Miracle-dz/UniFAR-FTUs huggingface dataset:
Satellite Analysis and Identification System Based on MATLAB

Currently, there are still cognitive blind spots and technological deficiencies in satellite analysis. On the other hand, facing massive signal data, there is a lack of an effective classification tool for rapid identification of satellite types. In order to improve the efficiency of satellite recognition, this paper designs a satellite analysis and recognition system based on MATLAB. The system uses Fourier transform and Hilbert transform to extract accurate frequency and phase feature information of the signal, and then judges the modulation method and signal system. The K-means clustering method is used to quickly classify satellites, achieving rapid analysis and classification of satellite signals.
Research on Advanced Detection Method of Target Spectrum of Radar Altimeter Based on VGG16-Net

Radar altimeter is an indispensable airborne electronic equipment in many aircraft. It has been widely used in various military and civilian equipment. The radar altimeter based on FM continuous wave constant beat frequency system has the advantages of strong anti-interference and no distance blind area, and has a good application prospect. However, since the radar altimeter's target is the ground, and the ground is a complex surface target, the echo information reflected from the ground is very rich, and it is difficult to accurately extract the height information. Therefore, to solve the above problems, this paper applies convolutional neural network to the spectrum frontier detection of altimeter radar, and proposes a spectrum frontier detection algorithm based on VGG16-Net.
Improving Robustness of Unsupervised Domain Adaptation with Adversarial Networks

Unsupervised Domain Adaptation (UDA) has been subject to comprehensive investigation and has achieved significant success in real-world scenarios by transferring information from labeled source domains to non-labeled target domains. Nonetheless, the vulnerability of UDA models to adversarial attacks remains a formidable challenge. While Adversarial Training (AT) is acknowledged as one of the most potent defense mechanisms, it cannot be directly applied to UDA settings. Furthermore, there have been few studies that explore the application of AT in UDA setting. In this paper, we strive to leverage Generative Adversarial Network (GAN) to generate adversarial examples for target data, subsequently incorporating them into AT. To generate high-quality adversarial examples and achieve better adversarial robustness of UDA models, we propose the AAT algorithm. We apply AAT to two different UDA algorithms and evaluate on three datasets. The results demonstrate that our model achieves improved adversarial robustness and a balance between accuracy on clean data and accuracy under adversarial conditions.
The Development of the Theory of Energy Conversion Processes in a Complex System Electromechanical Assemblage

The theory of energy process analysis in electromechanical complexes received further development. Systematization of approaches to the decomposition of the time function of the instantaneous power of polyharmonic signals in various circuit solutions and modes has been carried out. The mechanism of formation of instantaneous power components of polyharmonic signals is considered. A mathematical apparatus for determining canonical, non-canonical and pseudo-canonical components of instantaneous power is proposed. The effect of strengthening the alternating-signal component of the power due to the coincidence of the frequencies of the canonical and pseudo-canonical components is proven. The possibility of expanding the complex of energy process assessments in the space of instantaneous power is shown.
A Multimodal Virtual Keyboard in Fully Immersive Virtual Reality

Virtual reality (VR) technology can be used for multiple purposes, for simulating or enhancing real-life situations. VR-based interfaces can provide new operational advantages and reduce hardware requirements, i.e., weight, by offering various enhanced input modalities that otherwise could not be used in a physical cockpit. This study presents a comparative analysis of three input modalities for a virtual keyboard in a fully immersive VR environment. Each input modality has two steps: 1) pointing to the desired button, and 2) the selection of the item. We compare the hand laser pointer with trigger selection, the head laser pointer with trigger selection, and the head laser pointer with the dwell time selection (no need to use the hands). In addition, we propose new types of visual feedback: for all the input modalities, we display in the field of view of the user the last 5 letters. For the modality with the dwell time, we add a circular progress bar around the field of view of the user to provide a constant awareness of the possible button selection. The proposed virtual keyboard input modalities were assessed by 32 participants. The results support the conclusion that the hand pointer with the trigger control provides the best speed compared to other modalities.
Enhanced Fatigue Driving Behavior Detection Based on Iris Recognition and Facial Analysis

