Miracle-dz/UniFAR-FTUs huggingface dataset:
Upper Extremity Kinematic Parameters: Reference Ranges Based on Kinect V2

Stroke and other neurological diseases seriously affect the daily activities of patients. Automated assessment not only quantifies outcomes but also customizes the rehabilitation schedule. However, there are few prescriptive parameters range in automatic assessment. This study provided a set of referable kinematic parameters range based on Kinect V2 from eighteen healthy subjects when they performed a set of upper extremity movements, Fugl-Meyer assessment of upper extremity, reaching movement, and hand-to-mouth movement. We firstly computed kinematic parameters of healthy subjects, including the range of motion of shoulder joint, elbow joint, and the trunk, the relative displacement of elbow and wrist joint to local coordinate frame, the movement smoothness, and the inter-joint coordination. Normality and variance of these parameters were analyzed. To verify the feasibility of these parameters in distinguishing patients with different severity, eight stroke patients were recruited and performed the movements. Same kinematic parameters were calculated from stroke patients and were quantified by qualified coefficient (QC) for each patient. The results showed that the range of motion, movement smoothness, and relative displacement followed a normal distribution in healthy subjects for most movements. These parameters in general were related to the severity, i.e., severer the disease is, lower the QC is. It revealed that the parameters could be used to distinguish patients with different severity. The results contributed to automatic motor function assessment and home rehabilitation of neurological patients based on Kinect V2.
In-circuit ESR Measurement Method for Electrolytic Capacitors

This paper presents an Equivalent Series Resistance (ESR) measurement method that involves the application of short voltage pulses to an electrolytic capacitor. In the proposed method, the value of the ESR is given by linear interpolating the charging curve of the capacitor taking several appropriate points. In order to validate our measurement method, we compared the values obtained from the practical experiments with the results provided by a BK Precision 891 Bench LCR Meter. After comparison, we found that the experimental results are very close to the values provided by the LCR Meter, the maximum relative error being less than 12%.
Prediction of CardioVascular Disease (CVD) using Ensemble Learning Algorithms

Over the last decade, CardioVascular Diseases (CVD) and allied heart disorders have been the leading cause of death world wide. Early prediction of CVD can help high-risk patients make lifestyle changes and as a result can reduce complications. Researchers in the past have worked on developing computational models to aid health care professionals in the prediction of CVD. Most of the existing techniques lack precise feature sets and suffer from high overfitting and low accuracy. To present more accurate model of predicting CVD along with employing better feature set, ensemble learning models along with individual classification techniques are proposed. Extensive performance analyses on Kaggle Cleveland Heart Disease dataset clearly show our model can significantly improve on the accuracy and F1-score than some of the existing competitors.
Intelligent Detection of Marine Organisms with Deep Learning based on Attention Mechanism

With the increasing intensity of marine fishing, the living space of marine organisms has become more and more serious. The establishment of marine pastures effectively solved the decrease in the number of fish caused by marine fishing. How to analyze underwater environmental data by means of artificial intelligence, monitor the condition of underwater fish in real time, and promote the healthy growth of marine organisms is an important topic of current research. This paper aims at the problems of low accuracy of multi-scale object detection and slow model inference. Therefore, a deep learning model combined CNN network and Attention mechanism is proposed in this paper, and adopted the inference optimization algorithm of multi-algorithm fusion. Finally realizes multi-object and multi-scale fast detection of fish images.
Adaptation Of Recursive Least Squares Identification For Binary Output Systems

Two recursive identification algorithms for Finite Impulse Response (FIR) systems are presented in this paper. These algorithms are dedicated to systems with binary value measurements on the output. They are based on a weighted Least Squares criterion. Numerical simulations are provided to demonstrate the effectiveness of the proposed algorithms in different framework such as the input distribution, the presence of noise or the length of the impulse response.
Multi-Task Active Learning for Simultaneous Emotion Classification and Regression

Emotion recognition, which aims to identify an individual’s emotional state from the acquired physiological or body signals, is very important in affective computing. Emotions have two common representations: categorical, e.g., happy, sad, etc., and dimensional (continuous), e.g., valence, arousal and dominance. Training a good emotion classification or regression model usually requires a large number of labeled data. However, the labeling process is very difficult. As emotions are subtle and uncertain, it usually requires multiple assessors to label each emotional instance to obtain the groundtruth categorical label or dimensional values. In this paper, we propose a multi-task active learning (MTAL) framework to query the most useful samples for labeling, which enables the efficient training of an emotion classification model and multiple emotion regression models simultaneously. This is novel and challenging, as all previous research considered only emotion classification or regression alone, but not simultaneously. Experimental results on the IEMOCAP dataset demonstrated that MTAL outperformed random selection and several state-of-the-art single task active learning approaches, i.e., with the same number of labeled samples, MTAL can obtain better emotion classification and regression models simultaneously.
Distributed Control for Nonlinear Multi-Agent Systems Subject to Communication Delays and Cyber-Attacks: Applied to One-Link Manipulators

