A Holey Cavity for High-Capacity Ultrasound Imaging

2018 ◽  
Author(s):  
Ashkan Ghanbarzadeh-Dagheyan ◽  
Juan Heredia-Juesas ◽  
Chang Liu ◽  
Ali Molaei ◽  
Jose Angel Martinez-Lorenzo
Keyword(s):  
Ultrasound Imaging ◽  
Sampling Rate ◽  
High Capacity ◽  
Regular System ◽  
Physical Structure ◽  
Ultrasound Wave ◽  
Sensing Matrix ◽  
Beam Focusing ◽  
Sensing System ◽  
Scan Time

Compressive sensing (CS) theory states that, if certain conditions are met, a signal can be retrieved at a sampling rate that is lower than what Nyquist theorem requires. Among these conditions are the sparsity of the signal and the incoherence of the sensing matrix, which is constructed based on how the sensing system is designed. One effective method to render the sensing matrix incoherent is to use random processes in its construction. Diverse approaches have been proposed to randomize the sensing matrix including transmission at random transmitter positions and spectral coding with the use of a physical structure that responds very differently at disparate frequencies. In this work, a holey cavity with various frequency modes is used to spectrally code the ultrasound wave fields. Then, with the use of CS theory and simulations, it is shown that the sensing system that is equipped with such a cavity performs meaningfully better than a regular system in terms of sensing capacity, beam focusing, and imaging. What is more, the validity of Born approximation is investigated in this work to show its extent of applicability in imaging relatively small targets. Due to computational limitations, the simulation domain has been selected to be comparatively small; yet, the achieved results evidently show the concept and warrant further studies on holey cavities in ultrasound imaging, including their fabrication and experimental corroboration. The decrease in the number of measurements necessary for correct image reconstruction can make ultrasound sensing systems more efficient in size and scan time in a variety of applications including medical diagnosis, non-destructive testing, and monitoring.

Pipelined Sampled-Delay Focusing in Ultrasound Imaging Systems

1987 ◽  
Vol 9 (2) ◽  
pp. 75-91 ◽  
Author(s):  
Joo Han Kim ◽  
Tae Kyong Song ◽  
Song Bai Park
Keyword(s):  
Time Delay ◽  
Ultrasound Imaging ◽  
Beam Steering ◽  
Sampling Rate ◽  
Imaging Systems ◽  
Delay Lines ◽  
Cell Basis ◽  
Beam Focusing ◽  
First In First Out ◽  
Resolution Cell

A sampled-delay focusing technique was recently proposed by the authors which completely eliminates the use of analog L-C delay lines for beam focusing in ultrasound B-mode imaging systems. With this approach, the product of sampling rate and maximum time delay is required to be less than unity. To remove this constraint, we propose in this paper a first-in-first-out pipelining technique. This allows one to perform beam steering and dynamic focusing simultaneously on a resolution-cell basis and in a completely digital fashion without the use Of analog L-C delay lines.

scholarly journals Design and Performance Evaluation of a Wearable Sensing System for Lower-Limb Exoskeleton

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Chunfeng Yue ◽  
Xichuan Lin ◽  
Ximing Zhang ◽  
Jing Qiu ◽  
Hong Cheng
Keyword(s):  
Angular Velocity ◽  
Lower Limb ◽  
Feature Recognition ◽  
Reaction Force ◽  
Recognition Rate ◽  
Sensing Matrix ◽  
Lower Limb Exoskeleton ◽  
Sensing System ◽  
Wearable Sensing ◽  
And Performance

Because the target users of the assistive-type lower extremity exoskeletons (ASLEEs) are those who suffer from lower limb disabilities, customized gait is adopted for the control of ASLEEs. However, the customized gait is unable to provide stable motion for variable terrain, for example, flat, uphill, downhill, and soft ground. The purpose of this paper is to realize gait detection and environment feature recognition for AIDER by developing a novel wearable sensing system. The wearable sensing system employs 7 force sensors as a sensing matrix to achieve high accuracy of ground reaction force detection. There is one more IMU sensor that is integrated into the structure to detect the angular velocity. By fusing force and angular velocity data, four typical terrain features can be recognized successfully, and the recognition rate can reach up to 93%.

