High-speed Processing Research Articles

RESEARCH ON WIDE SPECTRUM FULL-COLOR NIGHT VISION ELDERLY FACIAL VIDEO HEART RATE AUTOMATIC MEASUREMENT AND WARNING SYSTEM

In recent years, the prevalence of cardiovascular diseases has been growing geometrically, especially among the elderly. Real-time heart rate monitoring becomes an effective measure for preventing cardiovascular diseases. Traditional contact-based real-time heart rate measurement methods require long-term wearing of contact electrodes, which can cause inconvenience to the elderly population. In this study, a wide spectrum full color night vision elderly face video heart rate automatic measurement and early warning system was designed and developed by combining imaging photoplethysmography (IPPG), video acquisition equipment based on starlight wide spectrum camera and high-speed face recognition signal processing algorithm based on deep learning. By constructing a software and hardware platform, a comprehensive evaluation of the system’s application in elderly facial video heart rate measurement experiments was conducted.

(Invited) Flexible Resistive Random-Access Memories Based on Carbon Nanotubes and Their Application for Reservoir Computing

Reservoir computing (RC) is one of the machine learning methods derived from a recurrent neural network (RNN) and has attracted attention because of the small amount of computation required for learning. In recent years, various forms of physical RC using nonlinear physical systems such as memristors, optical amplifiers, and soft octopus robots have been proposed for low power consumption and high-speed processing. [1] The physical reservoir system needs non-linearity, high dimensionality, and short-term memory. It has also been shown that transient response currents due to electrochemical reactions and ion migration in solution have the ability of reservoirs [2]. In this study, we report carbon nanotube (CNT)-based resistive random-access memories (ReRAMs) and their application for reservoir computing.The network-like CNT film is used as electrodes sandwiching a thin (10 nm) HfO2 oxide film to form CNT/HfO2/CNT ReRAMs. The CNT film was synthesized using the floating-catalyst chemical vapor deposition method. Then, the CNT film was transferred onto a plastic substrate. HfO2 was deposited by the atomic layer deposition method onto CNT film. Finally, another layer of CNT film was transferred to form CNT/HfO2/CNT stack. The ReRAM exhibited clear memory behavior with SET and REST voltages around +6 V and –6 V, respectively. The ReRAM can sustain the current level of each state for more than 1 × 104 s and can be predicted to store them for ten years. The ReRAMs also exhibited stable operation under bending stress with a 0.5 cm bending radius.We used the ReRAMs as a reservoir for reservoir computing. A time-series prediction task called NARMA10, which was often used as a benchmark of reservoir computing, was performed with a normalized mean square error (NMSE) of 0.38. The chaotic motion of a double pendulum was also successfully demonstrated by using the CNT ReRAM-based reservoir.[1] G. Tanaka et al., Neural Netw. 115, 100 (2019), [2] S. Kan et al., Adv. Sci. 9, 2104076 (2022)

PrunedYOLO-Tracker: An efficient multi-cows basic behavior recognition and tracking technique

Recognition and tracking of basic cow behaviors in natural environments are critical technologies for cows' health monitoring in smart farming. This study proposes an efficient cow behavior recognition and tracking method (PrunedYOLO-Tracker) to address the main problem of the lack of a real-time and identity-linked approach for analyzing and monitoring cow behaviors. Firstly, the channel pruning algorithm is employed to compress the size and parameters of the base model, YOLO v5l. Secondly, a multi-object tracking (MOT) method, Cascaded-Buffered IoU (C-BIoU), which expands the detection and trajectory matching space by increasing the buffer zone, is proposed to combine the behavior information from detection with the trajectory information from tracking, achieving multi-cows' behavior recognition and tracking. Through experimental verification, the pruned model maintains high detection accuracy while reducing the model size, floating-point operations (FLOPs) and parameters by 73.5%, 76.7% and 74.0%, respectively. Compared to the original model, the pruned model only experiences a slight decrease of 0.2% in F1 score, while achieving a 0.3% increase in mean Average Precision (mAP). In terms of cow tracking performance, when compared to six other multi-object tracking algorithms including DeepSort, DeepMot, OC-Sort, BotSort, StrongSort, and ByteTrack, the proposed method demonstrates the highest High Order Tracking Accuracy (HOTA), Multi-Object Tracking Precision (MOTP) and Identification F1 (IDF1) scores, reaching 72.4%, 86.1% and 80.3%, respectively. The results obtained from testing in multiple cow activity environments demonstrate that the proposed method exhibits excellent performance in behavior recognition and tracking, as well as high-speed processing capabilities, with an average video processing speed of 81 frames per second (FPS). This method possesses the ability to reliably monitor and manage cow behavior in real-time, providing technical support for promptly anomalies detection and cow health status monitoring for dairy farming managers.

