Miracle-dz/UniFAR-FTUs huggingface dataset:
MITEx: A Portable Hand Exoskeleton for Assessment and Treatment in Neurological Rehabilitation

This work describes the design and preliminary characterization of a novel portable hand exoskeleton for poststroke rehabilitation. The platform actively mobilizes the index-metacarpophalangeal (I-MCP) joint, and it additionally offers individual rigid support to distal degrees of freedom (DoFs) of the index and thumb. The test-bench characterization proves the capability of the device to render torques at the I-MCP level with high fidelity within frequencies of interest for the application (up to 3 Hz). The introduction of a feed-forward friction compensation at the actuator level lowers the output mechanical stiffness by 32%, contributing to a highly transparent behavior; moreover, the functionality of the platform in rendering different interaction strategies (patient/robot-in-charge) is tested with three healthy subjects, showing the potential of the device to provide assistance as needed.
Combining Forecasts using Meta-Learning: A Comparative Study for Complex Seasonality

In this paper, we investigate meta-learning for combining forecasts generated by models of different types. While typical approaches for combining forecasts involve simple averaging, machine learning techniques enable more sophisticated methods of combining through meta-learning, leading to improved forecasting accuracy. We use linear regression, knearest neighbors, multilayer perceptron, random forest, and long short-term memory as meta-learners. We define global and local meta-learning variants for time series with complex seasonality and compare meta-learners on multiple forecasting problems, demonstrating their superior performance compared to simple averaging.
Positioning, Orientation and Control of Virtual Robotic Arms Based on Collected Sensor Data Focused on the Kinematic Motion Analysis

The paper analyzes the motion of a six-degree-of-freedom (6 DoF) industrial robotic arm using a simulation environment with added sensors and Data Acquisition (DAQ) system. An accurate 3D assembly model of the 6R industrial robot was designed and tested. The output result of the project is the forward and inverse kinematic conversions using software and hardware modules and the user interface highlights the analysis results of the motion. The coordinates of the robot are set by a hand-controlled accelerometer, reading the signals by DAQ system. The achievements of the study are based on three folds: advanced training in mechatronics engineering, can contribute to designing more efficient performance-orientated DAQ systems for signal processing, and improve hand-controlled manipulators technology or control of robotic arms.
Relationships Between Distribution of Dislocation Glide Planes and Carrier Recombination Properties in InGaAs Solar Cells Using Microwave Photoconductivity Decay Mapping and Photoluminescence Spectroscopy

We investigated the relationships between the distribution of dislocation glide planes and carrier recombination properties in InGaAs solar cells with different open-circuit voltage (VOC) using microwave photoconductivity decay and photoluminescence measurements. The results showed the low VOC was due to the recombination of the photoexcited carriers with the dislocation-related defect.
CNTFET Based Unbalanced Ternary Master-Slave Delay Flip Flap Flop

While designing digital circuits at lower technology nodes, the primary objectives considered are minimization of delay and conservation of energy. CNTFET can be utilized in high performance analog and digital circuits because it offers low power consumption and high switching speeds as compared to the conventional silicon-based transistor. In this paper, an unbalanced ternary D-latch and a ternary master-slave D-flip-flap-flop (MSDFFF) is designed using a novel design of CNTFET-based basic ternary logic gates. The circuits have been designed and simulated using Synopsys HSPICE tool with a standard 32nm CNTFET model file at a VDD of 0.9 V. The power of the proposed MSDFFF is upto 99% less than the least power of the MSDFFF designs under consideration.
Ultrasound Imaging with Pre-charged Collapse-Mode CMUTs

Capacitive micromachined ultrasonic transducers (CMUTs) with a built-in charge layer are known as a pre-charged CMUT. In our prior work, we have shown how to model and characterize the charges inside the pre-charged collapse-mode CMUT and conducted life-time test that showed that the charges trapped inside the dielectric were stable in the order of years [1]. However, our prior work focused on the use of pre-charged collapse-mode CMUTs as a way to achieve ultrasound power reception, which does not require the CMUT to be actively driven. In this work, for the first time we use pre-charged collapse-mode CMUTs with an Al2O3 charge-trapping layer to create a B-mode ultrasound image. Thus, this work shows the first example that pre-charged collapse-mode CMUTs can fully operate with only an AC voltage.
An Empirical Analysis of the Long Short Term Memory and Temporal Fusion Transformer Models on Regional Air Quality Forecast