To more effectively identify and prevent fatigue driving behavior, this paper proposes an enhanced fatigue driving detection technique that incorporates iris recognition. This method integrates deep learning, facial feature analysis, and iris recognition technology to precisely identify and effectively combat fatigue driving. In its implementation, we use Convolutional Neural Networks (CNN) and three dimentional convolution (3D) to monitor the driver's eye, mouth, and iris status in real-time. This is done in conjunction with fatigue biological features such as blinking frequency, eyelid closure duration, and yawning frequency to determine the driver's state and categorize fatigue types. Further, we introduce an attention mechanism allowing the model to focus more on key fatigue features. As a result, the accuracy(ACC) has improved by 5%, and our algorithm can be successfully embedded into edge devices. Experimental evidence show that the proposed technique has high accuracy and reliability, making it of significant application value for preventing fatigue driving.
Contextual and Nonstationary Multi-armed Bandits Using the Linear Gaussian State Space Model for the Meta-Recommender System

Selecting an optimal recommendation method is crucial for an electronic commerce (EC) site. However, the effectiveness of recommendation methods cannot be known in advance. Although continuous comparative evaluation in an actual environment is essential, it results in opportunity loss. To overcome this problem, opportunity loss reduction has been studied as a multi-armed bandit (MAB) problem, and adaptive meta-recommender systems (meta-RS) were devised to automatically and continuously select the best recommendation method according to a policy. The following three factors cause opportunity loss: the context of the recommendation method, temporal variation, and response time. However, studies are yet to formulate an MAB policy that considers all three factors. Thus, reducing opportunity loss remains a problem. We propose an MAB policy that considers all three causes of opportunity loss by using a Kalman filter for a linear Gaussian state space model. We conducted extensive experiments to select the best recommendation method using data from a real EC site. The results revealed that the proposed policy is highly effective for reducing opportunity loss of the meta-RS during evaluation and increasing cumulative clicks compared with baseline policies.
Investigating the Impact of Intermediate Modulation Layer in RRAM on Multilevel Perceptron Performance

This paper investigates the impact of the intermediate modulation layer (IML) in resistive random access memory (RRAM) on the performance of multilevel perceptron (or multi-layer perceptron MLP). The study employs extensive simulations and analysis to quantify the accuracy, read disturbance, and energy efficiency enhancements. A comparative analysis highlights the advantages of incorporating IML layer in RRAM-based MLP architectures compared to traditional approaches. The results prove that IML layer improves the performance of the MLP, enabling higher accuracy, reduced read disturbance, and reduced energy consumption. The research findings provide valuable insights into the design and optimization of MLP architectures utilizing emerging memory technologies. Thereby allowing robust and efficient neuromorphic hardware implementation for many applications such as pattern recognition, machine learning, and data classification.
A Biofeedback-Enhanced Virtual Exergame for Upper Limb Repetitive Motor Tasks

Upper Limb (UL) Rehabilitation in Multiple Scle- rosis (MS) is an open research field due to the complex interplay between cognitive and physical dysfunctions. Virtual Reality (VR) can face such an issue by enriching physical training with engaging features, including biofeedback strategies to self- regulate autonomic functions according to the visualisation of indices like heart rate variability (HRV). In the present work, HRV biofeedback is introduced in a VR-based exergame (a game designed to promote exercising), tailored to rehabilitation of the dominant upper limb in Persons with MS (PwMS). The exergame is based on a dual-task paradigm, integrating a UL motor rehabilitative task with a breathing task. The aim is to investigate how the design developed for the HRV biofeedback affects engagement and performance during the exergame session. As a preliminary study, sixteen able-bodied subjects are tested in a within-subjects design, to assess the quality of the game features and design, before approaching MS patients. Two conditions are presented, with and without biofeedback. The proposed HRV biofeedback has two possible levels, depending on whether or not the desired respiratory rate of six breaths/min is successfully maintained. It is used to control game elements and change difficulty of the session. The main finding of this study is that biofeedback improves both user performance and experience in healthy subjects. These results underline the great potential of this technique to promote engagement. Thus, they point to fostering the rehabilitative effectiveness of repetitive motor tasks and encouraging adherence to the long- term training. Future studies will encompass fine tuning of the experimental setup and include PwMS to further adjust the game to patients' needs and observe the setup compliance to rehabilitation settings.
Unsupervised Learning-Based Methodology for Detection of Postural Anomalies in Wheelchair Users

Postural monitoring in wheelchair users is a topic of growing interest. The detection of changes in the sitting patterns of these patients may serve to detect changes in their functional status and be able to adapt rehabilitation early. For this reason, this paper presents a methodology for the detection of specific postural anomalies that, unlike previous works, adopts unsupervised learning. The proposed methodology involves data dimensionality reduction using Principal Component Analysis, and the application of K-means clustering to group different normal posture states. The anomalies are detected using a threshold approach, where data points that fall outside a certain threshold are considered as anomalies. The results show that the methodology is effective in identifying anomalies with a high degree of accuracy (around 90%).
Deep Learning Detection of Tiny Wood Splinters on Gymnasium Floor

