Miracle-dz/UniFAR-FTUs huggingface dataset:
Chest X-Ray Pneumonia Detection by Dense-Net

Chest diseases include various types with varying levels of riskiness. Pneumonia is among the most fatal infectious chest diseases and kills more children than any other infectious disease. In 2016, the United Nations International Children’s Emergency Fund (UNICEF) reported that pneumonia had infected the lives of over 800,000 children under the age of five. This paper presents a deep learning model architecture that aims to detect pneumonia disease in the chest using X-ray images. The proposed model is based on the Dense-Net model which consists of a 121-layer convolutional neural network. This research use the published dataset from Guangzhou Women and Children’s Medical Center. The dataset include chest X-ray images for patients age ranges from one to five years old labeled by physicians containing the required binary classes of Normal and Pneumonia classes labels. The model consists of the Dense-Net Model, three convolution neural network (CNN) layers, and a global average pooling (GAP) layer. The overlapping pooling helps in reducing the overfitting. The Decoupled Weight Decay Regularization is used as the optimizer for the proposed model with a small learning rate. The proposed model could detect pneumonia with an accuracy of 90%. Such a model could be relied on by radiologists for providing early detection of pneumonia to speed up the process of reprioritization of hundreds of chest X-rays needed to be diagnosed.
Ultra Low Loss - MMC Submodules favorable for SiC-FET enabling High Functional Safety

Further progress of Modular Multilevel Converters (MMC) is mainly related to advanced submodule topologies (SM), semiconductors (SiC) and improved control concepts. Fully electronic failure management for external failures (i.e. DC-short circuit) and protection against internal failures of the converter are key issues. A new submodule for MMC is presented, which meets these requirements. In addition, a novel SiC-JFET supercascode is investigated, suitable for these and other applications.
Brown-Peterson Homology: An Introduction and Sampler

Part 1. An introduction Complex bordism Formal groups Brown-Peterson homology Part II. A sampler Cooperations and stable homotopy Associated homology theories Morava's little structure theorem and the Conner-Floyd conjecture Hopf rings, the bar spectral sequence, and $K(n)_*\underline K_*$ $H_*\underline K_*$ and the Steenrod algebra Two formal groups and $BP_*\underline{BP}_*$ Chan's proof of no torsion in $H_*\underline{BP}_*$ Part III. Something new Unstable operations References.
IMPULSIVE SPOT HEATING AND THERMAL EXPLOSION OF INTERSTELLAR GRAINS REVISITED

The problem of the impulsive heating of dust grains in cold, dense interstellar clouds is revisited theoretically with the aim of better understanding the leading mechanisms of the explosive desorption of icy mantles. We rigorously show that if the heating of a reactive medium occurs within a sufficiently localized spot (e.g., the heating of mantles by cosmic rays (CRs)), then the subsequent thermal evolution is characterized by a single dimensionless number λ. This number identifies a bifurcation between two distinct regimes: when λ exceeds a critical value (threshold), the heat equation exhibits the explosive solution, i.e., the thermal (chemical) explosion is triggered. Otherwise, thermal diffusion causes the deposited heat to spread over the entire grain—this regime is commonly known as whole-grain heating. The theory allows us to find a critical combination of physical parameters that govern the explosion of icy mantles due to impulsive spot heating. In particular, our calculations suggest that heavy CR species (e.g., iron ions) colliding with dust are able to trigger the explosion. Based on recently calculated local CR spectra, we estimate the expected rate of explosive desorption. The efficiency of the desorption, which in principle affects all solid species independent of their binding energy, is shown to be comparable to other CR desorption mechanisms typically considered in the literature. Also, the theory allows us to estimate the maximum abundances of reactive species that may be stored in the mantles, which provides important constraints on the available astrochemical models.
Use of Facial Expressions to Improve the Social Acceptance of Level 4 and 5 Automated Driving System Equipped Vehicles