Real-time meteorological observation systems are of great importance in practical engineering use. Monitoring environmental data 24/7 not only provides a healthy and green living environment but also enables the prediction of future living conditions based on existing data, thus acting as a detection and warning. At this stage, weather monitoring stations usually use conventional detection, which often results in prediction deviations, thus bringing a more significant impact on weather detection in the whole region. Therefore, the prediction strategy of meteorological factors should be further strengthened and better defined. This study aims to fill this gap and compare the advantages and disadvantages of linear regression time series models, long and short-term memory models, and Temporal Fusion Transformer models theoretically and experimentally. Ultimately, we seek to provide researchers with information to aid them in choosing a prediction algorithm based on their available data volume.
Hybrid Deep Neural Network with CNN and RNN alongside 1st order B-spline Differential-Based Methodology for Real Time Fatigue Crack Growth Rate Monitoring Using Only AE Sensors

Prediction of crack growth rate (CGR) is crucial for the reliability and health management of complex structures (e.g., ships, bridges, aircraft). An autonomous prognostic and health management system is required in the shipping industry for safe and cost-effective maintenance of critical components. This study utilizes a hybrid deep neural network (DNN) comprising a one-dimensional convolutional neural network (CNN) and long short-term memory (LSTM) to predict CGR in DMR 249A high-strength low-alloy ship steel. Three fatigue tests with load ratios (R) of 0.1, 0.25, and 0.5 corresponding to sea-state IV conditions were conducted. The raw AE data was pre-processed with B-spline basis functions and first-order derivatives to enhance CGR prediction accuracy. The predicted crack growth rate achieved a correlation of 79.59%, 86.00%, and 95.97% for load ratio R 0.1, 0.25, and 0.5, respectively with the actual CGR. This work demonstrates the feasibility of using raw AE signals for online CGR determination with the proposed hybrid DNN model.
Enhanced Edge Prediction, a case study: predicting links in Wikipedia sites

This study introduces a scalable approach for link prediction in Wikipedia pages, specifically designed to handle the challenges arising from the large volume of data. The proposed solution combines partial reconstruction of the original graph using node descriptions, the generation of node pair vectors based on graph metrics, and the application of a threshold similarity using the TF-IDF method. Our proposed solution achieve a high F1 score of 0.948.
A Registration Algorithm for Point Clouds Obtained by Scanning Objects on Turntable

A registration algorithm for point clouds obtained by scanning objects on turntable with a stepper-motor is presented.Firstly three or more point clouds were used to calibrate the position of central axis of the rotating platform.So the relationship between the scanner and the rotating platform were defined.Then the rotate matrix was calculated with the angle of the rotating platform to rotate point clouds into the same coordinate system.Only the angle of the rotating platform was needed in this method.The method implemented with C++ language had proved stable,efficient,high degree of automation,good registration performance and high practical value,which could be used in many 3D scanning system such as laser finder system and structure light scanning system.
Active Noise Cancelling Method for An Electro-Optical Detection System

Recently "optrodes" a.k. a. optical-electrodes have been developed in the context of electrophysiological studies. We propose an applicable active noise cancelling method for better detection results, i.e. for an increased signal-to-noise ratio. This approach uses a tailor-made light receiver board combining two channels and filtering algorithms implemented on a field programmable gate array (FPGA) board to actively cancel noise. Experiments were conducted to test this approach's effect on the noise floor. Results show a 50% reduction on the noise floor and the ability to recover very low amplitude signals.
A Framework for Context-Sensitive Prediction in Time Series - Feasibility Study for Data-Driven Simulation in Medicine

The need to comprehend and predict the dynamics of complex systems has spurred developments of time series forecasting methods across several disciplines. Nowadays, time series are collected with event logs for an ever increasing number of systems. Event logs can contain prominent information about the system dynamics. This paper addresses joint modelling of time series and event traces, to enhance time series forecasting. We introduce the Non-Homogeneous Markov Chain AutoRegressive model, to best apprehend the combined influence of past events belonging to several event categories, on a system’s dynamics. The originality of our proposal stems from the synchronization of a Hawkes temporal point process with the classical first-order hidden Markov model, through contextual variables. We also instantiate a basic version whose contextual variables only take into account events’ latest occurrences. Our proof-of-concept experiments address a real-world case related to digitally assisted training in anaesthesiology. We demonstrate that the advanced instantiation outperforms the basic instantiation (maximal prediction error percentage: 5.6% versus 13.5%). Finally, we validate the suitability of the advanced instantiation for the desired simulation, by applying real sequences of medical actions to digital patients. We show that we obtain highly realistic simulations.
Scalable Industrial Control System Analysis via XAI-Based Gray-Box Fuzzing

Conventional approaches to analyzing industrial control systems have relied on either white-box analysis or black-box fuzzing. However, white-box methods rely on sophisticated domain expertise, while black-box methods suffers from state explosion and thus scales poorly when analyzing real ICS involving a large number of sensors and actuators. To address these limitations, we propose XAI-based gray-box fuzzing, a novel approach that leverages explainable AI and machine learning modeling of ICS to accurately identify a small set of actuators critical to ICS safety, which result in significant reduction of state space without relying on domain expertise. Experiment results show that our method accurately explains the ICS model and significantly speeds-up fuzzing by 64x when compared to conventional black-box methods.
A Review of the Evaluation of Ransomware: Human Error or Technical Failure?

