Miracle-dz/UniFAR-FTUs huggingface dataset:
A Framework for Assessing Physical Rehabilitation Exercises

Physical rehabilitation exercises effectively reduce the utilization of healthcare systems through exercises designed to restore and improve the level of functionality of each patient and track the recovery process. Thus, the rehabilitation exercises lower hospital admissions, length of stays, and readmissions. This study proposes a framework for evaluating physical rehabilitation exercises and monitoring patients' progress to reduce the costs associated with rehabilitation. The automated evaluation of the exercises also provides personalization options that enable clinicians to design personalized treatment plans. A fully-automated evaluation framework must identify the distinct skeletal parts, angles, and trajectories for each exercise to distinguish one exercise from another. The proposed framework starts by recording a patient's movements using an RGB camera and then extracts the different skeletal parts from the video for classification and monitoring purposes. Thus, the study also addresses the feasibility of employing an RGB rather than a Kinect camera. A feature ranking algorithm (the Fast Correlation Based Filter) was applied to select essential features. Then, the experiments investigated two classifiers using two sets of rehabilitation exercises (along with their respective mistakes) to classify the movements. The proposed approach achieved 99.64% and 90% accuracy using the Extra Tree Classifier and the One Dollar algorithm, respectively.
Android Malware Detection via Deep learning Approach

With the increasing number of malicious applications nowadays, the traditional methods (such as signature-based methods) cannot cope with these rapid changes and the need for new and effective approaches arises. Therefore, we present an Android malware detection model that depends on static analysis and deep learning. In static analysis, the source code of an application has been analyzed. Moreover, the proposed model combines two deep learning approaches: graph convolutional network and reservoir computing network. Empirical results indicate good performance with accuracy of 82.73% and recall of 91.86%. Moreover, the results show that our model can cope with real-time requirements.
Measuring Method and Experiments of Knee Joint Moment of Assisted Exoskeleton Based on Neural Network

The research on the knee joint moment measurement method is of great significance for the moment control and efficiency measurement of the assisted exoskeleton. For most of the existing measuring methods are not suitable when wearing the assisted exoskeleton, knee joint moment measuring methods of assisted exoskeleton system based on Back-propagation (BP) neural network and Long short term memory (LSTM) neural network is proposed, in which surface EMG to joint moment mapping model is created. The estimation of knee moment at different load levels is realized. The experiment of human walking with a natural gait is verified. The root mean square error of the prediction is less than 17%, and the correlation coefficient is above 0.95.
Novel Forward/Backward Signal Space for DOA Estimation of Coherent RF Signals

Direction of Arrival (DOA) estimation of multiple narrowband RF signals in multipath environments is crucial in real-time applications. In this paper, a novel method is presented to reduce the computational complexity of coherent signals in pairs while maintaining the estimation accuracy. The proposed method employs Cholesky decomposition in conjunction with a novel forward/backward operation (FBO) of the signal subspace matrix to de-correlate the incident signals while the existing methods apply spatial smoothing techniques to the covariance matrix. Employing FBO to the signal space along with Cholesky decomposition will significantly reduce the computational complexity and processing time. These advantages make the proposed method suitable for high-speed communication and real-time applications. Simulation results using Root Mean Square Error (RMSE) are presented to demonstrate the effectiveness of the proposed method and it is compared with Root-MUSIC method as a benchmark.
The Impact of Haptic Feedback During Sudden, Rapid Virtual Interactions

Haptic feedback is known to improve the realism and the performance of virtual tasks during manipulation or teleoperation tasks. However, these benefits might depend on the nature of virtual tasks or the intensity of haptic rendering. In this paper, we focused on the impact of the presence and the intensity of the haptic stimulus during sudden, rapid virtual interactions through a variation of an ISO 9241:411 task- instead of calm, exploration-based interactions. We conducted a user study where the haptic stimulus is rendered through a realistic 1-DoF fingertip haptic device with different intensity levels (full-strength, half-strength, and no-strength) as they are asked to choose highlighted targets on a 6-by-5 grid as fast and correctly as possible. Our results show that haptic feedback did not significantly affect user performance regarding time, throughput, or the nature of the selection behavior. However, participants made significantly more errors when haptic feedback was present in half-strength compared to full-strength and no-strength conditions. In the post-experiment questionnaire, participants reported having favored haptic feedback in full strength in terms of perceived realism, enjoyment, and immersion.
Robust Structured Sparse Subspace Clustering with Neighborhood Preserving Projection

