Miracle-dz/UniFAR-FTUs huggingface dataset:
KNTU-RoboGlove: Design of a Pneumatic Soft Robotic Glove

This paper introduces an innovative advancement in the field of wearable assistive technology by presenting a revolutionary portable soft robotic glove designed to assist individuals with hand impairments during rehabilitation exercises and daily tasks. The glove offers both active finger flexion and passive extension to aid in hand support and rehabilitative training. It achieves this by utilizing integrated soft actuators, including a variable stiffness layer and a pneumatic-driven layer, which guide specific bending, twisting, and extending movements when air pressure is applied. Notably, the variable stiffness layer takes inspiration from pangolins, whose scales are pliable during daily activities but become rigid when facing threats. To provide increased power with enhanced stiffness, a toothed pneumatic actuator is specially designed. The three-finger soft glove is manufactured by molding super-elastic material, creating the actuator mold using a 3D printer. The glove’s grasping abilities have been thoroughly tested, and experimental results demonstrate that, when compared to previously developed elastomeric-based actuators, these actuators can generate substantial force and torque to facilitate finger flexion even at lower air pressure levels. This capability allows the glove to grasp a wide range of objects, assisting individuals in their rehabilitative training. Furthermore, a control system for grasping has been developed, which includes motion processing unit sensors for collecting feedback signals aligned with the actuators, along with a closed-loop controller for regulating the pressure. In contrast to existing devices, this glove, thanks to its portable design, has the potential to significantly enhance user autonomy and independence.
Infinity-Norm Weighted Low-Order Sparse Iterative DOA Estimation Under Impulsive Noise

This paper investigates the subject of the direction of arrival (DOA) estimation with acoustic vector sensor array (AVSA) under impulsive noise. A low-order-based variant of the typical sparse reconstruction method is designed to address this challenge. The proposed scheme successively weights the array output snapshots using the infinity norm to remove high outliers. Subsequently, the Frobenius norm based approach is extended to lower order, and a new cost function is created to address the impulse characteristics of the residual. Finally, the expression of the signal power is quantified, and DOA is approximated on the signal power by spectral peak search mechanism. Numerical results demonstrate that the proposed algorithm offers significant performance enhancements over some current techniques in impulsive noise scenarios.
A Blockchain-Based Approach to Improving Smart Home Security With Situation-Aware Access Control

Recent advances in information technologies have made great improvements to smart homes with various services, but the security of smart homes remains a critical issue. Traditional security measures often overlook the importance of data and lack efficient tracking of incidents, and complicating access management across various user groups. In this paper, an effective approach to improving the security of smart homes based on blockchain and using situation-aware access control is presented. Specifically, this approach provides improved access control to securely store and protect transaction data, including residents’ personal information, behavior patterns, and devices in smart homes, ensuring robust data protection and increased transparency and auditability. This approach is compared to existing approaches and its major advantages are discussed.
A Novel Reconfigurable Cross-connected Cascaded H-bridge based Multilevel Inverter for Virtual Inertia Emulation

This paper introduces a cross-connected cascaded H-bridge (CHB) based photovoltaic (PV) multilevel inverter with ability to reconfigure its structure for inertia injection to power electronics dominated grid under any frequency event. The inverter requires only one PV source along with closed-loop voltage controlled floating capacitors in each phase for synthesizing the multiple voltage levels at its output. To emulate virtual inertia, the inverter utilizes the stored energies from two of its floating capacitors connected in parallel under any contingency situation. A simulation study in MATLAB/Simulink environment reveals that the individual floating capacitor voltages can be restored back to their desired levels rapidly after the contingency event is cleared which ensures quick transition of the inverter into its normal mode of operation. The performance of the proposed reconfigurable inverter is also compared with a conventional cross-connected CHB inverter under similar contingency events to establish the improvement in grid frequency profile due to this reconfigurable operation.
Long – Term operational Energy production analysis of 200KWp Roof Top Grid Interactive Solar PV System: A Research Study