According to the World Health Organization (WHO), more than one million people die yearly from car accidents. At the same time, between 20 and 50 million people suffer non-fatal injuries, which can also lead to permanent disabilities. Recently, vehicles equipped with SAE level 3, 4, and 5 Automated Driving System (ADS) have become one of the hottest topics in the automotive industry. In fact, their main expected benefit is that they could significantly reduce the number of road accidents. Their actual success will depend on how people react to their introduction: considering that the absence of the steering wheel and pedals is possible for levels 4 and 5, will people trust these advanced forms of driving automation? In this regard, the authors of this paper have proposed two different ideas. The first, which can be implemented during the proactive phase, consists in calibrating the driving algorithm of the vehicle based on the volunteers' reaction to simulations of common driving situations. The second, which can be implemented during the reactive phase, consists in dynamically adapting the driving style of the vehicle based on the average feeling inside the vehicle. Both of these ideas could help improve social acceptance and facilitate the transition to vehicles equipped with SAE level 4 and 5 ADS.
Model Reduction using Singular Perturbation Methods for a Microgrid Application

The paper deals with the modeling of an island grid for the purpose of simulation. Since the behavior of the system is affected by phenomena taking influence in different time-scales, a model reduction scheme is proposed that leads to simpler models which reduce the computational burden in simulations. Compromises in the modeling procedure are addressed by investigating simulation studies.
A POMDP-based Robot-Human Trust Model for Human-Robot Collaboration

Trust is a cognitive ability that can be dependent on behavioral consistency. In this paper, a partially observable Markov Decision Process (POMDP)-based computational robot-human trust model is proposed for hand-over tasks in human-robot collaborative contexts. The robot's trust in its human partner is evaluated based on the human behavior estimates and object detection during the hand-over task. The human-robot hand-over process is parameterized as a partially observable Markov Decision Process. The proposed approach is verified in real-world human-robot collaborative tasks. Results show that our approach can be successfully applied to human-robot hand-over tasks to achieve high efficiency, reduce redundant robot movements, and realize predictability and mutual understanding of the task.
Research on Natural Language Recognition based on Grey Correlation Degree and TF-IDF Algorithm

Natural language research topic in recent years is complying with the important topic of artificial intelligence development, when dealing with text information data, the training mechanism and combined with the feature of algorithm for natural language data analysis is an effective method to deal with complex text information. For the text classification and judgment process, in combination with the concept of deep learning algorithms have been proposed, and get the argument, combined with the feature of countable characteristic of text classification problem. In the process of research, it often need a lot of samples, the complexity of the samples affect the key content of text extraction, but the lack of samples will lead to extract text content and it is too simplified, the deviation is from the characteristics of information content, On the other hand, for the huge amounts of text sample, it will lead to more redundancy in the compilation process of the algorithm, which has certain requirements for the configuration of the hardware system, and it loses timeliness in the operation time. In this paper, the accuracy of feature extraction of complex text is studied by using an innovative idea of algorithm iteration, and the mathematical grey correlation degree is used to deduce and verify the accuracy.
Energy-aware Adaptive Approximate Computing for Deep Learning Applications

Application that use deep learning incur a substantial amount of energy consumption. Reducing this energy footprint is important, especially for applications such as Internet of Things (IoT) Embedded Systems (ESs), where resources are scarce. Here, we present computational self-awareness as a promising solution for intelligently adapt machine learning algorithms at runtime to reduce their energy consumption. In particular, we focus on approximation as a key enabler knob for such adaptivity. We show that the benefits of such an approach can be up to 2.5 × energy savings.
Design of a serial impingement cooling heatsink for a 30 kW PV string inverter

