Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Design of SIW-based Phase Shifter for Electronic Beam Switching in Millimeter Wave 5G

An advanced design of an antenna array with electronic beam switching is proposed for millimeter-wave 5G applications. Electronically reconfigurable phase shifters are employed to control the phase difference between the array elements. Eight rectangular dielectric resonator antennas (DRAs) are excited by using the radiating slots on the surface integrated waveguide (SIW). To realize the phase shifter, a number of electronically switchable Vias, made of liquid metal (LM), are implanted within the SIW. A maximum tilt of $\pm 38^{\circ}$ along the SIW guide is achieved by adjusting the phase difference between the array elements electronically. An impedance bandwidth of 16.2% is maintained for all switching states. About 13.5 dBi peak-gain is achieved from a compact SIW-fed DRA array $(5.3 \lambda \times 0.9 \lambda \times 0.2 \lambda)$, while the scan loss is kept below 3dB over the entire scanning range. Furthermore, the antenna array shows an inherent feature of frequency scanning, in which the beam tilting angle is possible to vary by controlling the operating frequency.
SVM-Based Optimization on the Number of Data Streams for Massive MIMO Systems

For millimeter-wave (mmWave) massive multiple-input and multiple-output (MIMO) systems, hardware cost and power consumption can be reduced via hybrid precoding, while keeping almost the same spectral efficiency (SE) as the fully digital precoding scheme. However, for different hardware configuration and channel environments, system SE varies with the number of transmitted data streams at the transmitter, and hence this should be determined primarily before hybrid precoding design. In this article, for single-user massive MIMO systems, a regression model based on the support vector machine technique is proposed, through which the optimal number of data streams can be obtained automatically based on the propagation characteristics of wireless channels, the number of antennas, and RF chains. To validate the correctness of the proposed model, simulation results are presented under various parameter settings.
Research on Data Management and User Profiling Technologies that Support Value-added Smart Energy Services

This paper discusses the connotation of big data-based smart electricity consumption technology with the premise of intelligent analysis and processing of massive amounts of data and information. This paper proposes the technical architecture of a flexible, interactive smart electricity consumption big data platform, including the sensing layer, analysis layer, and application layer, focuses on key technologies such as big data storage, processing, analysis, and presentation, and conducts in-depth research on functional applications such as user characteristic classification, load prediction, and energy efficiency analysis of electricity consumption. Finally, based on the actual promotion of flexible and interactive electricity consumption and the current research status of big data technology, several suggestions and considerations are put forward to promote the application of big data technology in intelligent electricity consumption.
Design of the Multiband Substrate Integrated Waveguide Filter using Coupling Matrix based on Frequency Transformation

This paper presents the design of substrate integrated waveguide (SIW) based multi-band filter using the frequency transformation approach. The steps involved in the extraction of coupling matrix elements and the equivalent circuit model for the generalized multi-band filter design are briefly explained. To validate the synthesis method presented in this paper a second-order dual-band bandpass filter is designed with the Chebyshev response. The two passbands are designated at 10.1 – 10.2 GHz and 10.4 – 10.5 GHz with fractional bandwidth (FBW) of 0.985% and 0.957% respectively. The maximum return loss for each passband is 20 dB. Very good agreements are achieved between theoretical responses and full-wave simulations.
Compact Wideband Uni-directional Circularly Polarized Antenna

A compact circularly polarized two-arm Archimedean spiral antenna backed by a shallow cavity is presented. Different miniaturization techniques were incorporated to reduce the overall dimensions of the conventional cavity-backed spiral antenna. Rigorous iterations on the spacing between the spiral and cavity back plate were carried out to achieve optimum performance. The overall dimension of this fabricated antenna is $0.326^{\star} \lambda_{\mathrm{L}}$ diameter and $0.0393^{\star}\lambda_{L}$ height. The fabricated prototype antenna exhibits a measured VSWR bandwidth (VSWR $\leq 2$) of 1.4 GHz to 9.7 GHz and measured Axial Ratio bandwidth $(\text{AR}\leq 3\text{dB})$ of 1.3 GHzto 7 GHz.
COMSOL 3D Simulation of Optimization of Waste Heat Recovery Using Thermoelectric Generator by Modifying the Design