This paper submits research study carried out on electrical energy production of a 200KWp Roof Top grid interactive Solar PV System examined over a period of more than 8 years. In this, solar plant Energy production analysis is proposed and evaluated based on IEC & IEEE Standards by using the quality real time monitored data. International Energy Agency has given several performance parameters to analyze the grid connected Solar PV systems. In this research study Reference Yield, Final system yield, Performance Ratio and Plant Availability Factor were chosen as prominent data parameters. A successive eight plus years of data was collected and used to perceive the operational energy production analysis as well as degradation. From the interpretation of real time monitored data of this plant, the disparity of real-time final energy yield with respect to reference yield was determined for the observation years. The real-time Performance Ratio calculations, proves that plant is operating in agreement with the PV Syst simulation results. The plant availability factor and the degradation of plant performance over the investigation period is presented. An economic evaluation of the Roof Top SPV system is also done based on financial parameters. The payback period for the investment and ROI is determined.
Application of LBWK Algorithm in Forward-looking Super-resolution Imaging of Scanning Radar

Scanning radar can achieve forward-looking imaging through antenna scanning, which is beneficial to obtaining target information in the radar’s forward-looking area. However, in practice, the azimuth resolution is limited due to the limitation of antenna size. In this paper, we first transform the scanning radar super-resolution problem into an L1 regularization problem. Then, based on the assumption of sparse objectives, the classic linearized Bregman with kicking (LBWK) is used to solve the L1 regularization problem. This algorithm linearly expands the quadratic terms and converges based on the Bregman distance with low computational complexity. At the same time, the use of kicking makes the algorithm converge faster. Finally, the performance is verified through experimental data.
A miniaturized UWB monopole antenna for sub-6 GHz 5G wireless applications

This article describes a miniaturized, ultra-wideband monopole antenna with dimensions of 23.00 x 20.00 x 1.60 mm3 (0.23λ0 x 0.23λ0 x 0.016λ0) designed for sub-6 GHz 5G wireless applications. The suggested layout is compatible with sub-6 GHz 5G, including satellite X-band (7.5-8.5 GHz), ITU n77 (3.3-4.2 GHz), and n79 (4.4-5 GHz) bands, 502.11a WLAN (5.15 - 5.85 GHz), and WiMAX bands (3.5/5.5 GHz). The suggested structure uses a fusion shape of rectangular, triangular, and trapezoidal slits on the ground plane and isosceles triangle, octagonal slot on the metal patch to improve the radiation characteristics and impedance bandwidth. The addition of rectangular slits which are incorporated into the bottom plane just below the microstrip line and trapezoidal-shaped slots shifts the lower cut-off frequency towards the left, while the octagon-shaped slot and the isosceles triangular slit on the radiating patch shift the higher cut-off frequency towards the right. The suggested structure operates over the range from 3.10 to 18.87GHz, having an equivalent fractional bandwidth of 143.56% and a peak gain of around 5.37 dBi. The antenna has a radiation efficiency of 80.00% and displays a decent S11 ≤-10 dB throughout the bandwidth. Results from measurement and simulation are good in agreement.
A 28GHz Low Jitter, Low Power Fully Differential Self-Biased Clock Buffer with Embedded Low Pass Filter Utilizing Enable Switch in 16nm FinFET

This paper presents a 28GHz fully differential self-biased clock buffer with an embedded low pass filter (LPF) on the self-bias gates. This LPF shields the gates from the output making the output rising and falling edge transitions steeper while reducing the power consumption. By utilizing several small enable switches as resistors to configure the LPF for small area, it conveniently allows to power down the circuit while preventing a shift in common-mode voltage caused by the voltage drop of a conventional power-gating switch. Fabricated in a 16-nm process with an active area occupation of 105μm2, the proposed design at 28GHz operation achieves 19.44fsec rms jitter according to the simulation result while consuming only 5.13mW in the measurement.
Predictive Modeling for Heart Disease Detection with Machine Learning