Downsampling Direct Sequence Spread Spectrum Signal Using Orthogonal Pretreatment

2014 ◽  
Vol 610 ◽  
pp. 944-948
Author(s):  
Jin Bo Zhang ◽  
Jian Cheng ◽  
Lei Yang
Keyword(s):  
Spread Spectrum ◽  
Strong Correlation ◽  
Sampling Rate ◽  
Direct Sequence Spread Spectrum ◽  
Direct Sequence ◽  
Sensing Matrix ◽  
Pretreatment Method ◽  
Reconstructed Signal ◽  
Nyquist Rate ◽  
Simulation Results

The Direct Sequence Spread Spectrum (DSSS) signal is widely used because of its good concealment and anti-jamming performance. The Compressive Sensing (CS) theory reduced the sampling rate of the DSSS signal effectively compared with the traditional Nyquist-rate sampling theory. While in the process of CS sampling the sensing matrix and the sparse basis generally have a strong correlation when the DSSS signal is decomposed with a complete dictionary. This paper presents a novel orthogonal pretreatment method with which the incoherence between sensing matrix and sparse basis can be improved. As a result, the reconstructed signal is more accurate. Simulation results demonstrate that this method is effective and efficient.

scholarly journals Multi-Channel Data Acquisition System Based on FPGA and STM32

2020 ◽  
Vol 38 (2) ◽  
pp. 351-358
Author(s):  
Yue Zhang ◽  
Linwei Tao
Keyword(s):  
Power Consumption ◽  
Low Power ◽  
Dynamic Range ◽  
Sampling Rate ◽  
High Capacity ◽  
Acquisition System ◽  
Total Power ◽  
Low Power Consumption ◽  
Signal Acquisition ◽  
And Storage

In order to realize the acquisition and storage of underwater acoustic signals for aiming at the requirements of multi-channel, low power consumption and small volume for underwater receiver extension of sonar system, a multi-channel signal acquisition and storage system based on FPGA and STM32 with variable number of working channels and sampling frequency is designed, in which the system is consisted of 8 pieces, 8 channel and 24 bits high dynamic range Δ-Σ ADS1278 ADC chip to synchronous multi-channel analog signal acquisition. FPGA, as the acquisition sequence and logic control, reads and collates the ADC chip data and writes it into the internal high-capacity FIFO, and adds corresponding operations according to the characteristics of FIFO in an application. SMT32 single-chip microcomputer reads the FIFO data through the high-speed SPI interface with FPGA and writes the multi-channel data into the high-capacity SD card. The testing results have verified that the system has characteristics such as stable and reliable, easy configuration, low power consumption, can guarantee the multichannel data serial transmission, storage, accurate, up to 64 analog signals at the same time the real-time collection and storage, top 20 kHz sampling rate, the system total power of the system of about 3W, data rates up to 100 Mb/s, fully meet the needs of underwater sound acquisition system.

scholarly journals Measurement Matrix Optimization for Compressed Sensing System with Constructed Dictionary via Takenaka–Malmquist Functions

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1229
Author(s):  
Qiangrong Xu ◽  
Zhichao Sheng ◽  
Yong Fang ◽  
Liming Zhang
Keyword(s):  
Compressed Sensing ◽  
Singular Values ◽  
Tight Frame ◽  
Gram Matrix ◽  
Signal Recovery ◽  
Measurement Matrix ◽  
Sensing Matrix ◽  
Matrix Optimization ◽  
Sensing System ◽  
Iterative Minimization