The advancement of technology has led to a significant rise in cybercriminal activities, with ransomware emerging as a prominent threat to both individuals and businesses. Ransomware attacks involve encrypting a victim's data or entire computer system and then demanding a ransom payment in exchange for the decryption key. This study examines the evolution of ransomware from its initial type, 1.0, to the latest type, 3.0, while also delving into the key factors contributing to its spread, including technical vulnerabilities and human errors. Our research explores the relationship between technological weaknesses and human behavior, assessing the effectiveness of current measures in mitigating and preventing these vulnerabilities. We conducted a survey among various organizations to identify the primary causes of ransomware attacks and provide recommendations for bolstering defenses against this threat. The survey results indicate that 74% of individuals seldom update their computer's operating system, 72% do not utilize two-factor authentication, and 66% of users download software from untrusted sources, among other findings. The outcomes of our study offer valuable insights for organizations and individuals to establish cybersecurity objectives and safeguard themselves against the harmful consequences of ransomware incidents.
Development of Multi-Layer Tissue-Mimicking Breast Phantoms for Microwaves and Millimeter-Waves Imaging

In this paper, multi-layer phantoms of the human breast, obtained from recipes previously proposed by some of the authors of this work, are presented. Heterogeneous and multiple-layer phantoms allow the performance evaluation of microwave-based medical devices on increasingly realistic scenarios. The proposed two-steps preparation procedure is very simple and involves cooling part of the phantom. It has been shown that this does not alter the dielectric properties of the individual layers.
Estimation of Lung Surface Roughness by Means of an Ultrasound Multifrequency Approach, in silico and in vitro results

Lung ultrasound (LUS) is an important tool to evaluate the state of the lung surface, but is limited to the visual evaluation of imaging artifacts. Specifically, it is mainly focused on the vertical artifacts, which correlate with pathologies characterized by a reduction of air-spaces’ dimensions. In contrast, there exist pathologies, e.g., chronic obstructive pulmonary disease (COPD), characterized by an enlargement of the air-spaces. This enlargement causes the lung surface to behave essentially as a perfect reflector, which does not allow the ultrasound penetration. As air-spaces of different sizes may generate a different roughness level at the lung surface, the roughness estimation could provide a way to characterize the state of lung surface. In this study, we show how a multifrequency quantitative approach has the potential to estimate the roughness of the lung surface by measuring image intensity variations along the lung surface as a function of frequency.
Potential Solutions to Challenges in C Program Repair: A Practical Perspective

Automated program repair is to reduce the manual work for bug fixing by human developers. In recent 15 years, the research community of program repair has created many novel techniques. However, these techniques share several assumptions that cannot always be satisfied in daily software development. This badly hurts the application of program repair in practice. For example, many repair techniques assume that test cases are well written before patch generation; many techniques assume that specific language features can be ignored (or already-processed). In this paper, we propose a framework of C program repair, which mainly addresses two challenges: test-independent repair and preprocessor directive processing. Our solution to test-independent repair is to automatically construct patch conditions for C programs via parsing the syntax structures; our solution to preprocessor directive processing is to generate code symbols to replace preprocessor directives. We plan to implement these potential solutions with program analysis techniques. The goal of this paper is to present practical solutions for developers to automate C program repair.
Advanced Web Tool for the Optimization of Antenna Positioning based on Evolutionary Algorithms

In this paper, the development of an advanced web tool for the optimization of antenna positioning based on evolutionary algorithms is presented. The system includes a web interface that allows the user to configure the parameters of the evolutionary algorithms. The configuration of these optimization parameters, as well as the subsequent visualization of the results, are integrated into the web interface itself. The results are displayed in real-time, making the optimization process easy to follow. Java and TypeScript programming languages were used in the implementation of this tool. Optimizations are performed using the JMetal framework, which provides facilities and varieties of evolutionary algorithms. A Web interface was created with React, which made the integration with the application more feasible. Finally, the tool, in addition to optimizing antennas, can be used to obtain a comparative analysis of the different algorithms.
A research scanner coupled to a 3072-element dense array for advanced 3-D imaging