Diagnostic performance of a novel automated CT-derived FFR technology in detecting hemodynamically significant coronary artery stenoses: A multicenter trial in China

Computed tomography-derived fractional flow reserve (CT-derived FFR) algorithms have emerged as promising noninvasive methods for identifying hemodynamically significant coronary artery disease (CAD). However, its broad adaption is limited by the complex workflow, slow processing, and supercomputer requirement. Therefore, CT-derived FFR solutions capable of producing fast and accurate results could help deliver time-sensitive results rapidly and potentially alter patient management. The current study aimed to determine the diagnostic performance of a novel CT-derived FFR algorithm, esFFR, on patients with CAD was evaluated. 329 patients from 6 medical centers in China were included in this prospective study. CT-derived FFR calculations were performed on 350 vessels using the esFFR algorithm using patients' presenting coronary computed tomography angiography (CCTA) images, and results and processing speed were recorded. Using invasive FFR measurements from direct coronary angiography as the reference standard, the diagnostic performance of esFFR and CCTA in detecting hemodynamically significant lesions were compared. Post-hoc analyses were performed for patients with calcified lesions or stenoses within the CT-derived FFR diagnostic "gray zone." The esFFR values correlated well with invasive FFR. The sensitivity, specificity, accuracy, positive and negative predictive value for esFFR were all above 90%. The overall performance of esFFR was superior to CCTA. Coronary calcification had minimal effects on esFFR's diagnostic performance. It also maintained 85% of diagnostic accuracy for "gray zone" lesions, which historically was <50%. The average esFFR processing speed was 4.6 ± 1.3 minutes. The current study demonstrated esFFR had high diagnostic efficacy and fast processing speed in identifying hemodynamically significant CAD.

Sistem Keamanan Kartu NFC Menggunakan Metode AES pada Sistem Pembayaran Elektronik

To maintain data security, a method is needed that can improve data security, one of which is by using cryptographic techniques. In an effort to meet the needs of data security, cryptography focuses on understanding and applying techniques to protect messages. There are several methods used in cryptography, one of which is the AES method which is recognized as the best encryption algorithm for several reasons, such as using a sufficiently secure key, high processing speed, and ensuring data integrity and confidentiality. In this study, the AES method is used to secure data in the form of nominal balances stored on NFC cards. Based on the test results, it can be seen that the NFC card can function as a balance storage with a maximum limit of IDR 1,000,000. Then based on the confusion matrix method, the security system on the NFC card has an accuracy value of 81,81%, 83.3% precision, and 93,75% recall.&#x0D;

Realization and Control of Bulk and Surface Modes in 3D Nanomagnonic Networks by Additive Manufacturing of Ferromagnets.