Injuries during the practice of sports in gymnasiums have been reported, and one of the causes of injuries is due to environmental factors as tiny wood splinters on the gymnasium floor. Although it is important to regularly inspect gymnasium floors, it is difficult for humans to inspect the entire gymnasium floor, as it is done manually and visually, and requires a lot of time and manpower. We have developed an automatic inspection system to detect tiny splinters on the gymnasium floor. The system attaches cotton to tiny splinters and detects the attached cotton by using an image processing technique. Using this system, the entire gymnasium floor can be inspected automatically by using simply creating a 2D map. After the inspection, the system can show where splinters are located on the map. In this paper, the method for detecting splinters attached to cotton using deep learning object detection-YOLO was proposed. The detection ratio of the proposed method was improved by 25.0 % compared to the conventional method of threshold color segmentation process. In an inspection of an entire gymnasium, the proposed method detected 33 markers and was able to detect splinters that could cause injury.
Piezoelectric Active Vibration Control of a Cylindrical Shell

This paper is concerned with the dynamic modelling, active vibration controller design and simulation analysis for a cylindrical shell equipped with piezoelectric sensors and actuators. Based on Donnell-Mushtari shell theory, the kinetic equations of piezoelectric plate and cylindrical shell coupling are derived by using Hamilton principle. The first six natural frequencies and modes of the cylindrical shell were analyzed by ANSYS. To verify the validity of the simulation analysis, an aluminum shell was fabricated. In the experimental analysis, the first six natural frequencies of the cylindrical shell are measured by the accelerometer and compared with the simulation analysis. The first two free vibration modes agree well with the simulation results. Finally, the piezoelectric active control of cylindrical shell is simulated by sine wave and MATLAB. The simulation results show that the piezoelectric actuator and the proposed controller can effectively suppress the vibration of the cylindrical shell.
Follow My Lead: Designing an ADAS that Shares Decision Making and Control with the Driver

Driving through traffic often involves sequences of distinct maneuvers, for example changing lanes and overtaking slower vehicles. To assist drivers in these situations, a system for shared decision making and control (SDMC) is developed, taking inspiration from the lead-follow relationship in partner dancing. The SDMC system plans maneuvers through parallel nonlinear optimizations, infers the driver's intended maneuver, and shares control over steering, throttle, and braking actuators to jointly execute the maneuver. Experimental results in overtaking and lane changing scenarios demonstrate the driver's ability to guide the system through sequences of maneuvers and the system's support of the driver via shared lateral and longitudinal control.
Energy-aware Time-optimal Routing of Battery Electric Trolley Trucks

Catenary overhead lines have recently drawn attention as a means of extending the range of battery electric trucks. In order to characterize the potential stemming from such an infrastructure, our paper presents a framework to optimize the routes of battery electric trolley trucks jointly with their driving and charging strategies, whereby both static and dynamic charging is possible by either stopping at a station or by connecting to the catenary line, whenever present, while driving. Specifically, we first devise a drive cycle convex optimization model to obtain a Pareto front of optimal strategies in terms of energy usage and travel time. Thereafter, we compute such Pareto fronts for all road links present on a given road network, and leverage them to frame a time-optimal routing problem accounting for battery consumption and charging as a mixed-integer linear program that can be efficiently solved with global optimality guarantees. Our results for the BeNeLux highway network show that catenary lines can significantly reduce the travel time of battery electric trolley trucks, making them a promising alternative to internal combustion engine trucks.
Segmentation and Extraction of Maize Phytomers Using 3D Data Acquired by RGB-D Cameras

Based-field Individual-level crop organ extraction is an important prerequisite for high-throughput crop phenotyping. Phytomer is the organ propagation unit that connects the leaf and stem nodes, and it can be used to measure a variety of morphological phenotypic parameters. However, the severe occlusion across neighboring crops in dense field scenes leads to difficulties for crop organ point cloud extraction. To tackle this challenge, we study phytomer extraction methods based on point density features. In this paper, we propose a point cloud segmentation approach for individual-level crop organs based on point density histograms and cubic B-sample curve fitting, and also propose a phytomer construction approach based on point cloud color information and density features. To verify the effectiveness of our approach, we conduct field experiments under open-field conditions with a mobile robot equipped with RGB-D cameras. The experimental results show that the phytomer construction approach based on point density features can accurately extract the organ units reflecting the real crop morphology. Based on the statistical analysis of 15 sets of test datasets, we found that the success rate of phytomer construction is up to 89.2%.
Deep Semi-supervised Anomaly Detection Using VQ-VAE

