def test_error_correction(arrEncoded, nErrorBits):
# data1
arrEncoded_1 = arrEncoded
arrEncoded_bin_1 = ct.toBinaryArray(arrEncoded_1)
# data2
arrEncoded_bin_2 = np.copy(arrEncoded_bin_1)
# introduce some errors
arrErrorPos = np.random.choice(range(len(arrEncoded_bin_2)), nErrorBits,
replace=False)
for i in arrErrorPos:
arrEncoded_bin_2[i] = (arrEncoded_bin_2[i] + 1)%2
# error correction
n = 7
k = 4
lsh = LSHash(k, n)
arrDecoded_bin_1 = lshDecode(ct.zeroPadding(arrEncoded_bin_1, n)[0],
lsh, n, k)
arrDecoded_bin_2 = lshDecode(ct.zeroPadding(arrEncoded_bin_2, n)[0],
lsh, n, k)
# BER
nErrorBits, dBER = ct.computeBER(arrEncoded_bin_1, arrEncoded_bin_2)
nErrorBits_ec, dBER_ec = ct.computeBER(arrDecoded_bin_1, arrDecoded_bin_2)
return nErrorBits, dBER, nErrorBits_ec, dBER_ec
def test_error_correction(eng, arrEncoded, nErrorBits, n=7, k=4, strCoder="hamming/binary"):
# data1
arrEncoded_1 = arrEncoded
arrEncoded_bin_1 = ct.toBinaryArray(arrEncoded_1, nBitBlock=n)
print "data1(%d): " % len(arrEncoded_bin_1), arrEncoded_bin_1
# data2
arrEncoded_bin_2 = np.copy(arrEncoded_bin_1)
# introduce some errors
arrErrorPos = np.random.choice(range(len(arrEncoded_bin_2)), nErrorBits, replace=False)
for i in arrErrorPos:
arrEncoded_bin_2[i] = (arrEncoded_bin_2[i] + 1) % 2
print "data2:(%d) " % len(arrEncoded_bin_1), arrEncoded_bin_2
# decode
print ("coder: %s" % strCoder)
arrDecoded_bin_1 = decode(eng, arrEncoded_bin_1, n, k, strCoder)
arrDecoded_bin_2 = decode(eng, arrEncoded_bin_2, n, k, strCoder)
print "decoded1(%d): " % len(arrDecoded_bin_1), arrDecoded_bin_1
print "decoded2(%d): " % len(arrDecoded_bin_2), arrDecoded_bin_2
# BER
nCount = 0
for i in xrange(len(arrDecoded_bin_1)):
if arrDecoded_bin_1[i] != arrDecoded_bin_2[i]:
nCount += 1
dBER = nCount * 1.0 / len(arrDecoded_bin_1)
print "BER: ", dBER
def test(eng, arrMsg, nErrorBits, n=7, k=4, strCoder="hamming/binary"):
arrMsg_bin = ct.toBinaryArray(arrMsg, nBitBlock=n)
# encode
arrEncoded_bin = encode(eng, arrMsg_bin, n, k, strCoder)
# introduce some errors
arrErrorPos = np.random.choice(range(len(arrEncoded_bin)), nErrorBits, replace=False)
for i in arrErrorPos:
arrEncoded_bin[i] = (arrEncoded_bin[i] + 1) % 2
# decode
arrDecoded_bin = decode(eng, arrEncoded_bin, n, k, strCoder)
print "message(%d): " % len(arrMsg_bin), arrMsg_bin
print "encoded(%d): " % len(arrEncoded_bin), arrEncoded_bin
print "decoded(%d): " % len(arrDecoded_bin), arrDecoded_bin
# BER
nCount = 0
for i in xrange(len(arrMsg_bin)):
if arrDecoded_bin[i] != arrMsg_bin[i]:
nCount += 1
dBER = nCount * 1.0 / len(arrMsg_bin)
print "BER: ", dBER
def test_random_secret(matlabEng, n, k, nErrorBits=1):
# data 1
arrData_1 = np.random.randint(0, 2, n)
arrData_bin_1 = ct.toBinaryArray(arrData_1)
print "d1 (%d): "%len(arrData_bin_1), arrData_bin_1
# data2
arrData_bin_2 = np.copy(arrData_bin_1)
# introduce some errors
arrErrorPos = np.random.choice(range(len(arrData_bin_2)), nErrorBits,
replace=False)
for i in arrErrorPos:
arrData_bin_2[i] = (arrData_bin_2[i] + 1)%2