The design of a cooling solution for a 30 kW string inverter is detailed in this paper. As opposed to traditional solutions, a novel approach has been proposed, consisting of three pinfin copper heatsinks tightly arranged with mutual airflow. This solution has been characterized on a dedicated test bench, partly based on a $\mathrm{T}_{\mathrm{j}}(\mathrm{I}_{\text{DS}},\mathrm{V}_{\text{DS}})$ TSEP itself precisely calibrated, both being described in this paper. Comparisons between traditional solutions, expected solution, characterized solution, and perspective solutions have been conducted based on the CSPI indicator, reaching up to 34 W.K−1.L−1.
A Countermovement Jump for the Midterm Assessment of Force and Power Exertion After Anterior Cruciate Ligament Reconstruction

Objective The aim of this study was to assess force and power exertion during a countermovement jump after anterior cruciate ligament reconstruction using either semitendinosus and gracilis or bone-patellar tendon-bone graft. Design One hundred-nineteen semitendinosus and gracilis and 146 bone-patellar tendon-bone participants performed a countermovement jump on two force platforms after 3 (T1) and 6–9 mos (T2) from surgery. Twenty-four healthy participants served as control group. Peak force of eccentric and concentric phases and peak power were obtained from the analysis of vertical components of the ground reaction forces. Asymmetry was quantified by means of limb symmetry index. Results Eccentric peak force was significantly (P < 0.05) lower than concentric peak force in both bone-patellar tendon-bone and semitendinosus and gracilis at T1 and T2. At T1, bone-patellar tendon-bone showed higher peak power, but lower limb symmetry index in eccentric and concentric compared with semitendinosus and gracilis. At T2, bone-patellar tendon-bone showed higher peak power than semitendinosus and gracilis, although there were no differences in limb symmetry index between the two groups, which however was significantly (P < 0.05) lower in both groups when compared with control group. Conclusions Bone-patellar tendon-bone and semitendinosus and gracilis participants showed asymmetries in eccentric and concentric force during a countermovement jump. Bone-patellar tendon-bone showed greater asymmetries and a higher peak power respect to semitendinosus and gracilis participants.
JU QUERY - Web App for community based learning

The primary goal of this work is to create an interactive and exclusive question-and-answer web application for university students, faculty, and anybody interested in knowing something about the University. The web application allows anyone to share their thoughts and questions about a variety of themes including "technology, technical events, innovation, placements, sports, cultural events, transportations, hostel life, canteen, and many other things" happening at the University. The main purpose of this website is to simply impart knowledge and assist those who are in search of it. Through effective collusion or collaborative learning strategy, the JU Query web app also assists students and universities in improving their academic outcomes. Q&A platforms are widely utilized as a knowledge repository in the modern era, and in the case of this system, students and faculty at the University can use it as a knowledge base, from the collections of the questions and answers submitted, either internally or externally.
Design of a Novel Dual-band Printed Dipole Antenna

A novel dual-band printed dipole antenna operating at 433 MHz and 2390 MHz is proposed and investigated. The antenna is composed of a two-element double-sided printed dipole array which works at high frequency and one arm of the low-frequency dipole. By loading a capacitor, high-frequency arms printed on one side are shared as the low-frequency dipole's another arm. Additionally, the length of the dipole for the lower band is further shortened by loading inductors. The proposed antenna offers a compact antenna configuration, details of the configuration and experimental results are discussed.
Modeling and Validation of Safe Space in the Operating Area of Minimally Invasive Thyroid Surgery Robot

The Minimally Invasive Thyroid Surgery Robot is a minimally invasive surgical robot with an areolar approach. Based on the intracavitary double-hole flexible arm of this robot, its mechanical structure is analyzed and simplified to obtain the D-H model of the flexible arm, and the working space of the flexible arm is analyzed on the basis of this model. Meanwhile, the mathematical modeling is carried out for the relevant tissues and organs in the working area of the flexible arm, i.e., the tissues and organs of the human neck. Point projection and secondary fitting are used in the modeling process to avoid the results of reduced accuracy of the fitting function due to the complexity of the 3D function, and on the basis of this mathematical model, the human neck pull compensation model is established by combining the relevant biomechanical parameters. Finally, the flexible arm workspace is combined with the human neck pull compensation model, and the validity of the model can be verified by simulation that the pull compensation model can optimize the reachable area of the flexible arm during surgery.
Modeling the arrangement of drill holes for orthogonal biasing in controllable inductors for power electronic converters

