Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-Time Strain Interrogation Based on an STS Structure and Wavelength-to-Time Mapping

A novel real-time interrogation technique based on an SMF-TMF-SMF (STS) structure and wavelength-to-time (WTT) mapping is proposed and experimentally demonstrated. An ultrashort optical pulse is first spectrally shaped by the homemade STS comb filter, which is formed by inserting a section of two-mode fiber (TMF) into single-mode fibers (SMF) using core-offset splicing. Linear WTT mapping is realized by employing a dispersive element and the spectrally-shaped waveform is mapped to the time domain. By monitoring the strain-encoded time shift, an ultrafast strain interrogation system with a measurement speed of 100 MHz and a strain sensitivity of 0.3 ps/ $\mu \varepsilon $ is obtained.
Radio Resource Management in 5G networks

In this work, we study the resource allocation problem for downlink in5G New Radio (NR) mobile networks in the case of orthogonal frequency-division multiple access (OFDMA). One of the key features of 5G mobile networks is the usage of antenna arrays that allows them to form beams in a specific direction and send data to several users at the same transmission time interval (TTI) via the same carrier or resource block (RB). Antenna arrays usually contain up to 64 single antennas. We modeled the process of the data transmission from the cell to the user device or user equipment (UE), formulated mathematical abstraction of the resource allocation problem, and proposed a lightweight algorithm that uses a proportional fair metric.
A Novel Energy Sharing Framework for a Residential Community

The local energy sharing in a residential community has becoming a research hotspot in the area of distribution system. Designing a transparent energy sharing price scheme, ensuring users who participate in the energy sharing to gain benefits, and distributing the cost savings fairly are three key factors for establishing the local energy trading market. In this paper, we propose a novel energy sharing framework among residential users (RUs) with considering the demand response. First, an individual cost optimization is solved to find the potential buyers and sellers in every time slot, and further to calculate the energy sharing price. Then, an energy sharing problem is formulated, in which the interest of each RU is guaranteed by an incentive constraint. Finally, a redistribution problem is proposed to attack the problem of multiple energy sharing schedules and to recalculate the payoffs. The results show that the RUs can gain benefits via energy sharing and it is necessary to carry out the redistribution problem.
Analysis of Intelligent Art Design Paradigm Based on Perception Model and Image Feature Extraction

Analysis of intelligent art design paradigm based on perception model and image feature extraction is studied in this research. In the study of information search behavior, in recent years, most of the measurement of the perceived risk focuses on the user's direct perception to enhance the general model, where the complex paradigm is considered. This research work obtains the locally associated frames of the visual pixel points of the color image space, reconstruct the edge contour feature distribution set of the color image, obtain the edge contour of the color image, and use the multifractal technology to match the shape feature of the color image. Besides this, the comprehensive model patterns are collected to support the construction of the framework. The model is applied into the art design and the applications are tested.
MVC-NET: Multi-View Chest Radiograph Classification Network With Deep Fusion

Chest radiography is a critical imaging modality to access thorax diseases. Automated radiograph classification algorithms have enormous potential to support clinical assistant diagnosis. Most algorithms focus solely on the single-view radiograph to make a prediction. However, both frontal and lateral images are valuable information sources for disease diagnosis. In this paper, we present multi-view chest radiograph classification network (MVC-Net) to fuse paired frontal and lateral views at both the feature and decision level. Specifically, back projection transposition(BPT) explicitly incorporates the spatial information from two orthogonal X-rays at feature level, and mimicry loss enables cross-view predictions to mimic from each other at decision level. The experimental results on 13 pathologies from MIMIC-CXR dataset show that MVC-Net yields the highest average AUROC score of 0.810, which gives better classification metrics as compared with various baseline methods. The code is available at https://github.com/fzfs/Multi-view-Chest-X-ray-Classification.
A Review of Mobile Ad hoc NETwork (MANET) Protocols and their Applications

