Channel Signal Research Articles

Efficient methods for iterative ultrasound image reconstruction using L1 and L2 norm regularization

Ultrasound image reconstruction can be posed as an inverse problem, with image pixels as parameters to a model of wave propagation that are estimated from raw ultrasound channel data. Such framework allows one to make assumptions about channel signals and image properties in form of regularization that can be used to improve reconstruction accuracy in the presence of electronic and acoustic noise. A simple model assumes spherical wave propagation from point sources in a homogeneous medium, similar to delay-and-sum (DAS) beamforming; solving such problems numerically can require many iterations and can result in blurred images when traditional L2 norm regularization is used. We use Fast Iterative Shrinkage Thersholding Algorithm (FISTA) to reduce the number of iterations to less than 10, and to apply L1-norm regularization to the data, which improves edge sharpness and image contrast. We demonstrate the concept using FIELD II simulated ultrasound signals from point, speckle, and anechoic targets with controlled levels of electronic and speckle noise. The FISTA-reconstructed point targets show reduced sidelobe levels by 15 dB compared to the traditional DAS image, and the reduction in full-width at half maximum by a factor of 2. We also implement iterative reconstruction in omega-k frequency domain, which allows factoring the forward and adjoint operators at each spatial frequency and paves the way for an intuitive and more memory efficient, multi-threaded implementation of the method.

Blind passive signal detection via dictionary learning in unknown multipath time-spreading distortion underwater channels

Blind passive signal detection is a challenging endeavor since typically there is no prior knowledge of the transmitted signal. This becomes further complicated in time-spreading distortion (TSD) underwater channels [R. Rashid, E. Zhang, A. Abdi, and Z. H. Michalopoulou, “Theoretical and experimental multi-sensor signal detection in time spreading distortion underwater channels,” Proc. Oceans (2022)], where due to the presence of multiple propagation paths, the unknown signal is convolved with an unknown channel impulse response. In this paper, we introduce and develop a blind passive signal detection method, using dictionary learning [M. Sadeghi, M. Babaie-Zadeh, and C. Jutten, “Dictionary learning for sparse representation: A novel approach,” IEEE Signal Process. Lett. (2013)]. In our blind passive signal detection method, we use the received data to estimate the unknown signal, and also to separate it from the unknown channel impulse response. We have conducted simulations and underwater experiments, to generate receiver operating characteristic curves, to study the performance of the proposed method. The high detection probabilities of the blind method, compared to the replica correlation integration method—that needs to know the transmitted signal for matched filtering—demonstrate the usefulness of the method for passive detection of unknown signals in unknown TSD channels.

Enhancing AmBC Systems With Deep Learning for Joint Channel Estimation and Signal Detection

Enhancing AmBC Systems With Deep Learning for Joint Channel Estimation and Signal Detection

From apathy to activism: Shareholder dissent on director's pay proposals and board effectiveness

Using a hand-collected dataset of shareholder voting on 10,805 director compensation proposals at general shareholder meetings of Chinese listed firms from 2010 to 2019, we provide novel empirical evidence on board reactions to shareholder dissent. We find that boards of directors facing shareholder dissent significantly improve their monitoring and advising effectiveness. Specifically, they reduce the likelihood of financial restatements, improve CEO performance-turnover sensitivity, and improve corporate investment efficiency. Our findings are robust to propensity score matching, instrumental variable estimation and the exogenous shock of mandatory online voting. Moreover, independent directors facing dissent are more likely to vote against board proposals and to reduce their absences from board meetings. The mechanism analysis shows that shareholder dissent operates through the reputation concern and market signal channels. The effect of dissent is more prominent for non-SOEs, for firms with more high-profile directors, and for firms with less powerful CEOs. Besides, the improved board effectiveness induced by shareholder dissent leads to better firm performance. Overall, our findings highlight the role of shareholder voting in emerging markets with concentrated ownership and weak investor protection.

