Miracle-dz/UniFAR-FTUs huggingface dataset:
Glioma/Glioblastoma Detection in Brain MRI using Pre-trained Deep-Learning Scheme

Convolutional Neural Network (CNN) supported medicinal image examination is extensively accepted due to its reputation and improved accuracy. The investigational outcome obtained with DLS along with a chosen classifier helps to achieve better detection results than the traditional and machine-learning methods. The proposed research examines the performance of pre-trained VGG16 and VGG19 schemes in detecting the brain tumour (Glioma/Glioblastoma) grade using different pooling methods. The classification is performed using SoftMax with five-fold cross-validation, and the products are compared and presented. The brain tumour images considered in this study are collected from The Cancer Imaging Archive (TCIA) dataset. This work considered 2000 images (1000 Glioma and 1000 Glioblastoma) of axial-plane with dimension of 224×224×3 pixels for the assessment, and the attained results are compared. The experimental outcome achieved with Python® confirms that the VGG16 with average-pooling provides a better classification accuracy (>99%) with Decision Tree (DT) compared with other methods considered.
Incremental Method of Permutation Alignment For Frequency-Domain Blind Source Separation

This study proposes an incremental permutation-alignment method for frequency-domain (FD) blind source separation (BSS). This method is used to extract the elements belonging to a distinct source at all frequency bins and to reduce the permutation-alignment problem of the N-channel to that of the (N − 1)-channel. Permutation ambiguity is resolved by repeating the extraction. According to the simulation, the proposed method combined with a sparsity-based FD independent component analysis method proposed in 2020 is competitive in terms of the signal-to-distortion ratio with the adoption of recent FD-BSS approaches such as auxiliary function-based independent vector analysis and independent low-rank matrix analysis that can handle inter-frequency dependencies without using permutation alignment.
Architecture Design of Intelligent Monitoring System for PV Power Station in the 5G Framework

To improve the performance of monitoring system for photovoltaic power station (PVPS), the architecture of intelligent monitoring system in the 5G framework was designed in this paper. Firstly, we analyzed the deficiency of traditional PVPS monitoring system, and discussed the application scenarios of intelligent monitoring for PVPS. For realizing the designed intelligent monitoring scenarios, the communication requirements were analyzed, and the overall architecture of the monitoring system was designed. To build the private network of 5G PVPS, the deployment scheme and network topology of the network were designed using the SA networking, mobile edge computing, 5G network slice and UPF sinking. The proposed scheme fully combines the characteristics of 5G technology and functional requirements of PVPS, which is highly feasible and is expected to be popularized on a large scale.
A First Look at Accurate Network Traffic Generation in Virtual Environments

The generation of synthetic network traffic is necessary to several fundamental networking activities, ranging from device testing to path monitoring, with implications on security and management. While literature focused on high-rate traffic generation, for many use cases accurate traffic generation is of importance instead. These scenarios have expanded with Network Function Virtualization, Software Defined Networking, and Cloud applications, which introduce further causes for alterations of generated traffic. Such causes are described and experimentally evaluated in this work, where the generation accuracy of D-ITG, an open-source software generator, is investigated in a virtualized environment. A definition of accuracy in terms of Mean Absolute Percentage Error of the sequences of Payload Lengths (PLs) and Inter-Departure Times (IDTs) is exploited to this end. The tool is found accurate for all PLs and for IDTs greater than one millisecond, and after the correction of a systematic error, also from 100 us.
Fault Diagnosis of Rolling Bearing based on VMD Feature Energy Reconstruction and ADE-ELM

Aiming at the problem that the vibration signal characteristics of rolling bearings are not obvious and the diagnosis accuracy of traditional intelligent fault diagnosis algorithms is not high, a rolling bearing fault diagnosis method based on VMD feature energy reconstruction and ADE-ELM is proposed. Firstly, the VMD is used to process the bearing vibration signal to obtain the intrinsic mode function. Then, each IMF component is calculated, and the fault feature vector is reconstructed based on the feature energy ratio. Finally, the adaptive differential evolution (ADE) algorithm is introduced to optimize the hidden layer input weights and biases of the extreme learning machine. The simulation shows that the VMD feature energy reconstruction can accurately extract fault features, and the ADE-ELM model can effectively improve the fault classification accuracy. Compared with other methods, this method is more accurate and reliable.
DC-Bus Control Considerations of Asymmetrical Multilevel Inverters with Embedded Buck-Boost Converter

