High-speed Parallel Research Articles

High‐Resolution Optical Convolutional Neural Networks Using Phase‐Change Material‐Based Microring Hybrid Waveguides

In the More‐than‐Moore era, the explosive growth of data and information has driven the exploration of alternative non‐von Neumann computational paradigms. Photonic neuromorphic computing has emerged as a promising approach, offering high speed, wide bandwidth, and massive parallelism. Herein, a high‐resolution optical convolutional neural network (OCNN) is introduced using phase‐change material Ge2Sb2Te5 (GST)‐based microring hybrid waveguides. This on‐chip optical computing platform integrates GST into photonic devices, enabling versatile programming and in‐memory computing capabilities. Central to this platform is a photonic convolutional computational kernel, constructed from photonic switching cells embedded with GST on a microring resonator. This programmable photonic switch leverages the refractive index modulation during the GST phase transition to achieve up to 64 discrete levels of transmission contrast, suitable for representing matrix elements in neural network algorithms with 6‐bit resolution. Using these matrix elements, an OCNN capable of performing parallelized image edge detection and digital recognition tasks with high accuracy is demonstrated. The architecture is scalable for large‐scale photonic neural networks, offering ultrahigh computational throughput, a compact design, complementary metal‐oxide‐semiconductor‐compatible fabrication, and broad bandwidth.

Research and implementation of a large-bandwidth real-time radar jamming simulator.

The electromagnetic environment faced by modern radar is becoming increasingly complex. One effective means to improve the performance of radar systems is testing in an anti-jamming ability test chamber, where the increased complexity has also led to higher performance requirements for radar jamming simulators. Based on the requirements for modern radar system testing, this paper presents a study of a large-bandwidth real-time radar jamming simulator and describes its overall design architecture; the simulator covers the L-Ku and Ka frequency bands and the instantaneous bandwidth is ≥2GHz, which means that the system is able to simulate 11 interference patterns. Synchronous control of the system is realized in 1ms through use of the reflection memory interrupt mechanism, the synchronous pulse signal mechanism, synchronous timing design, and a real-time control software architecture. An overall design scheme for real-time simulation of a radar target jamming echo is given and baseband signal processing resources are saved through information preprocessing, a large-capacity high-speed storage board is designed to improve the data reading speed, a multiphase filtering structure is used to achieve high sampling rates and save hardware resources, and a high-speed parallel computing method is used to improve computing efficiency; the actual measured baseband signal processing time is less than 500ns. Finally, a measurement platform is built, and the main interference patterns are verified through experimental measurements.

Research on trajectory tracking control of delta high-speed parallel robot based on PTNTSMC

Research on trajectory tracking control of delta high-speed parallel robot based on PTNTSMC

Method of field measurement of electromagnetic environment of high speed maglev train

Abstract In order to reduce the complexity of the magnetic field test of maglev trains and improve the accuracy of measurement data, this paper proposes a field measurement method of maglev train radiation emission based on high-speed parallel adaptive filter denoising technology. This measurement method is not limited by special measurement sites, and has the characteristics of high-speed measurement, real-time data processing, and large-capacity data storage. It breaks through the technical bottleneck of the traditional measurement system. The electromagnetic radiation emission of high-speed maglev trains is measured in real-time through multi-antenna synchronous measurement technology, and multi-channel high-speed RF signal measurement and acquisition of external signals are further carried out by blind source separation technology, environmental pre-scanning is carried out, environmental interference characteristics are obtained through feature statistics, and the radiation signal to be measured is estimated by blind source separation algorithm. It helps to achieve the purpose of accurately measuring the electromagnetic radiation emission of high-speed maglev trains. Through field testing, the denoising performance of the method reached 20 dB. Through field testing, the denoising performance of the method reached 20 dB.

Optical diffractive neural network imaging through double diffusive mediums

The optical diffractive neural network (ODNN) offers the benefits of high-speed parallelism and low energy consumption. This kind of method holds great potential in the task of reconstructing diffusive images. In this work, we capture a double-scattering dataset by designing optical experiments and use it to evaluate the image reconstruction capability of the constructed ODNNs under more complex scattering scenarios. The Pearson Correlation Coefficient, which is used as a quantitative index of the reconstruction performance, shows that the constructed diffractive networks enable to achieve high performance in the direct recovery of double-scattering data, as well as in the recovery task of stitching images based on two different kinds of double-scattering data. Meanwhile, due to the high redundancy of valid information in the speckle patterns of scattering images, even if parts of the information in the speckle patterns are blocked, the constructed diffractive networks can also show high reconstruction performance without retraining. The capability of the proposed ODNN to reconstruct double-scattering images indicates that the optical diffractive network has the potential to bring transformative applications in more complex scattering scenarios.