Linear Anti-interference Algorithm for Digital Signal Transmission in Fiber Optic Communication Networks based on Link Analysis

In order to achieve accurate transmission of protection signals in fiber optic communication networks, it is necessary to perform channel balancing configuration of fiber optic communication networks and adaptive forwarding control processing of relay protection signals, the author proposes an accurate transmission method for relay protection signals in fiber optic communication networks based on time-varying multipath fading suppression and adaptive beamforming. The system analyzes the sources of wireless long-distance pain signal interference signals, introduces anti-interference technologies such as two-dimensional joint processing (STAP), provides anti-interference algorithms and related gain analysis, and conducts signal processing gain simulation using MATLAB. Based on the analysis of comprehensive simulation results, at a given symbol length, the signal bandwidth increases, and the processing gain infinitely approaches the given theoretical limit value, rather than increasing nonlinearly. The reason is that the channel is affected by noise, and the channel estimation value and signal conjugate multiplication produce a noise quadratic term. At this point, the estimated value of the coherent region channel is reduced by the influence of noise, and the signal-to-noise ratio loss caused by the noise quadratic term is reduced, so the processing gain increases. During the process of infinite increase in signal bandwidth, the input signal-to-noise power ratio of the receiver tends to decrease towards an infinite value, limited by the size of the coherent region. The channel estimation value increases under the influence of noise, and the noise quadratic term is the main factor affecting the output noise power. When the symbol length is greater than the coherent time, the smaller the maximum Doppler frequency shift and the larger the coherent detection area, the greater the processing gain.

Allyl isothiocyanate, a TRPA1 agonist, protects against olanzapine-induced hypothalamic and hepatic metabolic aberrations in female mice

Olanzapine, a widely prescribed atypical antipsychotic, poses a great risk to the patient’s health by fabricating a plethora of severe metabolic and cardiovascular adverse effects eventually reducing life expectancy and patient compliance. Its heterogenous receptor binding profile has made it difficult to point out a specific cause or treatment for the related side effects. Growing body of evidence suggest that transient receptor potential (TRP) channel subfamily Ankyrin 1 (TRPA1) has pivotal role in pathogenesis of type 2 diabetes and obesity. With this background, we aimed to investigate the role of pharmacological manipulations of TRPA1 channels in antipsychotic (olanzapine)-induced metabolic alterations in female mice using allyl isothiocyanate (AITC) and HC-030031 (TRPA1 agonist and antagonist, respectively). It was found that after 6 weeks of treatment, AITC prevented olanzapine-induced alterations in body weight and adiposity; serum, and liver inflammatory markers; glucose and lipid metabolism; and hypothalamic appetite regulation, nutrient sensing, inflammatory and TRPA1 channel signaling regulating genes. Furthermore, several of these effects were absent in the presence of HC-030031 (TRPA1 antagonist) indicating protective role of TRPA1 agonism in attenuating olanzapine-induced metabolic alterations. Supplementary in-depth studies are required to study TRPA1 channel effect on other aspects of olanzapine-induced metabolic alterations.

Single channel blind source separation of rolling bearing compound faults based on self-learning sparse decomposition and feature mode decomposition

Feature mode decomposition (FMD) has advantages over the other newer time-frequency methods such as ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) in single channel blind source separation (SCBSS). However, FMD has the defect of needing to determine the precise number of fault sources manually. To solve the above defect of FMD, an adaptive method for determining the number of fault sources based on the shift invariant sparse code (SISC) is proposed. SISC was used to train a set of basis functions from the single channel signal, and the corresponding potential components were reconstructed firstly. Subsequently, the structural similarity of these potential components was used for clustering, and each of the obtained clustering signals represented one kind of fault. Then the number of clustering was determined by minimizing the structural correlation among the clustering signals. It was considered that the source separation had achieved the best effect when the structural difference among the clusters was the largest, and the number of clustering at this time was used as the optimal estimated value, which was used as the modal inputs number of FMD calculation model to realize SCBSS of rolling bearing. Simulation and experimental analysis were carried out to verify the effectiveness of the proposed method, and its superiority was also verified through comparison.

Convolutional Neural Networks for Raw Signal Classification in CNC Turning Process Monitoring.

