Reviewerly/exHarmony huggingface dataset:
Thin film photonic resonator weight stacks

We propose a device composed of stacked thin film optical microcavities to perform compact reconfigurable weighting of wavelength division-multiplexed (WDM) signals. We validate this concept through transfer-matrix simulations of a concrete system composed of rugate filters and phase-change thin films targeting near infrared optical signals.
FPGA-Based Architectures for Random Forest Acceleration

Random Forests (RF) are a widely used group of classification and regression algorithms, mainly implemented in software. Software approaches, however, can take a long time to train when using large datasets due to their complexity. Several hardware architectures have been proposed, implemented and discussed previously for hardware implementation of RFs. However the solutions are focused on a tradeoff of run time and memory. This paper proposes a method to decrease the classification training time by expanding on memory usage while keeping high accuracy compatible with CPU implementations. The proposed method is shown to decrease the timing required for training an RF by an average of 50× compared to a software implementation. The proposed architecture also improves the resulting accuracy compared to the previous literature methods while reducing run time.
Social Distance Measuring Based on Monocular Vision

Socia1 distance has been a growing concern since the COVID-19 pandemic broke out globally. Statistics indicate that keeping social distance is of great practical significance in slowing the spread of the pandemic. Traditional ranging methods rely on ultrasonic, infrared, laser or others. Unfortunately, most of these methods require Bluetooth modules or particular measuring sensors and need to fix hardwire devices on objects, which makes it costly and difficult to apply for measuring distances in various scenes. In order to reduce cost and extend application scope, this paper studies a novel ranging method based on monocular vision, which is proposed to estimate the distance between people in surveillance images. Our approach is to measure the social distance via the world coordinate relationship transformation or the principle of pinhole imaging after performing pedestrian detection. It is worth mentioning that this method only needs computer monocular vision technology, which is low in cost and suitable for an abundance of application scenarios. Through the experiment and analysis, our method shows good performance of social distance measuring in application.
Joint Detection and Tracking with Movable Camera and Its Application to a Drilling Robot in Underground Coal Mine

The movable camera usually has two states, namely static and moving, which makes it difficult to detect or track the object. The following three problems may rise. First, when the camera and the object are relatively stationary, the stationary object may possibly be misjudged as the background or even be gradually absorbed by the background, due to the complexity and limitation of the background model construction. Second, when the camera and the object are relatively moving, the object may be blurred due to camera vibration since the relative movement between them is irregular, which affects the tracking effect. Third, the background is constantly changing when the camera is moving, which may cause confusion between the object and the background. In order to solve the above problems, this paper proposes a joint detection and tracking method with moving camera. The relative status between the camera and the object is judged in real time through the Lucas-Kanade (LK) algorithm at first. For relative stationary state, object detection can be conducted with foreground segmentation method. For relative moving state, object tracking can be implemented with color based adaptive tracking method. Then, the objective can be simultaneously detected and tracked with moving camera, by distinguishing relative stationary and moving states. Finally, to verify the effectiveness of the proposed method, it is applied to borehole detection and tracking for a drilling robot in underground coal mine.
Empirical Study of Proposed Meltdown Attack Implementation on BOOM v3

In recent years, the discovery of transient attacks such as Spectre and Meltdown has brought a huge impact on many processor manufacturers. Therefore, these manufacturers must pay more attention to the security verification of their products and understand the principles of these transient attacks in order to avoid developing products with security vulnerabilities. The Meltdown attack exploits the hardware vulnerability of permission checking and the out-of-order execution feature that is implemented on modern processors, thereby leaking some protected important data. In this paper, we provide a sample code of Meltdown attack to show the open source RISC-V processor - BOOM v3 (Berkeley Out-of-Order Machine) which is claimed to have mitigation mechanism for Meltdown attack is still vulnerable. We also analyze the implementation of BOOM’s permission check and the features of out-of-order to illustrate why our sample attack can attack it successfully. According to our experimental results, the simulation time of this code costs 3.9 ms to show that BOOM v3 will be affected by Meltdown attack.
Least Squares, Minimax and Peak Constrained Designs of Polynomial Hypergraph Filters

In this paper, the polynomial hypergraph filter (PHF) designs using least squares, minimax and peak constrained methods are presented. First, the hypergraph Laplacian matrix (HLM) is used to define the hypergraph Fourier transform (HFT) by using the eigen-decomposition of HLM. Then, the transfer matrix and spectral response of PHF are defined by using HLM and HFT. Next, the filter coefficients of PHF are determined by minimizing three error measures between actual response and ideal response including least squares (LS) error, maximum error, and LS error with peak constraint. Finally, the distributed implementation structure of PHF is studied and noise reduction application example is demonstrated to show the effectiveness of the proposed methods.
An Improved Dynamic Arc Fitting Trajectory Tracking Method

