


performance characteristics of converged LPDA antennas are presented. In all, four hundred and ten LPDA antennas were investigated and the total outcomes of this investigation are presented as ‘result’. The simulations were done such that, for any operating bandwidth, the number of frequency steps will be the same, to ensure same average values. In the same vain, for any boom-length and operating band-width, the number of element is doubled for each incremental step, starting with N = 4 where possible. This paper does not cover all varieties of Log Periodic Array Antennas, as there are an infinite number of such antennas. This paper investigates LPDA Antennas within the bandwidth of 300MHz – 3GHz, [except for one of the built models where validation of Super NEC was the main aim], with boom lengths ranging from 0,5max to 2max (where max is the wavelength at the lowest operating frequency). The variations of these parameters are limited to ranges, which are of practical interest. It is to be noted here that Super NEC [11] is not an infallible tool. It does have its own limitations and rules, which, if not well followed, can yield erroneous results. Finally, these investigations are limited to free space environment only.

2. DESCRIPTION AND VALIDATION OF NUMERICAL MODELLING TOOL (SUPERNEC)

2.1 The modelling tool The numerical modelling tool used in these investigations is Super numeric electromagnetic code version2 (SuperNec). SuperNec [11] is an enhanced version of the numeric electromagnetic code 2 by G. Pogio et al [13]. It is an object oriented program with C++ at its core. It is a hybrid tool combining the methods of moment (MOM) [14] with optical theories such as geometric theory of diffraction (GTD) for simulation of antennas and structures. SuperNec requires Matlab to run, thus it combines the immense computational and graphical capabilities inherent in Matlab with the user friendliness of object oriented based c++ environment. SuperNec models wire antennas and large structures and have rules which if not strictly adhered to, can yield embarrassing results. The modelling platform is interactive and prompts the user for key parameters of the antenna like operating band widths, characteristic impedance of the boom, space, scaling constants and length to diameter ratio. The simulated output is weighed against known performance characteristic of the antenna and a decision is then required as to accept the output or make some adjustments and simulate again. These parameter adjustment and repeated simulations is part of what this paper seeks to overcome by presenting all probable performances in form of parametric curves. In

order to simulate the large number of antennas investigated here, group of antennas with similar characteristics were run as batch files and their outputs combined and viewed in SuperNec output engine [11].

2.2 Tests on Practical LPDA Antennas Four pairs of antennas were built and measured for three antenna output parameters: impedance, voltage standing wave ratio and gain. Certain parameters of the antennas were varied such as length to diameter ratios, operating bandwidth and boom-length. Fixed boom impedance ‘Zo’ of 75 was used for all the four models. This is because the transmission line (stripe line) is lossy, resulting in average feed point resistance of about 50, which can easily be matched to a coaxial cable of the same characteristic impedance value. For each pair of antennas, dipole elements of fixed diameters were used. The antenna was fed directly using coaxial cables in all models. This involved passing 50, RG 58 coaxial cable through aluminium tubes to the smallest dipole elements of each model. The measurement was performed in a shielded anechoic chamber using HP8753C network analyser. Table 1 below, gives the simulated and measured values of the antenna models built. The simulated values are denoted with an extension ‘out’, while the measured values are denoted as ‘models’. Table1: Measured and simulated results for the six models of antenna built

A fair agreement between the predicted values from simulations and measured values is evident as can be seen in table 1 (last page). In all instances, real, imaginary impedance (and VSWR are compared for simulated and measured results. The numerical means and standard deviations of input resistance, VSWR and gain were presented as opposed to parametric curve presentation of the same parameters. The reason for this format of presentation is justified on the basis that trends in the variation of numerical values of these parameters form the criteria for determining the convergence or otherwise of a LPDA Antenna which is introduced in the

