Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Lung Tissue Densities for Lung Cancer Classification Using Deep Learning Methodologies

Identification of lung ailments constructed on the analysis and processing of therapeutic images is vital for helping doctors reach a diagnosis. This article deals with the investigation of the density of tissue in Lung ailments, offers an initial feature extraction technique based on human tissue density patterns. The features extraction on thorax computer tomography images is performed using the suggested approach that computes radiological densities of human tissues in-terms of Hounsfield Units. Support vector machine (SVM) and convolution neural network (CNN) are utilized in the investigation. The outcomes of the investigation exposed that the proposed SVM (87) and CNN (98) performed with the best accuracy rate and also having the shortest runtime among all experiments. As a result, we see our proposal as a good option for usage in real-time applications.
Implementation of Multiple Kernels for Edge Detection using Pipelined Architecture on FPGA

Nowadays, image processing plays a vital role in various applications like medical imaging, robotics, and autonomous vehicles. This paper proposes an optimized pipeline architecture for accurate detection of edges by an efficient implementation of multiple kernels on FPGA. The presented pipeline architecture combines the Sobel and Sharpen kernels for detecting edges with low noise. This is designed in such a way to reduce the cost, utilization of resources and time. The simulation is carried out using Xilinx Vivado Design Suite for the target of ZedBoard Zynq Evaluation and Development kit and implemented on the ZYBO Zynq™-7000 Development Board, the results shows that the proposed design is better to earlier implementations in terms of less resource utilization and latency. The presented architecture is capable of detecting edges in real-time and can be used for applications such as image enhancement, motion detection, and object detection, which can be achieved with lesser resources and time savings by concurrent convolution of kernels. This architecture is also scalable and can be easily implemented on other FPGA devices using the Zynq 7000 SoC. The results state the proposed architecture is capable of reducing noise, detects edges in real-time with accuracy, and can be used for real-time applications.
Unsupervised Domain Adaptation via Variational Autoencoder with Explicit and Implicit Feature Alignment for Cardiac Segmentation

Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) is of vital importance in the diagnosis of myocardial infarction (MI). However, the LGE image annotations are limited, it is urgently needed to transfer the learned knowledge in other modalities. We propose an unsupervised domain adaptation (UDA) algorithm based on variational autoencoder (VAE) with a Combination of Explicit and Implicit Alignment of feature distributions (VCEIA). First, we re-derive the Kullback-Leibler (KL) divergence of the features extracted in the source and target domains as a measure of distribution discrepancy. Secondly, for explicit category-level distribution alignment, we use contrast learning to reduce the distance between prototypes of the same category in the source and target domains. Finally, we add a domain discriminator to the output space to implicitly align the feature distributions. Extensive experiments were conducted to validate the effectiveness of VCEIA on the MS-CMRSeg 2019 dataset, which showed competitive results.
Fault Diagnosis Based on Feature Mode Decomposition of Whale Optimization Algorithm

For the increasingly complex problems of mechanical fault signals, a recently introduced adaptive signal processing technique called feature mode decomposition (FMD) has been effectively utilized to diagnose mechanical equipment faults. Since FMD requires prior knowledge, its parameters need to be input manually, and its parameter adaptability directly affects the decomposition performance of FMD. Based on this, this work suggests a method for choosing FMD parameters by the Whale optimization Algorithm (WOA) and uses it to identify mechanical equipment bearing faults. First, the WOA is utilized to search for the best combination of FMD’s mode number (K) and filter length (L) after utilizing the sample entropy as the fitness function. The modal components are then obtained through signal decomposition, and the mode component with the highest kurtosis index is chosen. To identify the issue, resonance demodulation analysis is carried out for the target mode, and fault characteristic data is collected. The research on the Xi’an Jiaotong University dataset and MFPT dataset has demonstrated that this technique, which is quicker and more accurate than variational mode decomposition (VMD), can extract fault signals, identify fault types, and be applied to bearing fault detection.
Secure Edge Computing-Assisted Video Reporting Service in 5G-Enabled Vehicular Networks

Since traffic accidents occur frequently, a real-time video traffic reporting service is necessary in vehicular networks for a prompt response to accidents. Although the fifth-generation (5G) network provides a solution for real-time services in vehicular networks, the security and privacy of the services needs to be addressed first. The existing secure video reporting service schemes in 5G-enabled vehicular networks have significant computing, communication, and storage overheads, because of public key certificates, expensive bilinear pairing operations, and repeated video reporting to the cloud. To address these issues, we propose a secure edge computing-assisted video reporting service in 5G-enabled vehicular networks. In the proposed method, edge nodes complete the message verification and classification. Moreover, these nodes send the first received report message of the same accident to the designated official vehicles to realizes a local upload and download of video reports and to minimize the storage of repeated accident reports in the cloud. Security analysis indicates the proposed scheme is secure under the random oracle model and meets a series of vehicular networks requirements. In addition, performance evaluations show that the scheme achieves lower authentication overhead than existing signature schemes, and has lower total delay than other relevant video reporting service schemes.
Exploring the Energy Metaverse: Potential Benefits and Challenges

