Miracle-dz/UniFAR-FTUs huggingface dataset:
Model Predictive Event-Triggered Non-Fragile Fault-Tolerant Control for IT2 T-S Fuzzy Systems with Static Output Feedback

This paper presents an innovative event-triggered non-fragile fault-tolerant control approach towards the interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy systems subject to bounded disturbances and actuator faults based on model predictive control (MPC) strategy. The proposed fault-tolerant control method integrates both an event-triggered strategy to alleviate the transmission pressure and energy consumption of the network and a non-fragile controller design strategy to remain resilient to potential gain perturbations. Quadratic bounded (QB) technology is used to design static output feedback controllers. Moreover, the proposed method guarantees recursive feasibility and quadratical stability of closed-loop dynamics. The validity of method is verified through an example.
Coyote Optimization Algorithm-Based PV Planning Strategy for Maximizing Hosting Capacity in A Distribution System

This paper declares a Coyote Optimization Algorithm (COA)-based method of determining both optimal PV locations and sizes simultaneously in a distribution system with the largest hosting capacity (HC) of solar photovoltaic (PV) generation and minimizing system voltage deviation. The objective function includes PV hosting capacity (PVHC) and voltage deviation amplitude at each bus. The RMS bus voltage, the maximum size of each PV placement at an installed bus, the total active power losses, and reverse power flow at the substation are constraints to be met. The problem is solved using the Coyote Optimization Algorithm (COA). The IEEE 123-bus system is applied to test the proposed method. Results are compared with those obtained by other metaheuristic methods, including Genetic Algorithm and Particle Swarm Optimization. Test results show that the COA method yields exceptional solutions compare to GA and PSO methods in both optimality and convergence.
Review of the Current Research Status and Constraints of Wall-climbing Robots

Wall-climbing robot refers to the electromechanical system that can attach to the surface of the object to carry out multi-degree-of-freedom movement and complete the work, especially suitable for the execution of special tasks, so it has a good application prospect and a wide range of market demand. Based on reading a large number of related papers, this paper firstly summarizes the research background and main classification of wall-climbing robots, and then summarizes the current research status of wall-climbing robots in three aspects: wheeled wall-climbing robots, crawler wall-climbing robots, and foot-type wall-climbing robots. On this basis, the challenges on the development of wall-climbing robots are pointed out.
Performance and Inter-Comparison of Novel 4π-Optimized Compton Imagers

Compton imagers determine the location of a gamma-emitting radioactive source by tracking the interactions of a gamma ray within a position-sensitive spectroscopic gamma detector. The Compton imaging technology is naturally capable of delineating multiple and extended sources as well as localizing point sources. With their graphic output of source location probability contours overlaid on a photograph, Compton imagers are finding application in astronomy, medical imaging, environmental remediation, nuclear nonproliferation and national security. Over the past several years this group has been developing the Silicon photomultiplier-based Compton Telescope for Safety and Security (SCoTSS) imager using a traditional two-plane design with a forward "scatter" plane and a rear "absorber’ plane [1]. At the 2019 IEEE NSS MIC conference we described adapted SCoTSS designs optimized for 4π imaging including cubic, spherical and cruciform geometries and discussed their performance determined from GEANT4 simulations [2]. In this submission we present the results of studies performed on the realized instruments in a laboratory setting, quantifying and comparing their point source response and their imaging uniformity over large fractions the 4π incident angle space. These results, using real data, permit verification of the performance expectations for the various design principles of the detectors.
Designing An Overseas Experiential Course in Data Science