This paper focuses on the DC-bus control of asymmetrical Multilevel inverter family featuring a Buck-Boost converter to boost the input voltage. To investigate the dynamic performance of the system a thorough analysis is presented on the DC-bus dynamic behavior. It is shown that with higher system bandwidth the input capacitance requirements and the peak current through the boosting inductor are increased, compromising the power density and the efficiency of the whole DC/AC converter. To improve the transient performance of the system without increasing the volume of the passive components, a control scheme with a feedforward current estimator term is proposed. Finally, the correctness of the theoretical analysis is validated via experimental probing on a laboratory prototype.
Modeling and control of a soft-rigid hybrid robotic glove for hand rehabilitation and assistance

Soft robotic gloves have merits on assisting individuals with hand pathologies to perform continuous exercises to hand-functions restoration, which could accelerate the rehabilitation process and reduce costs. However, most of the soft gloves are composed of silicone-based actuators, whose time-consuming fabrication process and relatively low output force limit the development of soft robotic gloves. In this paper, we propose a novel design of a robotic glove based on soft-rigid hybrid joint actuators. We first introduce structures and working principles of each functional part of the glove in detail and then present the comprehensive theoretical modeling of the deformation and force response of the novel actuator which were experimentally validated. Furthermore, we evaluate the performances of the proposed robotic glove. The results show that the theoretical model can accurately predict the force-pressure response of the hybrid actuator. Besides, the proposed 10-DoF robotic glove is able to assist various training exercises and grasping various objects with different hand postures. This paper focuses on the modeling and control of a novel soft-rigid hybrid robotic glove, which will have an impact on the development of soft robots and wearable devices.
A comprehensive Review: Cardiac Disease Detection using IoT based Wearable Sensor and Deep Learning Techniques

The clinical field is making huge measure of information that doctors can't unravel and utilize productively. Additionally, rule-based master frameworks are wasteful in settling convoluted clinical assignments or for making experiences utilizing Big Data. The Deep Learning has arisen as a more precise and powerful innovation in a wide scope of clinical issues like analysis, expectation, and intercession. Deep Learning is a portrayal learning technique that comprises of layers that change information nonlinearly, subsequently, uncovering various leveled connections and designs. Wearable technologies can provide creative solutions to medical care problems. Certain Wearable Technology uses, like weight management or ongoing work tracking, are intended to assist people in avoiding sickness and maintaining their health. These wearable gadgets were being used for other purposes by heart patients and executives who are unwell. Wearable apps can have a direct effect on treatment direction. In this audit, Wearable Technology applications in medical services and Deep Learning application papers that utilization organized information, and sign and imaging modalities from cardiology are concentrated exhaustively. The benefits and constraints of condition of-craftsmanship strategies Deep Learning in Cardiology and wearable advances that likewise apply in medication by and large, while proposing specific bearings as the most practical for clinical use.
Payload-Based Network Traffic Analysis for Application Classification and Intrusion Detection

Network traffic characterization has become an important topic with the development of evasion techniques. Preventing malicious activities is vital in terms of network performance. On the other hand, defining which applications are run through the network traffic has become a significant issue with the diversification of applications. In this work, a payload-based flow analysis tool that provides both application classification and intrusion detection is proposed. Payload features that characterize network flows efficiently are used to classify network traffic and detect malicious attacks. Application classification performance analysis is performed on a publicly available up-to-date dataset containing traces from most popular applications such as Spotify, WhatsApp, etc. Attack detection performance is evaluated on IDS 2012 and IDS 2017 datasets containing different kinds of attack traces.
Improving Classification Results in Network Data Analysis using Interpretability Methods

Developing network intrusion detection and prevention systems usually leverage a rule-based approach, which is derived from rules defined by network security experts who can utilize logic from both low and high network layers. However, in recent times, machine learning methods have also achieved promising results in developing Network Intrusion Detection Systems, and their popularity is steadily rising. Unfortunately, the usage of these machine learning methods in real-life problems has regularly proved that no good out-of-the-box solution exists for production or deployment. Also, due to the increasing volume and complexity of processed data that machine learning methods are faced with over time, improvements and adaptions are frequently required. As the problem at hand becomes more convoluted, so does the the nature of the applied solution. This complexity is further compounded by the fact that certain machine and deep learning methods intrinsically do not offer a way of understanding how they make decisions, effectively behaving like black boxes. All of this significantly lowers the understandability of implemented solutions in production environments that are already quite complex, which justifies the need of interpretability methods. While interpretability methods are commonly designed to be used by humans, in this paper we propose a way of improving a model's classification performance by applying data mining methods on explanation data generated by interpretability methods. The paper's main contribution is improving on a previously built network intrusion detection system through proposing an automated process of integrating explanations into original data with the purpose of improving the interpretability and score of the used machine learning model
Subspace Constrained Independent Vector Extraction