Technology advancements have a huge impact on conventional methods and practices, which causes the energy sector to undergo significant change. The digitalization of energy systems is what will undoubtedly shape the energy sector's future. The Metaverse may be the next phase of digitalization that will make the energy sector vastly different from what it does now, by providing exceptional chances to reorganize workflows, increase productivity, and reduce costs. Although the use of the Metaverse in the energy sector is still in its early stages, the popularity of the Energy Metaverse is rising as a result of its numerous advantages. Nevertheless, a number of challenges must be carefully considered for Metaverse to be widely adopted in the energy sector. This paper aims to provide a thorough overview of the Energy Metaverse in order to stimulate further research and hasten the adoption of this ground-breaking idea in the energy sector, which is currently lacking in research studies.
EcoTILLING Reveals Natural Allelic Variations in Starch Synthesis Key Gene TaSSIV and Its Haplotypes Associated with Higher Thousand Grain Weight

Wheat is a staple food commodity grown worldwide, and wheat starch is a valuable source of energy and carbon that constitutes 80% of the grain weight. Manipulation of genes involved in starch synthesis significantly affects wheat grain weight and yield. TaSSIV plays an important role in starch synthesis and its main function is granule formation. To mine and stack more favorable alleles, single nucleotide polymorphisms (SNPs) of TaSSIV-A, B, and D were investigated across 362 wheat accessions by Ecotype-Targeting Induced Local Lesions IN Genome (EcoTILLING). As a result, a total of 38 SNPs in the amplified regions of three TaSSIV genes were identified, of which 10, 15, and 13 were in TaSSIV-A, B, and D, respectively. These 38 SNPs were evaluated by using KASP and six SNPs showed an allele frequency >5% whereas the rest were <5%, i.e., considered to be minor alleles. In the Chinese mini core collection, three haplotypes were detected for TaSSIV–A and three for TaSSIV–B. The results of an association study in the Chinese mini core collection with thousand grain weight (TGW) and spike length (SPL) showed that Hap-2-1A was significantly associated with TGW and Hap-3-1B with SPL. Allelic frequency and geographic distribution indicated that the favored haplotype (Hap-2-1A) has been positively selected in Chinese wheat breeding. These results suggested that the Kompetitive Allele Specific PCR (KASP) markers can be applied in starch improvement to ultimately improve wheat yield by marker assisted selection in wheat breeding.
Allele Frequency Difference AFD–An Intuitive Alternative to FST for Quantifying Genetic Population Differentiation

Measuring the magnitude of differentiation between populations based on genetic markers is commonplace in ecology, evolution, and conservation biology. The predominant differentiation metric used for this purpose is FST. Based on a qualitative survey, numerical analyses, simulations, and empirical data, I here argue that FST does not express the relationship to allele frequency differentiation between populations generally considered interpretable and desirable by researchers. In particular, FST (1) has low sensitivity when population differentiation is weak, (2) is contingent on the minor allele frequency across the populations, (3) can be strongly affected by asymmetry in sample sizes, and (4) can differ greatly among the available estimators. Together, these features can complicate pattern recognition and interpretation in population genetic and genomic analysis, as illustrated by empirical examples, and overall compromise the comparability of population differentiation among markers and study systems. I argue that a simple differentiation metric displaying intuitive properties, the absolute allele frequency difference AFD, provides a valuable alternative to FST. I provide a general definition of AFD applicable to both bi- and multi-allelic markers and conclude by making recommendations on the sample sizes needed to achieve robust differentiation estimates using AFD.
A Scalable Unsupervised Feature Selection With Orthogonal Graph Representation for Hyperspectral Images

Feature selection (FS) is essential in various fields of science and engineering, from remote sensing to computer vision. Reducing data dimensionality by removing redundant features and selecting the most informative ones improves machine learning algorithms’ performance, especially in supervised classification tasks, while lowering storage needs. Graph-embedding (GE) techniques have recently been found efficient for FS since they preserve the geometric structure of the original feature space while embedding data into a low-dimensional subspace. However, the main drawback is the high computational cost of solving an eigenvalue decomposition problem, especially for large-scale problems. This article addresses this issue by combining the GE framework and representation theory for a novel FS method. Inspired by the high-dimensional model representation (HDMR), the feature transformation is assumed to be a linear combination of a set of univariate orthogonal functions carried out in the GE framework. As a result, an explicit embedding function is created, which can be utilized to embed out-of-samples into low-dimensional space and provide a feature relevance score. The significant contribution of the proposed method is to divide an $n$ -dimensional generalized eigenvalue problem into $n$ small-sized eigenvalue problems. With this property, the computational complexity (CC) of the GE is significantly reduced, resulting in a scalable FS method, which could be easily parallelized too. The performance of the proposed method is compared favorably to its counterparts in high-dimensional hyperspectral image (HSI) processing in terms of classification accuracy, feature stability, and computational time.
Modelling and Testing Transformer Differential Protection in Inverter-Based Generation Networks