Content-addressable memory using selective-charging and adaptive-discharging scheme for low-power hardware search engine

Single clock cycle access feature of content-addressable memory (CAM) suits well for high-speed parallel content search operation in data-intensive hardware search engines. The diverse applications span from accelerating databases and routing networks to processing images, implementing machine learning, processing biomedical data, and compressing data. Nevertheless, the CAM macro consumes significant energy due to the high switching of most match-lines (MLs), which comprise CAM words, during parallel access. Segmented ML schemes reduced power yet the cell and ML delay, and the extra sequential cycles affect search-speed. A novel selective-charging and adaptive-discharging (SCAD) scheme in the form of dynamic ML architecture is proposed to reduce CAM power consumption at no extra cycle cost. Additionally, a full-swing CAM cell forms the basis of storage and comparison-evaluation to lessen ML delay. Based on 45-nm technology under 1-V supply, the proposed 64 × 32-bit and 256 × 144-bit SCAD-CAM arrays dissipate only 0.45–0.46 fJ/bit/search energy and achieve high-speed. Compared to CAMs based on low-power ML schemes, viz., low-swing precharge, division and control, and master–slave, and the conventional CAM as baseline design, the SCAD-CAM reduces 13.49%–89.35% energy-delay. The average-power reduction of 1.8×–2.4× establishes the SCAD-CAM as a promising memory architecture for emerging search-intensive applications involving large-scale data workloads.

An Experimental Investigation of the Dynamic Performances of a High Speed 4-DOF 5R Parallel Robot Using Inverse Dynamics Control

High-speed pick-and-place industrial applications often use parallel kinematic robots due to their high stiffness and dynamic performance; furthermore, the latter not only depends on the mechanical characteristics of the robots but also on the control algorithm. The literature shows several theoretical contributions to such controllers, mainly tested at the simulation level or on simple proof-of-concept laboratory equipment that execute low-speed and simple trajectories. This paper presents an experimental investigation of the dynamic performance of an industrial high-speed 4-DOF 5R parallel robot designed for pick-and-place applications on moving objects. The inverse dynamics control in the task space is used as a control algorithm. The results show the contribution of all the components of the control algorithm to the motor torque, and the inverse dynamics controller performances are discussed also in comparison to those achievable with simpler PD or PID controllers in a joint space. Moreover, the paper shows the controller synthesis from a modern mechatronic point of view, and the effectiveness of the proposed solution for the tracking of complex high-speed trajectories in an industrial application.

APPLICATION OF THE NEXT GENERATION SEQUENCING IN BIOLOGY AND MEDICINE

Next-Generation Sequencing (NGS), also known as high-throughput sequencing, refers to a set of modern DNA sequencing technologies that have revolutionized the field of genomics. Advantages of NGS techniques involving high speed (parallel sequencing is faster than traditional methods, allowing researchers to obtain results more quickly), cost-effectiveness (ability to sequence multiple fragments simultaneously reduces the cost per base compared to traditional sequencing), and scalability (platforms can be scaled to accommodate varying levels of throughput depending on experimental needs). NGS has significantly accelerated genomics research, enabling breakthroughs in fields such as personalized medicine, cancer genomics, and evolutionary biology. However, challenges such as data analysis complexity, error rates, and cost still exist and are areas of ongoing research and improvement within the field of sequencing technologies. Paper contains the brief explanation of the current NGS platforms and their features. NGS biomedical application is described with the main advantages and abilities of the analysed tools.

