Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hybrid Clustering Pipeline for Mining Baseline Local Patterns in 3D Point Cloud

Three-dimensional (3D) imaging provides detailed geometry of real-world objects, unlike 2D image texture. The rudimentary form of 3D imaging is point clouds that are distinctly different from image pixels in terms of structure and processing methods. The 3D computer vision literature primarily retrieves global shape patterns in 3D data for object and face recognition tasks. In contrast, mining local deformation patterns in 3D data that are independent of global shape is a nontrivial task. This paper proposes a computational pipeline for mining baseline local patterns in 3D point clouds and identifies informative segments of point clouds for data selection and interpretation. We investigate the performance of several clustering algorithms in 3D point cloud segmentation and propose a computationally fast multi-stage clustering pipeline with parametric modeling of local patterns in point clouds. The proposed pipeline has achieved an area under the ROC curve of 0.72 in classifying seven emotional expressions (including the neutral expression) using 3D human facial point clouds. Our results demonstrate the baseline efficacy of raw 3D point coordinates in mining local patterns without involving feature engineering or deep learning. Therefore, the proposed pipeline can serve as a baseline for 1) rapid mining of informative local patterns and 2) selecting important segments of 3D point cloud data. The source code is made publicly available to promote future work in this area.
Impedance Bandwidth Improvement of a Planar Antenna Based on Metamaterial-Inspired T-Matching Network

In this paper a metamaterial-inspired T-matching network is directly imbedded inside the feedline of a microstrip antenna to realize optimum power transfer between the front-end of an RF wireless transceiver and the antenna. The proposed T-matching network, which is composed of an arrangement of series capacitor, shunt inductor, series capacitor, exhibits left-handed metamaterial characteristics. The matching network is first theoretically modelled to gain insight of its limitations. It was then implemented directly in the 50- $\Omega $ feedline to a standard circular patch antenna, which is an unconventional methodology. The antenna’s performance was verified through measurements. With the proposed technique there is 2.7 dBi improvement in the antenna’s radiation gain and 12% increase in the efficiency at the center frequency, and this is achieved over a significantly wider frequency range by a factor of approximately twenty. Moreover, there is good correlation between the theoretical model, method of moments simulation, and the measurement results.
Towards Understanding Tool-chain Bugs in the LLVM Compiler Infrastructure

LLVM is a widely adopted compiler tool-chain, and its quality is critical to the projects relying on it. The bugs in LLVM may lead to an unpredictably large impact on the community. Usually, tools in LLVM need to interact with each other to finish a particular task. This working style brings up a specific type of bugs that involve multiple tools, namely, tool-chain bugs, which complicates the bug detection and debugging. Though there exist prior works about LLVM bugs, none of them has investigated this particular type of bugs in-depth. Thus, in this paper, we conduct an empirical study of the LLVM tool-chain bugs, aiming to provide the first comprehensive understanding of these bugs. Overall, we find 1723 tool-chain bugs. We reveal frequently occurred tool combinations, six typical interaction reasons, as well as four commonly seen failure symptoms. Through the linkages between these bugs and their fixing commits, we identify six common root causes, and summarize debugging manners. Based on the above findings, we highlight three insights into testing and debugging these bugs.
CPN.Net: An Automated Colored Petri Nets Model Extraction From .Net Based Source Code

Multithreaded and parallel software systems are notably difficult to test due to their nature of non-determinism. Researchers from the literature suggested formal modeling and model checking to verify such systems. However, manual construction of models and abstractions of such systems could be time consuming, tiresome, and error prone. Automated models extraction approaches are necessary. In this study, we propose an approach to automatically extract Colored Petri Nets model from source code. Moreover, we establish a set of mapping rules to translate control flow graphs to Colored Petri Nets.
A Single-Phase Inverter Topology with Seven-Level Output based on T-type Structure

This paper introduces a novel single-phase seven-level inverter structure that is based on a modified T-type structure. This topology employs a reduced number of devices compared to classical multilevel inverter (MLI) topologies. It also uses a single DC source that renders this topology suitable for PV applications. The DC-link capacitors required in this topology have reduced size which in turn reduces the overall cost of the system. The working modes of the circuit and modulation method of the proposed structure are presented comprehensively. Finally, the introduced topology is simulated in the environment of MATLAB/SIMULINK and the obtained output results are presented.
Efficient Image Transmission over Cluster-Based Cooperative Visual Sensor Networks