Debilitating health symptoms brought on by heart disease reduce people's quality of life and impose serious pain, discomfort, and restrictions on daily activities. It places a heavy burden on economies, healthcare systems, and society at large. Accurate cardiac disease prediction has the ability to significantly contribute to prevention, treatment, and essential assistance for healthcare personnel facing this ailment given its influence on public health. This study uses the most recent developments in machine learning techniques to build an accurate model for heart disease prediction. Heart disease prediction and the Cleveland datasets, which combine approximately 13 important patient history variables, are used to analyze data from people with and without heart disease. XGBoost, naive bayes, logistic regression, decision trees, support vector machines, random forests, and k-nearest neighbors are just a few of the machine learning techniques used in the model development for classification. We can increase the precision and effectiveness of identifying persons at risk of heart disease and enabling prompt therapies by applying these machine learning techniques. According to the findings of this study, XGBoost, decision trees, and random forests have consistently produced high accuracy predictions of heart disease.
Large-Signal based Design Approach in Boost PFC under Constant On-Time Digital CMC with Load Feedforward for Ultra-Fast Transient and Stability

The growing demand for high power-density power factor corrector (PFC) solutions for compact adaptors, electric vehicle chargers, light-emitting diode (LED) drivers, etc, necessitates high-performance energy-efficient control to achieve ultra-fast load transient with reduced output capacitor size. In this paper, a trajectory-based controller tuning approach is proposed in a boost PFC converter under digital constant on-time average current mode control (CMC) with normalized load current feedforward, which can achieve near-time-optimal recovery with reduced voltage undershoot/overshoot. Also, a CCM-DCM (continuous & discontinuous conduction mode) boundary detection technique is incorporated, which offers a natural transition with reduced switching/driving losses. Structural stability analysis is carried out using a discrete-time modeling framework, and the effect of parameter variations is discussed. The effectiveness of the proposed control technique and the performance improvement are demonstrated using simulation results.
Data Science Challenges; A Whole Systems Lens for Energy Network Solutions

The rapid digitalisation of the energy system requires advanced and robust data driven solutions as well as the necessary technical skills to implement them. Unfortunately, many solutions are only tested on private data using a single method only compared (if at all) to simple, non-competitive benchmarks. This approach limits the reproducibility and usefulness of the outputs. This report demonstrates, through three distribution network focused problems, the opportunities, and limitations of data science competitions in supporting open science innovation. With each challenge this report includes a description of the problem, the data utilised, and the solutions given. Through the outputs, it is demonstrated how different models compare (including against competitive benchmarks), how to best utilise the available data, and what unique techniques can help to optimize the solutions. The paper concludes with some of the limitations and possible improvements in future competition implementations.
Robust Emotion Recognition in EEG Signals Based on a Combination of Multiple Domain Adaptation Techniques

Conventional classification approaches for EEG- based emotion recognition cannot often adapt to different domains, such as cross-subject or cross-dataset scenarios, leading to poor performance. To handle this challenge, we introduce a novel fusion method using a combination of multiple domain adaptation techniques to improve the emotional states in EEG datasets via classification accuracy. For this aim, Our proposed approach exploits domain adaptation approaches such as Transfer Component Analysis (TCA), Correlation Alignment (CORAL), Transfer Joint Matching (TJM), Geodesic Flow Kernel (GFK), and Joint Distribution Adaptation (JDA), to enhance the overall classification performance. Later, a new fusion approach called Multiple Domain Adaptation based on a Neuro-Fuzzy Inference System (MDA-NF) is applied to combine the classifiers using proper fuzzy membership functions and deliver maximum separation between classes. The main contribution is by applying the fusion approach using MDA- NF technique, adaptability is sufficiently enhanced. Another advantage is to employ multiple adaptation techniques that improve separation between classes. In experimental test results conducted with cross-subject and cross-dataset scenarios, the MDA-NF approach demonstrates superior performance in terms of accuracy for both the valence and arousal aspects, as observed in two public DEAP and DREAMER datasets.
Transformer-based Deep Frame Rate up Conversion for Hybrid High-Efficiency Video Coding