print "d2 (%d): "%len(arrData_bin_1), arrData_bin_2
# create secret at random
arrSecret = np.random.randint(0, 2, k)
arrSecret_bin = ct.toBinaryArray(arrSecret)
arrCodeword_bin = fec.encode(matlabEng, arrSecret_bin,
n, k, 'hamming/binary')
# compute the difference
arrDelta = np.bitwise_xor(arrData_bin_1, arrCodeword_bin)
# deduce securet
arrCodeword_bin_2 = np.bitwise_xor(arrData_bin_2, arrDelta)
arrSecret_bin_2 = fec.decode(matlabEng, arrCodeword_bin_2,
n, k, 'hamming/binary')
# compute the BER
nMismatchedBits = np.size(arrSecret_bin) - \
np.sum(arrSecret_bin==arrSecret_bin_2)
print "error bit=", nMismatchedBits, ", BER=",\
nMismatchedBits*1.0/np.size(arrSecret_bin)
def test_reconciliation(nTrials, matlabEng, nKeySize, nErrorBits=1,
n=7, k=4, m=3, strCoder='hamming/binary'):
"""
test the reconciliation method mentioned in 'proximate, mobisys'11'
"""
lsStat = []
for i in xrange(nTrials):
# data 1
arrData_1 = np.random.randint(0, 2, nKeySize)
arrData_bin_1 = ct.toBinaryArray(arrData_1)
# data2
arrData_bin_2 = np.copy(arrData_bin_1)
# introduce some errors
arrErrorPos = np.random.choice(range(len(arrData_bin_2)), nErrorBits,
replace=False)
for i in arrErrorPos:
arrData_bin_2[i] = (arrData_bin_2[i] + 1)%2
# padding
nPaddingSize = m*n if strCoder == CODER_RS else n
arrData_bin_1 = ct.zeroPadding(arrData_bin_1, nPaddingSize)[0]
arrData_bin_2 = ct.zeroPadding(arrData_bin_2, nPaddingSize)[0]
# compute the difference
arrDelta = computeDelta(matlabEng, arrData_bin_1, n, k, m, strCoder)
# reconciliation
arrDeducedData_bin_1 = reconciliate(matlabEng, arrDelta,
arrData_bin_2, n, k, m, strCoder)
# compute the BER
nMismatchedBits = np.size(arrData_bin_1) - \
np.sum(arrData_bin_1==arrDeducedData_bin_1)
dBER = nMismatchedBits*1.0/np.size(arrData_bin_1)
lsStat.append({'err_bits': nMismatchedBits, 'BER': dBER})
if (nMismatchedBits != 0):
print nMismatchedBits, arrErrorPos
# print "error bit=", nMismatchedBits, ", BER=", dBER
dAvgErrBits = np.mean([i['err_bits'] for i in lsStat ])
dAvgBER = np.mean([i['BER'] for i in lsStat ])
print "----\n AvgErrorBits=%.3f, BER=%.3f" % (dAvgErrBits, dAvgBER)
def sourceEncode(lsData_rectified, nSCWndSize, nNeighborWnd=3):
"""
encoding data
Parameters:
----------
lsData_rectified:
list of rectified EMG data
nSCWndSize:
source coding window size (in points)
nNeighborWnd:
searching area of nearby neighbors
Returns:
-------
lsSourceCode:
list of source code lists
lsSourceCode_bin:
list of source codes in binary
lsSourceShape:
list of source approximated shape arrays
"""
lsSourceCode = []
lsSourceCode_bin = []
lsSourceShape = []
for i, arrData in enumerate(lsData_rectified):
lsDataSrcCode, arrDataShape = encoder.shapeEncoding(arrData,
nSCWndSize, nNeighborWnd)
arrDataSrcCode = np.array(lsDataSrcCode)
lsSourceCode.append(arrDataSrcCode)
arrDataSrcCode_bin = ct.toBinaryArray(arrDataSrcCode, 2)
lsSourceCode_bin.append(arrDataSrcCode_bin)
lsSourceShape.append(arrDataShape)
return lsSourceCode, lsSourceCode_bin, lsSourceShape