In this paper, an investigation on protocol issues for mobile ad hoc networks (MANET) is conducted. The capacity and relevance of nomadic computing are evident with the quick propagation of wireless devices like laptops, wireless sensors, and smartphones. A MANET is a non-temporary infrastructure, consisting of a group of mobile hosts which have no central management and dynamically create their network or connections. This type of network introduces complexities such as regular changes to the topology. The constraints of bandwidth, low energy, and storage capacities of such mobile nodes impose severe limitations on their abilities. Some nodes also cannot communicate directly as there are generally small transmission ranges within nodes in MANETs. Therefore, routing paths can be of multiple hops, and it is the responsibility of neighbouring nodes to operate as routers. A comparative analysis using qualitative techniques is performed by categorizing different routing protocols as proactive, reactive, hybrid and nature-inspired. Protocols such as the Destination-Sequenced Distance Vector (DSDV), Wireless Routing Protocol (WRP), Ad Hoc On-demand Distance Vector Routing (AODV), Zone Routing Protocol (ZRP) and AntHocNet are described to know their suitability for real-life applications. This study reveals that hybrid protocols are better as they consume less power and uses bandwidth more efficiently.
AdaBoost-CNN: A Hybrid Method for Electricity Theft Detection

Electricity theft causes great economic losses and potential safety hazards to society. With the development of smart meters and artificial intelligence, data-driven methods have gradually been applied to electricity theft detection, which improves efficiency and reduces costs. At present, data-driven electricity theft detection methods are dedicated to improving accuracy. To achieve higher accuracy, this paper proposes a hybrid method combining adaptive boosting algorithm (AdaBoost) and convolutional neural networks (CNN) for electricity theft detection. In the proposed method, multiple CNN-based classifiers are trained to extract different features from the electricity consumption data. Then, AdaBoost combines these classifiers into a strong one according to their performance. The experimental results based on the smart energy data from the Irish Smart Energy Trial show the hybrid classifier has better performance than other conventional data-driven methods in electricity theft detection.
Obstetrics and Gynaecology Ultrasound image Analysis Towards Cryptic Pregnancy Using Deep Learning-A Review

A bizarre type of pregnancy, which will be called as pseudo pregnancy or concealment of pregnancy or denial of pregnancy is a false pregnancy, that cannot be detected by customary medical testing methods like scanning, blood and urine analysis and ultrasound due to its smaller size or the presence outside the uterus, so that it is veiled and goes undetected. Some women are not conscious about pregnancy up to the last weeks of gestation or in some circumstances till the last moment of delivering a baby. The symptoms such as nausea, sickness, vomiting will not be there if it is a symptomless pregnancy. The appearance of normal pregnancy will not be there like abdominal swelling so it will be very difficult to detect. Current statistics shows that, one in every 475 women come across such an unexpected situation till the end of twentieth week of the pregnancy, where it is referred as pseudo-mensural bleeding. The main reason behind this condition is psychological disorders and hormonal imbalance like low HCG (human chorionic gonadotropin) hormone levels and so it is treated as delusion pregnancy. This paper outlines about the various research work done in the area of obstetric ultrasound image analysis by particularly focusing on a special type of pregnancy. By using most advanced technology like deep learning, a sub field of machine learning, an effective tool can be developed for obstetrics/gynecologic image analysis. This paper focuses on performing ultrasound image analysis using deep learning technique for pregnancy analysis. The early and automatic detection is very important to avoid the unnecessary complications and risk till last moment.
Genesis and Taxonomy of Black Soils from Basalt and Basaltic Alluvium in Rajasthan