With the increasing penetration of inverter-based power generation, the evaluation of network protection performance and coordination of protection systems deserves closer examination. The electrical protection of power transformers with fault contribution from electronic interfaced power sources imposes new application challenges that have to be dealt with by protection engineers. Even though transformer differential protection has been successfully employed as the main protection of power transformers for many years due to its simple principle of ampere-turn balance and first Kirchhoff’s law, one of the major challenges to its successful application is the discrimination of fault currents from inrush currents due to transformer energization and overexcitation. In the case of an electronic-based power interface, the current waveform and harmonics content (2nd, 3rd, 5th) in the fault currents may significantly impact the inrush stabilizing algorithm used in conventional transformer differential protection. The advanced transformer protection may use the current waveform analysis (CWA) algorithm to discriminate internal faults from inrush. The CWA algorithm is able to detect internal faults just after the first cycle after fault inception and block the standard second harmonic inrush stabilization. The investigated power system is a large-speed drive with an active front end in a four-quadrant rectifier that can be operated in motor and generator mode. The active front end is a modular multilevel converter with LVRT capability. For a detailed analysis of the transients, the modular multilevel converter is modelled in MATLAB/Simulink with an adequate control strategy. The transformer is modelled with nonlinear characteristic. The MATLAB/Simulink environment tests included external faults, internal faults at the grid and converter side and turn-to-turn faults. Selected cases are shown and discussed in detail.
Kernel Code Integrity Protection at the Physical Address Level on RISC-V

An operating system kernel has the highest privilege in most computer systems, making its code integrity critical to the entire system’s security. Failure to protect the kernel code integrity allows an attacker to modify the kernel code pages directly or trick the kernel into executing instructions stored outside the kernel code pages. Existing prevention mechanisms rely on the memory management unit in which certain memory pages are marked as not-executable in supervisor mode to prevent such attacks. However, an attacker can bypass these existing mechanisms by directly manipulating the page table contents to mark the memory pages with malicious code as supervisor-executable. This paper shows that a small architectural extension enables a physical address-level mechanism to stop this threat without relying on page table integrity. PRIV LOCK lets, at boot time, the kernel specifies the physical address ranges containing its code. At run time, PRIV LOCK ensures that the content within the range is not manipulated and that only the instructions from those pages are executed while the processor runs in supervisor mode. Despite this protection, the kernel can still create new code pages (e.g., for loadable kernel modules) and make them executable with the help of PRIV LOCK ’s secure loader. The experimental results show that PRIV LOCK incurs low performance (<0.5%), area (0.14–0.3%), and energy/power (0.053–2%) overhead.
Voxel-based Representations for Improved Filtered Appearance

Volumetric representations allow filtering of mesh-based complex 3D scenes to control both the efficiency and quality of rendering. Unfortunately, directional variations in the visual appearance of a volume still hinder its adoption by the real-time rendering community. To alleviate this problem, we propose two simple structures: (1) a virtual mesh to encode the directional distribution of colors and normals, and (2) a low-resolution subgrid of opacities to encode directional visibility. We precompute these structures from a mesh-based scene into a regular voxelization. During display, we use simple rendering methods on the two structures to compute the image contribution of the appearance of a visible voxel, optimizing for efficiency and/or quality. The improved visual results compared to previous work are a step forward to the integration of volumetric representations in real-time rendering.
Predicting autism in children at an early stage using eye tracking

Autism spectrum disorder (ASD) refers to a collection of conditions characterized by challenges in areas such as social interactions, communication, and repetitive behavior. Children with autism spectrum disorders often experience difficulties in processing and responding to social cues, which can lead to deficits in social skills and nonverbal communication. Children with ASD have been observed to have problems in maintaining eye contact. The main aim of this study is to use the eye tracking scan path images as a biological indicator to identify children with autism. The dataset used in this study has 547 visualized scanpath images collected from 59 children. The aim of this study is to utilize these scanpath images and formulate an autism diagnosis technique with the help of machine learning algorithms. The proposed model extracts the trainable features from the images and it is fed to a logistic regression classifier and a multi-layer perceptron classifier (MLP). A comparative study between the performance of the proposed model and a custom convolutional neural network is also presented.
Image retrieval in astronomical interferometers affected by atmospheric turbulence

