Miracle-dz/UniFAR-FTUs huggingface dataset:
The Optimum Interference Suppression Technique in the MBER Sense for Spread Spectrum Systems

By examining the relationship between the minimum bit error rate (MBER),minimum mean square error (MMSE) and the constrained minimum mean output energy (MMOE),we resolve the interference suppressor optimized in the MBER sense into the optimum suppressor in the MMSE criterion and the constrained MMOE criterion which lend themselves to adaptive implementation more readily than the MBER suppressor.This paper addresses the recursive least squares (RLS) algorithm and the blind RLS (BRLS) algorithm without known desire signal.The suppressed effect of the BRLS algorithm is shown to be significantly inferior to the RLS algorithm with known data sequences.This paper proposes the scheme of combining RLS with BRLS to solve the interference suppression in time-varying environment.
Environmental Impact of Coherent Point-to-Multipoint Pluggables in Metro Aggregation Optical Networks

The environmental impact of telecommunications is becoming increasingly relevant in our society as more data are transmitted every day. On the one hand, telecommunication networks help, for example, by reducing the number of travels and visits; on the other hand, the energy consumed by the network elements and devices is reaching unprecedented levels as a consequence of the growing bandwidth demand needed by novel applications and services. In this contribution, we analyze key metrics to estimate the energy efficiency, in building an optical network, of several types of optical transceivers when employed in the most common topologies of metro aggregation networks, i.e., horseshoes, rings, and hub-and-spoke. We considered three different types of coherent pluggable modules: (i) 400G ZR point-to-point (P2P); (ii) 400G ZR+ P2P; and (iii) 100G/400G point-to-multipoint (P2MP) which can be enabled by digital subcarrier multiplexing (DSCM). We show that the P2MP approach needs—due to its inherent flexibility and the ability to simplify network design—the lowest number of devices, and consequently the lowest amount of CO2 emissions among the three considered metro aggregation network scenarios.
About the peculiarities of searching for Information Sets of Block-Permutation LDPC-Codes when Correcting Error Bursts

Modern communication standards use error-correcting coding, especially LDPC codes. Various algorithms are used to decode them, including decoding by information sets. The algorithm for decoding over information sets has exponential complexity. Errors may occur on any communication channel when transmitting messages. Traditionally, independent errors are considered when researching decoding algorithms. However, in real systems, errors are grouped into bursts. This article discusses the probability of detecting information sets while correcting error bursts. Random matrices and block permutation matrices are considered. Graphs of the dependence of the probability of finding information sets on the number of positions that are considered when searching for information sets are given.
A Survey on Smart City Surveillance System

The significance of smart cities is constantly growing as the world’s population is moving towards urban areas in search of new opportunities for their development and the betterment of their future. This rapid influx of migrants has increased the pressure on the services deployment, traffic congestion within the city, prevention of criminal activities, energy usage and waste reduction. This can lead to the impending failure of the management system within the cities, looming with the burden of issues. These events have triggered the remediation of such issues through the development of smart cities projects. In order to obtain a realistic answer to this challenge, several pilot programs have been initiated around the world. Smart city surveillance systems are being designed to make the urban areas smart enough to monitor the changes and activities in their ambient environment. For the handling of such rapidly growing issues, the governments along with their supporting organisations, private organisations, other non-governmental agencies working non-profitable, academics and researchers and the general public of the nations must work collaboratively on innovative ideas with the use of different technological advancements. This paper provides a survey of the various smart city initiatives for the betterment of the living standards of the residents. Various open issues and challenges have been discussed. Solutions proposed in the field, for the remediation of the looming issues, have also been looked upon. Finally, the future perspective in the domain has also been reviewed and the impact of smart city surveillance system into the people’s lives has also been looked upon.
Machine Learning enabled Fault-Detection Algorithms for Optical Spectrum-as-a-Service Users