Magnetic devices are essential components of power electronic converters. Unfortunately, they usually are also the bulkiest and heaviest components in the converter system. Controllable magnetic devices using active premagnetization are a promising approach regarding further volume and weight reduction. One method of active premagnetization is orthogonal biasing. To be able to use this method, drill holes in the ferrite core material are necessary. Because of the material's fragility and brittleness, manufacturing ferrite cores is a difficult and expensive process. For this reason, an effective arrangement of the drill holes is critical. In this paper a basic model of drill hole arrangement based on congruent circle packing is presented for circular and square core cross sections. 2D-FEM-simulations and measurement results are made for comparison and verification. The deviations between calculation and measurement results are discussed and opportunities for model improvement are given.
Operator-Norm-Based Variable-Wise Diagonal Preconditioning for Automatic Stepsize Selection of A Primal-Dual Splitting Algorithm

We propose a diagonal preconditioning method for automatically selecting the step sizes of a primal-dual splitting method (PDS). The conventional preconditioning method for PDS has several limitations, such as the need to directly access all the entries of the matrices representing the linear operators in the target optimization problem, and the possibility that the proximity operator cannot be solved analytically due to the element-wise preconditioning. In this paper, we establish operator norm-based variable-wise diagonal preconditioning (ON-VW) to resolve these issues. ON- VW has two features that are preferred in real applications. First, the preconditioners constructed by ON-VW are defined using only (an upper bound of) the operator norm of the linear operators, which eliminates the need for their explicit matrix representations. Furthermore, the stepsizes automatically selected by our preconditioners are variable-wise, which allows us to keep the proximity operator computable. We also prove that our preconditioners satisfy the convergence condition of PDS and demonstrate its effectiveness through its application to denoising of hyperspectral images.
State of Charge Estimation of Lithium-ion Battery based on Minimum Error Entropy Square Root Cubature Kalman Filter

To account for the impulsive non-Gaussian noises, a minimum error entropy square root cubature Kalman filter (MEE-SRCKF) is proposed to estimate the state of charge (SOC) of lithium-ion batteries. It adopts the robust minimum error entropy (MEE) criterion as the optimization criterion, instead of the MMSE or maximum correntropy criterion (MCC). And then the square root cubature Kalman filter (SRCKF) is used to deal with the nonlinearity. The performance of the proposed algorithm is validated by a pulsed discharge test and an urban dynamometer driving schedule (UDDS) test. Experimental results demonstrate that the proposed approach has significantly improved the SOC estimation accuracy of the lithium-ion battery under non-Gaussian measurement noise compared with the SRCKF algorithm. In addition, given different initial SOC values, MEE-SRCKF algorithm also converges rapidly to its true value.
Method and Strategy for Increasing Wearability of Electrocardiogram

Wearable electrocardiogram (ECG) can provide more comprehensive health detection and effective preliminary risk alerts for patients with arrhythmia. The performance of the existing clinical stationary ECG can meet most of the diagnostic needs, but there are still many difficulties in the wearability of ECG. This paper focus on improving ECGs’ wearability in different aspects and methods, including the use of flexible electrodes, the optimal design of electrodeposition to reduce the restriction of equipment on human activities, and the algorithm of motion artifact elimination. Existing researches demonstrate that the flexible electrode can greatly improve the comfort of wearing and allow strenuous movements. Some more ideal electrode connection positions can obtain high-quality signals without affecting human activities. Motion artifact elimination algorithm can effectively extract more accurate ECG signals in a high noise environment. However, an optimal option of strategies to improve the wearability of ECG cannot be easily declared based on current researches. Therefore, a more comprehensive consideration and application should be made for specific demands in the future development of wearable ECG.
A Novel Multimodal Speech Emotion Recognition System