Six typical pedons representing black soils, two (P1 & P2) developedfrom basalts and the other four (p3 to P6) developed from basaltic alluvium in Rajasthan were selectedfor this purpose. Soil colour was found to be the function of free iron oxide to clay ratio while vertic properties were found to be governed by free iron to total iron ratio. The irregular distribution of organic carbon and regular depthwise function of CaCO3 indicate differential pedoturbation with respect to these constituents. Fe2O3 and MgO content of the soils from basalts were more than the soils from basaltic alluvium while SiO2, Al2O3 and K2O content showed a reverse trend. The weathering indices, like lower Al2/Fe2O3, CEC, SiO2/R2O3 and K2O/Na2O molar ratios in clays and CEC of clay/clay ratios, reflected advanced stage of weathering in basaltic soils compared with their basaltic alluvial counterpart. CEC/clay ratio was found to decrease with the increase in Fe2O3 content of clays. Taxonomicallythese soils were classified under Chromic Haplusterts and Typic Haplusterts subgroups of Vertisol soil order.
ArtGAN: Artwork Restoration using Generative Adversarial Networks

We propose a method to recover and restore art-work that has been damaged over time due to several factors. Our method produces great results by completely removing damages in most of the images and perfectly estimating the damaged region. We achieved accurate results due to (i) a custom data augmentation technique which depicts realistic damages rather just blobs (ii) novel CResNetBlocks that subsequently upsample and downsample features to restore the image with efficient backpropagation measures, and (iii) the choice of using patch-discriminators to achieve sharpness and colorfulness. Our network architecture is a conditional Generative Adversarial Network where the generator uses a combination of adversarial loss, L1 loss and the discriminator uses binary cross-entropy loss for optimization. While the expressiveness of existing comparison methods is limited, we present our results with several metrics for future comparison and showcase some visuals of recovered artwork. PyTorch implementation is available at: https://github.connamasl91297/artgan.
A Cad System For Accurate Diagnosis Of Bladder Cancer Staging Using A Multiparametric MRI

In this paper, a computer-aided diagnostic (CAD) system is developed using a multiparametric magnetic resonance imaging (MPMRI) (T2-MRI and DW-MRI) to differentiate between BC staging, especially T1 and T2. The segmentation of the bladder wall (BW) and the localization of the whole BC area (At) and its extent inside the wall (Aw) is first performed. Secondly, a set of functional, texture, and morphological features are estimated. Due to the massive difference between the wall and bladder lumen cells, At is split into nested equidistance contours (i.e., iso-contours), and features are estimated for each iso-contours. The functional features are based on the cumulative distribution function (CDF) statistical measures for the estimated apparent diffusion coefficient (ADC) from DWMRI. Texture features, namely radiomic features, are derived from T2W-MRI, At both carcinoma intensity and gradient images for each iso-contours from At, T2-MRI. Besides, morphological features are also incorporated to describe the tumors’ geometric from T2W-MRI, Aw. Finally, the estimated iso-features are augmented and used to train and test neural networks classifier as well as a statistical machine learning (ML) classifier. The system has been tested using a leave-one-subject-out approach on 42 data sets. The overall accuracy, sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristics (ROC) are 92.86%, 97.05%, 100%, and 0.9705, respectively. We introduce the diagnostic accuracy of individual MRI modality for our proposal to highlight the advantage of fusion multiparametric iso-features that is confirmed by the ROC analysis. Furthermore, the accuracy of two different techniques statistical ML classifiers (i.e., random forest (RF) and support vector machine (SVM)) and end-to-end convolution neural networks (i.e., ResNet50) is compared against our pipeline.
Enhanced Connectivity and Reduced Mind Wandering after Tactile Training in Young Adults

The intensive practice of specific cognitive activities can lead to improvements of relevant cognitive capability in human beings, which may transfer to gain in untrained activities. Although there are a growing number of studies investigating the behavioral benefits of attention training in mind wandering, few studies have directly examined the neurophysiological basis of the training effects. Here using 128-channel electroencephalography (EEG), we examined whether the tactile training can reduce the mind wandering as measured by the sustained attention to response task (SART), and how the dynamic neurophysiological connectivity changes following training in young adults. The trainees showed significantly less occurrence of mind wandering after the five-day tactile training. Furthermore, the functional connectivity within and between the frontal and parietal regions was enhanced after training. Our findings suggest that the tactile training-induced brain plasticity may provide new therapeutic strategies for attention-related disorders.
Mastering the NEC Vector Engine Accelerator for Analytical Query Processing