The growing usage of high-bandwidth, low-latency applications has led to a significant increase in data traffic in recent years. To meet this demand, optical network operators have begun upgrading to Elastic Optical Networks (EONs), powered by Flexible Bandwidth Variable Transceivers (Flex-BVTs). Encouraged by the disaggregation trend, where Flex-BVTs and Open Line Systems (OLS) are owned and controlled by different parties, the operators are introducing new service models like Optical Spectrum-as-a-Service (OSaaS) in their networks. The OSaaS user in this service model perceives OLS as a transparent light tunnel with no monitoring points other than the Flex-BVTs. As multiple OSaaS users share the same OLS, these networks are more susceptible to failures caused by power degradation or channel interference. To reduce system disruptions and repair costs, it is therefore crucial to detect, identify, and counter such failures timely.In this work, we investigate the methods for OSaaS users to detect and identify failures as early as possible using only the telemetry data available from the end Flex-BVTs. Deploying and monitoring five Flex-BVTs within a 400-GHz dedicated OSaaS channel on a pan-European live network for 45 days, we evaluate the applicability of two Machine Learning (ML) based algorithms for EON failure detection, namely, an Artificial Neural Network (ANN) model with dynamic threshold calculation, and a One-Class Support Vector Machine (OCSVM) model. Our results show that the ANN-based approach can detect all artificially introduced failures, with a misclassification rate of 0.01% as compared to the OCSVM-based approach which was unable to detect up to one-third of artificially introduced failures, along with a misclassification rate of 0.6%.
NR Enhancements for Downlink MIMO in Rel-18

Massive multiple-input multiple-output (MIMO) has been a key technology in 5G New Radio (NR) standard, which empowers significant system performance improvement in previous 5G NR Releases. To support higher data rate and multi-user simultaneous transmission in downlink, enhancement in massive MIMO is essential for NR Release 18 (Rel-18). In this article, we explore performance requirements, challenges, and potential technologies for MIMO enhancement. First, we summarize major performance requirements for MIMO in Enhanced Mobile Broadband scenario. Based on these requirements, we identify three critical challenges of MIMO enhancement for single transmission reception point (TRP) and multiple TRPs scheme, in which we highlight the promising coherent joint transmission (CJT) technique. For each challenge, we present specific description from requirement perspective and technique perspective, evaluate the pros and cons of potential solutions, and carry out simulations to illustrate the potential performance gain. Some insights on how to solve these challenges to enhance massive MIMO performance in NR Rel-18 are also provided.
DyCroNO: Dynamic Cross-layer Network Orchestration and Real-time Deep Learning-based Network Load Prediction

In this paper, we present Dynamic Cross-layer Network Orchestration (DyCroNO), a dynamic service provisioning and load balancing mechanism for IP over optical networks. DyCroNO comprises of the following components: i) an end-end (E2E) service provisioning and virtual path allocation algorithm, ii) a lightweight dynamic bandwidth adjustment strategy that leverages the extended duration statistics to ensure optimal network utilization and guarantee the quality-of-service (QoS), and iii) a load distribution mechanism to optimize the network load distribution at runtime. As another contribution, we design a real-time deep learning technique to predict the network load distribution. We implemented a Long Short-Term Memory-based (LSTM) method with a sliding window technique to dynamically (at runtime) predict network load distributions at various lead times. Simulations were performed over three topologies: NSFNet, Cost266 and Eurolarge using real-world traffic traces to model the traffic patterns. Results show that our approach lowers the mean link load and total resources significantly while improving the resource utilization when compared to existing approaches. Additionally, our deep learning-based method showed promising results in load distribution prediction with low root mean squared error (RMSE) and ∼90% accuracy.
Based on Improved Single Shot MultiBox Detector construction site Helmet Detection Algorithm

For the existing engineering construction now the worker's helmet wear detection method consumes labor and time and also easy miss detection problems, to protect the safety of construction site personnel, proposed an improved Single Shot MultiBox Detector(SSD) helmet wear detection algorithm. First, the backbone network with Resnet-50 model instead of VGG-16 is used as the SSD algorithm, and the residual structure in Resnet can improve the feature extraction ability of the network; CA (Coordinate Attention) module is added before the feature layer enters the prediction to enhance the capture of localization information of the target. The experimental results show that the average accuracy (mAP) of the improved algorithm can reach 94.5% on the homemade helmet data set, which is 4.49 percentage points higher than the original algorithm, and can meet the accuracy requirements of helmet-wearing detection under construction sites.
Parachute Deployment Fault Identification and Landing Point Prediction of Spacecraft Recovery System Based on Swin Transformer and Edge Detection

The recovery and landing process of spacecraft requires a high level of safety and reliability. In this paper, we focus on the parachute of the spacecraft recovery system, investigating the method of open state identification and landing point prediction. A deep-learning-based method of opening fault identification and accurate landing point prediction of the spacecraft recovery system is proposed. On the basis of a 3-DOF dynamic model of the parachute and payload system, combined with the effects of wind fields, we built a framework for predicting the landing point of the recovery system. A dataset of parachute-opening images and complete labels was established from video data photographed in previous airdrop experiments. Integrate the well-trained Swin Transformer in the landing-point prediction framework to determine whether the parachute opens normally. Calculate the characteristic drag area of the parachute with edge detection using Sobel filter. Furthermore, real-time correction of the recovery system dynamics parameters is used to obtain a complete opening fault identification and landing point prediction system of the spacecraft recovery system.
Displacement field of a screw dislocation in a 〈 011 〉 Cu nanowire: An atomistic study