To improve the speed and accuracy of path tracking of a skid-steered robot, an improved algorithm based on the dynamic arc fitting method is presented. A dynamic look-ahead distance model is formulated to cut the global path when the points at which the heading angle change drastically are detected. In addition, the obstacle potential field model with the trajectory collision detection equation is considered to ensure the safety of the fitted trajectory. The average arc radius model, radius uniform variation model and hard constraints of attitude adjustment range are formed to improve the moving speed and stability of the robot. Furthermore, a prediction and compensation model of slip error is modeled to reduce the trajectory tracking error. Finally, the validity of the proposed methods is verified by simulations and experiments.
Effects of Medical Clothing on Person Re-Identification Algorithms

Accurate camera-based human action recognition over longer periods of time or in different camera views requires re-identification of individuals to correctly associate the actions. This is especially important if you want to track people's actions over time. Most work in person re-identification currently focuses on improving the performance of re-identification models for images of people wearing everyday clothing. This becomes a problem when the re-identification scenario changes, and with it the typical appearance of people in that specific environment. Therefore, this work examines the effects of medical clothing on five different person re-identification algorithms. As artificial intelligence and computer vision find more and more applications in the medical field, the question arises to what extent current implementations of person re-identification algorithms are able to generalize from non-medical data, so that the algorithms can be applied in a medical scenario. Since person re-identification is a well-studied topic in the computer vision community, and can also be used in medical settings, this work focuses on the impact of medical clothing on such algorithms. This becomes relevant because the medical clothing is highly uniform and covers many features of a person's characteristics. In addition to examining the effects of clothing as described, ways to overcome the resulting limitations are discussed. In the absence of medical datasets for person re-identification, a suitable dataset was generated containing images of people in medical clothing and the required annotations. Five different existing re-identification models were trained on a non-medical dataset and then tested with the medical data created for this study. The results show a general drop in performance when subjects are wearing medical clothing instead of normal cloths. By additionally marking all people with individual colored hairnets, the re-identification performance can be improved compared to the unmarked subjects.
Context-based Data Augmentation for Improved Ballet Pose Recognition

As computer vision technology continually advances and expands into various application fields, modern methods enable researchers to extract the most important and relevant key features from visual data. Human action recognition is one area that has gained much interest due to its potential in a variety of domains. Ballet, an art form filled with movements and poses, is a domain where the analysis of key features is particularly relevant. At a fundamental level, well-defined features used in a computer vision pipeline, generally provide more accurate predictions. This paper presents a feature engineering and pose analysis approach with OpenPose that produces pose feature templates from distance data. The calculated distance data is used to explore feature augmentation strategies for improved ballet pose recognition results. In addition to the OpenPose distance feature data, this paper investigates geometric and spatial feature approaches to determine optimal feature configurations for the ballet pose recognition task. A ballet dataset of eight distinct poses with multiple dancers was used for the study. The results demonstrate that the proposed approach provides a way to derive baseline pose skeletons from which statistical metrics are derived to aid in determining a valid ballet pose feature space. The augmentation of calculated OpenPose distance feature data yields improved results where the leading SVM classifier for the OpenPose feature space achieves an accuracy of 99.713%. The ensemble-based feature extraction approach which uses MobileNetV3 along with Augmented OpenPose features yielded an excellent Area Under the Curve (AUC) score of 99.999%. The proposed study, therefore, provides a valuable approach that results in state-of-the-art performance for the ballet pose recognition task.
Quantifying resolution and power use in integrated photonic accelerators

Energy efficiency is a key value proposition of photonic vector-matrix accelerators. Quantifying their efficiency presents a challenge. We describe a system-level approach that yields new device metrics, power contributor comparisons, and roadmapping strategies.
Research on the Performance Test System of Automotive Alarms