This study addresses the need for advanced machine learning-based process monitoring in smart manufacturing. A methodology is developed for near-real-time part quality prediction based on process-related data obtained from a CNC turning center. Instead of the manual feature extraction methods typically employed in signal processing, a novel one-dimensional convolutional architecture allows the trained model to autonomously extract pertinent features directly from the raw signals. Several signal channels are utilized, including vibrations, motor speeds, and motor torques. Three quality indicators-average roughness, peak-to-valley roughness, and diameter deviation-are monitored using a single model, resulting in a compact and efficient classifier. Training data are obtained via a small number of experiments designed to induce variability in the quality metrics by varying feed, cutting speed, and depth of cut. A sliding window technique augments the dataset and allows the model to seamlessly operate over the entire process. This is further facilitated by the model's ability to distinguish between cutting and non-cutting phases. The base model is evaluated via k-fold cross validation and achieves average F1 scores above 0.97 for all outputs. Consistent performance is exhibited by additional instances trained under various combinations of design parameters, validating the robustness of the proposed methodology.

Automatic MTF Conversion between Different Characteristics Caused by Imaging Devices.

Depending on various design conditions, including optics and circuit design, the image-forming characteristics of the modulated transfer function (MTF), which affect the spatial resolution of a digital image, may vary among image channels within or between imaging devices. In this study, we propose a method for automatically converting the MTF to the target MTF, focusing on adjusting the MTF characteristics that affect the signals of different image channels within and between different image devices. The experimental results of MTF conversion using the proposed method for multiple image channels with different MTF characteristics indicated that the proposed method could produce sharper images by moving the source MTF of each channel closer to a target MTF with a higher MTF value. This study is expected to contribute to technological advancements in various imaging devices as follows: (1) Even if the imaging characteristics of the hardware are unknown, the MTF can be converted to the target MTF using the image after it is captured. (2) As any MTF can be converted into a target, image simulation for conversion to a different MTF is possible. (3) It is possible to generate high-definition images, thereby meeting the requirements of various industrial and research fields in which high-definition images are required.

Improving the Resolution of Flexible Large-Area Tactile Sensors through Machine-Learning Perception.

Industrial robots are the main piece of equipment of intelligent manufacturing, and array-type tactile sensors are considered to be the core devices for their active sensing and understanding of the production environment. A great challenge for existing array-type tactile sensors is the wiring of sensing units in a limited area, the contradiction between a small number of sensing units and high resolution, and the deviation of the overall output pattern due to the difference in the performance of each sensing unit itself. Inspired by the human somatosensory processing hierarchy, we combine tactile sensors with artificial intelligence algorithms to simplify the sensor architecture while achieving tactile resolution capabilities far greater than the number of signal channels. The prepared 8-electrode carbon-based conductive network achieves high-precision identification of 32 regions with 97% classification accuracy assisted by a quadratic discriminant analysis algorithm. Notably, the output of the sensor remains unchanged after 13,000 cycles at 60 kPa, indicating its excellent durability performance. Moreover, the large-area skin-like continuous conductive network is simple to fabricate, cost-effective, and can be easily scaled up/down depending on the application. This work may address the increasing need for simple fabrication, rapid integration, and adaptable geometry tactile sensors for use in industrial robots.

Quantitative measurements of reactive oxygen species partitioning in electron transfer flavoenzyme magnetic field sensing.

Biological magnetic field sensing that gives rise to physiological responses is of considerable importance in quantum biology. The radical pair mechanism (RPM) is a fundamental quantum process that can explain some of the observed biological magnetic effects. In magnetically sensitive radical pair (RP) reactions, coherent spin dynamics between singlet and triplet pairs are modulated by weak magnetic fields. The resulting singlet and triplet reaction products lead to distinct biological signaling channels and cellular outcomes. A prevalent RP in biology is between flavin semiquinone and superoxide (O2 •-) in the biological activation of molecular oxygen. This RP can result in a partitioning of reactive oxygen species (ROS) products to form either O2 •- or hydrogen peroxide (H2O2). Here, we examine magnetic sensing of recombinant human electron transfer flavoenzyme (ETF) reoxidation by selectively measuring O2 •- and H2O2 product distributions. ROS partitioning was observed between two static magnetic fields at 20nT and 50μT, with a 13% decrease in H2O2 singlet products and a 10% increase in O2 •- triplet products relative to 50µT. RPM product yields were calculated for a realistic flavin/superoxide RP across the range of static magnetic fields, in agreement with experimental results. For a triplet born RP, the RPM also predicts about three times more O2 •- than H2O2, with experimental results exhibiting about four time more O2 •- produced by ETF. The method presented here illustrates the potential of a novel magnetic flavoprotein biological sensor that is directly linked to mitochondria bioenergetics and can be used as a target to study cell physiology.