In many application scenarios, we wish to extract the target source signal from an observed mixture, which is contaminated by interfering source signals. To solve this problem, a blind source extraction (BSE) method called independent vector extraction (IVE) has been proposed recently. In this paper, we propose an extension of the IVE by introducing a subspace constraint. First, we estimate the subspaces of the target source signal and interfering source signals, given the rough information about the target source signal’s direction of arrival (DOA). Second, the estimated subspaces are used to construct the constraint for the demixing filter of the target source signal. This constraint regularizes the cost function of the IVE to find the target source signal. The experimental results show that the subspace constrained IVE outperforms the conventional IVE in terms of source separation accuracy.
Characterization of GaN-on-AIN/SiC transistors towards monolithic integrability

A GaN-on-AlN/SiC technology is proposed for monolithic GaN power switch integration. As opposed to conventional GaN-on-Si devices, the insulating SiC substrate results in immunity to back-gating effects and enables monolithic integration without degradation of switching characteristics resulting from the shared substrate. This is validated for a discrete half-bridge, with the substrate of both transistors shorted together, as well as for a monolithic half-bridge. Hard switching transients up to 300 V in a double-pulse test with both half-bridges reveal faster switching transients and reduced switching losses for the monolithically integrated half-bridge.
Function Theory: Interpolation and Corona Problems

These lecture notes take the reader from Lennart Carleson's first deep results on interpolation and corona problems in the unit disk to modern analogues in the disk and ball. The emphasis is on introducing the diverse array of techniques needed to attack these problems rather than producing an encyclopedic summary of achievements. Techniques from classical analysis and operator theory include duality, Blaschke product constructions, purely Hilbert space arguments, bounded mean oscillation, best approximation, boundedness of the Beurling transform, the Koszul complex, use of trees, the complete Pick property, and the Toeplitz corona theorem. An extensive appendix on background material in functional analysis and function theory on the disk is included for the reader's convenience.
Design and Development of Unmanned Combat Game Platform

The unmanned combat game platform is the technical carrier to research unmanned combat issues. Although most current unmanned combat simulation platforms can effectively simulate the unmanned combat process, their scenes are fixed and single. They cannot carry out the secondary design of the scene. At the same time, they also lack an effective description of the scene information. Therefore, this paper takes air-ground unmanned combat as a specific task scenario and independently develops an unmanned combat deduction platform based on the Hybrid Stochastic Time Delay Petri Net (HSTPN) modelling theory and PyQt development tool. The platform supports scene modelling and model changes, deep reinforcement learning algorithm battles, animation deductions, file storage, etc. At the same time, a battle decision algorithm is designed based on Proximal Policy Optimization (PPO). The experimental results show that the unmanned combat game platform can fully realize the combat process simulation. Its scene modelling function can show all the hybrid characteristics of the combat process and support the modification of the scene model and its secondary development. The battle function can complete the confrontation game of the decision-making algorithm.
Photonic hook through a microlens with a tip deposited at the bottom

A design that couples the transparent silica microsphere to the AFM probe and deposits the diamond tip at the bottom of the microsphere is studied when illuminated with a plane wave. A controllable curved photonic nanojet (PNJ) named photonic hook (PH) can be generated on the backlight side, which can be explained as an interference phenomenon caused by the breaking of particle symmetry. The influence of the size, position of the tip and the diameter of microlens on the PH focusing on the outside of the microlens is studied. The simulation results show that these parameters can be used to control the bending characteristics of PH.
Evaluation of a Fabric Channel Cooling Apparatus for Twisted Coiled Actuators

Wearable robotic systems have the potential to help many individuals with rehabilitation and to support activities of daily living. Unfortunately, these systems are not widespread due to their cost, limited portability, and size. Twisted Coiled Actuators (TCA), novel artificial muscles made from nylon thread, are inexpensive, lightweight, and slim. However, the natural cooling time of these thermally activated muscles is too slow to support rehabilitation or voluntary motions. This paper presents and assesses the feasibility of a novel cooling apparatus for the TCA. The cooling method involves a flexible fabric channel and a miniature air pump to cool the TCA with forced convection. The channel is lightweight, flexible, and can easily be sewn onto other materials to allow easy fabrication of wearable devices. ANSYS Fluent simulations were performed to determine the relationship between the input air velocity and the cooling time constant of the system. The results indicate that the miniature air pumps currently available are not powerful enough to cool the TCA at the required frequencies. There was a 13.2% difference between the cooling time constant predicted by the model and the time constant found experimentally for an input of 1 m/s.
Evaluation and Comparison of Gmapping and Karto SLAM Systems

Gmapping and Karto are two classic laser-based SLAM algorithms widely used in various applications. This paper evaluated and compared the performances of these two algorithms. A series of experiments were conducted within the self-built outdoor environments. The parameters of algorithms were tuned, the performances of different parameter settings were evaluated and compared, and the pros and cons regarding mapping and localization accuracy and computational cost of two algorithms were discussed.
Current distortion study for hybrid multi-level grid inverter with active neutral-point-clamped 4-Leg topology