Due to the EU’s energy strategy in order to improve the efficiency, researchers look for solutions to recover the waste heat. Heat can accumulate inside the system which might have a reduction effect on the electrical parameters. Consequently, transporting the accumulated heat can increase the overall efficiency of the system and can reduce the degradation effect of the accumulated heat. Thermoelectric generator directly converts the heat to electrical current via Seebeck effect. In order to directly convert the thermal energy that passes through the device into electricity, hight temperature difference has to occur. Consequently, the use of thermoelectric generator provides the opportunity to increase the overall efficiency of the system. Currently, Bismuth Telluride material is used for industrial thermoelectric modules. The simulation of the thermoelectric generator is a crucial part in the investigation of the performance. In the current article the optimization of waste heat recovery is studied using thermoelectric generator by modifying the leg geometries. During the comparison hexagon, trapeze, cube and cylinder geometries were taken into consideration. Based on the results, the hexagon geometry was the most optimal.
High Gain Cylindrical Dielectric Resonator Antenna Design using Aluminum Cavity for Wireless Applications

a novel high gain cylindrical dielectric resonator antenna is proposed by using aluminum cavity as a gain booster and also radiations converter. Here the aluminum cavity acts as gain booster from 6dBi to 14dBi, and the broadside radiations of conventional CDRA is converted into highly directional radiations. This proposed antenna configuration is very compact and it can be easily adopted in any wireless applications. The optimum design is obtained using High Frequency Structure Simulator (HFSS). The conventional CDRA is excited with dominant HEM11δ mode by the coaxial probe feed, which is resonating at 3.93GHz with broadside radiations and the peak gain of 6.73dBi. The proposed CDRA is resonating at 4.11 GHz with directional radiations and with co-polarized peak gain of 14.66dBi. The total gain of 8dBi is enhanced with an isolation of 36dB. The prototypes are fabricated and the experimental verification is under process.
Mechanism of Binaural Fusion in the Hearing of Speech

The mechanism of binaural fusion is here discussed as a form of statistical operation based upon the brain's execution of running cross‐correlation of the two ear signals. The technique of measurement of the degree of binaural fusion, outlined in our earlier paper, is here extensively applied to sine waves, to chords of sine waves, to intoned vowels and to running speech; such technique results in curves which assess listener's judgments as to whether a source of sound lies in the right or left hemisphere of subjective space. An extended theory is also developed here explaining these judgment curves in detail. Finally, a discussion is made of the importance of having an understanding of the mechanism of binaural fusion, in any consideration of our binaural directivity sense and our speech discrimination sense (the “cocktail, party problem”).
Analytical Comparison of Deep Learning Algorithms for Fracture Detection

Image processing is used in a variety of applications such as image restoration, satellite imaging, and medical imaging, to name a few. Many new applications in the computer vision and image processing sectors are being developed every day thanks to the proliferation of image processing libraries, particularly OpenCV and MATLAB. The bone fracture detection method was created in MATLAB using the “Image Processing” module. The paper's goal is to use an X-ray image to classify whether or not there is a fracture. The fundamental goal of this research is to determine which Advanced Machine Learning model works best on each fracture detection dataset and provides the highest accuracy with the least amount of error. As a result, we're required to run our dataset through a variety of Machine Learning Models, compare their accuracy, and submit a report based on the findings. Different Captured X-ray Images of Different Body Parts are sent into the Software Systems that we are meant to design. The Best Model is CNN Model with Batch Wise Learning with Accuracy of 77.52% with loss function value 0.587.
Fake News Detection using 5L-CNN

With the advent of websites and social media technologies, the internet has been a great method of transmitting news and information around the globe. However, due to lack of editorial scrutiny and monitoring by the media authorities, the news distribution has been seriously abused leading to the fast spreading of fake news. Fake news involves misleading broadcasting of information from sources that target to intentionally manipulate the way how people view some events or statements. This study seeks to develop a fake news detection model which will be able to analyze the title and text information attached to the news articles. From the researches done by previous scholars, it showed that the models did not perform very well because of the lack of sufficient feature extraction and fine tuning of the classification of the text. This research will therefore, articulate the gap by employing 5L-CNN deep learning model which will use inbuilt tokenizers for word embedding and will show better accuracy compared to the traditional machine learning models. In this paper, we compare machine learning algorithms (Decision trees, Random Forest, Logistic Regression & Naive Bayes) and deep learning algorithms (RNN, 5L-CNN & LSTM) to classify the authenticity of news articles. The model developed in this paper attained the best accuracy with 5L-CNN which had a result of 99.99%.
Compact Coplanar-waveguide based Bandpass to Bandstop Switchable Filter with Distinct Inductive and Capacitive coupling

A compact coplanar-waveguide (CPW) based switchable bandpass to bandstop filter has been presented in this manuscript. The bandpass consists of open-loop resonator which is centrally connected to the ground plane of CPW. The inductive and capacitive couplings are realized using grounding at the center and open ends of resonator respectively. These two couplings lead to two transmission zeros across the passband which improves the selectivity of the filter. These two paths has been analyzed using even and odd-mode stydy. A coupled-feed scheme has been implemented to couple RF energy to the resonator. A PIN diode has been integrated between input and output feed line to achieve bandpass (BP) to bandstop (BS) responses. An example filter has been fabricated for 1.1 G Hz. The filter has a power handling capacity upto 26 dBm.
Research on Small Object Pattern Recognition Technology based on Computer Deep Learning: Improved YOLOX Model as an example