Sparse subspace clustering algorithm cluster the data points located on the union of low-dimensional subspaces through the ℒ1 minimization program. However, the ℒ1-norm is not rotation invariant, and utilizing original data containing noise as the dictionary leads to poor performance. This paper proposes a method named robust structured sparse subspace clustering with neighborhood preserving projection (RSSSC). Firstly, RSSSC replaces the ℒ1 minimization program with a structured re-weighting sparse regularization term, effectively recovering the sparse representation. Secondly, RSSSC uses the extracted features as the dictionary in the self-representation problem to replace the original dataset containing noise and outliers, thus making the model more robust. By fully considering the low-dimensional manifold structure of samples in the original high-dimensional space, RSSSC preserves the neighborhood structure while learning the optimal projection. We verify the effectiveness of the proposed method through experiments on real-world image datasets.
A Haptic Exploration and Surface Classification of Objects with Four Typical Surface Properties

To effectively interact with the physical world, an intelligent robot is required to have the ability to obtain the detailed features of an unknown object. Visual devices are commonly used to detect the global geometry of an object; however, detailed information such as surface properties cannot be identified using these devices. The current study proposes an efficient haptic exploration method to recognize the physical properties of surfaces using an intelligent fingertip. Our surface-following algorithm utilizes the normal force vector and the tangential force vector at the contact point between the fingertip and the target object to predict the moving direction and implement the surface exploration. And a correction index $K$ is introduced to adjust the sliding velocity. The algorithm is proved to be robust and high performance both in simulation and lab experiments. Finally, we propose and explore a haptic prediction neural network, which enables our robot to have an accurate feel through physical interaction. In the classification experiment of four types of objects with different surface properties, the average accuracy of the proposed model is 90.2%.
Multi-View Token Clustering and Fusion for 3D Object Recognition and Retrieval

3D object recognition has received extensive attention in recent years. Many existing methods tackle the task by rendering 3D objects from multiple views. However, most multi-view recognition methods do not utilize fine-grained information from different views, which is found to be crucial for improving 3D object representation in the multi-view setting. In this paper, we propose a transformer-based method, referred to as MVCFormer, for multi-view feature clustering and fusion. MVCFormer clusters semantically similar tokens at the same stages and selects representative fine-grained features, which helps to eliminate feature redundancy and remove cluttered backgrounds and make the selected features more diverse. On the other hand, our model also integrates selected features from all stages to obtain a discriminative 3D object representation by a cross-attention fusion method. Extensive experiments on benchmark datasets (e.g., ModelNet40, ModelNet10, ShapeNetCore55, and RGBD) clearly demonstrate the effectiveness of our proposed MVCFormer over existing baselines.
An Improved Interpolation Algorithm for the Damped Signal Based on Hann Window

Up to date, numerous studies for parameter estimation have been carried out for the tapered signals. This paper proposes a novel interpolation algorithm with the Hann window to obtain the estimations of the frequency and damping factor. Firstly, the interpolation algorithm with the Hann window is derived, and a criterion for determining the root related to the frequency and damping factor estimations is analyzed in detail. Secondly, the estimation variances are theoretically analyzed, and verified by simulations. Comparative simulations are performed to show the capabilities against the white Gaussian noise. Then, the proposed interpolation algorithm is applied to estimate the instantaneous angular speed (IAS) from a practical vibration signal. Finally, the IAS estimation is further applied to the fault diagnosis of bearing, and the results show that it can recognize the fault accurately. Therefore, the improved interpolation algorithm with the Hann window has obvious advantages in the parameter estimation.
Early Detection of Colon Cancer Using Deep Learning Techniques

Colon cancer is the third most prevalent kind of cancer worldwide. A colonoscopy is the global standard for colorectal screening and treatment because it enables the elimination of polyps before they transform into cancerous tumours. Due to the wide variety of polyp types in terms of shape, texture, size, and color, as well as the presence of numerous mimic polyps during colonoscopy, automatic detection of colonic polyps remains an issue that needs more work. Deep learning architectures enhance polyp detection by extracting polyp characteristics. In order to train deep neural networks to recognise polyps in the sparsely available polyp photos, we looked into image enhancement techniques. In our research, we relied solely on public datasets that were supplemented with additional data to yield 3, 972 positive polyp images. Our model is made up of three stages that are preprocessing, polyp detection, and data analysis. In the preprocessing, techniques for image enhancement like rotation and flipping were applied. This model heavily relied on the YOLOv4 detection tool. Finally, the error analysis section aimed to compare the results of our detector with the ground truth for all polyp images. We accomplished 94% precision, 90% recall, and an F1 score of 92%.
Integrated Scheduling of Flexible Job Shop and Energy-Efficient Automated Guided Vehicles