Mechanical resonance suppression of the high-speed parallel robot based on fractional order disturbance observer

Considering the flexibility of the transmission shaft system, this paper presents an approach for mechanical resonance suppression of the high-speed parallel robot based on fractional order disturbance observer. First, in order to evaluate mechanical resonance, a decoupled mechatronic dynamics model is built. Based on the proposed decoupled dynamics model, the mechanical resonance under different influence factors is analyzed. Then, based on the three closed-loop control structure of servo system, a model-based parameter tuning method is adopted in the controller parameter tuning. Finally, inspired by the traditional disturbance observer, a fractional order disturbance observer is established by introducing a fractional order filter. Results show that due to the flexibility of the transmission shaft system, under the combined action of mechanical disturbance torque and electromagnetic driving torque, the joint transmission system will generate mechanical resonance phenomena. Therefore, this paper proposes a method for the mechanical resonance suppression of the high-speed parallel robot based on the fractional order disturbance observer. And the proposed method can be applied to other mechatronic device including flexible transmission shaft system.

Scalable Multi-Hierarchy Embedded Platform for Neural Population Simulations.

Brain-inspired structured neural circuits are the cornerstones of both computational and perceived intelligence. Real-time simulations of large-scale high-dimensional neural populations with complex nonlinearities pose a significant challenge. Taking advantage of distributed computations using embedded multi-cores, we propose an ARM-based scalable multi-hierarchy parallel computing platform (EmPaas) for neural population simulations. EmPaas is constructed using 340 ARM Cortex-M4 microprocessors to achieve high-speed and high-accuracy parallel computing. The tree-two-dimensional grid-like hybrid topology completes the overall construction, reducing communication strain and power consumption. As an instance of embedded computing, the optimized model for a biologically plausible basal ganglia-thalamus (BG-TH) network is deployed into this platform to verify the performance. At an operating frequency of 168 MHz, the BG-TH network consisting of 4000 Izhikevich neurons is simulated in the platform for 3000 ms with a power consumption of 56.565 mW per core and an actual time of 2748.57 ms, which shows the parallel computing approach significantly improves computational efficiency. EmPaas can meet the requirement of real-time performance with the maximum amount of 2000 Izhikevich neurons loaded in each Extended Community Unit (ECUnit), which provides a new practical method for research in large-scale brain network simulation and brain-inspired computing.

Computational ghost image encryption method based on sparse speckles

Optical encryption based on ghost imaging has the advantages of high-speed parallel processing and multi-dimensional information. However, in practical application, a large number of speckle patterns are required to achieve the encryption, resulting in a significant increase in transmission costs and low encryption efficiency. In this paper, a compressive sensing-based ghost imaging encryption method using sparse speckles is proposed. The sparse speckle used in this method has the characteristics of easy compression and greatly reducing the amount of encryption keys. According to calculation, the memory overhead of keys using sparse speckles can reduce to 2.44% of that using random speckles. And the decryption effect and anti-noise performance of this method are also verified better than that using random speckles by simulation and experiment results.

Observation of cavitation dynamics in viscous deep eutectic solvents during power ultrasound sonication.

Deep eutectic solvents (DESs) are a class of ionic liquid with emerging applications in ionometallurgy. The characteristic high viscosity of DESs, however, limit mass transport and result in slow dissolution kinetics. Through targeted application of high-power ultrasound, ionometallurgical processing time can be significantly accelerated. This acceleration is primarily mediated by the cavitation generated in the liquid surrounding the ultrasound source. In this work, we characterise the development of cavitation structure in three DESs of increasing viscosity, and water, via high-speed imaging and parallel acoustic detection. The intensity of the cavitation is characterised in each liquid as a function of input power of a commercially available ultrasonic horn across more than twenty input powers, by monitoring the bubble collapse shockwaves generated by intense, inertially collapsing bubbles. Through analysis of the acoustic emissions and bubble structure dynamics in each liquid, optimal driving powers are identified where cavitation is most effective. In each of the DESs, driving the ultrasonic horn at lower input powers (25%) was associated with greater cavitation performance than at double the driving power (50%).

MEConformer: Highly representative embedding extractor for speaker verification via incorporating selective convolution into deep speaker encoder

