Ultrasonic signal compressive detection using improved random equivalent sampling

With the rapid development of electronic science and technology, the signal frequency that engineers need to measure becomes higher and higher. Limited by the analog-to-digital converter (ADC) sampling rate and Nyquist sampling theorem, the digital oscilloscope can only measure waveform in a very limited range of frequency in real-time sampling mode. Therefore, it is necessary to introduce an equivalent sampling technique to increase the ability of the oscilloscope to measure higher frequency signals. In this paper, a random equivalent sampling reconstruction method based on field programmable gate array (FPGA) is proposed. In the random equivalent sampling mode, multiple sets of samples collected by the ADC are buffered in the FPGA, and the stretched circuit measures the time interval $\Delta$t between each set of trigger signals and the rising edge of the first sampling clock. Through these samples as well as the $\Delta$t in each group, the waveform can be reconstructed in FPGA, and then this reconstructed waveform can be read and displayed by the upper computer. The waveform refreshing rate of the random equivalent sampling can be greatly improved by this kind of reconstruction inside a FPGA.

The emerging compressive sampling (CS) theory makes processing ultra-wide-band (UWB) signal at a low sampling rate possible if the underlying signal has a sparse representation in a certain basis. The feasibility of model based compressive sampling for ultra-wide-band (UWB) signal is investigated. In this paper, a multichannel compressive sampling architecture is developed to capture UWB signal at a rate much lower than Nyquist rate. The proposed framework considers sub-Nyquist sampling stream of delayed and weighted versions of a known signal with finite support in time domain. A basis function is constructed to realize sparse signal representation. To reduce the hardware cost, a segmented architecture is suggested. In addition, a joint signal recovery algorithm is presented. Experimental results indicate that, with this system, a UWB signal sampled at about 4% of Nyquist rate still can be recovered with overwhelming probability.

A Random Equivalent Sampling (RES) circuit that has 16ps sampling resolution has been developed for a high-resolution Time-Domain Reflectometer (TDR). The high-resolution TDR uses an expensive programmable delay chip or a complex Time-to-Digital Converter (TDC) circuit to capture the waveform with very fine time interval. The Vernier time delay generation technique using two crystal oscillators of slightly different frequency is proposed, which is simpler and more cost-effective and provides subpicosecond time resolution. One of the two clocks is used for the reference time to generate incident periodic pulses, and another clock is used for the sampling, The implemented RES circuit consists of the Vernier clock generator, the pulse generator and the control logic for pulse generation, interface and high-speed memory control. Using the ADC of relatively low sampling rate, the periodic pulse waveform is reconstructed with tens of GSPS high equivalent sampling rate by the repetitive sampling utilizing the incremental Vernier time delay. The performance of RES circuit is measured through the operation of TDR. The resolution of the RES circuit is 16.8ps equal to 59.5GSPS sampling rate, which means that the signal waveform in the 66.7%VOP RG58C/U cable can be sampled by 1.6mm interval.

With the advent of ubiquitous computing there are two design parameters of wireless communication devices that become very important power: efficiency and production cost. Compressive sensing enables the receiver in such devices to sample below the Shannon-Nyquist sampling rate, which may lead to a decrease in the two design parameters. This paper investigates the use of Compressive Sensing (CS) in a general Code Division Multiple Access (CDMA) receiver. We show that when using spread spectrum codes in the signal domain, the CS measurement matrix may be simplified. This measurement scheme, named Compressive Spread Spectrum (CSS), allows for a simple, effective receiver design. Furthermore, we numerically evaluate the proposed receiver in terms of bit error rate under different signal to noise ratio conditions and compare it with other receiver structures. These numerical experiments show that though the bit error rate performance is degraded by the subsampling in the CS-enabled receivers, this may be remedied by including quantization in the receiver model. We also study the computational complexity of the proposed receiver design under different sparsity and measurement ratios. Our work shows that it is possible to subsample a CDMA signal using CSS and that in one example the CSS receiver outperforms the classical receiver.

ADC (Analog to Digital Converter) which follows the Nyquist rate has changed signal processing. Most of the real time applications required too many samples if Nyquist rate is followed. This may involve more cost or even practically not feasible to build systems capable of acquiring samples at Nyquist rate. Compressive Sensing (CS) is a recent trend emerged as a better concept than Nyquist technique by enabling reconstruction of sparse signals which are acquired bellow Nyquist rate. The authors propose reconstruction of Image using Compressive Sensing Technique. Various matrices like Partial Hadmard, Bernoulli Matrix, Uniform Spherical and Random Matrix with proper threshold are used. The reconstruction time, SNR and MSE are measured. Experiments are carried on various sized image with Foreward Hadmard Transform. The experimental results show that for an image of size 256×256 the reconstruction time is 9 sec with signal to noise ratio 23 dB. For inage of size 512×512 the reconstruction time is 15 sec with signal to noise ratio as 26dB. This gives the opportunity to build CS hardware as an alternative for ADC.