FILE NAME

REAL INPUT IMPEDANCE

IMAGINARY IMPEDANCE

G(dBi)av

MEAN STD MEAN STD C1.out

Model 1a Model

1b

52.78 51.29 54.19

8.87 7.83 9.32

-1.73 3.36 4.86

8.76 7.92 8.68

6.96 6.00 6.00

C2.out Model 2a

Model 2b

50.65 57.13 55.83

8.94 12.87 11.68

-2.34 6.13 5.73

8.37 12.26 11.49

6.96 5.82 5.82

C3.out Model 3a

Model 3b

49.50 58.69 60.00

8.81 14.43 18.05

-2.59 7.39 8.94

8.18 14.33 17.15

6.95 6.50 6.50


next section. In other to maintain a balanced presentation, one of the LPDA Antenna models designed to operate from 30MHz to 120 MHz with a boom-length of 1.5m and number of elements = 15 is presented in graphical format as can be seen in figures 2 & 3. Figure 2: Plot of real input impedance variation with frequency, L=1.5m, N=15 Figure 3: Variation of imaginary Zin with frequency N = 15,L=1.5m

The fair agreement between measured values and the theoretical prediction of the values by the modelling tool is enough validation of the modelling tool. Hence forth, it is to be assumed that the modelling tool has been validated and can then be used as the basis for extensive theoretical investigations.

3. CONVERGENT AND SPARSE LPDA ANTENNAS

The need to classify LPDA antennas arose due to the following reasons: It has been observed that some values of scale and

design constants (and by extension number of elements) for a given boom-length (L) and operating bandwidth (B), do not yield meaningful gain, VSWR and radiation pattern characteristics. That is, for these values of and , (or N) the antenna gain,

VSWR and radiation pattern characteristics are often not so good. There is therefore the need to avoid these values when designing LPDA antennas.

One of the main objectives of this paper is that the number of dipole element (N) should be the optimisation criteria. This is because, as pointed out by De Vito and Stracca [7], [8], the cost of the LPDA antenna is determined by the boom-length and the number of dipole elements. For this reason, it has become imperative to find a means of designating LPDA antennas, so that one can see at a glance, the minimum number of elements for any given Boom-length and operating bandwidth required for the antenna to yield acceptable performance characteristics. This delineation is therefore a means of minimising 'N' so as to produce a cost optimised LPDA antenna.

To achieve these objectives, LPDA antennas are designed such that every other parameter remains constant except the number of element, which are doubled in every step. Thus depending on the boom-length and operating bandwidth, the number of elements are given as N= [4 8 16 32 64 128]. The Boom-lengths are given as L = [0.5max, max, 1.5max 2max] where max is the wavelength at the lowest frequency of operation. The operating bandwidth is investigated in the range B= [1:2 1:5 1:10]. The boom-impedance Zo =75 and Ln/Dn =100. The antennas were simulated and the average and standard deviation of the gains are extracted from the simulated batch files. Trends in the patterns of variation of the average and standard deviations of the gain are used as the basis for deciding the element density, at which the antenna yields acceptable performance. Once this optimisation is done, the standard deviation of the gain has no further use and will not be referred to again. The justification for using the mean and standard deviation of the gain as the basis for determining the usefulness or otherwise of a LPDA antenna is that the mean (or average) value of a set of data (in this instance gain) is a measure of central tendency. It is a measure of the tendency of the gains of LPDA antenna to converge to a central value for variations of frequency within the operating bandwidth. The standard deviation of the gain on the other hand is a measure of dispersion of the gain values around the mean value. It gives an indication of how close a given gain value is to the mean. In looking at the trends in the variation in the standard deviation of the gain, a low standard deviation value is an indication of LPDA antenna convergence (or optimised performance). It is to be seen that once convergence is attained the STD values do not differ widely for doubling of 'N. It is also seen that for the mean gain, once convergence is attained, the mean values remain within a narrow band of values. As an example, consider a LPDA Antenna of length L = 1.5max, with operating bandwidth of 1:5. The number of

4 5 6 7 8 9 10 11 12-8

-4

0

2

6

10

Measured result Simulation

3

-6

-2

4

8

Frequency (MHz)

Imag

inar

y im

peda

nce

(Ohm

s)

4 5 6 7 8 9 10 11 12-8

-4

-2

0

4

6

8

10

Imag

inar

y im

peda

nce

(Ohm

s)