Unprecedented demand for data science professionals in the industry has led to many educational institutions launching new data science courses. It is however imperative that students of data science programmes learn through execution of real-world, authentic projects on top of acquiring foundational knowledge on the basics of data science. In the process of working on authentic, real-world projects, students not only create new knowledge but also learn to solve open, sophisticated, and ill-structured problems in an inter-disciplinary fashion. In this paper, we detailed our approach to design a data science curriculum premised on learners solving authentic data science problems sourced from an overseas project sponsor. The course consists of a local component in which students acquire requisite knowledge on data analytics through classroom-based learning and an overseas component where students were on-site with the overseas project sponsor. To evaluate the design of our course curriculum and uncover its potential benefits, we surveyed the project sponsor representatives and tasked our students with the submission of a personal reflection. Quantitative and qualitative analysis of the survey and reflections respectively revealed the sponsors' high satisfaction with the students' output and the students' perception that the course has enriched their learning. The results also indicated that the students attributed teamwork, communications, mentoring and data processing skills as key factors integral to successful project outcomes. In all, we have developed an experiential, project based and applied data science program in an overseas context offered to students from different disciplines which yields promising student learning outcomes.
A Splitting Modulation Strategy for Arm Multiplexing Modular Multilevel Converter

Aiming at the lightweight demand of modular multilevel converter (MMC), an arm multiplexing modular multilevel converter (AM-MMC) with high utilization of sub-modules is proposed. According to the characteristics of AM-MMC topology, the modulation signal is divided into three groups and input into different arms. a splitting modulation strategy is designed to meet its modulation requirements in the field of HVDC transmission. The simulation results based on Matlab/Simulink show that under the proposed strategy, AM-MMC can successfully complete the AC/DC voltage conversion and energy transmission, and the operation effect is good, and the voltage of the three sub-modules is stable at the rated voltage fluctuation under the split modulation strategy.
A Backflow Power Optimization Method for Three-Level ANPC-DAB Converter

With the increasing demands of high voltage level and high power level, conventional two voltage-level dual active bridge (DAB) applications are restricted, and the active neutral-point-clamped dual active bridge (ANPC-DAB) topology is introduced. In this paper, a minimization method of backflow power for three-level ANPC-DAB with low current stress under triple-phase-shift (TPS) modulation is proposed. Firstly, the operation principle and operation modes of ANPC-DAB converter are analyzed. Then, the mathematical model of the converter under different operating modes are established. On this basis, a minimum backflow power model is built, the optimal solution is obtained by MATLAB/Fmincon solver, and the mode transition principle is also discussed. Finally, experiments were conducted on an experimental prototype and the correctness and effectiveness of the proposed method is verified.
Timing Calibration with Depth of Interaction for the NeuroEXPLORER Brain PET

The NeuroEXPLORER (NX) is a next generation SiPM based dedicated brain PET scanner. It offers ultra-high sensitivity, spatial resolution, and an extended axial field of view. To provide previously unachievable imaging performance, NX uses novel U-shaped LYSO crystals with reduced sizes to provide depth of interaction (DOI) and time of flight (TOF) measurements. Timing calibration is critical to achieve the best possible timing resolution. In this work, we investigate timing calibration with DOI for the NX. We develop an iterative algorithm to accurately estimate time offsets on a per crystal and DOI basis using a line source. We first estimate the source position using, e.g., a non-TOF reconstruction. The difference time error for each event is computed by comparing the measured time and true time obtained based on the source position, which in turn is used to generate error histograms for each DOI of each crystal. The mean is estimated from a Gaussian fitting, which is used as a candidate for updating time offsets using an iterative algorithm. To further reduce data acquisition time, we introduce a separable time offset model (STOM), the time offsets are mainly contributed by crystal and DOI independently. The STOM allows timing calibration incorporating DOI information while reducing unknowns comparable to those in a non-DOI timing calibration. The overall mean timing resolution in FWHM is 271.3±13.9ps. The difference between STOM time offsets and full estimated time offsets is quite small; the standard error is about 1 TOF bin of 12.21ps. In summary, we develop effective timing calibration with DOI using a line source for the NX.
Efficient Disease Detection in Wheat Crops: A Hybrid Deep Learning Solution