Traditional dual-hop cooperative communication may not suitable for the clustered based cooperative wireless networks, since the intermediate nodes with improper channel condition may become the bottleneck of the overall communication. To address this problem, a cluster-based cooperative framework was presented in this paper. In this framework, all the candidate relays are divided into two clusters: relays near to the source (called source cluster) and nodes near to the destination (named as destination cluster). Further, to improve the performance, an algorithm for the efficient image transmission methodology was presented. Generally, in image transmission, Discrete Wavelet Transforms (DWT) is widely used for image compression. Since the DWT is a convolution-based approach, designing the hardware circuit for floating-point numbers is difficult. Here, we adopt lifting based discrete wavelet transform to reduce the hardware implementation complexity for the compression. Through the simulation results, we show the performance improvement of the proposed method against state-of-art approaches in terms of peak signal to noise ratio, mean square error, structural similarity index and bit error rate.
A Review: Object Detection Models

Object detection and recognition is an integral part of Computer Vision. It has a multitude of applications ranging from character recognition to video analysis. Object detection now play a crucial role in industries like security, video, medical, sports, and many more. With the latest research, rapid development in deep learning, and computational image understanding object detection will be more prevalent. In the future, we will be able to implement object detection in much more mundane workings of our life. Our review paper will focus on the existing techniques that are present in the community and how each technique is different from the other techniques. We will start with an introduction about object detection as a tool and technique. This will be followed by a brief background discussing the most fundamental steps involved and the basic architecture of object detection. Then, we focus on several existing techniques and perform comparative analyses of these techniques to draw meaningful conclusions. These techniques include two-stage models such as R-CNN, Fast R-CNN, and Faster R-CNN and single-stage models YOLOv1, YOLOv2, YOLOv3, YOLOv4, and SSD. Finally, we will conclude by presenting some directions that provide a scope for further analyses along that direction and hope to suggest different techniques that can be implemented in different sectors for optimal results.
CIA Analysis for LoraWAN Communication Model

IoT technologies have helped to advance numerous industries and innovations. Agriculture is one of the key industries in Thailand that is crucial for the country’s economic growth. Therefore, many IoT devices and applications for agricultural industries have been created. LoraWan is one of the IoT communication protocols that was applied to various smart farm applications. As a result, IoT deployments can create complex and vulnerable systems. This paper analyzes the security factors of the LoRaWan system based on the Cybersecurity Cube, which is a three-pronged framework for computer security and information protection. The study offers important security details for agricultural IoT communications.
Succinct representations of Intersection Graphs on a Circle

We consider the problem of designing succinct encodings for some intersection graphs on a circle, which include graph classes such as circle graphs, k-polygon-circle graphs, circle-trapezoid graphs among others. More specifically, we first prove a general counting lower bound, which is of independent interest, for these intersection graph classes, and then present a uniform encoding approach that lets us obtain matching lower and upper bounds for their succinct representation.
Core-Shell Dual-Gate Nanowire Synaptic Transistor with Short/Long-Term Plasticity

This work demonstrates design and performances of core-shell dual-gate nanowire synaptic transistor with short/long term plasticity. The novel structure helps equip better capacitive coupling between dual gates through full depletion of carriers from the Si channel and construct deeper potential well for charge storage, which eventually increases the probability for short-term-to-long-term memory transition by reducing the recombination. The dual-gate operation effectively realizes the short-and the long-term potentiation in the proposed device for hardware-driven neuromorphic system.
Avalanche Risk Estimation Automat System

This work comes to the aid of free ride skiers as well as people traveling through the mountains. It can help them know if they are exposed to an avalanche. Our system can detect an avalanche in its infancy or offer the possibility to find out if in that area, under the action of an external mechanical force such as the passage of a skier, snowboarder, a group of people or another force (e.g. pressure caused by waves sound) is likely to trigger an avalanche. The system is designed to send a text message to the mountain rescuer or the competent authorities with GPS coordinates. The system is implemented on a ski stick so that it can be accessible to any skier and not have to take other devices or utensils with him. Further development of our system is mentioned in the last chapter of this article.
A Load Adaptive Cascade PI Controller for Buck Converters Operating in Wide Load Range in Cathodic Protection Systems

