Search Results (867)

Pedestrian accidents involving heavy vehicles such as trucks and buses remain a critical safety issue, primarily due to structural blind spots. While existing systems like radar-based FCW and BSD have been adopted, they are not fully optimized for pedestrian detection, particularly under adverse weather conditions. This study focused on the empirical validation of a 360-degree pedestrian collision avoidance system using multichannel ultrasonic sensors specifically designed for heavy-duty vehicles. Eight sensors were strategically positioned to ensure full spatial coverage, and scenario-based field experiments were conducted under controlled rain (50 mm/h) and fog (visibility <30 m) conditions. Pedestrian detection performance was evaluated across six distance intervals (50–300 cm) using indicators such as mean absolute error (MAE), coefficient of variation (CV), and false-negative rate (FNR). The results demonstrated that the system maintained average accuracy of 97.5% even under adverse weather. Although rain affected near-range detection (FNR up to 17.5% at 100 cm), performance remained robust at mid-to-long ranges. Fog conditions led to lower variance and fewer detection failures. These empirical findings demonstrate the system’s effectiveness and robustness in real-world conditions and emphasize the importance of evaluating both distance accuracy and detection reliability in pedestrian safety applications. Full article

Spectroscopic observations of Earth-like exoplanets and ultra-faint galaxies–top scientific priorities for the coming decades–involve measuring broadband signals at rates of only a few photons per square meter per hour. This imposes exceptional requirements on the detector performance, necessitating dark currents below 1 e⁻/pixel/kilo second, read noise under 1 e⁻/pixel/frame, and the ability to handle large-format arrays–capabilities that are not yet met by most existing infrared detectors. In addition, spaceborne LiDAR systems require photodetectors with exceptional sensitivity, compact size, low power consumption, and multi-channel capability to facilitate long-range range finding, topographic mapping, and active spectroscopy without increasing the instrument burden. MCT Avalanche photodiodes arrays offer high internal gain, pixelation, and photon-counting performance across SW to MW wavelengths needed for multi-beam and multi-wavelength measurements, marking them as a critical enabling technology for next-generation planetary and Earth science LiDAR missions. This work reports the latest progress in developing Hg_1−xCd_xTe linear-mode e-APDs at premier industrial research institutions, including relevant experimental data, simulations and major project planning. Related studies are summarized to demonstrate the practical and iterative approach for device fabrication, which have a transformative impact on the evolution of this discipline. Full article

To improve the spatial processing performance in the low frequency and long range shallow water communication system, a multi-mode joint equalization scheme is proposed, which combines modal depth function estimation, mode filtering, and multi-input equalization. This method first estimates the modal depth function of the effective modes by Singular Value Decomposition (SVD) of Cross Spectral Density Matrix (CDSM), then separates the influence of each mode on the continuous-time signal by the vertical array mode filtering without any prior information. After these pre-processings, the separated signal is only affected by the single channel mode, and the output Signal-to-Noise Ratio (SNR) is enhanced, and channel delay spread is reduced simultaneously. All the separated parts are then sent to a multi-input equalizer to compensate for the channel fading between different modes.Simulation results verify that compared with single channel equalization after beamforming and multichannel equalization, the proposed multi-mode joint equalization can obtain 3 dB and 6 dB gain, respectively. Experimental results also show that the proposed equalization can achieve lower Bit Error Rate (BER) and higher output SNR. Full article

A wearable closed-loop transcutaneous electrical nerve stimulation (TENS) platform has been developed to address the limitations of conventional open-loop neuromodulation systems. Unlike existing systems such as CLoSES—which targets intracranial stimulation—and electromyography-triggered functional electrical stimulation (EMG-FES) platforms primarily used for motor rehabilitation, the proposed device uniquely integrates low-latency surface electromyography (sEMG)-driven control with six-channel current stimulation in a fully wearable, non-invasive format aimed at ambulatory pain modulation. The system combines real-time sEMG acquisition, adaptive signal processing, a programmable multi-channel stimulation engine, and a high-voltage, boost-regulated power supply within a compact, battery-powered architecture. Bench-top evaluations demonstrate rapid response to EMG events and stable biphasic output (±22 mA) across all channels with high electrical isolation. A human-subject protocol using the Cold Pressor Test (CPT), heart rate variability (HRV), and galvanic skin response (GSR) has been designed to evaluate analgesic efficacy. While institutional review board (IRB) approval is pending, the system establishes a robust foundation for future personalized, mobile neuromodulation therapies. Full article