Open scanners enable 3-D experimental imaging research but have been so far limited to the use of 2-D sparse or row-column arrays having a relatively low number of elements (typically ≤ 256). 2-D dense arrays remain the gold standard for high-quality 3-D imaging but have thousands of elements that are controlled through sophisticated application-specific integrated circuits (ASICs) in commercial high-end scanners. This paper reports on the development of a research scanner that fully controls an advanced 2-D 3072-element probe. The system’s performance was preliminarily evaluated by measuring the one-way beams corresponding to three different transmission strategies.
Robust observer and proximal policy optimization-based VTOL vehicle attitude stabilization research

In this paper, a robust observer-based proximal policy optimization control method (ROPPO), which combines a robust observer and a proximal policy optimization (PPO) algorithm in deep reinforcement learning, is studied for the attitude stabilization of vertical take-off and landing vehicles considering elastic vibration and model uncertainty disturbances. Firstly, a robust observer is designed to reconstruct the elastic vibration into easily controlled attitude-added angles and outputs the reconstructed pitch attitude angle and angular velocity. The deep reinforcement learning agent interacts continuously with the environment consisting of the robust observer and dynamics model, and learns based on the feedback from the environment to achieve attitude stabilization during the vehicle's vertical take-off and landing phase. The simulation results show that the designed ROPPO algorithm is more robust and converges faster than the proximal policy optimization (PPO) and the currently used adaptive fuzzy PID algorithm. The effectiveness of the proposed algorithm is verified on a self-made (Vertical take-off and landing) VTOL vehicle in the real world.
Performance Evaluation of PicoRV32 RISC-V Softcore for Resource-Constrained Devices

In this paper, an extensive analysis of the resource-efficient PicoRV32 softcore, which implements the RISC-V instruction set, is performed. The focus of the analysis is on performance, energy efficiency and resource utilization. For this, both CoreMark and Embench are implemented as benchmark tools.In contrast to previous evaluations, a resource-constrained target platform (Low Power (LP) FPGA) is explicitly used for the evaluation, to demonstrate the applicability on very small embedded systems. Furthermore, all configurations of the softcore are investigated, which helps in the estimation and subsequent configuration of the softcore and reveals significant bottlenecks like memory interface.
Plugging Energy Regeneration Improves Braking Torque at Low Speed Instead of Dynamic Energy Regeneration

Previously, we proposed a dynamic energy regeneration used in robotic prostheses. However, a dynamic energy regeneration cannot provide enough torque at low speeds and for a robotic prostheses, low torque may result in falling down in some cases. In this study, we proposed a plugging electrical energy regeneration instead of dynamic electrical energy regeneration, which can provide relatively larger torque at low speeds. Firstly, the mathematical model and formula of a dynamic energy regeneration and a plugging energy regeneration were given. Theoretically, for a plugging energy regeneration, due to the current drain from the power supply, the braking current is larger than the current for a dynamic energy regeneration, at the same low speed, indicating more braking torque. Further, we designed a drive circuit of energy regeneration, to verify two methods of a dynamic energy regeneration and a plugging energy regeneration. Experiment results showed that at low speeds, the braking torque is larger using a plugging energy regeneration than the torque using a dynamic energy regeneration, in accordance with the results from the mathematical model. From the mathematical model and physical experiments, this study showed the potential of a plugging energy regeneration used in a robotic prosthesis, to deal with the weak braking torque at low speeds.
One KHz-Order Linewidth-Stabilized Ring-Cavity Fiber Laser Based on Sub-Ring Cavities and Saturable Absorber Hybrid Scheme

This study demonstrates a narrow linewidth-stabilized ring-cavity fiber laser uses three sub-ring cavities and fiber saturable absorber. The measured laser output power and wavelength change are less than 0.03dB and 0.008nm within 60minutes, respectively. A narrow linewidth of 1.65KHz with and a high optical-signal-to-noise-ratio of 62dB is obtained.
Programming-by-Example with Nested Examples

As program synthesizers become integrated into IDEs, programmers combine synthesized code and manually-written code within the same project. When documenting the provenance of synthesized code, this can result in a larger segment of example-specified code encompassing a smaller one. In this state, the programmer can decide to re-run the synthesizer, either to refactor the code or to automatically correct manually-written code. State of the art Programming by Example synthesizers cannot handle this nested specification: synthesis will only consider the outer-most block, discarding any code or additional specification inside. Moreover, the outer-most scope contains less specifications, but describes more functionality, which can easily result in the synthesizer either timing out or overfitting. To address this information loss we propose a technique that can be used to re-synthesize code from previously documented nested specifications, preserving the information that the internal specifications contain. We then evaluated our implementation on 12 benchmarks based on SyGus competition benchmarks, and show that the technique allows the user to re-synthesize larger code segments while adding fewer examples compared to just using the specification from the outer-most block.