This paper proposes a simple cascade PI controller for Buck converter to be used in impressed current cathodic protection (ICCP) systems which aim to operate in wide voltage and current ranges for any load condition. The Buck converter with cascade controller structure is a frequently used topology in ICCP systems to prevent buried steel pipelines from corrosion. Changes in the environment in which the pipe is embedded force the converter to operate at wide load and voltage ranges. However, in average current mode controllers, the gain of the current loop varies significantly with the load. At light loads, the inner current loop slows down dramatically and may stay behind the outer voltage loop. In order to solve these issues and to maintain the regulation of the pipeline voltage and the average load current, this paper presents a simple cascade PI controller whose coefficients are adapted to the changing load. The small signal analysis of the Buck converter in both continuous conduction mode (CCM) and discontinuous conduction mode (DCM) is performed and variables determining the transfer characteristics are examined. For the proposed controller, a design criteria based on circuit parameters have been established. With the parameters obtained by the proposed method, a sample cascade controller is designed and compared with classical PI controller in simulation. Also, a digitally controlled 100-W converter prototype is built to validate the performance.
Investigation of Effects of Rising and Falling Time, Ringing Phenomena and Dead Time in GaN Inverter on Iron Loss with Very High Sampling Rate

Although general impacts of the ringing phenomena occurring at dozens of megahertz and the rapid rising time in several nanoseconds in voltage waveform of the gallium nitride-field effect transistor (GaN-FET) inverter on the iron loss in ring core tests were considered in some recent studies, those works only used oscilloscopes with a medium sampling rate (e.g. 100 MS/s), which may cause the analysis and evaluation to be not really sufficient and correct. Hence, our paper investigates the detailed relations and effects of the rising time, falling time, ringing phenomena and voltage spike at many pulses in voltage waveform on the iron loss in a ring core under the GaN-FET inverter excitation using a top-notch oscilloscope with a very high sampling rate of 10 GS/s for measurement. Furthermore, the unique characteristics of the input current in negative and positive ranges relating to the ringing phenomena are considered as another novelty and contribution of this research. Moreover, the relations between the total harmonic distortions of the magnetic flux density and input current with the iron loss are examined. The effect of large changes in the dead-time of GaN-FET devices on the iron loss of the ring core is also examined.
SATA: A New Students Attendance Tracking Application

The number of smartphone users has rapidly increased in the last decade due to their attractive features and useful applications (apps). On the other hand, manual attendance tracking may waste precious lecture time in addition to other problems. Accordingly, we have created a new Students Attendance Tracking Application (SATA). This app aims to enhance the process of tracking attendance in terms of time and effort in large universities. A course instructor generates a Quick Response code during a lecture and views it for a short time to the students who in turn scan the code and send back attendance requests to the instructor's device. Actually, a fingerprint authentication is required among other restrictions to prevent any possible manipulation. Moreover, SATA allows both instructors and students to show different types of reports and get useful notifications before and after lectures. In fact, our app was practically tested in a number of the University of Jordan faculties where it showed efficiency and credibility based on the outcomes of an evaluation survey filled by users.
Quasi-3D Thermal Simulation of Multi-Chip Stack Embedding Package

Multi-chip embedded PCBs were in resent years the mainstream technology of electronic packaging size reduction and at the same time providing improved performance. Ultra-small package size obviously strongly impacts the SiP module thermal behaviour. Unfortunately the complete thermal design methodology was not developed for this technology till now. In this work the problem of thermal design is completely solved using quasi-3D approach taking into account the main features of embedded PCB constructions: 1) 3D integration of ICs and board; 2) a large number of thinned layers of different materials; 3) vertical z-axis interconnections through the package layers. A ten times decrease of central processing unit (CPU) time is achieved as compared with the full 3D solutions obtained by commercial universal 3D simulators, while saving the sufficient accuracy. The simulation error of maximal temperature TMAX determination for different types of packages is not more than 10-20%.
Output-Feedback Based Simplified Optimized Backstepping Control for Strict-Feedback Systems with Input and State Constraints

In this paper, an adaptive neural-network (NN) output feedback optimal control problem is studied for a class of strict-feedback nonlinear systems with unknown internal dynamics, input saturation and state constraints. Neural networks are used to approximate unknown internal dynamics and an adaptive NN state observer is developed to estimate immeasurable states. Under the framework of the backstepping design, by employing the actor-critic architecture and constructing the tan-type Barrier Lyapunov function (BLF), the virtual and actual optimal controllers are developed. In order to accomplish optimal control effectively, a simplified reinforcement learning (RL) algorithm is designed by deriving the updating laws from the negative gradient of a simple positive function, instead of employing existing optimal control methods. In addition, to ensure that all the signals in the closed-loop system are bounded and the output can follow the reference signal within a bounded error, all state variables are confined within their compact sets all times. Finally, a simulation example is given to illustrate the effectiveness of the proposed control strategy.
Experimental Injury Biomechanics of Human Body Upper Extremities: Anatomy, Injury Severity Classification, and Impact Testing Setups