To solve the problem of measuring the performance index of car alarms, this paper proposes a similarity metric algorithm-based car alarm testing system, which directly reads the current and voltage through a programmable DC linear power supply, and the control of left turn, right turn and reverse mode is realized by a multiplex switch; based on the acquired sound waveform for background analysis, the similarity metric algorithm is used to determine the number of car alarm chirps by traversing the sound waveform to obtain the maximum value of similarity of sampling points. At the same time, the similarity calculation waveform obtained based on the similarity metric algorithm is displayed on the PC in real-time, so that the performance index of the number of car alarms can be monitored visually. Finally, it is proved that the system can test the car alarms according to the standard requirements, and the quality of car alarms can be improved accordingly.
Convolutional neural network model for gait classification of flexible lower limb exoskeleton

Gait refers to the way humans walk. Gait is a complex behavioral characteristic. It is very challenging to identify the intention of human lower limbs for lower limb exoskeleton robots. How to improve the accuracy of gait classification tasks under the premise of reducing complexity is our pursuit. In this paper, a convolutional neural network-based method is proposed to recognize and classify human motion states in outdoor activities while wearing a flexible lower limb exoskeleton. This method hopes that the exoskeleton can meet the tasks of convenient wearing and high accuracy of gait recognition for the elderly, and realize their good wishes of improving the quality of life and increasing the time of outdoor activities. The inertial measurement unit mounted on the hip joint of the lower limb collects motion data under different gaits, Including Euler Angle data, angular velocity data, acceleration data, and magnetic field strength data. After data collection and processing, a python script is used to segment the data into a certain size matrix, and then as the input of a convolutional neural network; various gait activity types are classified and recognized. By using this method, the recognition rate of wearers in different terrain can reach 98.6%, indicating that this method can ensure the good effect of the exoskeleton in gait activity recognition of human lower limbs
Recurrent Multi-view Stereo Depth Inference with Pyramid of Images

Reconstruction of 3D objects from multiple 2D images is to obtain 3D structural information of spatial objects from 2D images. This paper uses bilinear interpolation to downsample high-resolution input images to reduce memory consumption. A multi-stage depth map estimation method is proposed, which makes full use of the effective information at different scales of the image. The accuracy of the high-resolution depth map is improved by using the low-resolution depth map estimated in the previous stage as the input when estimating the high-resolution depth map. A more efficient regularization method is proposed by fusing Conv, LSTM and U-Net in the regularization stage. We conduct a series of experiments on the DTU dataset and confirm that our network outperforms the original algorithm. Our network improves the reconstruction quality as well as the reconstruction speed.
An optimization algorithm for path generated by Jump point search

This paper proposes an optimization algorithm to solve the problem of path redundancy in JPS algorithm. The optimization algorithm connects the nodes in the path into straight lines, and then selects the key nodes by judging whether the straight lines pass through obstacles. Then, a gird map collision detection method based on slope variation is proposed to speed up the screening process of key nodes. Finally, the effectiveness and advancement of the optimization algorithm are verified by simulation experiments.
Shared Round Core Architecture: A Novel AES Implementation for Implantable Cardiac Devices

The explosive growth of connectivity between IoT devices necessitates increased security measures to protect user data. Advanced Encryption Standard (AES) is one of the most popular and widely adopted symmetric encryption/decryption algorithms. The wide usage of AES (and other forms of encryption) in IoTs and other severely power constrained devices such as the implantable cardiac devices makes the low power implementation of AES critical. Traditionally, AES is performed with 128-bit data path, and frequently is aimed for high-speed processing. In this paper we introduce a novel ultra-low power global AES architecture, called Shared Round Core Architecture (SRCA), suitable for low frequency applications such as implantable cardiac devices. This architecture is obtained by reducing the data path from 128 to 32 bits and by merging the encryption and decryption data paths. Merging the data paths allows for the elimination of redundant calculation blocks and sharing of other blocks.
Integrating Augmented Reality in Science Education in South Africa: Applications in the Mancosa iTEACHlab

Integrating augmented reality (AR) in education merges the physical and virtual worlds to exhibit multi-dimensional content that would be difficult to display in a traditional classroom setting. Employing augmented reality in science education results in favourable outcomes since it enables learners to acquire and comprehend abstract science concepts. To achieve these positive outcomes, augmented reality must be successfully integrated into science lessons. However, there is limited material on how augmented reality can be implemented into science education in South Africa. Case studies enable individuals to learn from their experiences and to discover approaches that shape and accelerate progress toward resolving challenges. The purpose of this case study is to share experiences from augmented reality installations at the Mancosa school of education’s iTEACHlab. Also, the article presents an overview of augmented reality (AR) and its educational applications in South Africa. The paper adopts a document analysis technique to collect and analyse the data. Thus, it provides crucial information on how educational institutions might employ augmented reality into science education.
Compressed Sparse Kernel: Optimization of Pruning for Customized CNNs on FPGAs