Measured result Simulation

Frequency (MHz) 3

-6

2


elements is doubled per step as 4, 8, 16, 32 and 64. Trends in the pattern of variations of the VSWR, gain and radiation patterns will be investigated with the aim of establishing the criteria for convergence or Sparse LPDA Antennas. As can be seen from figure 4 below, this antenna does not yield acceptable VSWR (1:2 or below) performance for all frequencies within the operating bandwidth until the number of dipole elements became at least greater than 16. The same conclusion can also be reached, when the variations of gain and radiation pattern with frequency for fixed values of number of elements is investigated. The azimuth radiation pattern was taken at a mid- frequency of 900MHz (figures not included due to size). It can be shown that, the variation of gain with frequency was not acceptable until the number of dipole element 'N' became greater than 16. The radiation pattern is not acceptably directive until the number of dipole element became greater than 16 and for N greater than 32, the radiation pattern does not show marked improvement (as would have been expected) to justify using more than 16 elements. The implication of this is that even though increasing the number of elements beyond the convergence point does sometimes increase the average gain, the pattern of variation of gain with frequency may render this increased gain useless. Since the aim here is minimisation of 'N', using values of N greater than N at convergence does not necessarily result in improved performance of the LPDA as to justify the cost implications.

Figure 4 is a representative case of the graphical illustration of convergence and sparse LPDA antennas. In other to define LPDA Antenna convergence in detail, there is the need for numerical presentation of the trends in the variation patterns of VSWR and gain for fixed N as can be seen from table 2 below. The mean VSWR does not meet acceptable value until N greater than 16. This suggests a convergent value of N of about (16+32)/2 =24. The reason for this approach is based on the fact that the mean VSWR approaches 1:1 between N=16 and 32 and the mean gain appears to stabilize between these values. Also for N=16, the STD of gain =1.99 and for N=32, STD of gain =1.52. Now, if N exceeds 32, the STD of gain increases, suggesting that the point of convergence lies between N = 16 and 32, hence the justification for averaging. After convergence, doubling the number of elements does not result in any appreciable change in the VSWR. With respect to standard deviation of the VSWR, once the number of elements became greater than 16, the value of STD dropped drastically suggesting little dispersal from the central value. After convergence, the STD values do not vary greatly. Looking at the trends in the pattern of variation of the mean gain, it was also observed that for N greater than 16, the Antenna for the first time yields acceptable mean gain and doubling the number of elements does not result in appreciable change in gain values. The same is true for the STD of the gain.

Figure 4: SWR versus frequencies for values of N, L =1.5max


Table 2 : Trends in pattern of variations of VSWR and gain for fixed values of N, L = 1.5λmax , B = 1:5.

N (VSWR)avg (VSWR)STD (Gain)avg dBi

(Gain)STD dBi

4 5.29 9.37 -0.39 3.75

8 3.06 4.12 -3.31 4.55

16 1.08 0.08 6.40 1.99

32 1.12 0.15 6.81 1.52

64 1.32 0.60 6.80 1.64

This was the procedure adopted for all four hundred and ten LPDA Antennas (410) and information gathered from this is used for setting the convergence criterion, which follows next. 3.1 Convergence (Optimisation) Criteria for LPDA Antennas. A log periodic dipole array antenna shall be considered converged if it meets the following requirement:

I. It must yield a mean gain of at least 5dBi for a pair of scale factor '' and spacing constant '' (and by extension for a given value of number of dipole elements 'N') and for any given boom-length 'L' and operating band-width 'B'. A gain of 5dBi indicates that the LPDA Antenna radiates at least, three times more in the direction of maximum field compared to that of an isotropic radiator.

II. Doubling the number of dipole elements 'N' results in at most average increase in mean gain of about 1dBi and the STD averages 1.15dBi (deduced from observation).

III. It must maintain a VSWR value of 2:1 and below within the designed operating bandwidth.

IV. Its electrical characteristics such as input impedance, radiation pattern and gain must vary periodically as the logarithm of the operating frequency. This Antenna must therefore yield acceptable radiation pattern within its operating bandwidth.