Intelligent machines and automated guide vehicles (AGVs) have been widely applied to improve the flexibility of manufacturing systems. However, the energy consumption and battery management of AGVs are not well considered. Therefore, this paper proposes an integrated scheduling method for solving flexible job shop scheduling problems (FJSP) with energy-efficient AGVs to minimize the makespan and energy consumption. Besides, a novel hybrid genetic algorithm (HGA) with a local neighbor search (LNS) is developed to optimize the objective. Results indicate that the proposed HGA obtains a shorter makespan and lower energy consumption than the genetic algorithm. Finally, we verify the model and present the integrated scheduling solutions of FJSP with energy-efficient AGVs. It indicates the proposed method has significant potential for intelligent manufacturing systems.
GT-Net: Variational Autoencoder Networks based on Graph Transformer for 3D Shape Learning

In this paper, we introduce a novel structure-aware method to generate diverse, and realistic 3D shapes via semantic parts. Most previous works neglect the structural and context information between shape parts and only consider the geometric information. This sometimes leads to the wrong combination of parts during the generation process and brought down the generation quality. To address this issue, we learn a structure-aware latent representation for 3D shapes by training a variational autoencoder(VAE). Specially, we use a graph to express semantic parts and their structural relationship of the 3D shape. Based on that graph representation, we design a generative network based on graph transformer architecture, called Graph Transformer VAE networks(GT-Net), to encode and decode the graph-represented 3D shape. Our experimental results demonstrate that our method achieves better performance than previous methods among various shape families, especially in terms of capturing shape details information.
HDLcs1.0: A Compiler and Simulator Framework for Hardware Description Language

With the increasing scale of integrated circuits, the design of processor chips is highly dependent on EDA tools. Verilog-oriented HDL (Hardware Description Language) processing tools are the key software for front-end design. Apart from commercial EDA tools, open source tools still have room for improvement and optimization. Based on open source software, the required key tools can be further expanded, especially at the gate-level circuit level. This paper presents a compiler and simulator framework HDLcs1.0 for Verilog HDL. Based on LLVM technology and simulator design, we initially established a feasibility framework, which can meet the needs of RTL simulation.
Simulation of Real-time Collision-Free Path Planning Method with Deep Policy Network in Human-Robot Interaction Scenario

Collision avoidance is essential for manipulators working in human-robot interaction scenarios where humans and robots collaborate to ensure human safety and working efficiency. However, traditional path planning methods like RRT are not well-suited for the dynamic and real-time requirement because of the human intervention where the human arms move continuously and randomly. Therefore, we propose a deep policy network based on reinforcement learning to realize real-time collision-free trajectory planning. First, we use a state-of-art human pose estimation network for robot perception. Second, a reinforcement learning-based path plan method is designed to realize the real-time obstacle avoidance. Also, We create the simulation environment based on the human-robot interaction scenarios and the training results show that our method can realize real-time obstacle avoidance path planning in human-robot interaction scenarios by offline training and online deploying. To see the simulation results, please visit the project video at https://www.bilibili. com/video/BVlbW4ylq70Q.
Design and Optimization of FeFET Based CiM for Neural Network Acceleration

In the era of the Internet of Things and artificial intelligence, edge devices require low power consumption, realtime, and low hardware cost to address various information processing tasks and corresponding recognition, classification, and learning algorithm models. The compute-in-memory (CiM) architecture reduces the energy consumption and performance loss caused by data movement by integrating computing units into storage and has the potential to solve the “memory wall” problem caused by the Von Neumann architecture, making it a promising direction. The emerging Ferroelectric Field Effect Transistor (FeFET), with its low write power consumption, high CMOS compatibility, and high switching current ratio, has become one of the leading non-volatile memories (NVMs), attracting many researchers to study its application in the CiM architecture. This paper presents a FeFET-based crossbar array design based on the improved 1FeFET-1Resistor(1F1R) memory cell for neural network acceleration. Such design supports the most critical operation in neural network, multiply-accumulate (MAC) operation. The 1F1R CiM cell proposed in this paper greatly reduces the current variability compared to the traditional FeFET CiM cell and improves the operating range of the input current. In addition, this paper uses a 5-bit precision analog-digital converter (ADC) as the output interface and benchmarks the crossbar array and ADC system in the LeNET neural network model. The results show that the array proposed in this paper can achieve a peak computing efficiency ratio of 1445.3TOPS/W.
Material Classification in Laser Cutting using Deep Learning

Industrial laser cutting offers numerous advantages, including high speed, precision, and the ability to process a wide range of materials. However, the versatility of this technology presents a significant challenge when determining the appropriate power and speed for cutting materials. As the laser cutting machine operator selects the cutting parameters manually, confusion can quickly arise when cutting visually similar materials. This confusion can directly affect the cutting quality and result in errors and wasted materials. This study presents an automated material classification approach based on deep learning that utilizes convolutional neural networks (CNN) and laser speckle sensing. A comprehensive dataset of speckle pattern images for the most commonly used laser-cutting materials is proposed in this paper. The dataset is created by capturing the scattered speckle patterns of the material being cut using a USB camera and includes 6000 speckle pattern images. A deep-learning model based on the pre-trained VGG16 model is then trained on the proposed dataset. The performance of the model is experimentally evaluated on a test set of 200 images with varying orientations and lighting conditions for each sample. The F1-score, recall, and precision metrics validate the robustness of the model in distinguishing between the used materials. The results demonstrate the ability of the model to classify the materials, achieving a notably high classification accuracy of 98.1%. This approach provides an automated and cost-effective solution for organizing laser-cutting materials, enabling operators to select the appropriate power and speed for the cutting process based on the type of material being cut.
Contralateral Movement Constraint Based Gait Intervention Using a Powered Hip Exoskeleton