Compressed sensing (CS) has been proposed to improve the efficiency of signal processing by simultaneously sampling and compressing the signal of interest under the assumption that the signal is sparse in a certain domain. This paper aims to improve the CS system performance by constructing a novel sparsifying dictionary and optimizing the measurement matrix. Owing to the adaptability and robustness of the Takenaka–Malmquist (TM) functions in system identification, the use of it as the basis function of a sparsifying dictionary makes the represented signal exhibit a sparser structure than the existing sparsifying dictionaries. To reduce the mutual coherence between the dictionary and the measurement matrix, an equiangular tight frame (ETF) based iterative minimization algorithm is proposed. In our approach, we modify the singular values without changing the properties of the corresponding Gram matrix of the sensing matrix to enhance the independence between the column vectors of the Gram matrix. Simulation results demonstrate the promising performance of the proposed algorithm as well as the superiority of the CS system, designed with the constructed sparsifying dictionary and the optimized measurement matrix, over existing ones in terms of signal recovery accuracy.

Ultrasonic Phased Array Industrial Imaging with Sub-Nyquist Sampling Rate

2013 ◽  
Vol 347-350 ◽  
pp. 327-331
Author(s):  
Guang Zhi Dai ◽  
Guo Qiang Han ◽  
Xian Yue Ouyang
Keyword(s):  
High Speed ◽  
Phased Array ◽  
Signal To Noise Ratio ◽  
Signal Reconstruction ◽  
Sampling Rate ◽  
Sampling Frequency ◽  
Frequency Resolution ◽  
Signal Frequency ◽  
Beam Focusing ◽  
Ultrasonic Phased Array

this paper uses a new type of FRI (Finite Rate of Innovation) sampling pattern based Sub-Nyquist sampling model breaked through Shannon theorem that it can get accurate signal reconstruction based on signal information rate, which requires the sampling frequency lower than two times the max signal frequency. We apply the new model in the ultrasonic phased array industrial imaging. In the experiment, ultrasonic phased array realized dynamic focusing and the high speed scan by ultrasonic array transducer of various array time delays to get flexible controllable synthesis beam composed signals that received by 32 phased array elements . The results indicate that in the model it greatly reduces the signal sampling frequency and improves the signal-to-noise ratio, frequency resolution at the same of the beam focusing and steering flexible.

scholarly journals Wideband Sparse Signal Acquisition Based on Serial Multi-Coset Sampling

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Changjian Liu ◽  
Houjun Wang
Keyword(s):  
Sampling Rate ◽  
Binary Sequence ◽  
Sampling Technique ◽  
Signal Acquisition ◽  
Sensing Matrix ◽  
Pattern Design ◽  
Analog To Digital ◽  
Reconstruction Performance ◽  
Sequence Generator ◽  
Sampling Pattern

Traditional parallel multi-coset sampling (MCS), which has several sub-Analog-to-Digital-Converters (sub-ADCs) working parallelly, is an attractive sub-Nyquist sampling technique for wideband sparse signals. However, the mismatch among sub-ADCs in traditional parallel MCS, such as bias, gain, and timing skew mismatch, degrades the signal acquisition performance greatly. In this paper, a serial MCS scheme based on clocking single ADC with nonuniform clock is proposed. The nonuniform sampling clock is generated by a pseudo-random binary sequence generator. An additional Sample/Hold (S/H) is used to improve the analog bandwidth of the serial MCS. Moreover, universal sampling pattern is designed for the proposed serial MCS. The sampling pattern design should not only maximize the Kruskal rank of compressed sensing matrix but also take the ADC’s sub-Nyquist sampling rate into consideration. Numeral experiments are presented demonstrating that the mismatch among sub-ADCs in traditional parallel MCS degrades the reconstruction performance greatly, and the proposed serial MCS can avoid the mismatch tactfully.

scholarly journals Collision Sensing Using Force/Torque Sensor

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Yu-Quan Leng ◽  
Zheng-Cang Chen ◽  
Xu He ◽  
Yang Zhang ◽  
Wei Zhang
Keyword(s):  
Sampling Rate ◽  
Dynamic Environment ◽  
Physical Structure ◽  
Physical Principle ◽  
Normal Vector ◽  
Torque Sensor ◽  
Unified Modeling ◽  
Complex Design ◽  
Conflict Solution ◽  
Geometric Surface