A log periodic dipole array antenna that does not meet the requirements I to IV above is considered un-converged. Although the criteria for convergence have been stated in terms of the VSWR, radiation pattern and gain characteristics of the LPDA antenna, the latter will be used as the basis for establishing convergence. Any of the electrical characteristics can be used but I have chosen the gain for the sake of uniformity; also, the effect of boom-impedance 'Zo' and length to diameter ratio on the gain of LPDA antenna are the subject matters of a later paper. It is to be noted however, that a converged antenna must meet all of the requirements.

It is necessary at this stage to point out that the convergent value is not absolute. It is however more sensitive at the lower values than at higher values. Thus, if for a given boom-length and operating bandwidth, a given LPDA Antenna converges for N = 8. If the elements were reduced by 2 elements, the convergence of that Antenna may no longer be guaranteed. On the other hand, if convergence was attained at N=12, reducing the number of elements by two might not affect the convergence drastically. Convergence provides a means of using the number of elements as the optimisation criteria while maintaining the electrical characteristics of the LPDA antenna.

4. RESULTS In this section, results of investigations are presented in the order in which they were carried out. First generalised (figures 5-16), extended parametric plots are presented for both convergent and sparse LPDA antennas (i.e. for both minimised and non-minimised LPDA Antennas). Next, minimised (or convergent) LPDA antennas are presented as in figures 17, 18 and 19. 4.1 Probable Gain Performance Characteristics of LPDA Antennas (Convergent/Sparse) Fig 5: G (dBi) versus N for L and varying B Fig 6: G (dBi) versus N for L and varying B

Number of elements (N) 0 20 40 60 80 100 120 140

-5

0

5

10

15

Ave

rage

Gai

n (d

Bi)

L=1m, B=1:5

L=1m, B=1:10

L=0.5m, B=1:2

L=0.5m, B=1:5

L=1m, B=1:2

L=0.5m, B=1:10

0 20 40 60 80 100 120 140 -8

-6

-4

-2

0

2

4

6

8

10

12

Number of elements

Ave

rage

Gai

n (d

Bi)

L=1.5m,

B=1:2

L=1.5m,B=1:5

L=2m,B=1:2

L=2m,B=1:

5

L=2m,B=1:10

L=1.5m,B=1:10


Fig 7: N versus τ for L and varying B Fig 8: N versus τ for L & varying B Fig 9: N versus τ for L and varying B Fig 10: N versus τ for L and varying B

Fig 11: N versus σ for L & varying B

Fig 12: N versus σ for L & varying B Fig 13: N versus σ for L & varying B Fig 14: N versus σ for L & varying B

0 0.5 0.6 0.7 0.8 0.9 10

20 40 60 80

100 120 140

Scale factor (tau)

Num

ber

of e

lem

ents

(N

)

L=1m,B=1:2 L=1m,B=1:5

L=1m,B=1:10

0.4 0.5 0.6 0.7 0.8 0.9 10

20 40 60 80

100 120 140

Scale factor (tau)

Num

ber

of e

lem

ents

(N

)

L=1m,B=1:2

L=1m,B=1:10

L=1m,B=1:5

0.4 0.5 0.6 0.7 0.8 0.9 10

20 40 60 80

100 120 140

Scale factor (tau)

Num

ber

of e

lem

ents

(N

)

L=2m,B=1:2 L=2m,B=1:5

L=2m,B=1:10

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

20

40

60

80

100

120

140

Space constants (sigma)

Num

ber

of e

lem

ents

(N

) L=2m,B=1:10

L=2m,B=1:5

L=2m,B=1:2

0 0.1 0.2 0.3 0.4 0.5 0.6 0.70

20

40

60

80

100

120

140

Space constant (Sigma)

Num

ber

of e

lem

ents

(N

) L=1m,B=1:10

L=1m,B=1:5

L=1m,B=1:2

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90

20

40

60

80

100

120

140

Number of elements (N)

Spa

ce c

onst

ant (

sigm

a) L=1.5,B=1:10

L=1.5m,B=1:5

L=1.5m,B=1:2

1 Scale factor (tau)

0.4 0.5 0.6 0.7 0.8 0.90

10

20

30

40

50

60

70

Num

ber

of e

lem

ents

(N

)