A Compressive Sensing (CS) approach is applied to utilize intrinsic computation capabilities of Spin-Orbit Torque Magnetic Random Access Memory (SOT-MRAM) devices for IoT applications wherein lifetime energy, device area, and manufacturing costs are highly-constrained while the sensing environment varies rapidly. In this manuscript, we propose the Adaptive Compressed-sampling via Multi-bit Crossbar Array (ACMCA) approach to intelligently generate the CS measurement matrix using a multi-bit SOT-MRAM crossbar array. SPICE circuit and MATLAB algorithm simulation results indicate that ACMCA reduces reconstruction error by up to 4dB using a 4-bit quantized CS measurement matrix while incurring a negligible increase in the energy consumption of generating the matrix. Additionally, we introduce an algorithm called Energy-aware Adaptive Sensing for IoT (EASI) which determines the frequency of measurement matrix updates within the energy budget of an IoT device.

It is of great significance to construct deterministic measurement matrices with good practical characteristics in Compressed Sensing (CS), including good reconstruction performance, low memory cost and low computing resources. Low-density-parity check (LDPC) codes and CS codes can be closely related. This paper presents a method of constructing compressed sensing measurement matrices based on quasi-cyclic (QC) LDPC codes and arithmetic sequence sets. The cyclic shift factor in each submatrix of QC-LDPC is determined by arithmetic sequence sets. Compared with random matrices, the proposed method has great advantages because it is generated based on a cyclic shift matrix, which requires less storage memory and lower computing resources. Because the restricted isometric property (RIP) is difficult to verify, mutual coherence and girth are used as computationally tractable indicators to evaluate the measurement matrix reconstruction performance. Compared with several typical matrices, the proposed measurement matrix has the minimum mutual coherence and superior reconstruction capability of CS signal according to one-dimensional (1D) signals and two-dimensional (2D) image simulation results. When the sampling rate is 0.2, the maximum (minimum) gain of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) is up to 2.89 dB (0.33 dB) and 0.031 (0.016) while using 10 test images. Meanwhile, the reconstruction time is reduced by nearly half.

Millimeter wave (mmWave) communication is one viable solution to support Gbps sensor data sharing in vehicular networks. The use of large antenna arrays at mmWave and high mobility in vehicular communication make it challenging to design fast beam alignment solutions. In this paper, we propose a novel framework that learns the channel angle-of-departure (AoD) statistics at a base station (BS) and uses this information to efficiently acquire channel measurements. Our framework integrates online learning for compressive sensing (CS) codebook learning and the optimized codebook is used for CS-based beam alignment. We formulate a CS matrix optimization problem based on the AoD statistics available at the BS. Furthermore, based on the CS channel measurements, we develop techniques to update and learn such channel AoD statistics at the BS. We use the upper confidence bound (UCB) algorithm to learn the AoD statistics and the CS matrix. Numerical results show that the CS matrix in the proposed framework provides faster beam alignment than standard CS matrix designs. Simulation results indicate that the proposed beam training technique can reduce overhead by 80% compared to exhaustive beam search, and 70% compared to standard CS solutions that do not exploit any AoD statistics.

Reference-free fatigue crack detection using deep long short-term memory network (DLSTM) and nonlinear ultrasonic modulation

Monitoring corrosion is a vital step to prevent catastrophic structural failure. With accurate corrosion rate data, the risk of failure can be mitigated by performing routine maintenance on components that have become compromised. When corrosion occurs in an inaccessible area such as the inside of a pipe or back side of an underground storage tank, however, conventional monitoring techniques such as visual inspection are inadequate or impossible. Non-destructive evaluation (NDE) techniques must be used to determine the condition of the structure to maintain its integrity. Ultrasonic (UT) Thickness Sensors were investigated for corrosion monitoring use. The chosen sensors had a resolution of a limit of 25.4μm. A system was designed to utilize Electrochemical Machining (ECM) to simulate a corrosion at various rates in a localized region (approximately 1 cm in diameter) while its thickness was monitored by a permanently attached UT sensor. Multiple techniques such as a 3D profilometer, and depth-gauge were used to validate the measurement of the permanently attached UT sensor. Multiple ECM experiments were conducted using NaCl electrolyte solution at 1, 10, and 100 g/L concentrations. Occasionally, variations in pit formation were observed and likely caused by the orientation of the counter electrode to the steel surface. The smallest and most consistent surface roughness was obtained using the 10 g/L concentration. Larger variations in surface roughness were observed at 1 and 100 g/L NaCl concentrations leading to the selection of 10 g/L solution for experiments. The ECM system rendered reproducible machining rates showing that the UT sensors were capable of monitoring very low corrosion rates averaged to 3μm per day. Discrepancies up to a few mm were sometimes measured between the UT sensor, 3D profilometer and depth-gage values. The discrepancies were attributed to non-uniform corrosion in the vicinity of the permanently attached UT sensor. The sensor therefore provided readings of a specific localized spot of the corroded region, or the profile of the corroded region interfered with the UT signals. The accuracy of UT sensor readings was also studied for various geometrical pit profiles. Certain defect geometries made it impossible for the UT sensor to record accurate measurements. This was also observed on naturally corroded steel plates. Therefore, UT sensors may sometimes provide inaccurate or no reading over certain corrosion profiles and should therefore be used with prudence.