Collision sensing including collision position, collision direction, and force size could make robots smoothly interact with environment, so that the robots can strongly adapt to the outside world. Skin sensor imitates principles of human skin using special material and physical structure to obtain collision information, but this method has some disadvantages, such as complex design, low sampling rate, and poor generality. In this paper, a new method using force/torque sensor to calculate collision position, collision direction, and force size is proposed. Detailed algorithm is elaborated based on physical principle and unified modeling method for basic geometric surface. Gravity compensation and dynamic compensation are also introduced for working manipulators/robots in gravity and dynamic environment. In addition, considering algorithm solvability and uniqueness, four constraints are proposed, which are force constraint, geometric constraint, normal vector constraint, and current mutation constraint. In order to solve conflict solution of algorithm in redundant constraints, compatibility solution analysis is proposed. Finally, a simulation experiment shows that the proposed method can achieve collision information efficiently and accurately.

scholarly journals Compressed Sensing System for a Fingerprint Image Recognition Using Sensing Matrix with Chaotic Model-Based Deterministic Row Indexes

2021 ◽  
Author(s):  
Workneh Wolde Hailemariam ◽  
Pallavi Gupta
Keyword(s):  
Compressed Sensing ◽  
Matching Pursuit ◽  
Signal To Noise Ratio ◽  
Optimization Method ◽  
Security Assessment ◽  
Sensing Matrix ◽  
Sensing System ◽  
Chaotic Model ◽  
Assessment Techniques ◽  
Linear Congruential

Abstract This paper proposes a novel design approach for a secured compressed sensing system for fingerprint sensing and transmission. In the proposed design, the first stage is acquiring the signal followed by sparsely modeling it using Orthogonal Matching Pursuit (OMP) algorithm then compressing. In addition to compressing, we multiply the sparse modeled data by a novel, deterministic, and partially orthogonal Discrete Cosine Transform (DCT) sensing matrix to guarantee its security. Furthermore, the construction of the sensing matrix uses a modified Multiplicative Linear Congruential Generator (MLCG) to select the row index appropriately from chaotically re-arranged rows of DCT pseudo-randomly. On the other hand, the compressed image's simultaneous recovery and decryption accomplished using a convex optimization method—the proposed system tested by employing different image and security assessment techniques. The results show that we have archived a better Peak Signal to Noise Ratio (PSNR) than the recommended value for wireless transmission using samples below 25%.

scholarly journals Spectrum sensing of wideband signals based on cyclostationary and compressive sensing

2020 ◽  
Vol 20 (3) ◽  
pp. 1361
Author(s):  
Ali Mohammad A. AL-Hussain ◽  
Maher Khudair Mahmood Al Azawi
Keyword(s):  
Compressive Sensing ◽  
Spectrum Sensing ◽  
Compression Ratio ◽  
High Speed ◽  
Processing Time ◽  
Sampling Rate ◽  
Probability Of Detection ◽  
Signal Spectrum ◽  
Sensing Matrix ◽  
Recovery Algorithm

Compressive sensing is a powerful technique used to overcome the problem of high sampling rate when dealing with wideband signal spectrum sensing which leads to high speed analogue to digital convertor (ADC) accompanied with large hardware complexity, high processing time, long duration of signal spectrum acquisition and high consumption power. Cyclostationary based detection with compressive technique will be studied and discussed in this paper. To perform the compressive sensing technique, Discrete Cosine Transform (DCT) is used as sparse representation basis of received signal and Gaussian random matrix as a sensing matrix, and then 𝓁1- norm recovery algorithm is used to recover the original signal. This signal is used with cyclostationary detector. The probability of detection as a function of SNR with several compression ratio and processing time versus compression ratio are used as performance parameters. The effect of the recovery error of reconstruction algorithm is presented as a function of probability of detection.

Sign in / Sign up

Export Citation Format

Share Document