Mainstream state-of-the-art video compression standards such as High-Efficiency Video Coding (HEVC/H.265) and Versatile Video Coding (VVC/H.266) are block-based coding. On the other hand, neural video compression networks have obtained impressive results recently. However, neural video compression is not practical for use due to its high computations. In this paper, we propose a hybrid approach that exploits a deep frame rate up-conversion network to improve the coding efficiency of existing video codecs such as HEVC. We analyze a coding scenario of a combination between the Frame Rate Up-Conversion (FRUC) network and the random access coding configuration. In addition, we show that the FRUC network trained on compressed images outperforms the model trained on non-compressed images for the video frame interpolation task. The proposed framework skips encoding odd frames in the display order of original video sequences at the encoder side and reconstructs them at the decoder side via the FRUC network. Experimental results show the proposed framework improves the coding efficiency of HEVC 4.2 % for random access configuration.
Insect Classification Using Pretrained Deep Neural Networks and Transfer Learning

Ecosystems cannot function without the presence of insects, yet due to their enormous diversity and minute variances, recognising and categorising them may be difficult. It is possible to reliably categorise insect species using deep learning algorithms. Deep learning-based insect categorization may be used for a variety of purposes, such as advancing scientific research, enhancing agricultural pest management, and assisting environmental conservation. The development of a deep learning model allows us to rapidly and precisely identify insects, even when they are in different life stages or seem identical to other species. By doing so, their actions and effects on the environment may be better understood. Considering the advancements in the fields of AI, especially in the area of deep learning, we have proposed to use transfer learning mechanism to classify the insects on IP102 dataset. Various standard models like VGG, Inception, Xception, MobileNet, DenseNet, ResNet and EfficientNet are used as a base model and then fine tuned to learn the representations. The best training set and validation set accuracy are found to be 71.34% and 64.95% respectively for the EfficientNetB7 model.
Analysis on Pre-charging of Auxiliary Resonant Capacitor of Resonant DC Link Inverter

Based on the resonant DC link inverter with energy feedback function, in order to overcome the problem of voltage change rate (dv/dt) increase caused by the auxiliary resonant capacitor not being charged to the power supply voltage during its operation, this paper analyzes its pre-charging process in detail. When the energy stored in the auxiliary resonant inductor is not enough to charge the auxiliary resonant capacitor to the power supply voltage, the latter will not immediately participate in the resonance process, resulting in an increase in the voltage change rate at both ends of the bus and main switches. Under this condition, the working mode of this soft-switching inverter is given, and the parameters are designed to suppress the increase of voltage change rate. Finally, the correctness of theoretical analysis is proved by comparing the waveforms of switches under different resonance parameters.
The role of remote sensing in the development of SMART indicators for ecosystem services assessment

Abstract Human beings benefit from a wide range of goods and services from the natural environment that are collectively known as ecosystem services. However, rapid natural habitat loss, overexploitation and climate change is causing accelerating losses of populations and species, with largely unknown consequences on ecosystem functioning and the sustainable provision of ecosystem services. It is crucial, therefore, to develop a suite of indicators of the health and status of ecosystems, to monitor and quantify services delivery and to facilitate policy responses to stop and reverse negative trends. An effective framework to facilitate the development of suitable indicators is by using the SMART approach, which defines five criteria that could be applied to set monitoring and management goals, which are Specific, Measurable, Achievable, Realistic and Time-sensitive. Remote sensing provides a useful data source that can monitor ecosystems over multiple spatial and temporal scales. Although the development and application of landscape indicators (vegetation indices, for example) derived from remote sensing data are comparatively advanced, it is acknowledged that a number of organisms and ecosystem processes are not detectable by remote sensing. This paper explores several approaches to overcome this limitation, by examining the strong affinity of species with dominant habitat structures and through the coupling of remote sensing and ecosystem process models using examples drawn from a number of important ecosystems.
Tobacco Impurities Detection with Deep Image Segmentation Method on Hyperspectral Imaging