Hemiparetic gait patterns of stroke survivors has been widely focused on nowadays with various robot-assist gait intervention approaches. In this paper, we use a powered hip exoskeleton to provide resistance on one side of the wearer during walking and explored the variation of the other side of the lower limb in kinematics, ground reaction forces, and EMG signals. The preliminary results on healthy adults indicated that the difference in hip joint angles, EMG area under the curve, co-contraction index, and synergy weight were observed under the contra-lateral intervention. This study shows potentials that powered hip exoskeleton-induced non-hemiplegic side constraints may be a new rehabilitation way for stroke patients in the near future.
Chemical Imaging of Microparticles with Raman, FTIR and Quantum Cascade Laser Microscopy

Microplastic contamination of the environment and ecosystem has attracted much attention in recent years. Its presence has been detected in the most remote regions and its propagation through the food chain poses a serious health risk to humans and animals. To better understand their presence in the environment, their detection and identification plays a critical role in any remediation effort. FTIR and Raman imaging provide simultaneously size metrology and spectral information and have been gold standard tools for microplastic analysis. In this study, we will present state-of-the-art chemical imaging methods, Fourier transform infrared spectroscopy (FTIR) and Raman imaging as well as quantum cascade laser mapping, for the detection of microplastic particles. We will demonstrate enhanced detection through the use of reflective substrates and discuss the signal enhancement strategies of these chemical imaging techniques as well as their limitations.
A Statistical Evaluation Method to Quantify the Runtime Uncertainty of Accelerator-rich SoC Targeting EDA Acceleration

Integrating a host CPU with domain-specific accelerators is a promising way to improve the performance/power/area of EDA acceleration in the post-Moore age. But it is hard to estimate the runtime performance of the accelerator-rich SoC in the early stage, especially considering the uncertainty produced by PVT variation. To predict and further avoid the architectural risk, we propose a statistical model to evaluate the runtime distribution while targeting EDA acceleration. We first collect the dataset by the Latin hypercube sampling method and dump the output runtime data via gem5-SALAM simulator. After transforming the original dataset into the Gauss distribution through box-cox transformation, we integrate the least square boosting regression to achieve the deterministic estimation. Then we inject the uncertainties of frequency and unit yield, and thus the predictions become distributions. Based on the statistical evaluation method with a 4.3% normalized MSE, we can find that the experimental performance is usually worse than the original deterministic estimation, which proves the architectural risk.
An Extended Virtual Proxy Haptic Algorithm for Dexterous Manipulation in Virtual Environments

With the evolution of hand-based haptic interfaces, novel forms of controlled force feedback arise allowing multipoint interaction between virtual objects and the hand’s digits. With these advances, there must be an effective force display coupled with an intuitive visualization of the hand at its points of high manipulability. This is the basis for dexterous manipulation immersion in virtual environments. Still, there are challenges due to the complexity of force interaction, bandwidth limitations, and redundant visual configurations. In this paper, we present an extended proxy algorithm for digit-based interactions, which through configuration-based optimization, provides an efficient, robust, and visually plausible way to interact with virtual objects with a virtual hand. Additionally, we revisit a seldom-seen modality of haptic rendering, whole-hand kinesthetic feedback, with the Maestro exoskeleton in the implementation of our algorithm. We unify these methods and develop a CHAI3D module in a comprehensive visuo-haptic framework that was evaluated through demonstrations of joint-level haptic force data during interaction with static and dynamic objects alike. Our computationally-efficient approach sets the foundation for the visual display of in-hand virtual object manipulation with the effective rendering of stable haptic interactions under complex tasks.
Recurrent Neural Networks (RNNs) to improve EEG-based person identification

This study explored the potential of using EEG signals for biometric identification, and the performance of advanced deep learning models, such as LSTM and GRU, in extracting features from raw EEG signals was evaluated. It was demonstrated that these models could accurately identify individuals based on their unique brain activity patterns. The study’s findings have significant implications for secure and reliable biometric identification systems in healthcare and security. However, challenges remain in addressing data privacy and security, cross-domain generalization, and fairness and bias. Further research is needed to address these challenges and fully realize the potential impact of deep learning on biometric identification.