In multi-level inverters in particular, the switching blocking time (dead-time) can lead to undesired voltage errors, which have a significant influence on the current quality. When changing between levels, clamping must be applied to ensure the minimum switch-on and switch-off time of the power electronics. The trend towards higher switching frequencies and smaller grid filters increases the difficulty of feeding in a standard-compliant output current, especially in active neutral-point-clamped (ANPC) inverters. In this paper, the effects of clamping and a compensation method are simulated for ANPC inverters and verified on a 3-level grid forming demonstrator.
A framework for hierarchical single-copy MPI collectives on multicore nodes

Collective operations are widely used by MPI applications to realize their communication patterns. Their efficiency is crucial for both performance and scalability of parallel applications. For deriving efficient MPI implementations, significant effort is put to keep pace with advances and capabilities of the underlying hardware and interconnect. Recent processor advances have led to nodes with higher core counts and complex internal structures and memory hierarchies. Such nodes are able to host tens to hundreds of processes and thus, performance of MPI collectives at the intra-node level becomes critical. In this work, we propose a framework for collective operations at the intra-node level, that aims to lower latency and increase bandwidth. Our approach utilizes knowledge of internal node structure to construct hierarchical algorithms, and XPMEM to achieve single-copy transfers. Pipelining is used to overlap communication at different levels of the hierarchy. We evaluate the proposed approach through several microbenchmarks and real-world MPI applications. For evaluation purposes, we compare the proposed approach with implementations of similar schemes from two recent studies. Our evaluation with microbenchmarks for Broadcast and Allreduce shows speedup up to 2. $5x$ and $3x_{2}$ respectively, over UCC and OpenMPI's default collectives implementation. Compared to recent research studies, we improve Broadcast by up to $5x_{2}$ and Allreduce by up to $7x$. We reduce the time of three applications PiSvM, miniAMR and CNTK, by up to 12%, 52% and 12%, respectively, over the next best-performing alternative.
Multimodal Depression Detection Using Task-oriented Transformer-based Embedding

Automatic depression detection from multimodal signals assumes significance since the features from different modalities possesses contrasting but relevant information. Multimodal signal analysis involving the derivation of a joint representation, identification of correct alignment, and selection of appropriate fusing mechanisms is a challenging task. In this paper, we focus on an approach to detecting depression from multimodal signals using task-specific representation and late fusion. Our method uses a task-oriented embedding generated through a GPT2-medium language model that is fine-tuned on a conversational dataset. We present our experimental results benchmarking on Distress Analysis Interview Corpus - a Wizard of Oz (DAIC-WOZ) corpus. Our proposed task-oriented embedding on a simple BiLSTM network outperforms the previous text-based depression prediction methods. The results are further strengthened when audio and video modalities are fused with the text predictions on a late-fusion network.
AGV State Monitoring Based on Global and First-person View Fusion in Digital Workshops

AGV state monitoring is becoming increasingly important for production system safety in digital workshops. This paper develops a global and first-person view fusion to monitor the AGV (Automated Guided Vehicle) running state in the complex digital workshops. Based on the affine transformation model, the AGV running states, i.e., position, velocity and orientation are estimated by the global camera with the assistance of ArUco markers, which are pasted on the front side of the AGV. Besides, the first-person view camera is used to implement pedestrian detection and distance measurement based on the YOLOv3 model. It is noticeable that the pitch angle is considered for distance measurement, and an improved triangular model for distance measurement is proposed. The effectiveness of the proposed method has been verified in our built platform.
High Switching Frequency Operation of a Single-Phase Five-Level Hybrid Active Neutral Point Clamped Inverter with a Model Predictive Control Approach

Wide bandgap (WBG) devices such as Silicon-Carbide (SiC) MOSFETs can be utilized to increase the switching frequency of power electronic converters. The size of passive components of an output filter can be reduced by increasing the switching frequency of converters or the number of output levels thorough the employment of multilevel topologies. Therefore, the combination of multilevel converters and WBG switches with a high switching frequency can improve the dynamic of converters. Meanwhile, a high bandwidth controller is also required to achieve a fast dynamic response of the system. In this paper, an advanced model predictive control (MPC) approach, based on the concept of hysteresis current control, is presented for a single-phase five-level hybrid active neutral point clamped (ANPC) inverter. A hybrid modulation technique with different switching frequencies is considered in this paper. As a result, different semiconductor technologies including SiC and Si are employed in the structure of the converter. Considering the AC and DC sides mathematical modeling of the converter, an MPC with the ability to control the neutral point (NP) voltage is designed. Finally, experimental results show that by utilizing the SiC MOSFETs and the proposed advanced MPC structure, the inverter's switching frequency is increased, with lower current ripple and fast dynamic performance.