NEC Corporation offers a vector engine as a specialized co-processor having two unique features. On the one hand, it operates on vector registers multiple times wider than those of recent mainstream x86-processors. On the other hand, this accelerator provides a memory bandwidth of up to 1.2TB/s for 48GB of main memory. Both features are interesting for analytical query processing: First, vectorization based on the Single Instruction Multiple Data (SIMD) paradigm is a state-of-the-art technique to improve the query performance on x86-processors. Thus, for this accelerator we are able to use the same programming, processing, and optimization concepts as for the host x86-processor. Second, this vector engine is an optimal platform for investigating the efficient vector processing on wide vector registers. To achieve that, we describe an approach to master this co-processor for analytical query processing using a column-store specific abstraction layer for vectorization in this paper. We also detail on selected evaluation results to show the benefits and shortcomings of our approach as well as of the coprocessor compared to x86-processors. We conclude the paper with a discussion on interesting future research activities.
Location-Aware Caching via Predicting Heterogeneous File Preferences in Mobile Networks

We present a new caching method in content-centric networks (CCNs) where each mobile user is equipped with finite-size cache and device-to-device content delivery is employed. In this study, we exploit the heterogeneity in file preferences among users who are moving around different locations (e.g., points-of-interest) in caching. Moreover, to infer our model parameter more accurately, we apply data imputation, which is a technique to replace unknown data with estimated values, based on collaborative filtering ($CF$). Our experimental results with real-world datasets demonstrate the superiority of our method over benchmark caching methods utilizing no location information in terms of both average hit ratio and runtime complexity.
Lightweight U-Net For Lesion Segmentation In Ultrasound Images

Acquiring ultrasound images of suspected lesion areas allows radiologists to monitor the cancer development of patients. The goal of this paper is to provide an automatic lesion segmentation tool for assisting them on the analysis of ultrasound images, by relying on recent neural network methods. Specifically, we perform a comparative study for the segmentation of 348 ultrasound image pairs acquired in 19 centers across France, displaying different tumor types. We show that, with a careful hyperparameter tuning, U-net outperforms other state-of-the-art networks, reaching a Dice coefficient of 0.929. We then propose to introduce group convolution into U-net architecture. This leads to a lightweight network named Lighter U-net @128 that achieves comparable segmentation performance with obviously reduced model size, hence paving the way for an embedded integration within hospital environment. We made our code publicly available1, for reproducibility purpose.
A High-Power Broadband Multi-Primary DAT-Based Doherty Power Amplifier for mm-Wave 5G Applications

Silicon-based millimeter-wave (mm-Wave) power amplifiers (PAs) with high power and high peak/back-off efficiency are highly desired to efficiently amplify multi-Gb/s 5G NR signals. This article presents a fully integrated high-power broadband linear Doherty PA with multi-primary distributed-active-transformer (DAT) power combining. We introduce a transformer-based impedance inverter for active load modulation and a multi-primary DAT structure for hybrid series and parallel power combining. Based on this, we propose a transformer-based Doherty combiner with more design freedom and a multi-primary DAT-based Doherty PA for simultaneous active load modulation and low-loss power combining. The EM simulation results demonstrate that the proposed DAT-based Doherty output network achieves very symmetric and balanced load impedances among all the main and auxiliary PA ports. As a proof of concept, a 24–30-GHz prototype PA is implemented in a 0.13-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> SiGe BiCMOS process. The PA achieves 30.4% PAE<sub>max</sub>, 28.3-dBm <inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {sat}}$ </tex-math></inline-formula>, 30.2% PAE at 26.8-dBm <inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {1\,dB}}$ </tex-math></inline-formula>, and 21.2% PAE at 6-dB back-off from <inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {sat}}$ </tex-math></inline-formula> at 28 GHz. Modulation measurement with single-carrier 64-QAM signals and 5G NR FR2 orthogonal frequency-division multiplexing (OFDM) signals has been demonstrated. For a 200-MHz 1-CC 5G NR FR2 64-QAM signal, the PA achieves 18.1-dBm P<sub>avg</sub> and 13.8% PAE<sub>avg</sub> with −25.1-dB rms EVM at 28 GHz.
Preparation and Characterization of Mucoadhesive Buccal Film for Delivery of Meloxicam

