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Home > Documents > Zz 4.42×155 ¥42web.stanford.edu/class/rad225/subdir/HW3Solutions.pdf · Zz =) ¥2 = JUST = 67.08...

Zz 4.42×155 ¥42web.stanford.edu/class/rad225/subdir/HW3Solutions.pdf · Zz =) ¥2 = JUST = 67.08...

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Problem I . -4 1) Speed of sound in PTZ - SH n 4560 Ms . L = Iz = ¥ = 452=9 . Ixion 2×2.5×106 L = 0.9 mm 2) element # 4 : 2- y = ¥2 = ) 4-624 = 68 mm 44 = Z=f§ymoI% = 4.42×155 s element # 3 . . Zz = ) ¥2 = JUST = 67.08 mm es - ZI - - ffjf.omm.my , = 4.35×55 s element # 2 . Zz - - ¥82 = 438-4 = 66 . 21 mm ez - - ZZ - bfj7.IT#m , = 4.30×155 s element # 5 : Zs = J ¥42 =) # = 68.96 mm es = Z{- = g = 4 . 48×155 s Dtc = IT s - Ey 1=0.06 x to S = o . 6×156 S = 0.6 µs µe , yes . I = o . ,z× , £5 = , ,3× if s = , .rs µ , DEZ = Its - Tz I = o . 18×155 = 1.8×156 S = 1.8 µs 3) FD - - DIG = = Ditz - 41%143%3=6.49 mm chime the transducer is not circularly symmetric , I will accept any dimension you chose LD .
Transcript
  • Problem I.

    -4

    1) Speed of sound in PTZ - SH n 4560 Ms . L = Iz = ¥ = 452=9 .Ixion

    2×2.5×106

    ⇒ L = 0.9 mm

    2) element #4 : 2- y = ✓ ¥2 = ) 4-624 = 68 mm44 = Z=f§ymoI% =

    4.42×155s

    element # 3 .. Zz = ) ¥2 = JUST = 67.08 mm

    es - ZI -- ffjf.omm.my,

    =

    4.35×55s

    element # 2.

    - Zz - -✓ ¥82 = ✓ 438-4 = 66 . 21 mm

    ez - - ZZ - - bfj7.IT#m,

    =4.30×155selement # 5 : Zs = J

    ¥42=) # = 68.96 mm

    es = Z{- = g = 4 . 48×155 s

    Dtc = ITs - Ey1=0.06 x toS

    = o .6×156

    S = 0.6 µs

    µe , yes . ↳I = o . ,z× , £5 = , ,3× if s = , .rs µ ,DEZ = Its - Tz I = o . 18×155 = 1.8×156 S = 1.8 µs

    3) FD -- DIG = = Ditz - - 41%143%3=6.49 mm

    chime the transducer is not circularly symmetric, I will accept any dimension you chose LD .

  • Problem 2 .

    • Cosine aperture

    tdvantage : the first side lobe is at about - 23 dB

    disadvantage : the main lobe is wider .

    • Rect aperture

    quantage i the main lobe is tighter at -3 dB .

    disadvantage : the first side lobe appears at - 13 dB

  • Problem 3 .

    I .

    2. A common compression A use

    is : calm ( imam, varying ⇐ will compress the

    image .

    3.

    Come up aieh a function that will weigh the top rows heavily compared * the deeper

    rows . An example would be let weigh each row as : flu ) = K x *÷ :

    comparethis A the o . z - 0.8

    image in port 2 . You can see

    thetop rows in this one better .

  • 4.

    the point of this problem is to show that higher frequencies don't make is to the deeperlayers .

    As you can see in the frequency spectrum , at deeper layers ( orange ) high frequency

    components are vanished .


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