Wheat rust disease poses a significant danger to global food security and requires rapid, precise diagnosis to be effectively managed. Using a hybrid deep learning (DL) model consisting of a convolutional neural network (CNN) and a decision tree (DT), a new method for classifying wheat rust illness across six magnitude scales has been described in the proposed study. For training and assessing the model, a dataset of 50,000 wheat leaf photos representing a wide range of disease magnitude has been amazing. The suggested work developed a hybrid CNN-DT model with an amazing overall accuracy of 93.47% by carefully analyzing the data and crafting the model. The model's resilience in identifying multiple levels of disease magnitude was proved by the performance metrics for each disease magnitude class. The proposed hybrid model also outperformed state-of-the-art models in terms of accuracy, as shown by the comparisons conducted. The findings provide important new information on the potential of DL methods for wheat rust disease classification, which can then be used as a trusted resource for early disease diagnosis and smarter agricultural policymaking. In the face of agricultural diseases, the suggested model has important implications for improving crop management, reducing yield losses, and guaranteeing food security.
Contemporary Trend Analysis on Depression Detection Using EEG, Eye Gazing and Facial Emotion Recognition

Depression, being one of the scorching issues of the present era and contributes as a major cause of suicidal death over the world, affects around 280 million people worldwide. Like the other mendable disease, depression can be cured completely with proper and proactive diagnosis. The lack of awareness and reluctance to visit a clinical practitioner may make the situation more critical for a patient having depressive disorder. In this paper, we have reviewed available literature on three prime focuses- Eye gaze measuring, EEG and facial emotion recognition. The main aim of this paper is to perform a systematic review of the available literatures in the selected area and explore the trend in this area and try to indicate the future direction also. Around 130 papers from peer-reviewed, High-Impact journals and conferences like Elsevier, IEEE, Springer, Lancet, NCBI, MDPI etc. have been selected and a systematic review has been performed on those papers to determine the trends as well as to indicate the future direction of this research.
Observer-Based Sliding Mode Control for Omni-Directional AGV

This paper presents a new strategy of a high-gain extended observer-based adaptive integral sliding mode control (HGEO-AISMC) on omni-directional AGV with mecanum wheels. Model uncertainties and unknown external disturbances (MUUED) are also taken into account. The proposed high gain extended observer (HGEO) is used to estimate the MUUED and unmeasured states. Based on HGEO, adaptive integral sliding mode control (AISMC) strategy is designed to ensure effective tracking capability, an adaptive law is used for the estimation of unknown parameters. Simulations were carried out to test the effectiveness and tracking performance of the designed HGEO-AISMC strategy.
Attention-based Deep Neural Network for Early Detection of Parkinson's disease using Diffusion Tensor Imaging

Accurate and early diagnosis of Parkinson's (PD) disease is a compelling need due to the lack of motor symptoms. In previous studies, statistical analysis and support vector machine-based (SVM) classification performed on fractional anisotropy (FA) and mean diffusivity (MD) maps extracted from diffusion tensor imaging (DTI) images showed significantly reduced fractional anisotropy (FA) and increased mean diffusivity (MD) in many brain regions of PD group compared to healthy control. Though the diagnostic accuracy of PD based on statistical analysis and SVM of DTI images are relatively good, convo-lutional neural networks (CNNs) are the leading techniques for classifying patients into PD and healthy. However, conventional CNNs generally use the whole image as input for network learning, while in some cases, the focus would be only on specific regions within the image. In this work, we propose an attention-based neural network using a 3D ResNet-18 model with an additional compartment, which converts the region of interest (ROI) masks into an attention map to diagnose PD from diffusion tensor imaging (DTI). In this regard, the focus would be only on the specific brain regions affected by PD. The accuracy and area under the receiver operating characteristic (ROC) with and without attention module were 67%, 83%, and 69%, 84%, respectively. The results demonstrated that the attention-based model exhibited noticeably superior performance owing to the concentration on the key regions of the brain. Therefore, the attention-based 3D ResNet-18 can reduce the number of misdiagnoses as a decision support tool for neurologists.
Data Driven Surrogate Signal Extraction for Dynamic PET Using Selective PCA