Influence of quadrature transformation imbalance on the noise immunity of signal reception with amplitude-phase shift keying

Objectives. At the present time, amplitude-phase shift keyed (APSK) signals are actively used in satellite communication systems. In particular, they are applied in systems which operate in a limited radio frequency spectrum with increased data transmission quality requirements. Such systems use multi-channel type receivers with maximum likelihood decision on the received symbol (correlation receiver) or quadrature type receivers. The noise immunity of these receivers is directly dependent on the quality of the formation of reference oscillations. These oscillations are reference signals for correlation receivers and in-phase and quadrature components for quadrature receivers. The aim of the work is to analyze the influence of the amplitude and phase parameter spread of the in-phase and quadrature channels on the noise immunity of receiving APSK signals with a circular shape of the signal constellation.Methods. Methods of statistical radio engineering, theory of optimal signal reception, and computer simulation are used.Results. The study established the characteristics of noise immunity of the APSK signal reception depending on the spread of parameters of the quadrature converter. The theoretical calculations were confirmed by the results of modeling the transmission of APSK signals in a Gaussian communication channel. A comparison with systems using quadrature amplitude modulation (QAM) was carried out, in order to assess system stability in the presence of spread parameters among other similar systems.Conclusions. The studies enabled us to conclude that an imbalance of the quadrature reference oscillations can lead to a significant decrease in the noise immunity of radio systems using APSK signals. The minimum energy loss due to imbalance of quadrature reference oscillations is achieved when the imbalance value is less than 10% in amplitude and 2°–3° in phase. The amplitude imbalance of quadrature reference oscillations when receiving QAM signals is more pronounced than in the case of APSK signals. The phase imbalance affects approximately the same.

Construction of an acoustic information transmission system of a horizontal directional drilling device

Currently, the constant growth in the volume of the horizontal directional drilling procedure dictates the improvement of the drilling technology and the technical requirements. One of the important technical requirements for the horizontal directional drilling device is the transmission of information about drilling parameters to the operator (depth, rotation angle, inclination angle). The solution to this problem is possible with the help of measuring systems installed inside the drilling head. Traditionally, in real drilling conditions, three communication channels are distinguished: electrically conductive, hydraulic and electromagnetic, each of which has some advantages and disadvantages. The use of acoustic waves excited in the internal space of the drilling head and propagating in the material of the drilling column to the receiver allows to eliminate the disadvantages of the listed communication channels and ensure the highest quality horizontal directional drilling procedure. The paper investigates the acoustic method of information transmission in a horizontal directional drilling device. An acoustic system for positioning a drilling tool has been developed, which provides the transmission of information about the coordinates of the drilling head. The acoustic channel of information transmission has been studied in terms of constructing its structural components: the design of the probe-transmitter (electromechanical transducer) has been developed, as well as the hardware of the system. In order to determine the requirements for the modulator-demodulator of signals of an acoustic information transmission channel, the propagation of an information package was simulated taking into account the resonant properties of the converter using the Multisim circuit simulation program. Thus, the use of an acoustic system for monitoring and transmitting information about the positioning of the drilling head makes it possible to abandon expensive contact methods for determining the position of the drilling head, which give point information and require a lot of time. The proposed acoustic information transmission channel allows for tight acoustic contact between the protective container and the drilling column to eliminate power losses, increases the efficiency of the device and reduces the loss of a useful signal during its propagation and also simplifies the operation of the device. As a result, the developed acoustic system provides an opportunity to perform the highest-quality horizontal directional drilling procedure with the necessary requirements for the volume of transmitted information and stability of operation in complex noise-signal conditions, which eliminates the disadvantages of previously used methods.