This note addresses the problem of distributed control for a class of nonlinear multi-agent systems over a communication graph. In many real practical systems, owing to communication limits and the vulnerability of communication networks to be overheard and modified by the adversary, consideration of communication delays and cyber-attacks in designing of the controller is important. To consider these challenges, in the presented approach, a distributed controller for a group of one-link flexible joint manipulators is provided which are connected via data delaying communication network in the presence of cyber-attacks. Sufficient conditions are provided to guarantee that the closed-loop system is stable with prescribed disturbance attenuation, and the parameter of the control law can be obtained by solving a set of linear matrix inequities (LMIs). Eventually, simulations results of four single-link manipulators are provided to demonstrate the performance of the introduced method.
Successive Interference Cancellation Decoding With Adaptable Decision Regions for NOMA Schemes

In this work, we propose adaptable decision regions in the successive interference cancellation (SIC) decoder for the three-user uplink/downlink non-orthogonal multiple access (NOMA) scheme by exploiting the knowledge of the channel gains. We present scenarios in which the modified SIC decoder outperforms the traditional one and is able to replicate the performance achieved by a deep learning-based decoder. We present analytical symbol error rate (SER) expressions for the three users, compare them to simulation results, and discuss possible consequences of our analysis, such as the optimal power allocation. We also employ heatmaps to analyze the joint influence on the SER of some system parameters, such as signal-to-noise ratio and channel gains.
CSAGAN: Channel and Spatial Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation

Unsupervised image-to-image translation is to learn a mapping function from one image domain to another with unpaired samples, which is an important task of computer vision. However, current unsupervised image-to-image translation methods only perform well on certain datasets. To handle the limitation, this paper proposes a novel framework termed as CSAGAN which contains a new discriminator structure, a novel attention module, and a new normalized function. The discriminator is an attention-guided feature pyramid discriminator which makes use of low-level and high-level features to determine an image’s realness. The new attention module integrating channel attention and spatial attention can guide generators focus on the most discriminative regions of feature maps to generate high-quality translated images. Moreover, our attention module embedded into generators requires less computation compared with other self-attention methods. In addition, the new normalized function helps generators limberly control the variation of shape, color, and texture through learning parameters. Experimental results indicate that our approach performs better than the current state-of-the-art methods.
Performance of DSP operations implemented using a soft microprocessor: a case study based on Nios II

Digital Signal Processors are microprocessing units specially designed for performing operations on signals while optimizing performance. Soft-core processors are fully implemented using a hardware description language and are synthesized using the programmable logic resources available in a Field Programmable Gate Array.The objective addressed in this paper is measuring the ability of soft-core processors in performing digital signal processing algorithms. This paper presents the implementation of some signal processing operations in the Nios II soft-core Reduced Instruction Set Computer based processor. The performance of these operations is then evaluated by ensuring proper execution of functions and benchmarked against the TMS320DM64x+ DSP by Texas Instruments.
Predictive Maintenance of an Electro-Injector through Machine Learning Algorithms

This work aims to define a system for measuring the "lift" of the anchor (the final part of the shutter) present inside the injector based on the use of Machine Learning classification algorithms. The measurement method determined is a non-invasive method, which guarantees that the internal organs of the injection system are not damaged to carry out the measurement and that it can be performed after welding the injector to prevent the "lift" from changing later. This measurement method provides for the classification of the currents circulating inside the solenoid, each of which can be associated with a specific value of the "injector lift. This approach is part of predictive maintenance techniques, a type of maintenance that tries to predict incorrect behavior of the system avoiding that critical operating conditions are reached.Finally, an analysis of the possible techniques for measuring the injector "lift" is carried out through the use of Machine Learning algorithms
Time Series Similarity Analysis Framework in Fresh Produce Yield Forecast Domain