The high-density integration in information technology fuels the research on functional 3D nanodevices. Particularly ferromagnets promise multifunctional 3D devices for nonvolatile data storage, high-speed data processing and non-charge-based logic operations via spintronics and magnonics concepts. However, 3D nanofabrication of ferromagnets is extremely challenging. In this work, wereport an additive manufacturing methodology and fabricate unprecedented 3D ferromagnetic nanonetworks with a woodpile-structure unit cell. Weinvestigate their collective spin dynamics (magnons) at frequencies up to 25 GHz by Brillouin Light Scattering (BLS) microscopy and micromagnetic simulations. A clear discrepancy of about 10 GHz is found between the bulk and surface modes which weengineer by different unit cell sizes in the Ni-based nanonetworks. The angle- and spatially-dependent modes demonstrate opportunities for multi-frequency signal processing in 3D circuits via magnons. The developed synthesis route will allow one to create 3D magnonic crystals with chiral unit cells which are a prerequisite towards surface modes with topologically protectedproperties. This article is protected by copyright. All rights reserved.

A novel programming circuit for memristors

Memristor has attracted a lot of interest due to its high processing speed, low power consumption and high integration ability, which is critical for electronic systems and memory-centric computing. However, the memristor programming circuit and strategy are still inflexible and complex, since the signal generator/collector and stimulate pulse must be carefully matched and designed based on memristor intrinsic characteristics without reconfigurable. Here, a simple and effective circuit only consists a parallel reference-resistor-and-NMOS is designed to program memristor with a >99% memristance precision. And the amplitude and width of stimulate pulse are fixed to ±4 V and 5 ms, respectively. In order to cope with the device variation, such as ±10% tolerance of transition voltage, an optimized programming strategy was proposed and demonstrated great robustness. Additionally, a set of reference resistors and NMOSs have been added to facilitate multi-level memristance operation without requiring any changes to the circuit structure. This program circuit was also employed to program memristor crossbar remains 99% precision. In the end, a memristor-based convolutional neural network which controlled by our optimized programming circuit was used for image recognition, and 89.36% accuracy can be achieved even under 15.8% memristance tolerance. This novel circuit demonstrates a simple and flexible strategy in memristor programming, providing a new way to control memristor crossbar for practical application.

High-speed recursive-separable image processing filters with variable scanning aperture sizes

In the process of development of computer technologies, the number of areas of their application naturally grows and, along with it, the complexity of the tasks to be solved, which entails the need for new research. Similar tasks include digital filtering of images in the field of medical technologies and active-pulse television measuring systems. There are many methods and algorithms of digital filtering designed to solve the problem of improving the quality; algorithms that can improve the quality of images while reducing computational costs are widely used. High demands, which are made due to the constant growth in the size of the generated images, as well as the requirement for modern television systems, is real-time operation. When solving practical problems, it is required to use different filter aperture sizes, which provide an increase in quality and preservation of image details. The solution of these problems was the reason for the emergence of adaptive filters that are able to change the parameters in the process of processing the received data, while not spending additional time on processing with an increase in the size of the aperture. The paper presents the principles of constructing adaptive image processing filters, which, by obtaining an input parameter indicating the required dimension of a multi-element aperture, are able to implement the construction of the required aperture. The Laplacian “Truncated Pyramid” filter and the “double pyramid” Laplacian were modified. A feature of these filters is the oddness of the multi-element aperture, so the coefficient used to build the mask is always set to odd. When using these filters, it is possible to use two coefficients that are responsible for increasing the filtration efficiency, since, in their original form, the Laplacian filters have a sum of coefficients equal to zero. The experiment shows a comparison with high-dimensional filters that work when using classical two-dimensional convolution. The next stage of the presented research will be the application of parallel computing techniques, which will increase the speed of the developed filters.

To the issue of decomposition of mathematical models of disturbed motion of complex discrete-continuous dynamic systems