Guest Editorial: Channel Modeling and Signal Processing for Terahertz Communications

Guest Editorial: Channel Modeling and Signal Processing for Terahertz Communications

Design and Analysis of Clustering-Based Joint Channel Estimation and Signal Detection for NOMA

Design and Analysis of Clustering-Based Joint Channel Estimation and Signal Detection for NOMA

Interplay between beta-adrenergic signaling and ion channel expression elucidate remodeling and arrhythmia risk in a simulated population of human ventricular myocytes with heart failure

Interplay between beta-adrenergic signaling and ion channel expression elucidate remodeling and arrhythmia risk in a simulated population of human ventricular myocytes with heart failure

A remote self-calibration design for three-phase power monitoring terminal

In view of the situation that the existing three-phase power metering device is prone to excessive error and inaccurate measurement during long-term operation, and the device must be removed for re-calibration, a self-calibration power monitoring terminal based on NB-IOT communication is proposed. The power monitoring terminal adopts an external voltage transformer and current transformer, and the input signal is mV and mA level AC small signal. The integrated design of single-chip microcomputer and metering chip is used in the power monitoring terminal. The single-chip microcomputer reads and writes the data collected by the metering chip and displays and transmits the power data. After the remote measurement instruction is issued, the three-phase sinusoidal signal generator chip inside the system is used to simulate the voltage signal and current model, and the external voltage and current input channel are switched with the analog signal channel through the analog switch to complete the internal self-verification work of the device. This scheme can significantly reduce the workload caused by dismantling, installing and verifying the instrument, ensure the measurement accuracy of the instrument for a long time, and has great practical value.

Abstract B022: Targeting epithelial-mesenchymal plasticity and CD73 to enhance responses of breast cancers to immune checkpoint blockade therapies

Abstract Immune checkpoint blockade (ICB) therapy has generated dramatic responses in certain types of human tumors however, the response of breast cancers has been largely limited. Epithelial-mesenchymal plasticity (EMP) enables carcinoma cells to metastasize, gain tumor-initiating ability and mount resistance to chemotherapies. In addition, we have demonstrated that this plasticity program also contributes to the establishment of an immunosuppressive tumor microenvironment and confers resistance to ICB therapy (Dongre and Weinberg 2019). By establishing novel, preclinical models of more-epithelial or more-mesenchymal breast tumors, we observed that epithelial tumors recruit CD8+ T-cells to their microenvironment and are sensitive to anti-CTLA4 ICB. In contrast, mesenchymal tumors recruit Tregs and M2-like macrophages and are resistant to the same treatment (Dongre et al., 2017). Strikingly, abrogation of CD73 from more-mesenchymal cells completely sensitizes otherwise refractory tumors to anti-CTLA4 ICB (Dongre et al., 2021). These findings demonstrated for the first time that more-mesenchymal breast cancer cells can be completely sensitized to anti-tumor immunity by interrupting certain cell-intrinsic signaling channels. However, the underlying mechanisms of such sensitization remain elusive. Additionally, the regulatory networks that control the expression of CD73 itself in more-mesenchymal but not epithelial breast cancer cells are not well defined. To determine the subset of T-cells that is functionally important in sensitizing mesenchymal tumors lacking CD73, we depleted either conventional CD4+ T-cells or CD8+ cytotoxic T-cells in combinatoin with ICB. While responding mice recruited both T-cells to their tumors, only CD4+ T-cells (and not CD8+ T-cells) were functionally important in driving sensitization. Along these lines, more-mesenchymal cancer cells lacking CD73 failed to respond to anti-CTLA4 ICB when propagated in genetically engineered mice lacking CD4+ T-cells relative to wild type controls. Importantly, depletion of CD4+ T-cells also prevented the recruitment of peripheral CD8+ T-cells to responding tumors, thereby driving resistance to ICB. In addition, we observed that human triple negative breast cancer cell lines expressed the highest amount of CD73 relative to hormone receptor-positive cell lines. Most importantly, activating EMP in human breast cancer cells by doxycycline-controlled expression of various transcription factors, induced the expression of CD73. Taken together, our work demonstrates that (i) the phenotypic plasticity of breast cancer cells can predict responses to ICB (ii) targeting CD73 can completely sensitize more-mesenchymal tumors to anti-CTLA4 ICB in a CD4+ T-cell dependent fashion and; (iii) altering the cellular plasticity of cancer cells can regulate CD73 expression. Given the highly refractory nature of more-mesenchymal cancer cells, our findings hold tremendous potential in aiding translational efforts to target cancer plasticity to enhance responses of breast cancers to ICB therapy. Citation Format: Anushka Dongre. Targeting epithelial-mesenchymal plasticity and CD73 to enhance responses of breast cancers to immune checkpoint blockade therapies [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr B022.