A 16-channel dual-sided flexible electrode based on a polyimide substrate was designed and fabricated using micro-electromechanical system (MEMS) technology. The electrode exhibited an average impedance of 5.9 kΩ at 1 kHz and a charge storage capacity (CSC) of 10.63 mC/cm². Concurrently, a three-dimensional finite element model incorporating electrical stimulation and micromotion-induced damage was established. The simulation results demonstrated that the implantation trauma caused by the bilateral electrode was significantly lower compared with silicon-based and cylindrical electrodes, while also enabling directional stimulation. Furthermore, leveraging the design of experiments (DOE) methodology, a multivariate regression model was developed to investigate the influence of key stimulation parameters—namely, current amplitude, frequency, and pulse width—on the volume of tissue activated (VTA). The results indicated that the regression model provided accurate predictions of VTA (R² = 0.912). Among the parameters, current amplitude and pulse width exerted a statistically significant influence on VTA size (p < 0.001), whereas the effect of frequency was comparatively minor (p = 0.387 > 0.05). This study presents the first successful fabrication and comprehensive dose–response analysis of a flexible bilateral DBS electrode. Its attributes of low implantation trauma, multi-channel capability, and directional stimulation offer a novel paradigm for precise neuromodulation. Additionally, the established stimulation parameter–VTA response model provides a robust theoretical foundation for optimizing therapeutic parameters in subsequent clinical applications. Full article

This study presents a reconfigurable optical convolutional neural network (CNN) architecture that integrates a crossbar switch network into a smart-pixel-based optical CNN (SPOCNN) framework. The SPOCNN leverages smart pixel light modulators (SPLMs), enabling high-speed and massively parallel optical computation. To address the challenge of data rearrangement between CNN layers—especially in multi-channel and deep-layer processing—a crossbar switch network is introduced to perform dynamic spatial permutation and multicast operations efficiently. This integration significantly reduces the number of processing steps required for core operations such as convolution, max pooling, and local response normalization, enhancing throughput and scalability. The architecture also supports bidirectional data flow and modular expansion, allowing the simulation of deeper networks within limited hardware layers. Performance analysis based on an AlexNet-style CNN indicates that the proposed system can complete inference in fewer than 100 instruction cycles, achieving processing speeds of over 1 million frames per second. The proposed architecture offers a promising solution for real-time optical AI applications. The further development of hardware prototypes and co-optimization strategies between algorithms and optical hardware is suggested to fully harness its capabilities. Full article

This paper introduces Neighborhood-Constrained Decomposition-based Prediction (NCD-Pred), the first system to leverage Neighborhood-Constrained Variational Mode Decomposition (NCVMD) for multichannel forecasting by integrating time series decomposition and neural networks. NCD-Pred leverages NCVMD to decompose a multichannel signal into simpler, band-limited components—referred to as intrinsic mode functions or simply modes—by prioritizing the most informative channel (the main channel) over less informative ones (the auxiliary channels) and bringing their central frequencies into alignment up to a tunable extent. This frequency synchronization provides a framework for cooperative mode forecasting, where predictions of signal components are recombined to produce the original signal prediction. For mode-level forecasting, Long Short-Term Memory (LSTM) networks are utilized. NCD-Pred’s performance is evaluated against similarly designed mode-level forecasting systems using a multichannel dataset with weak cross-correlation, representing power load on a large vessel. The results show that NCD-Pred outperforms benchmark methods, demonstrating its practical utility in real signal processing scenarios. Full article

Micro-motors are essential for power drive systems, and efficient fault diagnosis is crucial to reduce safety risks and economic losses caused by failures. However, the fault signals from micro-motors typically exhibit weak and unclear characteristics. To address this challenge, this paper proposes a novel fault diagnosis method that integrates jump plus AM-FM mode decomposition (JMD), symmetrized dot pattern (SDP) visualization, and an improved convolutional neural network (ICNN). Firstly, we employed the jump plus AM-FM mode decomposition technique to decompose the mixed fault signals, addressing the problem of mode mixing in traditional decomposition methods. Then, the intrinsic mode functions (IMFs) decomposed by JMD serve as the multi-channel inputs for symmetrized dot pattern, constructing a two-dimensional polar coordinate petal image. This process achieves both signal reconstruction and visual enhancement of fault features simultaneously. Finally, this paper designed an ICNN method with LeakyReLU activation function to address the vanishing gradient problem and enhance classification accuracy and training efficiency for fault diagnosis. Experimental results indicate that the proposed JMD-SDP-ICNN method outperforms traditional methods with a significantly superior fault classification accuracy of up to 99.2381%. It can offer a potential solution for the monitoring of electromechanical structures under complex conditions. Full article