Problem. Usually, complex dynamic systems are characterized by a high order of equations in their mathematical models and intricate interconnections among subsystems that constitute the complex dynamic system. Naturally, the analysis and synthesis of complex dynamic systems require the use of powerful computational systems with vast memory and high processing speed. Goal. The aim is quantitative assessment of the influence of the continuum part of a dynamic system on the behavior of its discrete part and the possibility to perform decomposition of the mathematical model of perturbed motion of a discrete-continuum system based on this assessment. Methodology. The measures are aimed at addressing issues that are based on the discrete-continuum capabilities and their decomposition. Results. For three types of complex dynamic systems described by infinite-dimensional systems of ordinary differential equations, quantitative assessments of the influence of the continuum part on the discrete part of the system have been proposed. These assessments determine the decomposition of the mathematical models of perturbed motion for such systems. Originality. Simplified mathematical models for three types of discrete-continuous dynamic systems were obtained. Practical value. The obtained results can be recommended for the study of the specialized course on the motion characteristics of cargo trucks during the transportation of liquids in tanks. Through optimization using the MATLAB software package, it is possible to simulate various parameters of both the cargo truck and the cargo being transported.

Design of a high speed rat whiskers tracking and symmetry analysis system based on FPGA.

This paper presents a high-speed rat whisker tracking and symmetry analysis system based on FPGA. The system utilizes high-speed image sensors recording rat face videos at 120 and 1000 fps. The Xilinx Ultra96 single computer board is chosen as the platform to implement the system's processing system (PS) and the programmable logic (PL) part. The PL part is responsible for high-speed image processing and whisker tracking, while the PS part analyzes the symmetry of rat face using the tracking results from the PL part. With a processing speed FoM of 118.5 fps/GHz on the Xilinx Ultra96 single computer board and 275.47 fps/GHz on a laptop with Intel Core i5-11500T@1.5GHz, the presented system achieves excellent performance. The proposed whisker detection method has a precision of 98.2% when a threshold with a 4-degree error is selected, with an average error angle of 0.98 degrees across more than 10,000 video frames. Moreover, the proposed system is capable of local video processing within millisecond delays. These results demonstrate the feasibility of developing a high-speed, accurate, and efficient whisker tracking and symmetry analysis system for rat behavior research.

DETECTION OF KNEE OSTEOARTHRITIS BASED ON CENTER OF PRESSURE DATA AND THE BAT ALGORITHM

The high rate of knee osteoarthritis has raised the need for accurate diagnostic methods. In this study, we propose a precise detection method using the center of pressure data obtained from the patients. The introduced automatic detection pipeline is based on the two modern algorithms of grey wolf and BAT. The extracted statistical features and the obtained data from healthy individuals and patients are processed with the grey wolf binary algorithm. The results are fed into the binary bat algorithm to select important features and increase the pipeline accuracy. Then the groups are classified using a four-layer neural network. We show that the proposed method with a simple four-layer neural network offers fantastic accuracy in high-speed processing large data and classifies the high-dimensional knee osteoarthritis center of pressure data with appropriate precision, recall, specificity, and F1 values. The proposed method has direct applications in knee osteoarthritis diagnostics in clinics.

Measurement method and results of divergence angle of laser-controlled solid propellants used in space propulsion

The plume divergence angle is an important reference index for evaluating the thrust efficiency and propellant utilization of space propulsion systems. However, the characteristics of the dynamic variation of plume divergence angle over time cannot be measured using current methods. This paper utilizes high-speed photography and image processing methods to develop a strategy that can give a quick, non-destructive and real-time detection of the divergence angle. Effectiveness of the strategy is verified, and the characteristics of plume divergence angles of different laser-controlled solid propellants were further analyzed and fitted. The experimental results indicate that graphene could effectively reduce the divergence angle, while oxide-doped samples had larger divergence angles than alloy-doped and carbon-doped samples.

Characteristic Analysis and Application of the Optoelectronic Neural Network

In recent years, optoelectronic neural networks have garnered significant attention due to their potential to facilitate high-speed and efficient information processing by employing light-based signals to transmit and process data. This review provides an overview of the current state-of-the-art in optoelectronic neural networks, including their design principles, fabrication techniques, and applications. The article also presents five different methods for constructing optoelectronic neural networks, which offer insights into current ONN research and solutions to overcome the limitations of traditional neural networks. Furthermore, the review discusses three different applications of ONNs, including basic tasks such as data classification, speech recognition, and image recognition, as well as hardware accelerators and SNN algorithms for object detection. The promising potential of optoelectronic neural networks in transforming various fields, such as artificial intelligence, image recognition, and data processing, is also highlighted. As research in this area continues to advance, further breakthroughs in optoelectronic neural networks are anticipated.