Convolutional neural networks (CNNs) have become a widely popular method for performing common computer vision tasks, such as image classification and object detection. While their performance is impressive, many applications struggle to make effective use of these algorithms due to their size and computation requirements. This is because many of the real-world applications of this technology are on small, resource-constrained edge devices. One possible solution to this problem comes from the development of customized hardware designed to perform the run-time computations of a CNN. Due to the low power consumption and high flexibility seen in field-programmable gate arrays (FPGAs), these devices are a popular choice for realizing these systems. In this work, we provide a resource and latency efficient implementation of popular CNNs using the weight pruning technique. Specifically, we propose a novel sparse matrix storage format to significantly reduce the amount of memory consumed by the storage of the parameters on the FPGA. We then derive neural network algorithms which use weights stored in this format directly, without the need to consider weights with a value of zero in any computation. Our design shows a latency improvement of 88% while also reducing the hardware by about 5% when compared to the state-of-the-art technique.
Laparoscopic automatic following motion planning of minimally invasive surgery robot based on safety constraints

In the master-slave operation stage of robot minimally invasive surgery, doctors need to frequently switch the control objects to adjust the position and posture of the laparoscope to obtain a better surgical field of vision, which will distract doctors' attention and lead to a decrease in the quality of laparoscopic surgery. Therefore, a laparoscopic automatic following motion planning is proposed to track the motion of surgical instruments quickly, smoothly and safely. Firstly, the position and posture adjustment conditions and adjusted visual field requirements of the distal reference point of the laparoscope are analyzed, and its path points and limit points in the task space are determined through linear trajectory planning. Then, the corresponding path points and limit points of each joint of the manipulator holding the laparoscope in the joint space are obtained by using the robotic kinematic model. Finally, the Chebyshev pseudo-spectral method and the sequential quadratic programming method are used to realize the trajectory planning of the robot joint space and the trajectory optimization based on the optimal time-smoothness. The laparoscopic following experiment shows that the maximum time of trajectory planning and optimization is 0.0362s, and the maximum speed and acceleration of the manipulator joint are no more than 1.68rad/s and 34.79rad/s <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> respectively, which meets the requirements of laparoscopic real-time and smooth tracking of the distal movement of surgical instruments during laparoscopic minimally invasive surgery.
Research on the Design and Prediction Method of Human Lower Limb Motion Perception System

In order to accurately obtain the motion information of the lower limbs of the human body and improve the control accuracy of the wearable lower limb exoskeleton. In this paper, on the basis of analyzing the characteristics of human lower limb movement joints, a set of human lower limb movement perception system based on multi-sensor information fusion is designed. The modules communicate quickly via the CAN bus. And study the use of Gated Recurrent Unit (GRU) to make short-term prediction of sensor signals to solve the problem of exoskeleton control hysteresis caused by sensor signal transmission delay. Finally, the feasibility of the designed system and method is verified by experiments, and short-term prediction can be achieved more accurately.
Phase and Amplitude Trimming of Photonic Integrated Circuits Using Phase Change Materials

Nonvolatile rewritable phase change materials are ideal for post-fabrication trimming of photonic integrated circuits by locally tuning the waveguide effective index. Using electrically configurable phase shifters based on Sb <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> Se <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> , we demonstrate devices with <0.3 dB insertion loss and “active” trimming in both phase and amplitude.
Energy Block Chain Trading Mechanism Based on Game Strategy

With the rapid development of photovoltaic, wind power and other new energy sources, more and more renewable energy sources have been applied to the power market. However, there is still a serious phenomenon of “abandoning light and wind” at the present stage. In Addition, China’s fossil energy still occupies a dominant position. Therefore, how to improve the utilization rate of renewable energy and reduce carbon emissions is a problem worthy of study. Based on micro the renewable energy power suppliers and power grid in the net in the thermal power suppliers as the research object, based on cooperative game model is established and the consensus league chain mechanism, combined with the carbon emission reduction contribution and load characteristics, set up power and new energy power plant net income for the biggest target of cooperative game model, and through the analysis of the trading main body’s contribution to the alliance, Improve Shapley value profit distribution model. Simulation results show that: based on the cooperative game formed by blockchain, the total profit of power suppliers is higher than the sum of the profits of power suppliers under the traditional mode, which improves the utilization rate of renewable energy, reduces the rate of abandoned light and wind, and reduces the carbon emissions.