Respiratory motion correction is beneficial in PET. Methods of motion correction include gated reconstruction, where the acquisition is binned, based on a respiratory trace. To acquire these respiratory traces, an external device, like the Real Time Position Management System, or a data driven method, such as PCA, can be used. Data driven methods have the advantage that they are non-invasive, and can be performed post-acquisition. However, data driven methods have the disadvantage that they are adversely affected by the tracer kinetics of a dynamic acquisition. This work seeks to evaluate several adaptions of the PCA method, through which it can be used with dynamic data. The methods explored in this work include, using a moving window (similar to the KRG method of Schleyer et al. (PMB 2014)), extrapolation of the principal component from later time points to earlier time points, as well as a method to select and combine multiple respiratory components. The respiratory traces acquired, were evaluated on 21 patients, by calculating their correlation with a Real Time Position Management System surrogate signal. The results indicate that all methods produce better surrogate signals than when applying static PCA to dynamic data. Extrapolating a late principal component, produced more promising results than using a moving window, and selecting and combining components held benefits for all methods.
Light-Load Efficiency Improvement of MHz RDCX Based on Output Voltage Ripple and Resonant Pulse Number Control

Regulated DC transformer (RDCX) achieves high-efficiency, high-power-density power conversion due to fully soft switching, transformer optimization, and partial-power regulation. However, the load-independent losses, especially the core loss at high switching frequency, make it difficult for RDCX to meet the increasing light-load efficiency requirements. Burst mode operation is an effective way to reduce the load-independent losses of the resonant DC transformer (DCX) in RDCX. However, when applying the conventional burst mode strategies in DCX, soft switching will be lost during the burst-on time, or the implementation have high requirements on the hardware or software. In this paper, a simple burst-mode strategy is proposed. Pulse sequence with fixed pulse widths in DCX maintains soft switching and simplifies the DSP calculation. To improve the efficiency in burst-mode operation, average output current during the burst-on time is controlled, and extra losses in the burst mode operation is minimized. Cooperating with the auxiliary DC-DC, automatic adjustment of average output current during the burst-on time is realized. Finally, a 1-MHz 66V/48V 500W prototype based on silicon devices is established for verification. The efficiency at 10% load is improved from 92.6% to 95.3%, which means a 37% loss reduction.
Compact DSP-based Time-to-Digital Converter IP-Core for Xilinx 7-Series

In this contribution, we present the implementation of a compact, multi-channel, and high-resolution Time-to-Digital Converter (TDC) IP-Core implemented using the Digital Signal Processor (DSP) blocks present in the 28-nm Xilinx 7-Series Field Programmable Gate Arrays (FPGAs). The very high-resolution is obtained using the classical tapped delay-line approach where the delay-line is implemented thanks to the adder chain of the DSP blocks, i.e. the DSP48E1 primitive. Exploiting this type of resource, instead of the classical CARRY primitive, makes possible to build a more compact structure also leading to relax the hardware utilization. Indeed, the sampling of the tapped delay-line is made by the flip-flops (FFs) integrated in the DSP itself, thus not requiring to use the FFs present in the FPGA fabric as in the CARRY approach.This device is also tunable, thanks to the possibility of setting the resolution (LSB), some picoseconds, and Full-Scale Range (FSR), up to some days, from the user-friendly graphical user interface (GUI) of the IP-Core. Last but not least, the TDC is also characterized by a maximum measurement rate in the order of hundreds of Msps per channel, thus satisfying the high count-rate capability required by state-of-the-art detectors. To obtain such an high-resolution over a wide FSR, the measurement is performed according to the Nutt-Interpolation technique, in which the timestamp of each channel is made by a coarse counter part and by a fine part performed by the DSP. From an experimental point of view, the TDC, with a few tens of channels, has been implemented and fully tested on an Artix-7 FPGA (xc7a100tftg256-2).
Framework for Generation of Annotated Corpus for Recognition & Categorization of Geo-Political Entities from Indian Election Domain