Understanding the human body's anatomical aspects helps capture key insights and gather increased information on the underlying human injury mechanisms, injury probability, and tolerance limits. Such knowledge, together with capable robots integrating safety-driven motion planning and control frameworks, enables the development of effective countermeasures for human arm/hand injury in robotics. In this work, thorough treatment for the anatomy, injury types, and severity classifications of human upper body extremities are provided. We also describe various impact testing setups and injury occurrence mechanisms encountered in sports, biomechanics, and forensics literature. The gathered insights provide solid grounds for designing/planning interactive impact setups and robot tasks in a well-informed way regarding the injury biomechanics of the human body upper extremities and their tolerances.
Effect of different gait phase-based assist patterns of a wearable robot on gait motion

Wearable walking assist robots are expected to improve the quality of life of the elderly, and a few such robots have been developed recently. However, the human response to such robots depending on the assist pattern must be evaluated. We aimed to compare the gait under multiple assist patterns to determine what assist pattern is effective in improving gait. We developed two assist patterns related to human motion or torque exerted on the gait cycle. We conducted an experiment using our exoskeleton MALO and measured the joint angle, muscle activity, and interaction force. As a result, we found that hip and knee flexion is assisted by our robot. The maximum flexion angle of the knee joint was high in motion-based assist, and a decrease in biceps femoris and gluteus maximus muscle activity was observed in torque-based assistance.
DeepRecipes: Exploring Massive Online Recipes and Recovering Food Ingredient Amounts

Ingredient amounts are crucial for food-oriented health systems, but this information is seldom used in food-oriented health systems due to the difficulty of fetching it from online recipes. This study proposes a predictive model named DeepRecipes to extract ingredient amounts from online textual recipes. The model predicts ingredient amounts according to a given recipe’s name and listed ingredients. We train the model on a small set of recipes containing all ingredients and their corresponding amounts. As we can extract the recipe names and ingredients from almost all online recipes, the proposed model can potentially recover ingredient amounts for massive online recipes. We first trained the model on a small set of recipes containing all ingredients and their corresponding amounts. Then, we compared ten models as references for their performances. The performance of DeepRecipes exceeds those of all the comparison models. The model’s mean absolute error (MAE) and mean absolute percentage error (MAPE) are $3.96\times {10}^{-1}$ and 18.57%, respectively, and its APEs are lower than 50% in more than 95% of the total predictions. This accuracy is sufficient for providing rough ingredient amount estimations for food-oriented health systems.
Optimal Time Window for the Integration of Spatial Audio-Visual Information in Virtual Environments

Sound duration and location may influence both auditory and visual perception with consequences for the judgement of both auditory-visual event location and integration. This study investigated audio-visual integration in a virtual environment using both short- and long-duration auditory stimuli with visual stimuli temporally offset from the start of the auditory stimulus, to investigate the effects of top-down neural effects on perception. Two tasks were used, an auditory localization task and a detection task (judgement of audio-visual synchrony). Eleven participants took part in the study using a HTC Vive Pro. The short-duration auditory stimuli (35-ms spatialized sound) and long-duration auditory stimuli (600-ms non-spatialized sound followed by 35 ms of spatialized sound) were presented at −60°, −30°, 0°, +30° and +60° degrees azimuth, with the visual stimulus presented synchronously or asynchronously with respect to the start of the auditory stimulus. Results showed that localization errors were larger for the longer-duration stimuli and judgements of audiovisual synchrony tended to be improved for stimuli presented at ±30°. Top-down neural processing can affect spatial localization and audio-visual processing. Auditory localization errors and audio-visual synchrony detection may reveal the effects of underlying neural feedback mechanisms that can be harnessed to optimize audio-visual experiences in virtual environments.
Ensemble Learning Approach to Retinal Thickness Assessment in Optical Coherence Tomography

Manual assessment of the retinal thickness in optical coherence tomography images is a time-consuming task, prone to error and inter-observer variability. The wide variability of the retinal appearance makes the automation of retinal image processing a challenging problem to tackle. The difficulty is even more accentuated in practice when the retinal tissue exhibits large structural changes due to disruptive pathology. In this work, we propose an ensemble-learning-based method for the automated segmentation of retinal boundaries in optical coherence tomography images that is robust to retinal abnormalities. The segmentation accuracy of the proposed algorithm was evaluated on two publicly available datasets that included cases of severe retinal edema. Moreover, the performance of the proposed method was compared to two existing methods, widely referenced in the relevant literature. The proposed algorithm outperformed reference methods at segmenting the retinal boundaries in both normal and pathological images. Furthermore, a thorough reliability analysis showed a strong agreement between the retinal thickness measurements derived from the segmentation obtained with the proposed method and corresponding manual measurements computed with the manual annotations.