The detection of impurities in tobacco raw materials is an important task, and the performance of detection algorithms directly affects the quality of tobacco products. The hyperspectral imaging system simultaneously captures the visible and near-infrared spectra containing rich spectral information that can accurately reflect material categories. In this paper, we propose a novel deep learning-based tobacco impurities detection method on hyperspectral data. It is a U-net like structure that progressively extracts multi-scale discriminant features and fuses them to find the impurities in the scene. Experiments conducted on the collected dataset show that the proposed segmentation model yields superior performance compared to other detection methods.
Sensor Fusion Image Processing for Autonomous Robot Blimps

This paper demonstrates a method for finding the goal in a robot competition called “Defend the Republic”. A flashing light and frame differencing image processing can be used to detect the goal and to estimate the distance to the goal. The flash and frame difference method eliminates false positives, but its operating range is 50% less than color detection only. Sensor fusion between color detection and frame differencing can be useful for first detecting potential goals at long range, and then eliminating false positives before getting too close.
A Study of Deep Learning Based Classification of Mandarin Vowels Using Spoken Speech EEG Signals

Brain computer interface (BCI) based on imagined speech provides an alternative access for human-computer interaction and can help patients who are unable to speak to communicate with the outside world. However, the complexity and variability of speech-related electroencephalographic (EEG) signal make decoding speech information from EEG signals a challenging task. This study aimed to explore feature extraction methods and classifiers that were suitable for speech-related EEG classification. Six feature extraction techniques and three classifiers were used to classify spoken speech EEG signals of four Mandarin vowels. The classification results showed that the accuracy reached 68.7% when using channel cross-covariance (CCV) with Riemannian manifolds as input features and deep convolutional neural network (Deep ConvNet) as the classifier, which was better than classification performance of other methods and previous studies. This study provides insights into the effectiveness of various feature extraction and classification methods in EEG-based speech classification. It suggests the potential of CCV combined with Riemannian manifold features and a Deep ConvNet based classifier for speech imagery studies.
A Modified Deadbeat Predictive Flux Control for Reducing Torque Ripple at Low Modulation Ratio

This paper proposes a modified accurate deadbeat predictive flux control (M-ADPFC) to reduce the torque ripple and improve the torque tracking accuracy at low modulation ratio. A torque ripple model with discretization error compensation and low computation burden is utilized to predict the torque ripple and tracking error in the space vector modulation (SVM) process. An optimization method based on the continuous-control set model predictive control (CCS-MPC) is proposed to minimized the torque ripple. Comparative simulations validate that the proposed method has reduced torque ripple and good tracking accuracy in a wide load range at low modulation ratio.
A Unified View of Decentralized Algorithms for Sparse Linear Regression

We investigate sparse linear regression in high-dimension via the projected LASSO estimator from distributed datasets across a network of agents. This model enables the ambient dimension to scale exponentially with the total sample size. We develop a unified algorithmic framework that encompasses a variety of distributed algorithms, new and old, such as primal, primal-dual, and gradient tracking-based methods, for which we establish both statistical and computational guarantees. Our findings demonstrate that, under standard data generation models, appropriate network connectivity and algorithm tuning, the studied schemes converge to statistically optimal estimates at a linear rate. However, the dependencies of convergence rate on the ambient dimension exhibit a noteworthy difference, ranging from linear to independent scaling. This fact is a new, interesting phenomenon distinctive of the high-dimensional setting and revealed by this study.