Transformer models have demonstrated superior performance across various domains, including computer vision, natural language processing, and speech recognition. The success of these models can be attributed to their robust parallel capacity and high computation speed, primarily reliant on the attention layer. In the domain of speaker recognition, state-of-the-art results have been achieved using convolutional neural network (CNN) architectures, particularly with speaker embeddings represented by x-vectors and r-vectors. However, existing CNN-based methods tend to focus on local features while overlooking the global dependence of voiceprint features, resulting in the loss of crucial information. Moreover, the presence of noise in audio data is an influential factor that cannot be disregarded, as it significantly impacts the extraction of discriminative speaker embeddings. To address these challenges, we propose a novel model called the Multi-Scale Expand Convolution Transformer (MEConformer). This model aims to convert variable-length audio into a fixed low-dimensional representation. The MEConformer leverages a CNN framework with expanded receptive fields to capture frame-level features effectively. Additionally, we introduce a transformer encoder that incorporates contextual dependencies, enabling the extraction of both frame-level and discourse-level feature representations. Furthermore, we present a multi-scale residual aggregation strategy, which facilitates the efficient transmission of voiceprint information across the model. By combining these innovative components, the MEConformer achieves a state-of-the-art Equal Error Rate (EER) of 3.72% on the VoxCeleb1 test set. Furthermore, it demonstrates EERs of 5.94% and 3.72% on the VoxCeleb1-H and VoxCeleb-E datasets, respectively. The code for the proposed MEConformer model will be made publicly available at https://codeocean.com/capsule/4563012/tree.

Trajectory Tracking Control of Fast Parallel SCARA Robots with Fuzzy Adaptive Iterative Learning Control for Repetitive Pick-and-Place Operations

Aiming at enhanced suppression of external disturbances and high-precision trajectory tracking of parallel SCARA robot dedicating to fast pick-and-place operations, this work presents the integrated control design of iterative learning algorithm, adaptive control and fuzzy rules, namely, fuzzy adaptive iterative learning control, for such type of robots. A step-design approach is adopted to ensure the adaptability of the designed control law, which is reflected in two aspects: ① the feedback gain of the controller is adjusted by the fuzzy rules; ② the adaptive unknown parameters are obtained by means of iterative learning estimation to suppress the uncertainties and external disturbances. The stability of the designed controller is analyzed and proved by the Lyapunov theory, and the effectiveness is verified by observing the tracking errors in joint space along with the testing pick path, in comparison with different iterative learning based algorithms. After the first-iteration learning, the motion errors of the four actuated joints can be reduced by 56.5%, 45.8%, 46.4% and 39.8%, respectively, and after 15 iterations of learning control, the final angular errors by the designed control law converge to 0.7×10−4 degree maximally. The varying maximum, root-mean-squared and mean angular displacement errors of the actuation joints can converge to zero values with the increasing iterations rapidly, which shows the robustness, effectiveness and advantages of the designed control law. The designed control law can be generalized to high-speed parallel pick-and-place robot to ensure high-precision trajectory tracking for high-quality material handling tasks.

Optical essential secret image sharing using unequal modulus decomposition and gyrator transform

Essential Secret Image Sharing (ESIS) decomposes a secret image into a set of shares that are distributed among categorized participants, and ensures that only authorized subsets of these participants can restore the image. All ESIS schemes to date have been based merely on computational techniques. In this paper, an optical ESIS system is introduced which uses unequal modulus decomposition (UMD) and optical gyrator transform (GT), offering high-speed parallel processing and dispensing with any pre-processing stages. The presented (1, 2, n) ESIS system generates n shares, including one essential share, such that any two shares that include the essential one, can reconstruct the initial secret image with no distortion. Any other unauthorized subset will not gain any information about the image. The scheme generates essential and nonessential shares that are of equal size, eliminating the need to concatenate sub-shares during the reconstruction of the secret image. The results verify that the secret image was completely retrieved in cases of authorized access, while full distortion occurred in cases of unauthorized access. The GT rotation angle serves as an additional authentication factor to validate the essential share and bolster the security. The optical ESIS system exhibits a high level of sensitivity to the changes in the GT rotation angle - that a variation of just 0.001 radians can cause the correlation coefficient to drop below 0.05.

High-speed parallel processing with photonic feedforward reservoir computing.

High-speed photonic reservoir computing (RC) has garnered significant interest in neuromorphic computing. However, existing reservoir layer (RL) architectures mostly rely on time-delayed feedback loops and use analog-to-digital converters for offline digital processing in the implementation of the readout layer, posing inherent limitations on their speed and capabilities. In this paper, we propose a non-feedback method that utilizes the pulse broadening effect induced by optical dispersion to implement a RL. By combining the multiplication of the modulator with the summation of the pulse temporal integration of the distributed feedback-laser diode, we successfully achieve the linear regression operation of the optoelectronic analog readout layer. Our proposed fully-analog feed-forward photonic RC (FF-PhRC) system is experimentally demonstrated to be effective in chaotic signal prediction, spoken digit recognition, and MNIST classification. Additionally, using wavelength-division multiplexing, our system manages to complete parallel tasks and improve processing capability up to 10 GHz per wavelength. The present work highlights the potential of FF-PhRC as a high-performance, high-speed computing tool for real-time neuromorphic computing.