The article is devoted to the control of multichannel objects by the method of numerical optimization. In this area, there are several not entirely accurate, but deeply rooted, ideas and approaches that we would like to analyze. This analysis is presented through typical examples and with the use of usually inexpensive software, which can also be obtained for free in a demo version. Using this software is easy with the help of intuitive buttons and options. Thus, solving problems in the design of a regulator for multichannel systems becomes accessible to anyone who has a mathematical model of the object. The principles of control of two-channel objects are considered, i.e., objects of size 2 × 2, which by induction can be extended to objects of size 3 × 3 and, possibly, even more. At the beginning, a solution to the problem of controlling a two-channel object of size 2 × 2 is proposed using a PID regulator of the same size or its simplest modifications. Then the problem with 3 × 3 objects is solved. There are two main factors that influence the result: the presence or absence of dominance of the absolute value of the diagonal elements of the object’s transfer function matrix over the remaining elements; the selected optimization objective function. In the case of dominance, the control task is significantly simplified. In its absence, in some cases it can be ensured by changing the numbering of the channels. Recommendations for forming the objective function are given. For the first time, the use of a fractional degree in the objective function is proposed, and the effectiveness of this approach is justified and shown. For the first time, an additional modification of the test signals during optimization is proposed, and the effectiveness of this modification is shown. It is also shown how the control quality of some channels can be improved at the expense of some deterioration in the control quality of other channels. Full article

This paper presents the development and validation of a cost-effective 3D-printed conductive phantom for EEG sensing system validation that achieves 85% cost reduction (£48.10 vs. £300–£500) and 48-hour fabrication time while providing consistent electrical properties suitable for standardized electrode testing. The phantom was fabricated using conductive PLA filament in a two-component design with a conductive upper section and a non-conductive base for structural support. Comprehensive validation employed three complementary approaches: DC resistance measurements (821–1502 $\mathsf{\Omega }$), complex impedance spectroscopy at 100 Hz across anatomical regions (3.01–6.4 k$\mathsf{\Omega }$ with capacitive behavior), and 8-channel EEG system testing (5–11 k$\mathsf{\Omega }$ impedance range). The electrical characterization revealed spatial heterogeneity and consistent electrical properties suitable for comparative electrode evaluation and EEG sensing system validation applications. To establish context, we analyzed six existing phantom technologies including commercial injection-molded phantoms, saline solutions, hydrogels, silicone models, textile-based alternatives, and multi-material implementations. This analysis identifies critical accessibility barriers in current technologies, particularly cost constraints (£5000–20,000 tooling) and extended production timelines that limit widespread adoption. The validated 3D-printed phantom addresses these limitations while providing appropriate electrical properties for standardized EEG electrode testing. The demonstrated compatibility with clinical EEG acquisition systems establishes the phantom’s suitability for electrode performance evaluation and multi-channel system validation as a standardized testing platform, ultimately contributing to democratized access to EEG sensing system validation capabilities for broader research communities. Full article

The vertical distribution of volatile organic compounds (VOCs) within the planetary boundary layer (PBL) is critical for understanding ozone (O₃) formation, yet knowledge remains limited in complex urban environments. In this study, vertical measurements of 117 VOC species were conducted using an unmanned aerial vehicle (UAV) equipped with a VOC multi-channel sampling system, up to a height of 500 m in Shenzhen, China. Results showed that total VOC (TVOC) concentrations decreased with altitude in the morning, reflecting the influence of surface-level local emissions, but increased with height at midday, likely driven by regional transport and potentially stronger photochemical processes. Source apportionment revealed substantial industrial emissions across all altitudes, vehicular emissions concentrated near the surface, and biomass burning primarily impacting higher layers. Clear evidence of enhanced secondary formation of oxygenated VOCs (OVOCs) was observed along the vertical gradient, particularly at midday, indicating intensified photochemical processes at higher altitudes. These findings underscore the importance of considering vertical heterogeneity in VOC distributions when modeling O₃ formation or developing measures to reduce emissions at different altitudes, and also demonstrate the potential of UAV platforms to provide high-resolution atmospheric chemical data in complex urban environments. Full article