By performing atomistic calculations with a tight-binding potential, we study the displacement field induced by a screw dislocation lying along a free 011 Cu cylindrical nanowire. For this anisotropic orientation that is often encountered experimentally, we show that the displacement field u z along the nanowire can be seen as the superposition of three different fields: the screw dislocation field in an infinite medium, the warping displacement field caused by the so-called Eshelby twist, and an additional image field induced by the free surfaces. A Fourier series analysis of this latter image displacement and stress fields is given. For a circular cross section of the wire, this image field corresponds mainly to an additional warping displacement u z ∝ xy. The dissociation mechanism of the dislocation into partials and the surface stress effects being also captured in our simulations, the present study enables one to quantify the various contributions to the formation of the x-ray diffractograms.
Techniques for Correlating Power Distribution Network Simulations with Physical Measurements

Power Distribution Network (PDN) noise management is becoming increasingly challenging; increasing clock frequencies create faster current transients for the same application, shrinking die sizes crowd the current into a smaller area of the package increasing current density and effective inductance, and higher utilization of on die circuits results in less static circuit capacitance for decoupling. Thus, accurate prediction of PDN noise continues to be critical to the design process. In this paper, we will touch upon a range of strategies for verifying PDN noise simulation results. Novel aspects include use of on-chip Power Glitch Detector (PGD) to measure peak noise, direct simulation of realistic application code and comparison to on-die measurements, high resolution PDN frequency response characterization through parameterized application code, and findings for board and package model extraction tool selection.
Face Detection Using Haar Cascade Algorithm

Nowadays, machines are becoming friendlier and can react to people in a faster way. Human face detection has attractive wide spread interest in recent years and become a vital factor in Human Machine Interaction and one of the most popular topics in computer vision and biometrics. The aim of this paper is to detect human face from an image containing simple and complex backgrounds using the Haar cascade algorithm. The three principal of this algorithm are: Haar-like Filter, Integral Image and the AdaBoost cascade classifier. The available Labeled Faces in the Wild (LFW) is used as the research dataset in support with OpenCV library and python programming. Our experiments show that our proposed method confirms the state-of-the-arts.
Survey on Modern Applications of Multiple Unmanned Aerial Vehicles(UAV) Systems

A single unmanned aerial vehicle (UAV) can be used for a variety of applications. , including reconnaissance, mapping and surveying, search and rescue, and many other applications. But the use of several UAVs has more advantages than previous applications and opens the way for more new applications. This paper aims to compare single and multi-UAV systems by mentioning the most important advantages offered by the use of more than one UAV, classifying the applications that use several UAVs, and providing a general review of some of the research that has been developed in recent years. Finally, a discussion of current trends and challenges is presented.
A New High Voltage Gain Active Switched-Inductor Based High Step-Up DC–DC Converter With Coupled-Inductor

This paper propounds a new high step-up DC-DC topology based on the active switched-inductor (A-SL) technique, which utilizes a coupled inductor (CI) for performance enhancement. Among the features of the presented topology are the following: (a) Better voltage gain (VG) and efficiency, (b) Since there are low voltages across power switches, it is possible to reduce the amount of power that is lost as a result of using MOSFETs with a low voltage rating and reduced internal resistance, (c) By reducing the turn ratio of the CI, the VG is enhanced, (d) Through the series connection of a capacitor with the CI primary winding, zero DC bias is achieved, and the core saturation problem is solved, (e) Input current division between input inductors and switches, (f) There is no limitation on duty cycle ( $0< D< 1$ ), and (g) Simple switching patterns and simultaneously driven switches. The propounded converter’s operation principle and steady-state analysis are explained in detail, and parasitic elements’ effects on VG and efficiency are examined. In addition, the VG, the number of components, and the voltage tension on power switches, diodes, and capacitors are compared and analyzed. An experimental laboratory prototype with 30V input and 400V output voltage is used to verify the validity of the study and the performance of the propounded topology.
Modeling S-parameters of Interconnects using Periodic Gaussian Process Kernels

In this paper, we present a novel technique to model wide-band scattering parameter (S-parameter) curves of high-speed digital interconnects. The proposed technique utilizes a new kernel function with periodic components for Gaussian process (GP) models. After proper training, the GP models are able to predict the S-parameter values at arbitrary frequency points inside the trained interval. The performance of the proposed technique is reviewed by means of correlation with standard Gaussian Processes with squared exponential kernel and Matern kernel. Results for the proposed technique show an increased prediction accuracy when applied to interconnects.
A Reinforcement Learning-Based Distributed Control Scheme for Cooperative Intersection Traffic Control