Bidirectional feature pyramid attention-based temporal convolutional network model for motor imagery electroencephalogram classification.

As an interactive method gaining popularity, brain-computer interfaces (BCIs) aim to facilitate communication between the brain and external devices. Among the various research topics in BCIs, the classification of motor imagery using electroencephalography (EEG) signals has the potential to greatly improve the quality of life for people with disabilities. This technology assists them in controlling computers or other devices like prosthetic limbs, wheelchairs, and drones. However, the current performance of EEG signal decoding is not sufficient for real-world applications based on Motor Imagery EEG (MI-EEG). To address this issue, this study proposes an attention-based bidirectional feature pyramid temporal convolutional network model for the classification task of MI-EEG. The model incorporates a multi-head self-attention mechanism to weigh significant features in the MI-EEG signals. It also utilizes a temporal convolution network (TCN) to separate high-level temporal features. The signals are enhanced using the sliding-window technique, and channel and time-domain information of the MI-EEG signals is extracted through convolution. Additionally, a bidirectional feature pyramid structure is employed to implement attention mechanisms across different scales and multiple frequency bands of the MI-EEG signals. The performance of our model is evaluated on the BCI Competition IV-2a dataset and the BCI Competition IV-2b dataset, and the results showed that our model outperformed the state-of-the-art baseline model, with an accuracy of 87.5 and 86.3% for the subject-dependent, respectively. In conclusion, the BFATCNet model offers a novel approach for EEG-based motor imagery classification in BCIs, effectively capturing relevant features through attention mechanisms and temporal convolutional networks. Its superior performance on the BCI Competition IV-2a and IV-2b datasets highlights its potential for real-world applications. However, its performance on other datasets may vary, necessitating further research on data augmentation techniques and integration with multiple modalities to enhance interpretability and generalization. Additionally, reducing computational complexity for real-time applications is an important area for future work.

Thermal infrared observations of a western United States biomass burning aerosol plume

Abstract. Biomass burning smoke particles, due to their submicron particle size in relation to the average thermal infrared (TIR) wavelength, theoretically have negligible signals at the TIR channels. However, nearly instantaneous longwave (LW) signatures of thick smoke plumes can be frequently observed at the TIR channels from remotely sensed data, including at 10.6 µm (IR window), as well as in water-vapor-sensitive wavelengths at 7.3, 6.8, and 6.3 µm (e.g., lower, middle, and upper troposphere). We systematically evaluated multiple hypotheses as to causal factors of these IR signatures of biomass burning smoke using a combination of data from the Aqua MODerate resolution Imaging Spectroradiometer (MODIS), Aqua Cloud and the Earth Radiant Energy System (CERES), Geostationary Operational Environmental Satellite 16/17 (GOES-16/17) Advanced Baseline Imager, and Suomi-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) and Cross-track Infrared Sounder (CrIS). The largely clear transmission of light through wildfire smoke in the near infrared indicates that coarse or giant ash particles are unlikely to be the dominant cause. Rather, clear signals in water vapor and TIR channels suggest that both co-transported water vapor injected to the middle to upper troposphere and surface cooling by the reduction of surface radiation by the plume are more significant, with the surface cooling effect of smoke aloft being the most dominant. Giving consideration of the smoke impacts on TIR and longwave, CERES indicates that large wildfire aerosol plumes are more radiatively neutral. Further, this smoke-induced TIR signal may be used to map very optically thick smoke plumes, where traditional aerosol retrieval methods have difficulties.