To enable multi-channel parallel spectral analysis in array-based devices such as micro-light-emitting diodes (Micro-LEDs) and line-scan spectral confocal systems, the development of compact array spectrometers has become increasingly important. In this work, a novel spectrometer architecture based on a microlens array grating (MLAG) is proposed, which addresses the major limitations of conventional spectrometers, including limited parallel detection capability, bulky structures, and insufficient spatial resolution. By integrating dispersion and focusing within a monolithic device, the system enables simultaneous acquisition across more than 2000 parallel channels within a 10 mm × 10 mm unit consisting of an f = 4 mm microlens and a 600 lines/mm blazed grating. Optimized microlens and aperture alignment allows for flexible control of the divergence angle of the incident light, and the system theoretically achieves nanometer-scale spectral resolution across a 380–780 nm wavelength range, with inter-channel measurement deviation below 1.25%. Experimental results demonstrate that this spectrometer system can theoretically support up to 2070 independently addressable subunits. At a wavelength of 638 nm, the coefficient of variation (CV) of spot spacing among array elements is as low as 1.11%, indicating high uniformity. The spectral repeatability precision is better than 1.0 nm, and after image enhancement, the standard deviation of the diffracted light shift is reduced to just 0.26 nm. The practical spectral resolution achieved is as fine as 3.0 nm. This platform supports wafer-level spectral screening of high-density Micro-LEDs, offering a practical hardware solution for high-precision industrial inline sorting, such as Micro-LED defect inspection. Full article

Approximately 185,000 people in the United states experience limb loss each year. There is a need for an intuitive neural interface that can offer high-fidelity control signals to optimize the advanced functionality of prosthetic devices. Regenerative peripheral nerve interface (RPNI) is a pioneering advancement in neuroengineering that combines surgical techniques with biocompatible materials to create an interface for individuals with limb loss. RPNIs are surgically constructed from autologous muscle grafts that are neurotized by the residual peripheral nerves of an individual with limb loss. RPNIs amplify neural signals and demonstrate long term stability. In this narrative review, the terms “Regenerative Peripheral Nerve Interface (RPNI)” and “RPNI surgery” are used interchangeably to refer to the same surgical and biological construct. This narrative review specifically focuses on RPNIs as a targeted approach to enhance prosthetic control through surgically created nerve–muscle interfaces. This area of research offers a promising solution to overcome the limitations of existing prosthetic control systems and could help improve the quality of life for people suffering from limb loss. It allows for multi-channel control and bidirectional communication, while enhancing the functionality of prosthetics through improved sensory feedback. RPNI surgery holds significant promise for improving the quality of life for individuals with limb loss by providing a more intuitive and responsive prosthetic experience. Full article

This paper proposes an optimized control filter (OCF) based on the Filtered-x Least Mean Square (FxLMS) algorithm for multi-channel active noise control (ANC) systems. The proposed OCF-McFxLMS algorithm delivers three key contributions. Firstly, even in difficult noise situations such as White Gaussian, Brownian, and pink noise, it greatly reduces error, reaching nearly zero mean squared error (MSE) values across all Microphone (Mic) channels. Secondly, it improves computational efficiency by drastically reducing execution time from 58.17 s in the standard McFxLMS algorithm to just 0.0436 s under White Gaussian noise, enabling real-time noise control without compromising accuracy. Finally, the OCF-McFxLMS demonstrates robust noise attenuation, achieving signal-to-noise ratio (SNR) values of 137.41 dB under White Gaussian noise and over 100 dB for Brownian and pink noise, consistently outperforming traditional approaches. These contributions collectively establish the OCF-McFxLMS algorithm as an efficient and effective solution for real-time ANC systems, delivering superior noise reduction and computational speed performance across diverse noise environments. Full article

In order to achieve 0.1 m resolution and fully polarimetric observation capabilities for airborne SAR systems, the adoption of stepped-frequency modulation waveform combined with the polarization time-division transmit/receive (T/R) technique proves to be an effective technical approach. Considering the issue of range resolution degradation and paired echoes caused by multichannel amplitude–phase mismatch in fully polarimetric airborne SAR with 0.1 m resolution, an amplitude–phase error estimation algorithm based on echo data is proposed in this paper. Firstly, the subband amplitude spectrum correction curve is obtained by the statistical average of the subband amplitude spectrum. Secondly, the paired-echo broadening function is obtained by selecting high-quality sample points after single-band imaging and the nonlinear phase error within the subbands is estimated via Sinusoidal Frequency Modulation Fourier Transform (SMFT). Thirdly, based on the minimum entropy criterion of the synthesized compressed pulse image, residual linear phase errors between subbands are quickly acquired. Finally, two-dimensional cross-correlation of the image slice is utilized to estimate the positional deviation between polarization channels. This method only requires high-quality data samples from the echo data, then rapidly estimates both intra-band and inter-band amplitude/phase errors by using SMFT and the minimum entropy criterion, respectively, with the characteristics of low computational complexity and fast convergence speed. The effectiveness of this method is verified by the imaging results of the experimental data. Full article