This paper presents a comprehensive framework for generating an annotated corpus that facilitates the recognition and categorization of geo-political entities within the Indian Election domain. It focuses on the generation of an annotated corpus and the development of a customized Named Entity Recognition (NER) model for the identification and categorization of geo-political entities within Indian Election domain text. The entities of interest include states, districts, parliamentary constituencies, and assembly constituencies. The proposed work involves extensive research and data analysis from various resources such as ECI Publication manuals, several websites, Wikipedia, among others. The collected data is then annotated using the specialized annotation tool called Prodigy. Subsequently, a customized NER model is built and trained using the annotated data. The performance of the developed model is thoroughly tested and analyzed. The results of this project indicate the successful generation of a dataset specifically tailored for the Indian election domain, achieving an accuracy rate of 68%. Additionally, a framework is established for the generation of similar datasets in the future. It combines data analysis, annotation techniques, and machine learning to create a robust NER model that can effectively recognize and categorize geo-political entities in Indian Election domain texts. The generated dataset and framework contribute to the advancement of natural language processing techniques in the context of political analysis.
Assessing the Efficacy of Virtual Teaching versus Face-to-Face Instruction and the Dunning-Kruger Effect: A Ten-semester Comparative Analysis

Virtual courses proliferated during the global Covid-19 pandemic. Universities offered synchronic online classes featuring real-time interactions to engage students from a distance. As the pandemic has subsided, it is important to analyze and review the effectiveness of virtual teaching. This study examined the efficacy of virtual teaching as compared to face-to-face instruction over ten semesters, focusing on students' perceived learning experiences and academic outcomes. The findings revealed that while students indicated improved learning experiences in virtual teaching due to its flexibility and perceived effectiveness, their academic performance actually declined compared to face-to-face instruction. This discrepancy was attributed to the Dunning-Kruger Effect, as students in virtual learning environments tended to overestimate their competency due to insufficient self-awareness and metacognitive skills when physically apart from their peers. The study suggested exploring the Hyflex model for course design which can integrate the best aspects of both virtual teaching and face-to-face instruction.
Deep Vision Transformers for Prognostic Modeling in COVID-19 Patients using Large Multi-Institutional Chest CT Dataset

The importance of prognosis is the assessment of the disease progression, providing more effective management, decreasing mortality, and lowering the time of hospital stay. In convolutional neural network (CNN)-based algorithms, explicit long-range and global relation modeling is a major challenge because of the locality of convolution operations. These challenges result in weak performance because of large inter/intra-patient variabilities, specifically in COVID-19 patients. Transformers successfully used in natural language processing (NLP) tasks could potentially address the limitation of CNN-based algorithms. In the current study, we evaluated deep transformers-based algorithms’ performances in the prognostication of COVID-19 patients. Patient data from 19 centers were enrolled in this study. After inclusion and exclusion criteria, 2339 patients remained (1278 alive and 1061 deceased). We implemented a pure Transformer that consists Swin Transformer block. Images split into non-overlap patches (4 by 4) are used as tokens by the patch partitioner, followed by a linear embedding layer. These tokens pass through the patch merging layer and the Swin transformer block (feature representation learning). In addition, we implemented CNN-based algorithms, including ResNet-18, ResNet-50, ResNet-101, and DensNet, for comparison. Data were split into train/validation (70%) and test sets (30%), and all evaluations were performed on test sets. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported for the test sets (unseen during training). Regarding AUCs, 0.66, 0.75, 0.72, 0.77, and 0.81 were achieved by ResNet-18, Resnet-50, Resnet-101, DensNet, and Transformers.Considering all parameters Transformer significantly (p-value <0.05) outperformed all CNN-based models with Accuracy, Sensitivity, Specificity, and AUC of 0.75, 0.78, 0.71, and 0.81, respectively.