mirror of
https://github.com/RfidResearchGroup/proxmark3.git
synced 2024-09-21 15:56:21 +08:00
3b2fee43ea
This is a new LF edge detection algorithm for the FPGA. - It uses a low-pass IIR filter to clean the signal (see https://fail0verflow.com/blog/2014/proxmark3-fpga-iir-filter.html) - The algorithm is able to detect consecutive peaks in the same direction - It uses an envelope follower to dynamically adjust the peak thresholds - The main threshold used in the envelope follower can be set from the ARM side fpga/lf_edge_detect.v, fpga/lp20khz_1MSa_iir_filter.v, fpga/min_max_tracker.v: New file. fpga/lo_edge_detect.v, fpga/fpga_lf.v: Modify accordingly. armsrc/apps.h (FPGA_CMD_SET_USER_BYTE1, FPGA_CMD_SET_EDGE_DETECT_THRESHOLD): New FPGA command. fpga/fpga_lf.v: Modify accordingly/Add a 8bit user register. fpga/fpga_lf.bit: Update accordingly. fpga/tests: New directory for testbenches fpga/tests/Makefile: New file. It compiles the testbenches and runs all the tests by default (comparing with the golden output) fpga/tests/tb_lp20khz_1MSa_iir_filter.v, fpga/tests/tb_min_max_tracker.v, fpga/tests/tb_lf_edge_detect.v: New testbenches fpga/tests/plot_edgedetect.py: New script to plot the results from the edge detection tests. fpga/tests/tb_data: New directory for data and golden outputs
59 lines
1.5 KiB
Python
Executable file
59 lines
1.5 KiB
Python
Executable file
#!/usr/bin/env python
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#-----------------------------------------------------------------------------
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# Copyright (C) 2014 iZsh <izsh at fail0verflow.com>
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#
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# This code is licensed to you under the terms of the GNU GPL, version 2 or,
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# at your option, any later version. See the LICENSE.txt file for the text of
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# the license.
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#-----------------------------------------------------------------------------
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import numpy
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import matplotlib.pyplot as plt
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import sys
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if len(sys.argv) != 2:
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print "Usage: %s <basename>" % sys.argv[0]
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sys.exit(1)
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BASENAME = sys.argv[1]
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nx = numpy.fromfile(BASENAME + ".time")
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def plot_time(dat1):
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plt.plot(nx, dat1)
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sig = open(BASENAME + ".filtered").read()
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sig = map(lambda x: ord(x), sig)
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min_vals = open(BASENAME + ".min").read()
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min_vals = map(lambda x: ord(x), min_vals)
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max_vals = open(BASENAME + ".max").read()
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max_vals = map(lambda x: ord(x), max_vals)
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states = open(BASENAME + ".state").read()
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states = map(lambda x: ord(x) * 10 + 65, states)
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toggles = open(BASENAME+ ".toggle").read()
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toggles = map(lambda x: ord(x) * 10 + 80, toggles)
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high = open(BASENAME + ".high").read()
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high = map(lambda x: ord(x), high)
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highz = open(BASENAME + ".highz").read()
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highz = map(lambda x: ord(x), highz)
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lowz = open(BASENAME + ".lowz").read()
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lowz = map(lambda x: ord(x), lowz)
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low = open(BASENAME + ".low").read()
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low = map(lambda x: ord(x), low)
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plot_time(sig)
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plot_time(min_vals)
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plot_time(max_vals)
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plot_time(states)
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plot_time(toggles)
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plot_time(high)
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plot_time(highz)
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plot_time(lowz)
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plot_time(low)
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plt.show()
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