In the design process of blue-printed cloth patterns, it is important to classify the pattern. In order to classify patterns more intelligently, we established a dataset and used artificial intelligence and computer algorithms to improve the rate and accuracy of pattern identification. Then, a new pattern recognition model was proposed to improve the efficiency of the blue-printed cloth pattern recognition. The dataset for Blue printed cloth was constructed with a proposed Mosaic+Mixup data enhancement method. The YOLOX network was improved to replace the backbone network with the GhostNet network and reduce the model parameters. The CBAM attention module was added to enhance the feature semantic learning ability of the model. DIOU loss function was also adopted instead of the IOU loss function to enhance the recognition ability of small target patterns. The final results show that the improved model achieved 85% of mAP on the Blue printed cloth dataset, which is 5.6% higher than that of the original YOLOX model. The detection speed of a single image was 24.2 ms, which was 10.5% faster than that of the original model. This model showed the real-time and high-precision identification of Blue printed cloth.
Intrusion Detection Using Enhanced Transductive Support Vector Machine

The world is getting more interconnected and reliant on the Internet and the services it provides today. The protection of networks and apps from unauthorized attacks is one of the biggest difficulties in internet communication. Numerous solutions have been put out to deal with security concerns, yet the vast majority of these solutions consistently fall short of rapidly and effectively detecting security threats. In order to detect new attacks with high accuracy, a method for intrusion detection employing machine learning techniques is proposed in this article. Here, the Enhanced Transductive Support Vector Machine (ETSVM) method is used to classify the data in order to more accurately detect the different types of intrusion attacks. The more pertinent and ideal features are chosen using the Improved Glowworm Swarm Optimization (IGSO) technique. This method performs better at detecting intrusions on the KDD CUP99 and CSE-CIC-IDS2018 datasets. Precision, recall, and accuracy are used to assess the proposed model's performance in identifying the four types of cyber attacks-DoS, U2R, R2L, and Probe. In order to validate the proposed methodology, comparative findings are presented.
Matching, Scanning and Radiation Characteristics of Wide-Scanning Angle Phased Array with Printed Dipole Antenna with Parasitic Elements at Ku-band

This paper presents a wide-scanning angle phased array with printed dipole antenna with parasitic elements at Ku-band. Printed dipole antenna is designed with a parasitic strip and a parasitic director to control active matching results in E-and H-planes respectively. Active matching in E-plane can be controlled by optimizing the length of parasitic strip and in H-plane by introducing a parasitic director. Introducing parasitic director to improve active matching in H-plane is the novelty of this work. Active matching results of printed dipole in an infinite array environment are presented. Matching, scanning and radiation characteristics of finite circular 164-element array are also presented. Design of antenna array shows a scan volume of ± 60 degrees, with active scan reflection coefficient (ARC) of better than -12 dB in E-plane and better than -15 dB in H-plane. A linear array of 1x14 is realized for validation of simulation results. Comparison of measurement and simulation results of 1x14 linear array are presented.
Initial Results of Ground based SAR Experiment in an Opencast Coal Mine for Slope Stability Monitoring

Slope stability monitoring in opencast mines is very important for the safety of workers and the equipment close to the overburden dump. Ground based Synthetic Aperture Radar (GBSAR) system is widely used due to its high accuracy in measurement and all weather capability. We developed FMCW GBSAR system first time in India and operated at Dudhichua, Northern Coal Ltd. This paper presents the initial results of its performance. GBSAR images are acquired over the mining area with a corner reflector placed at different locations. The raw data were processed for single look complex images so that radar interferometry technique can be used for monitoring the slopes. The backscattered power images show the benches in the mines very clearly with the SNR of 22 dB in VV polarization, A MATLAB tool was developed for processing the raw data. The validation of the system for phase stability, interferometric coherence, and time series real time monitoring is in progress.
S-Band Dual Circularly Polarized Multi-Beam Shared Aperture Phase Array Antenna for Ground Reception System

A dual circularly polarized S-band multi-beam shared aperture phase array antenna for ground based communication and data acquisition system has been discussed in this manuscript. The objective is to simultaneously produce multiple beams--using the same antenna aperture. Each beam is independently assigned to separate airborne flying objects for tracking and health data analysis purpose. The antenna system is capable of producing simultaneous dual circular polarization, namely left hand and right hand circular polarization. Each beam is independently steered electronically over ±45° without much degradation of the gain and beamwidth at scan edge. Side lobe level is reduced below 30 dB using Taylor distributed amplitude tapering. The overall array antenna produces a 3dB beamwidth of about 14°with boresight gain of is 20dBi over the entire operating band. However, the gain decreases by ~ 3dB at scan edge of $\pm 45^{\circ}$ The system has been realized and experimentally validated. The realized impedance bandwidth is wider than 10% at each of the ports with inter-port isolation $\geq 18$ dB. Measured axial ratio is better than 3 dB over the entire operating band.
Mixed-Type Wafer Failure Pattern Recognition (Invited Paper)