An auditory virtual reality of meeting room acoustics using wave-based acoustic simulations: A content for intuitive understanding of room-acoustics control effect by sound absorbers

Recently, wave-based room acoustic simulation technologies are becoming a realistic option as a small-room acoustics design tool and a virtual indoor sound environment creation tool for research and education. The present paper shows an auditory VR meeting room content, which makes us easily understand how sound absorbers play an essential role in creating a better acoustic environment. Unity creates the 3D virtual reality model, and a binaural room-acoustic auralization is realized by a hybrid technique combined with Ambisonics and the head-related transfer function using RIRs computed by a high-speed parallel wave-based room acoustics solver. The VR meeting room is constructed under various acoustic treatments with sound absorber modeling by wave-based material models.

Dynamic response investigation of high-speed structural redundant parallel manipulator considering multiple spatial revolute joints with radial and axial clearances

In order to grasp dynamic characteristics of the kind of high-speed structural redundant parallel manipulator in actual work, the dynamic response of a novel high-speed structural redundant parallel manipulator considering multiple spatial revolute joints with radial and axial clearances is studied. Firstly, the dynamic model of the novel high-speed parallel manipulator considering the multiple spatial revolute clearance joints is developed and verified. Then, the influences of different factors on dynamic characteristics of parallel manipulator are explored respectively. This paper contributes to explore the effect of revolute joints clearance on total property of the manipulator.

Time-energy-jerk optimal trajectory planning for high-speed parallel manipulator based on quantum-behaved particle swarm optimization algorithm and quintic B-spline

This paper presents a technique for multi-node trajectory planning for high-speed parallel manipulators that optimizes the execution time, energy consumption and jerk (the time derivative of the acceleration of the manipulator joints) to improve working efficiency, decrease energy consumption, dampen vibration and ensure mechanism protection. Initially, an improved quintic B-spline interpolation model is proposed, incorporating additional jerk boundary constraints to dampen residual vibrations at the start and end of the trajectory. Subsequently, the time-energy-jerk optimization model is formulated, with the maximum angular velocity, acceleration, and jerk serving as constraints. The normalized parameter of the B-spline curve is considered to divide the whole model into two submodels. In the first submodel, the satisfaction function is proposed to convert energy and jerk indices into one comprehensive objective for optimization with the quantum-behaved particle swarm optimization (QPSO) algorithm. Time parameters are to be decided as decision variables, regardless of kinematic constraints and execution time. In the second submodel, the optimal time parameters obtained in the first submodel are used as input arguments, and the optimal execution time is determined with respect to the kinematic constraints. The simulation results on a planar parallel manipulator show that the proposed method achieves an efficient trajectory, which is energy-saving and possesses reduced vibration. Application of the comprehensive trajectory generated by the proposed method, rather than the classical method, enhances the maximum productivity of the fast pick-and-place operation to 155 picks per minute from 97 picks/min. This improvement is accomplished by using less energy and reducing vibration.

Package Board Co-Simulation Using Equivalent Circuit Modeling Method for High-Speed System

This research paper focuses on the challenges of system-level simulations for high-speed data transfer and the importance of modeling methodology in achieving accurate results. The simulating of PCB alone without considering the package model is not enough. Instead, to ensure optimal system performance, simulating the packages and PCBs together is essential. When simulating the high-speed parallel bus at the PCB level, it's crucial to consider the package model. If the package model is missing, achieving successful results can be challenging. In this paper, a method is proposed for developing a package model that is specific to the nets required for simulation purposes. The paper explores the challenge of simulating high-speed interfaces with a DDRx example that includes an RLC package model. An interconnect model technique is developed to apply the package RLC model through the equivalent circuit modeling method for the high-speed DDRx test board. This solution aims to simplify the co-design process by using simulation and verifying the model with the assistance of an eye diagram of the data bus. The proposed package model reduces jitter in co-simulation and ensures timing compliance for high-speed interfaces.