L=0.5m,B=1:2

L=0.5m,B=1:5

L=0.5m,B=1:10

0 0.05 0.1 0.15 0.2 0.25 0.3 0.350

10

20

30

40

50

60

70

Space constants (Sigma)

Num

ber

of e

lem

ents

(N

) L=0.5m,B=1:10

L=0.5m,B=1:5

L=0.5m,B=1:2


Table 3: Convergent LPDA Antennas

Fig 15: σ versus τ for L and varying B 4.2 Convergent LPDA Antennas The convergence table 3 from figure 5 to 16 above by applying the convergent criteria. A look at the table clearly indicates that the values of tau and sigma are all within the optimum region. Therefore convergent antennas are somewhat also optimised LPDA antennas. In this region tau ranges from 0.85 to 0.98, while sigma ranges from 0.08 to 0.13. Fig 16: σ versus τ for L & varying B

Fig 17: Convergent pair of [N, G] for L & B Fig 18: Convergent [G, L] for N & B Fig 19: Convergent pair of [N, L] for changing

values of B It is to be noted that the convergent table given here is not absolute. The values are extendable to about 2 or 3 elements depending on boom-length and operating bandwidth. Figures 17 to 19 give the convergent curves.

5. CONCLUSION The main theme throughout this paper has been the design of a functional LPDA antenna. In other to do this, there is a need to know all the various trade-offs available to better optimise the probable performance characteristics of the antenna given a set of situations.

N G(dBi) L(METER) B

0.90 0.095 8 7 0.5max 1:2 0.86 0.086 12 5.6 0.5max 1:5 0.85 0.082 16 6.7 0.5max 1:10

0.936 0.125 12 8.23 max 1:2 0.93 0.086 24 6.61 max 1:5

0.928 0.08 32 8.03 max 1:10 0.97 0.09 24 8.87 1.5max 1:2 0.93 0.128 24 8.30 1.5max 1:5

0.928 0.119 32 8.00 1.5max 1:10 0.978 0.089 32 9.45 2max 1:2 0.948 0.129 32 9.25 2max 1:5 0.952 0.107 48 8.72 2max 1:10

Space factor (tau) 0.4 0.5 0.6 0.7 0.8 0.9 10

0.1 0.2 0.3 0.4 0.5 0.6 0.7

Spa

ce c

onst

ant (

sigm

a)

L=1m,B=1.2

L=1m,B:1.5 L=1m,B=1:10

L=0.5, B=1:10

L=0.5m,B=1:2

L=0.5,B=1:5

Scale factor (tau) 0.4 0.5 0.6 0.7 0.8 0.9 1

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

Spa

ce c

onst

ant (

Sig

ma)

L=2m,

B=1:2 L=2m,

B=1:5

L=2m,

B=1:1

L=1.5m,

B=1:10

L=1.5m,

B=1:5 L=1.5m,

B=1:2

5 10 15 20 25 30 35 40 45 505.5

6

6.5

7

7.5

8

8.5

9

9.5

Number of elements (N)

Gai

n (d

B)

B=1:2

B=1:5

B=1:10

Boom-Length in metes 0.5 1 1.5 2 2.5

5.5

6

6.5

7

7.5

8

8.5

9

9.5

10

Gai

n (d

B)

B=1:5B=1:10

B=1:2

0.5 1 1.5 25

10

15

20

25

30

35

40

45

50

Boom-Length in meters

Num

ber

of e

lem

ents

(N

)

B=1:2

B=1:5

B=1:10


These include: (i) The need to be able to approximate the probable gain

of a LPDA Antenna for a given length and operating bandwidth.

(ii) Approximate the number of dipole elements which will result in a desired gain value.

(iii) Determine what values of tau and sigma would result in that gain.

(iv) Determine what effect(s) if any, a given antenna parameter have on the performance of the antenna.