Single aperture speckle interferometry (Labeyrie, 1970) is a method that deciphers diffraction limited spatial Fourier spectrum and image features of stellar objects by counteracting blurring effect caused by the atmospheric turbulence. Together with pupil-plane techniques, as well as hybrid methods (speckle techniques with nonredundant pupils), it has made impacts in several important fields in astrophysics. The field of research that has benefited the most from such high angular resolution techniques and will still benefit in the future, is the origin and evolution of stellar systems. However, classical speckle interferometry falls short of obtaining phase information of the object, but provides a second-order moment (power spectrum) analysis which is the modulus of the object Fourier transform. Triple Correlation technique and other advanced image retrieval methods has been developed which also allows the reconstruction of the phase information. Such algorithmic techniques allows to retrieve diffraction limited information from the short exposure images. The thesis discusses the development of two image reconstruction algorithms based on triple correlation to be used with direct images from optical interferometers. Direct bispectrum algorithm uses a computationally intensive yet efficient triple correlation technique to reconstruct object information from two dimensional speckle images. Tomographic speckle masking algorithm has been developed to offer a computationally efficient method to reconstruct images from speckle data. It uses one dimensional projections and Radon transform to gain considerable savings in computational time and memory. Both these algorithms were tested with numerical simulations, real data and experimental simulations. Numerical simulations of different optical interferometric techniques was built to understand the possibility of reconstruction using these methods in multi-aperture optical interferometry. In the simulations the advantages of the all-in-one beam combination is over the use of pair-wise combination was also analyzed. We layout a study of the signal advantage we obtain at low light levels through all-in-one beam combination in cophased and non-cophased speckle mode. The usability of these algorithms with diluted aperture interferometers which use pupil densification (Hypertelescopes) is also explored in this thesis. It is seen from the numerical simulations of image recontructions that the developed algorithms can be used to restore atmospherically degraded images from hypertelescopes with good signal to noise ratio.
Compact Rat-race Ring Couplers with T type Capacitor Loading and T Type Stepped Impedance Loading

Compact rat-race ring couplers with T type capacitor loading and T type stepped impedance loading are presented in this paper. The occupied area of the coupler with T type capacitor loading is a little larger than that of the coupler with T type stepped impedance loading. The bandwidth of the coupler with T type capacitor loading is better than that of the coupler with T type stepped impedance loading. The conclusions are supported both by simulation and measurement.
Recurrent Variational Information Bottleneck for Time Series Trend Prediction

Time series with large dispersion can be challenging for learning algorithms to predict intrinsic value, such as trend. To deal with this types of time series, we propose a novel method, namely the Recurrent Variational Information Bottleneck (RVIB). We first derive the objective function inspired by the Information Bottleneck principle. Then, the objective is integrated into a Gated Recurrent Unit architecture. Experiments on simulated time series with large and varied dispersion show that RVIB properly approximates their trends.
Coordinated Group Codec for Systems with Highly Correlated Signals on the ESP32 Microcontroller

The rapid development of the Internet of Things in recent years has led to a rapid increase in the number of wireless network devices and, as a result, in the energy consumption required for data transmission. In this work, we experimentally investigate the issue of reducing power consumption of the Internet of Things devices using coordinated group transformation of correlated signals. For this purpose, we designed the layout based on the ESP32 microcontroller. We used light sensors (photoresistors) located in the same room to obtain correlated signals. ESP32 microcontroller carried out the signals group transformation algorithm using the MicroPython environment. The bit/Joule ratio (the amount of energy required to transmit one bit of information) was used as an energy efficiency metric. The effect of using the group transformation was about 25%. The results show the effectiveness of the proposed method for systems with highly correlated signals, including networks of the Internet of things and vehicle-2-everything.
A New Family of XDEVS Simulators for Enhanced Performance

Modeling and Simulation (M&S) techniques are extensively used for analyzing complex systems. Among different approaches, the Discrete EVent System specification (DEVS) mathematical formalism presents a robust methodology for defining complex models. The xDEVS framework is a cross-platform tool that provides a uniform Application Programming Interface (API) to simulate DEVS-based models using different programming languages like C++, Java, or Python. However, executing DEVS models on embedded devices equipped with small processors usually requires lower-level programming languages. This paper introduces xDEVS/Rust and xDEVS/C, two new xDEVS interfaces written in Rust and C, respectively. These new implementations bring xDEVS closer to supporting the execution of DEVS models in embedded systems. Also, they feature remarkably high performance, making them a valuable option for simulating complex systems in traditional platforms. We illustrate how competitive these new implementations are by comparing them with some of the fastest state-of-the-art simulators.