Aims: Preparation of mucoadhesive buccal films able to deliver the meloxicam drug to the site of application through oral mucosal tissues. This dosage form is advantageous due to absence the problems of the ordinary dosage forms. Study Design: In this research, it was prepared a lot of formulations from different polymers and plasticizers to select the best one which has the optimum and required characteristics.
Analysis of TikTok’s Success Based on Its Algorithm Mechanism

In modern society, smart phone is one of the most important things for people. A number of companies which produce software for smart phones definitely acquire huge profits. In recent years, TikTok has become one of the most popular and potential app in software market. This success is not only dependent on the trend of big data era, but also closely related to TikTok’s own operation. In this paper, the author discusses three main parts, including the reason why TikTok is attractive, the approaches how TikTok can build an effectively short video recommendation, and how TikTok makes money. The reasons behind TikTok’s rapid rise are analyzed and some benefits that TikTok brings to society and other businesses are revealed.
Evaluation of Active Photovoltaic Array Based on PSIM Software in Actual Scale

Photovoltaic (PV) power generation systems are often affected by the external environment (animals, plants, or buildings, etc.) during operation, causing the problem of partial shadowing of the PV array. In the case of partial shadows, conventional photovoltaic array connection and control methods cannot guarantee that each panel works at maximum power. In response to the above-mentioned situation of PV arrays in practical applications, this paper proposes a novel active PV array (APV) to control each PV panel to operate in the optimal state. Through the PSIM simulation analysis of the actual PV power station situation, the effectiveness of this control method is verified.
A New Ensemble Model for Phishing Detection Based on Hybrid Cumulative Feature Selection

A New Ensemble Model for Phishing Detection Based on Hybrid Cumulative Feature Selection(PDCFS) proposes a model that partitions the main dataset into $n$ partitions based on the features available in the dataset. This process is done by feeding the dataset into multiple feature selection methods: Chi-Square, Gain Ratio, Information Gain, Pearson Correlation Coefficient, and Principal Components Analysis, and arranging the dataset into $n$ datasets taking top-n features including the class label, given by the filter method, and discarding the remaining ones each time. Then each partitions of the dataset are used for training and testing using 5-fold cross validation, applying a number of classifiers: Support Vector Machine, Naïve Bayes, C4.5, Random Forest, JRip, PART, and k-Nearest Neighbors. In the next, the results are voted to get the best possible result. Majority voting is applied on both reduced feature subsets gained through feature selection steps, and full feature set to draw a comparison between the feature sets followed. The overall speculation suggests that Random Forest with reduced feature set of 32 tops the result chart with and accuracy of 98.36 %, while proposed PDCFS scores 98.24 % of accuracy. PDCFS also demonstrates a comparative performance when compared with other hybrid models.
TCAD Evaluation of the Substrate Bias Influence on the Carrier Transport of Ω-Gate Nanowire MOS Transistors with Ultra-Thin BOX

In this work, the effects of substrate biasing on the electrical behavior of n-type Ω-gate SOI nanowire MOS transistors with thin buried oxide (BOX) and variable fin width are analyzed. The analysis is carried over through 3D TCAD simulations calibrated with experimental data. Mobility degradation is observed for negative back bias due to surface-related scattering mechanisms on the front gate channel, while mobility increase is verified for positive back bias values that induce conduction in both front and back channels. High back bias values, however, which activate the back channel prior to the front one, lead to degradation in carrier mobility. On-state-off-state current ratio reduces for positive back bias due to degradation in the subthreshold slope, while DIBL is worsened due to the reduced front gate control when the substrate’s positive electric field takes ahold of the inversion charges.