Anomaly detection is a fundamental and challenging task in computer vision, which determines whether an image contains anomaly or not. Prior works using autoencoders for anomaly detection are based on pixel-wise learning in the continuous latent space, which is inefficient since images contain a lot of redundant information. Meanwhile, for most of the anomaly detection methods, the training set only contains normal data due to the unavailability or paucity of labeled anomalous data. However, an exposure to a fraction of labeled anomalous images, even infinitesimal in size in comparison to the amount of normal data, can significantly improve the anomaly detection performance while slightly increasing labeling costs. In this paper, we propose a Semi-Supervised Vector Quantized Variational Autoencoder (ss-VQ-VAE) for anomaly detection. Our ss-VQ-VAE leverages discretized latent space embeddings of VQ-VAE [1] to reduce noise and redundancies for better reconstruction of normal data in comparison with anomalous data. At the core of ss-VQ-VAE, we introduce a new loss to incorporate a few anomalous images available to train the model. In addition, based on the VQ-VAE architecture, we further propose an anomaly score that compares the encoded features of the input with the dictionary embeddings in VQ-VAE to make more accurate predictions. Experimental results on two datasets, MVTec and the corrosion dataset, show the significance of the novelties in our method. The code is available online1.1https://github.com/RenukaSharma/ss-vq-vae
Detecting Children Emotion through Facial Expression Recognition

Emotion detection has become an increasingly important area of research in recent years, as it has numerous applications in fields such as psychology, marketing, and human-computer interaction. Deep learning has shown success in emotion recognition due to the availability of large amounts of data and the ability to learn complex patterns in facial expressions, speech, and physiological signals when recognizing emotions. However, there are challenges associated with variations in lighting, pose, and facial expressions. This study introduces a novel deep-learning approach for emotion detection, leveraging the power of VGGFace2 to classify six emotional poses in children. The proposed approach outperforms the state-of-the-art in the field, achieving a success rate of 96.3% on the CAFE dataset. A comprehensive evaluation of the findings and a detailed discussion of their potential implications is offered as part of the study. Facial Emotion Recognition (FER), VGGFace2, emotion detection
Impact of Reconfiguration on Optimal Storage Deployment in Active Distribution Networks

Integrating sustainable energy sources such as photovoltaic (PV) and wind in an active distribution network brings advantages, such as reduced carbon emissions and free energy, and challenges, such as bidirectional power flow, voltage and frequency variations, a rise in uncertainty, etc. The problems caused by the growing use of renewable energy sources are addressed by integrating energy storage system. Furthermore, storage integration needs optimal planning and dispatch methodologies for the profitable operation of deployed energy storage systems. The study analyses a practical distribution network of Indian Institute of Technology, Roorkee (IITR) campus modeled in balanced and unbalanced formulations using MATLAB and OpenDSS, respectively. An optimal battery energy storage planning model as a mixed integer linear programming (MILP) problem is proposed for the distribution system, including reconfiguration, using the MATLAB-YALMIP interface. The method concludes that reconfiguration is an important criterion to be considered in the planning and operation problem of energy storage in distribution networks.
Time-Based Optical Receiver Featuring a Linear Current-to-Time Conversion with Equalization

This paper proposes a linear, low-power current-to-time converter in 28nm CMOS to enable time-domain signal processing in optical receivers with improved signal integrity and signal-to-noise ratio. The results show that the proposed converter outperforms other design options in linearity and power consumption as well as achieving a significantly higher eye margin and signal-to-noise ratio.
OpenCADP: Open-Set Intrusion Detection with a Cluster Anomaly Detection Plugin

Open-set recognition has gained significant attention in intrusion detection due to its ability to classify known attacks and identify novel attacks. However, current approaches based solely on discriminative features may fail to identify new data with non-discriminative feature differences and are prone to adversarial attacks. To overcome these limitations, this paper proposes an open-set intrusion detection framework that incorporates a cluster anomaly detection plugin to identify non-discriminative unknown classes by introducing primitive features and detecting adversarial examples through cluster design. Specifically, 1) we use the anomaly detection plugin trained with primitive features from the raw data to discover unseen data. Samples rejected by either deep discriminative classifiers or detection plugins are considered unknown. 2) We design the plugin as a cluster of one-class anomaly detectors. This approach effectively isolates adversarial examples carefully crafted to deceive the deep classifier. 3) We provide theoretical evidence of the proposed framework's ability to detect non-discriminative unknown classes and adversarial examples. Extensive experiments demonstrate the effectiveness of our approach when applied to open-set intrusion detection.