Searching similarity in time series (TS) datasets has gained widespread attention lately in databases classification and forecast domain. In this study, a TS similarity detection framework is proposed to explore alike-behavior fresh produce (FP) in the yield forecast domain through several factors. The sequential daily yield datasets of three types of FP, including strawberry, raspberry, and blueberry, as well as environmental information related to the Santa Maria region, California, between the years 2011 to 2019, are used to develop and evaluate the models. The framework's output is decided to be the similarity percentage (SP) by considering some thresholds that have been tuned using several synthetic yield datasets. According to the results, the SP is 82% and 52% for strawberry versus raspberry and strawberry versus blueberry, respectively. This indicates the fact that strawberry and raspberry have a relatively similar yield pattern compared to blueberry, which is a considerable matter in generalizing forecast models.
Structural design and analysis of a flexible circular soft gripper

This paper proposed a flexible circular soft gripper, its structure mainly consists of a 3-degree of freedom wrist and a pneumatic gripper which can achieve radial contractions. The combination of wrist and pneumatic gripper is able to achieve different angular grasping actions in spatial range. By combining the constant curvature assumption and the traditional D-H method, an improved D-H method applicable to the modeling of continuum robots is obtained, and the kinematic modeling of the soft wrist is performed to obtain the end position. The effects of different air pressures, air cavity cross-section shapes and the number of pressurizing cavities on the wrist bending characteristics are analyzed. The relationship of the number, thickness of chambers in pneumatic gripper versus the gripping performance is investigated. Finally, the feasibility of this gripper is verified and the optimal structural design is obtained.
Deep learning based pose estimation method using 3D point clouds

Pose estimation refers to the acquisition of a rigid transformation of an object relative to its original model coordinate system. This paper proposes a deep learning based approach for pose estimation with point clouds of textureless objects. The contribution of this paper can be summarized as follows: (1) A multi-scale local feature aggregation strategy for emphasizing the neighbor region of interest points. (2) The extension of the original spatial transformer network to point clouds, pose estimation and object classification as the output in a single proposed network. (3) A deep learning model combined with the symmetric function and the multi-scale features to improve the accuracy and robustness of the network model, and a new defined joint loss function by considering the objectives of pose estimation and classification. The experiments are conducted to verify the performance of pose estimation when the point clouds of textureless objects are taken as the input data, which shows that the proposed deep learning framework effectively performs on the pose estimation for both synthetic and real point clouds according to the experimental results.
MaCal - Macro Lens Calibration and the Focus Stack Camera Model

Macro photography is characterized by a very shallow depth of field, which challenges classical structure from motion and even camera calibration techniques, since images suffer from large defocussed areas. Computational photography methods such as focus stacking combine the sharp areas of many photos into one, which can produce spectacular images of insects or small structures. In this contribution we analyse the camera model to describe such focus stacked images in photogrammetry and computer vision and derive a camera calibration pipeline for macro photography to enable photogrammetry and 3D reconstruction of tiny objects. We demonstrate the effectiveness of the approach on ray-traced images with ground truth and real images.
Internet of Robotic Things: A Comparison of Message Routing Strategies for Cloud-Fog Computing Layers using M/M/c/K Queuing Networks

The Internet of Robotic Things (IoRT) has emerged as a game-changing player in the fourth industrial revolution (Industry 4.0) in which the robotic automation technologies are integrated with and reinforced by advanced computing paradigms like cloud/fog/IoT in order to maximize productivity of production chains of smart robotic agents in factories. A single robotic agent can comprise hundreds of sensors and actuators, and production processes performed by multiple agents can demand high computational costs, possibly only via remote resources in cloud or fog computing layers. In this context, it is paramount of importance to assimilate the performance of such computing paradigms for production chains of robotic agents in industrial factories and to provide IoRT system designers with assessment tools to comprehend and anticipate the operational performance of their projects at different development stages. This paper proposes an M/M/c/K model for the performance evaluation of IoRT systems integrated with fog-cloud computing paradigms. Focusing on routing strategies, we demonstrated that the proposed model allows evaluating how different configurations of components in an IoRT architecture impact the performance of the system using the performance metrics including mean response time, component utilization, number of messages, and drop rate. The performance modeling and evaluation of a specific IoRT in this paper can help computing system administrators to design and adopt advanced cloud/fog/IoT computing paradigms to maximize manufacturing productivity in industrial factories.
TSTrack: Tracking by Detector with Target Guidance and Self-Attention*