Damage localization and quantification in offshore jacket structures using signal processing and intelligent system

Offshore platforms operate in harsh environmental conditions, and a significant portion of their structure is submerged underwater. Consequently, inspection and repair operations are costly and complex, making it crucial to have a precise damage detection system to increase safety and reduce maintenance expenses. This paper presents an intelligent signal processing-based system for damage detection in offshore jacket platforms. First, two damage indicators based on the signal's entropy and power are introduced to localize damage in jacket-type offshore structures. Then, an intelligent system that combines an artificial neural network and several signal processing techniques is used to quantify damage in various parts of these structures. To this end, various signal processing techniques extract the response signal features. Next, the proposed neural network model is trained using a data set, including the extracted feature vectors and the severity of the known damage scenarios. As a case study, an offshore jacket platform installed in the Persian Gulf was examined to evaluate the proposed method's effectiveness. The results reveal that the provided method detects the damage locations and severities with high processing speed and accuracy. Further, selecting suitable features and performing initial processing on training feature vectors can increase the accuracy of detecting damage intensity.

An Experimental Study of Radar-Centric Transmission for Integrated Sensing and Communications

This study proposes a dual-function radar and communication (DFRC) system that utilizes radar transmission parameters as modulation indexes to transmit data to the users while performing radar sensing as its primary function. The proposed technique exploits index modulation (IM) using the center frequency of radar chirps, their bandwidths, and polarization states as indexes to modulate the communication data within each radar chirp. By utilizing the combination of these indexes, the proposed DFRC system can reach up to 17 Mb/s throughput, while observing a robust radar performance. Through our experimental study, we also reveal the trade-off between the radar sensing performance and communication data rate, depending on the radar waveform parameters selected in the DFRC system. This study also demonstrates the implementation of the proposed DFRC system and presents its real-time over-the-air experimental measurements. Consequently, the simulation results are verified by real-time over-the-air experiments, where ARESTOR, a high-speed signal processing and experimental radar platform, has been employed.

An Edge Neuromorphic Hardware With Fast On-Chip Error-Triggered Learning on Compressive Sensed Spikes

This brief proposes an edge neuromorphic hardware design for real-time energy-efficient applications. It is capable of fast on-chip learning on compressive sensed spikes utilizing an error-triggered learning mechanism. Our hardware architecture consists of two event-driven arrays of parallel computing cores, and adopts fine-grained pipeline circuits to boost processing speed. The architecture has good scalability and provides a flexible trade-off among processing speed, recognition accuracy and resource cost. Our design was prototyped on the very-low-cost Xilinx Zynq-7010 FPGA chip on a Zybo platform. The FPGA prototype realized a high processing speed of 4843 and 4931 frames/s with a high energy efficiency of 31.39 lJ/image and 30.83 lJ/image on the MNIST image set during training and inference, respectively. The prototype reached comparably high on-chip learning accuracies across a serial of datasets including both static images as well as spike event streams, e.g., 95.02% on the MNIST images and 84.46% on the N-MNIST streams.

Physical reservoir computing using vertically aligned graphene/diamond photomemristors

Reservoir computing is one of the most promising machine learning architectures and could allow highly efficient, high-speed processing of time-series data. Physical reservoir computing based on various physical phenomena that exhibit complicated dynamics has been widely investigated in recent years. The present work demonstrates vertically aligned graphene/diamond junctions (photomemristors) could be employed for physical reservoir computing involving image recognition of single digits. Exceptional image recognition performance of 92% was obtained due to their complex photoconducting behaviors. This work is expected to assist in the realization of novel visual information processing systems using photomemristors that mimic human brain functions.