The recognition of speech emotions is a popular study area. Speech signal is a combination of audio and text. The audio of speech is used as an input for detecting emotions in an Audio Emotion Recognition systems. The textual content of speech is used as an input for detecting emotions in Text Emotion Recognition systems. First, we focused on a unimodal technique, in which emotion recognition can be done using either audio or text. The study then shifted its focus to a multimodal approach, in which audio and text models are integrated to form a single modality. The AlexNet audio model had a 55% accuracy rate, and the text emotion Recognition model using the BERT model had 69% accuracy. The accuracy of the emotion detection system increased to 98% with the multimodal approach. The multimodal system comprises of both audio and text models, which improves the emotion recognition system.
Leveraging Fog Computing for Geographically Distributed Smart Cities

Recently, the emergence of smart cities (SC), where data streams come from various geographically distributed places, has posed new challenges. Cloud Computing provides excellent services for smart cities, such as powerful computation and storage. However, processing the geographically distributed data using cloud computing only is not an ideal solution in some cases. Additionally, moving all the big raw data to the remote cloud is another challenge for cloud computing since there will be shortcomings in terms of delay and high bandwidth consumption. A solution that allows fog-to-cloud or fog-to-fog communication can address these limitations as fogs are typically located locally near the data sources. However, the questions related to the efficient frameworks design, workload distribution, cost, and various key technologies and communication challenges remain. To this end, this research investigates the impact of fog, employing our proposed architecture, on the efficient utilization and management of resources in highly distributed systems through experiments. The comparison showed that fog computing reduces the cost in terms of time and resource utilization. Additionally, the collaboration of autonomous agents locally (within one fog) or globally (across multiple fogs and cloud) supports scalability and automation. It also facilitates large-scale data processing across various real-world distributed locations.
SA-Unet: A Phase Unwrapping method based on DCNN

Phase unwrapping is critical in many applications. Its purpose is to retrieve continuous phase information from discrete unwrapped phases, eliminate periodic aliasing of phases, and generate absolute phases with uniform spatial distribution. The phase unwrapping has been widely used in various cutting-edge fields, such as interferometry, fringe projection, and MRI. Phase unwrapping is an ill-posed problem, and its key point is to obtain accurate periodic ordinal information. However, when there is a lot of noise and aliasing, effective phase unwrapping still faces huge challenges. This paper converts phase unwrapping into a semantic segmentation problem. We propose a new network architecture SA-Unet, which adopts an encoder-decoder structure and adds a spatial attention module. To train SA-Unet, a simulated dataset with noise is produced. In this paper, we introduce focal loss and L1 norm to avoid dataset class imbalance problem. Experiments show that the SA-Unet proposed in this paper has higher accuracy and better robustness than other phase unwrapping methods.
Extension and Implementation of a Model-based Lifetime Monitoring System with Parallel Calculation of Multiple Power Semiconductors

The importance of power electronics for future energy supply is steadily increasing. This implies a lot of semiconductor-based power converters and thus, a huge number of power semiconductors. Their operational availability becomes a critical feature of the power converters. Therefore, predictable maintenance is a key element for a stable and reliable energy supply. A new approach for a FPGA based implementation of a lifetime model for parallel monitoring of semiconductors will be presented.
Regulated Cascode Current Mirror with Improved Voltage Swing

This paper presents a simple and easy-to-use method allowing to improve output swing of regulated cascode CMOS current mirror with short channel devices. It is based on matched offset voltage generators, implemented simultaneously at the input and output of the current mirror. These voltage generators translate the drain voltages of the reference and source transistors to the saturation voltage VDSAT, i.e. to minimal level, ensuring high output resistance of the cascode. The design aims to provide a nanosecond current generator for optical laser source, with fast short channel devices facilitating the integration. This paper presents a concept of circuit implementation, a mathematical description of the optimal value of voltage offset, and simulation results in 40nm process.