Traffic congestion is a major source of discomfort and economic losses in urban environments. Recently, the proliferation of traffic detectors and the advances in algorithms to efficiently process data have enabled taking a data-driven approach to mitigate congestion. In this context, this work proposes a reinforcement learning (RL) based distributed control scheme that exploits cooperation among intersections. Specifically, a RL controller is synthesized, which manipulates traffic signals using information from neighboring intersections in the form of an embedding obtained from a traffic prediction application. Simulation results using SUMO show that the proposed scheme outperforms classical techniques in terms of waiting time and other key performance indices.
Bilingual COVID-19 Fake News Detection Based on LDA Topic Modeling and BERT Transformer

The spread of fake news has become more prevalent given the popularity of social media and the various news that circulates on it. As a result, it is crucial to discern between real and fake news. During the COVID-19 pandemic, there have been numerous tweets, posts, and news about this illness in social media and electronic media worldwide. This research presents a bilingual model combining Latent Dirichlet Allocation (LDA) topic modeling and the BERT transformer to detect COVID-19 fake news in both Persian and English. First, the dataset is prepared in Persian and English, and then the proposed method is used to detect COVID-19 fake news on the prepared dataset. Finally, the proposed model is evaluated using various metrics such as accuracy, precision, recall, and the f1-score. As a result of this approach, we achieve 92.18% accuracy, which shows that adding topic information to the pre-trained contextual representations given by the BERT network, significantly improves the solving of instances that are domain-specific. Also, the results show that our proposed approach outperforms previous state-of-the-art methods.
A compact ultra-wideband circularly polarized antenna array for vehicular communications

In this paper, a new compact ultrawideband (UWB) circularly polarized (CP) antenna array for vehicular communications is proposed. The antenna array consists of a 2 × 2 sequentially rotated T-shaped cross dipole, four parasitic elements, and a feeding network. By loading the T-shaped cross dipoles with parasitic rectangular elements with cut corners, the bandwidth can be expanded. On this basis, the radiation pattern can be improved by the topology with sequential rotation of four T-shaped cross-dipole antennas, and the axial ratio (AR) bandwidth of the antenna also can be further enhanced. In addition, due to the special topology that the vertical arms of all T-shaped cross dipoles are all oriented toward the center of the antenna array, the gain of proposed antenna is improved while the size of the antenna is almost the same as the traditional cross dipole. Simulated and measured results show that the proposed antenna has good CP characteristics, an impedance bandwidth for S11 <−10 dB of about 106.1% (3.26:1, 1.57–5.12 GHz) and the 3-dB AR bandwidth of about 104.1% (3.17:1, 1.57–4.98 GHz), a wide 3-dB gain bandwidth of 73.3% as well as the peak gain of 8.6 dBic at 3.5 GHz. The overall size of antenna is 0. 56λ × 0. 56λ × 0. 12λ (λ refers to the wavelength of the lowest operating frequency in free space). The good performance of this compact UWB CP antenna array is promising for applications in vehicular communications.
Advanced Phase Jitter Analysis with Power Noise Induced Jitter Flow in PCIe Gen 3

In this work, we present a method to derive the phase jitter (PJ) of PCI Express (PCIe) Gen3 by analyzing both random jitter (RJ) and deterministic jitter (DJ) induced by power noise in a clock network. In the previous jitter analysis methods, the impact of power noise has not been considered when analyzing the jitter of PCIe Gen3 reference clock networks. We apply the proposed method to analyze and validate the main noise sources of PCIe Gen3 PJ violations observed in a system-on-chip (SoC) design implemented at Samsung's 4-nanometer process node. Through various experiments, we found that the main cause of the violation is jitter due to power noise below 50 MHz. By modifying the power management integrated circuit (PMIC) to reduce the low-frequency noise at the PMIC output, we observed a 50% reduction in jitter in the clock network.
Beam holography of OVRO-LWA antennas using the OVRO 40-m telescope

The OVRO-LWA radio telescope is an array of 352 dipole antennas optimized for high red-shift 21-cm cosmology and the detection of magnetospheric radio emission from exoplanets. These scientific cases impose some strict calibration requirements including a very accurate knowledge of the beam for each antenna in the array. This paper outlines a conceptual design to map the beams using pulsar holography. The technique will use the 40-m dish with a feed similar to a single dipole pair in the array, which will drastically increase the sensitivity. Another method is also discussed where the phased array beam is used instead of the dish potentially giving better results.