ПІДСУМКИ ТА ПЕРСПЕКТИВИ ПІДВИЩЕННЯ ХАРЧОВОЇ ЦІННОСТІ ПАЙКІВ ДЛЯ ВІЙСЬКОВОСЛУЖБОВЦІВ ПІДРОЗДІЛІВ, ЩО ВЕДУТЬ АВТОНОМНІ БОЙОВІ ДІЇ

The article considers the results and prospects of increasing the nutritional value of rations for servicemen of units conducting autonomous combat operations. As an example, the intelligence unit of the Ground Forces of the Armed Forces of Ukraine (SV AFU) is considered. An analysis of improving the quality of rations due to the introduction of modern production technologies, such as microwave (ultra-high-frequency) processing and sublimation, was carried out. The main advantages of microwave processing are determined: the preservation of more nutrients, high processing speed, more uniform heating, economic energy consumption, a wide range of applications. The main advantages of sublimation have been determined: the preservation of the high quality of the product, natural aroma, taste, chemical structure, increase in shelf life, the stability of the process, which allows to ensure the same quality, and the quantity of the product in each batch, the maximum possible reduction in the mass of the finished product. The obtained research results indicate that the selectivity of microwave heating of microorganisms in a non-resonant chamber allows to reduce the exposure time of the product when it is processed in a uniform electromagnetic field by 4-5 times. It is noted that rational nutrition is an important factor in preserving and maintaining the health of military personnel, as well as maintaining their high combat capability. In order to increase the nutritional standards of servicemen of the units of the Armed Forces of the Armed Forces of Ukraine and provide them with high-quality food products enriched with vitamins and minerals, a commodity analysis of dry food rations used in Italy, Spain, Lithuania and Poland was carried out. It is proposed to improve the set of food products to the complex of combat equipment of a soldier, to which are attached: tablets for water disinfection (based on 1 liter per meal); a set of dry products with a high iodine content (for example, "kelp"); a set of dry products, dry products with a high protein composition (for example, "dried banana"). Keywords: ration, quality, nutritional value, microwave processing, sublimation, dry products.

Optimal Reactive Power Dispatch in Electric Transmission Systems Using the Multi-Agent Model with Volt-VAR Control

The optimal dispatch of reactive power is a fundamental task in the operational planning of electrical power systems. This task aims to minimize active power losses and improve voltage levels within the electrical power system. This paper presents the application of the particle swarm optimization methodology to achieve optimal reactive power dispatch. The methodology’s performance is demonstrated by its high processing speed and the results obtained through a comprehensive global search for reactive power dispatch. Additionally, experimental results confirm the algorithm’s effectiveness in optimizing the objective function across different case studies, highlighting its ability to achieve optimal reactive power dispatch. This study represents a significant advancement in the field of power system optimization and provides a useful tool for managing and controlling these systems.

Single-shot switching in Tb/Co-multilayer based nanoscale magnetic tunnel junctions

Magnetic tunnel junctions (MTJs) are elementary units of magnetic memory devices. For high-speed and low-power data storage and processing applications, fast reversal of the magnetization by an ultrashort laser pulse is extremely important. We demonstrate single-shot switching of Tb/Co-multilayer based nanoscale MTJs by combining the optical writing and the electrical read-out methods. A 90-fs-long laser pulse switches the magnetization of the storage layer (SL). The change in the tunneling magnetoresistance (TMR) between the SL and a reference layer (RL) is probed electrically across the oxide barrier. Single-shot switching is demonstrated by varying the cell diameter from 300 nm to 20 nm. The anisotropy, magnetostatic coupling, and switching probability exhibit cell-size dependence. By suitable association of laser fluence and magnetic field, successive commutation between high-resistance and low-resistance states is achieved. The nature of the magnetization reversal of both SL and RL in a continuous film is probed with a depth-resolved magneto-optical Kerr effect (MOKE) magnetometry. The ultrafast dynamics in the continuous full-MTJ stack is investigated with the time-resolved pump–probe technique. Our experimental findings provide strong support for the growing interest in ultrafast spintronic devices.