The ongoing evolution in process fabrication enables us to step below the 5nm technology node. Although foundries can pattern and etch smaller but more complex circuits on silicon wafers, a multitude of challenges persist. For example, defects on the surface of wafers are inevitable during manufacturing. To increase the yield rate and reduce time-to-market, it is vital to recognize these failures and identify the failure mechanisms of these defects. Recently, applying machine learning-powered methods to combat single defect pattern classification has made significant progress. However, as the processes become increasingly complicated, various single-type defect patterns may emerge and be coupled on a wafer and thus shape a mixed-type pattern. In this paper, we will survey the recent pace of progress on advanced methodologies for wafer failure pattern recognition, especially for mixed-type one. We sincerely hope this literature review can highlight the future directions and promote the advancement of the wafer failure pattern recognition.
Industrial application of the frequency-domain aspects of the Kalman filter

Several classical industrial filtering applications have used notch filters to reject the unwanted dominant frequencies. For a class of industrial systems, it has been shown that the filter gain between the observations and the signal estimates is unity. While the gain between the observations and the disturbance state estimates is zero, the zeros of the Kalman filter are determined by the poles of the disturbance model. These results are generalised to a more representative case of a block-diagonal structure with feedback coupling.
Loop Rectenna Unit for Far Field Wireless Power Transfer Application

This article describes a 2.4 GHz two-port Loop Rectenna Unit (LRU) designed for a distant field wireless power transmission application. Two diodes are used in the configuration with intrinsic combiner characteristics to rectify the signal. Simulations in the full wave EM solver of ANSYS HFSS and Keysight ADS are used to analyse the performance of the suggested structure. When excited, the proposed LRU provides a total gain of 4 dBi and a peak rectification efficiency of 64%. At 2.4 GHz, source power levels between -6.8 dBm and 11.6 dBm exhibit above 40
Vulnerability Detection in Source Code using Deep Representation Learning

Everyyear, whether or not they may be discovered internally in personal code or are made public, software program issues are found extra frequently. Those weaknesses might be exploited, ensuing in denial of provider, gadget compromise, or statistics leaks. We developed a large-scale characteristic-stage vulnerability detection method using gadgets getting to know employing open-source C and C++ code. We advanced a giant dataset of hundreds of thousands of open-source features and tagged it with cautiously selected findings from 3 separate studies companies intending to augment previously labeled vulnerability datasets. Capacity vulnerabilities are determined using static analyzers. The marked dataset is to be had at https://osf.io/d45bw/. Using those datasets, we created a brief and scalable vulnerability detection approach based on deep characteristic illustration gaining knowledge that reads source code lexis at once.
AI based Technologies for International Space Station and Space Data

Billions of galaxies, stars, solar systems, planets, and other undiscovered mysterious objects are present in the continuously expanding space. Humans took a giant step forward when the International Space Station was deployed into Earth's lower orbit for a better understanding of space. This research focuses of the Artificial Intelligence based technologies that are deployed in the International Space Station and potential models. The study proposes some Machine Learning models and technologies that could be deployed in the International Space Station to increase its efficiency and provide security to the crew. Powerful and trending Machine Learning/Deep Learning Algorithms like ANN and Clustering algorithms are suggested by the paper to get insights from the data gathered from the space and to promote the Industry Automation. A detailed explanation of the requirement, operation, and construction of NASA's “Robonauts” designed for the International Space Station is discussed in the research. The paper also put light on the ATLAS, an asteroid detecting system. The methods for providing medical aid to the crew, debris and its influence, analyzing data and extracting insight from space research data using machine learning are also highlighted. Investigation on how technologies used in space exploration could be used in the ISS to improve its performance and an overview of some of the existing AI-based technologies deployed in the International Space Station (ISS) is discussed.
Efficient fault diagnosis in an antenna array incorporating mutual coupling

The inverse problem for locating faulty elements in an antenna array from electromagnetic field measurements is an interesting and relevant research topic. We present a novel idea based on compressive sensing to detect the faulty elements from fixed probe measurements, which is based on the optimizing of the element excitations. Unlike many previous works, our approach considers the inter-element mutual coupling effects and identifies their importance in fault diagnosis. This study illustrates the effects of coupling in a loop antenna array with varying array sizes. We used two coupling models, namely, the average embedded pattern and the coupling matrix approach to include coupling effects in the analysis. Our work significantly improves antenna array fault diagnosis using the coupling-aware method compared to the existing methods.