Random measurement matrices play a critical role in successful recovery with the compressive sensing (CS) framework. However, due to its randomly generated elements, these matrices require massive amounts of storage space to implement a random matrix in CS applications. To effectively reduce the storage space of the random measurement matrix for CS, we propose a random sampling approach for the CS framework based on the semi-tensor product (STP). The proposed approach generates a random measurement matrix, where the dimensions of the random measurement matrix are reduced to a quarter (or 1/16, 1/64, and even 1/256) of the number of dimensions, which are used for conventional CS. We then estimate the values of the sparse vector with a modified iteratively re-weighted least-squares (IRLS) algorithm. The results of numerical simulations showed that the proposed approach can reduce the storage space of a random matrix to at least a quarter while maintaining quality of reconstruction. All results confirmed that the proposed approach significantly influences the physical implementation of the CS in images, especially on embedded system and field programmable gate array (FPGA), where storage is limited.

Direct-write piezoelectric ultrasonic transducers for pipe structural health monitoring

In practical compressive sensing (CS) communication system, Analog to Digital Converter (ADC) is a necessary component to convert analog signals into digital ones. However, nonlinear distortion in ADC is unavoidable and definitely affects the reception accuracy. Though recent works have studied various methods to combat the negative effect of ADC nonlinear distortion, few of them discuss the solution in communication system with CS. This paper studies the CS recovery method when compressive measurements suffer from ADC nonlinear distortion. A double-sparsity model is first formulated, where the original signal and the ADC nonlinear distortion are both sparse. Then, for the type of clipping ADC, we propose a corresponding algorithm based on the Alternating Direction Method of Multipliers (ADMM) strategy to solve the double-sparsity (DS) problem, named as DS-ADMM. For the type of self-reset (SR) ADC, we explore its essence of rounding operation to design an integer constraint based feedback updating (ICFU) strategy, and accordingly propose the DS-ADMM-ICFU recovery algorithm. Experiment results show that the DS-ADMM algorithm for the double-sparsity problem improves the recovery performance compared with the existing counterpart, and DS-ADMM-ICFU for SR ADC exhibits preferable advantage in typical communication systems.

In order to solve the problem that it is difficult to detect the local ice layer debonding when the thermal protection system is working, an on-line ultrasonic detection method for the melting degree of interface between skin and ice layer based on the theory of ultrasonic transmission in multi-layer media was studied. A multi-layer medium acoustic model including skin, water film, ice water transition layer, ice layer and air layer is constructed. Ultrasonic pulse waves with different center frequencies are used as excitation sources, and the echo signals of multi-layer medium interface are obtained by using the numerical calculation. The ultrasonic signal processing algorithm for ice melting and debonding measurement is developed. The original echo signal is processed and the characteristic parameters of the echo signal of interface between skin and ice layer are extracted. The ultrasonic reflection coefficient of interface between skin and ice layer is proposed to evaluate the degree of ice melting. The ultrasonic ice detection system was built, and the low-temperature ultrasonic sensor was designed and fabricated. The ability of the system to measure the degree of ice melting under the action of electric heating was verified in the low-temperature environment. The ultrasonic ice detection system was built, and the low-temperature ultrasonic sensor was designed and fabricated. The ability of the system to measure the degree of ice melting under the action of electric heating was verified in the low-temperature environment. The theoretical results show that when the ice on the skin melts and produces a thin water film, the degree of ice melting can be reflected by the change of ultrasonic reflection coefficient at the skin-attachment interface. In this paper, the ultrasonic reflection coefficient more than 0.8 is used as the criterion of ice melting. With the increasing of center frequency of the ultrasonic signal, the thinner water film can be identified. The identification thickness of the water film corresponding to 7.5 MHz center frequency signal is 10 μm. The experimental results show that under the conditions of clear ice, mixed ice and frost ice, the ultrasonic detection system can out-put the ice layer debonding signal at the initial stage of electric heating. The propagation characteristics of ultrasonic pulse wave of interface between skin and ice layer in the thermal protection are preliminarily explored. The present method for detecting the degree of ice melting is expected to further reduce the energy consumption of thermal protection.

this study presents an eight array ultrasonic signal phased array sparse sampling experiment system based ultrasonic phased array technology and Compressed Sensing (CS). Proposed system considers recovery ultrasonic beam signal received eight phased array elements with sparse samples captured using sub-Nyquist model in CS recovery algorithm. We have the block defect detection test in the system. The test result approximated the actual block defect position. Based on block defect detection test, We compared sparse sampling value using spectrum estimation to Compressed Sensing recovery algorithm imaging, and no focus and focus detection effect, proved the phased array experiment system based on Compressed Sensing .it can greatly improve the detection signal to noise ratio (SNR) and sensitivity. So we verify the phased array focus can improve the detection ability.