Attempt has been made in this paper to provide these answers. It is therefore anticipated that, the use of the parametric curves (listed as figures 5 to 19) in conjunction with traditional design curves such as Peixeiro [9], would provide an easy means of designing a functional and cost optimised LPDA antenna given any set of requirements. Parametric curves beyond the ranges provided by Peixeiro [9] have been provided. In addition, parametric curves in terms of boom-length (L) and number of elements (N) for any given operating bandwidth has been provided so as to keep the cost of producing any LPDA Antenna acceptable, as these two parameters determine the cost of the Antenna, De vito and Stracca [7], [8]. A means of optimising LPDA Antenna in terms of performance, using minimised number of dipole element (N) as the criterion was introduced. The interesting thing about this technique is that although the minimisation is based on N, The value of N at which the LPDA Antenna is considered converged, is based on trends in the pattern of variation of the gain, VSWR and radiation pattern. The implication of this is that although minimisation is given in terms of N, the methodology used in obtaining N includes every practically useful performance evaluating parameter of the LPDA Antenna. The minimisation process ultimately results in optimised performance of the LPDA Antenna [15]. It is important to note here that all the parametric curves given in this paper are valid only for a transmission line of characteristic impedance 50Ω and length to diameter ratio of 100. A follow up paper attempts at studying the effects of boom impedance and length to diameter ratios on the gain performance of an LPDA antenna. It aims to model these effects numerically and if successful will present correction factors, which takes cognisance of the impact of these parameters on the gain performance of an LPDA antenna. In conclusion, the paper met its aims of: (i) Extending the range of parametric curves beyond those of Peixeiro [9]. (ii) Finding means of minimising 'N', leading to cost optimisation of the LPDA Antenna. (iii) Presentation of the parametric curves in terms of the main cost determinants namely L and N for values of operating bandwidths 'B'.

6. REFERENCES [1] D.E.Isbell, ''Log periodic dipole arrays,'' IRE

trans. Antennas and propagation, vol. AP-8, pp 260, May 1960.

[2] R.L.Carrel, ''Analysis and design of the log-periodic dipole antenna,'' Antenna Lab.., University of Illinois, Urban, tech. Rept.52, 1961.

[3] S. El-khamy et al.., “Design of Multiband Fractal Antenna arrays with wideband LPDA elements” International conference of computer theory and applications, 13th ICCTA, Alexander, Egypt, Aug. 2003.

[4] M. Hilbert, M. A. Tilston, and K. G. Balmain, “Resonance phenomena of log –periodic antenna characteristic- mode analysis,” IEEE Trans. Ant. Prop.., vol.37, no. 10, pp. 1224-1234, Oct. 1989.

[5] CISPR 16-1 specification for radio disturbance and immunity measuring apparatus and methods, part 1: Radio disturbance and immunity measuring apparatus, Intl. Electrotechnical commission, Geneva, Switzerland, 1993.

[6] ARRL Antenna hand book 4th edition, Mc-Graw Hill publishers, 1986.

[7] G. De. Vito and B. Stracca, “Comments on the design of log- periodic dipole array antennas,” IEEE. Trans. Ant. Prop.., AP-21, pp. 303-308, 1973.

[8] G. De. Vito and B. Stracca, “Further comments on the design of log-periodic dipole array antennas,” IEEE. Trans. Ant. Prop.., AP-22, pp. 714 – 718, 1974.

[9] C. Peixeiro, “Design of log-periodic dipole array antennas,” IEEE. Proc.., Vol. 135, pt. H, April 1988.

[10] P.C. Butson and G.T. Thompson, “A note on the calculation of the gain of log-periodic dipole array antennas,” IEEE. Trans. AP- 24, PP. 105 – 106, 1976

[11] Super Numeric Electromagnetic code (SuperNec) version 2 user’s guide.

[12] C. C. Balntin and K.G. Balmain, “Study of compressed log-periodic dipole array antennas,” IEEE. Trans. Ant. Prop.., Vol. AP-18, PP. 195-203, March 1970.

[13] G. Burke and A. Poggio, “Numerical electromagnetic code (NEC): “A user oriented computer code for analysis of electromagnetic response of Antennas and other structures,” Lawrence Livermore laboratory, Jan. 1981.

[14] R.F. Harrington, “Field computation by method of moments (MOM),” Macmillan Co. reissued edition 1996.

[15] Y.C. Chung and R. L. Haupt, “Log-periodic dipole array antennas optimization,” Journal of electromagnetic waves and application, Vol. 15, no. 9, pp. 1269-1280, 2001.

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