Object tracking can be considered as an object detection task. However, in general, an object detector cannot differentiate among intraclass instances and cannot fully exploit the spatiotemporal clues between successive frames. In order to solve these problems, in this paper, we proposed a novel object tracking framework based on object detection. We name the new framework TSTrack. Firstly, transformer is used in the target guidance module to extract the object’s features to guide the backbone network to complete the positioning task, which can solve the problem of distinguishing the instances within the classification. Secondly, self-attention mechanism is introduced into the model, which is mainly completed by spatial attention module (SAM) and channel attention module (CAM). By using the characteristics of self-attention mechanism, the attention matrix is dynamically self-learned in two dimensions of space and channel to obtain a more robust object representation. The proposed model is evaluated on the OTB-2013, OTB-50, OTB-2015 and VOT-2016 benchmarks, and the experimental results demonstrate the excellent tracking performance and robustness of our model.
Point2FFD: Learning Shape Representations of Simulation-Ready 3D Models for Engineering Design Optimization

Methods for learning on 3D point clouds became ubiquitous due to the popularization of 3D scanning technology and advances of machine learning techniques. Among these methods, point-based deep neural networks have been utilized to explore 3D designs in optimization tasks. However, engineering computer simulations require high-quality meshed models, which are challenging to automatically generate from unordered point clouds. In this work, we propose Point2FFD: A novel deep neural network for learning compact geometric representations and generating simulation-ready meshed models. Built upon an autoencoder architecture, Point2FFD learns to compress 3D point clouds into a latent design space, from which the network generates 3D polygonal meshes by selecting and deforming simulation-ready mesh templates. Through benchmark experiments, we show that our proposed network achieves comparable shape-generative performance than existing state-of-the-art point-based generative models. In real world-inspired vehicle aerodynamic optimizations, we demonstrate that Point2FFD generates simulation-ready meshes of realistic car shapes and leads to better optimized designs than the benchmarked networks.
Robust Fitting with Truncated Least Squares: A Bilevel Optimization Approach

We tackle the problem of large-scale robust fitting using the truncated least squares (TLS) loss. Existing approaches commonly optimize this loss by employing a smooth surrogate, which allows the problem to be solved using well-known methods such as Iteratively Re-weighted Least Squares (IRLS). In this work, we present a new approach to optimize the TLS objective, where we propose to reformulate the original problem as a bi-level program. Then, by applying the Optimal Value Reformulation (OVR) technique to this new formulation, we derive a penalty approach to solve for the best fitting models, where the penalty parameters can be adaptively computed. Our final algorithm can be considered as a special instance of IRLS. As a result, we can incorporate our new algorithm into existing IRLS solvers, where we only need to modify the weight evaluation procedure. Our experimental results show promising results on several instances of large-scale bundle adjustment and non-linear refinement for essential matrix fitting.
Effective Modeling of Interconnects with Domain Partitioning and Model Order Reduction

This paper is a commented review of our contributions related to the modelling and simulation of interconnects, presented throughout the years in scientific communities that belong mainly to electrical engineering and mathematics. The emphasis is on important topics that we feel that are not yet much used by the electronics packaging community, such as the electric circuit element boundary conditions in the electromagnetic field formulation as well as complexity reduction techniques throughout the modelling flow.
Heart Rate Variability and Breathing Rate Variability Analysis During Four Limbs Exercise

In this study, a four limbs-based exercise equipment was used to examine the effects on heart rate variability (HRV) and breathing rate variability (BRV) during lower limbs, upper limbs, and all four limbs exercise. As a result, a significant reduction of HRV and BRV was observed between the start and the end of each type of exercise. Further comparison between statistics of HRV and BRV information revealed that the minimum coefficient of variance of both HRV and BRV occurred during all limbs exercise, the minimum HRV and BRV were observed during lower limbs exercise and the highest mean of HRV and the highest mean of BRV were detected during upper limbs exercise. Therefore, it is suggested that different variations in HRV and BRV parameters can occur during different types of physical activities which can be useful to distinguish as the lower limbs, upper limbs, or all limbs activities.
Empirical Analysis of Upgrade the Manufacturing Industry in Guangdong——Base on Dynamic Shift-Share Method

In this paper,a single structure-DSSA(Dynamic Shift-Share Analysis) model has been created,and the model applied to obtain the difference between the industrial structure data,focusing on an analysis of the structure of major industries in Guangdong dynamics.On this basis,this paper put forward the main path of the upgrading of industrial structure.
Active Modeling and Control for Manipulators

In this paper, an active model-based control scheme is proposed for the manipulators with parameter uncertainties and disturbances. Firstly the Euler-Lagrange dynamic model of n-link manipulator is linearized by Lie derivation. Then Kalman Filter is utilized to estimate the model error in real-time. Moreover, an active model enhanced state feedback controller is proposed based on the estimated model error. Finally, several simulations are conducted on a manipulator to validate the feasibility of this method. With the proposed control scheme, the tracking errors of all joints are reduced by more than 47% comparing with the conventional state feedback controller.