Miracle-dz/UniFAR-FTUs huggingface dataset:
IndoorRSSINet - Deep learning based 2D RSSI map prediction for indoor environments with application to wireless localization

We consider the problem of 2D RSSI prediction in an indoor environment. Indoor RSSI prediction plays a vital role in localization, wireless network topology planning, and scheduling. The problem is challenging in an indoor scenario due to multiple reflections and refractions from walls, furniture, and other objects. Conventional RSSI predictions using on-field measurements, ray tracing, or electromagnetic simulations are time-consuming. By considering RSSI map prediction as an image prediction problem, we leverage recent advances in deep-learning to propose a deep learning model called IndoorRSSINet with faster RSSI map predictions. We motivate the design of IndoorRSSINet using a ray-tracing interpretation. We evaluate the performance of IndoorRSSINet on synthetic 2D indoor RSSI datasets generated using ray tracing. We show that predicting the 2D RSSI map using IndoorRSSINet is 21 times faster than a ray tracing approach with a Normalized Mean Square Error (NMSE) of 0.00962 for RSSI normalized to [0, 1]. We illustrate the use of IndoorRSSINet for transmitter position optimization in an RSSI fingerprint based wireless localization application.
Opinion Dynamics: Bots and the Spiral of Silence

The spiral of silence theory states that individuals with controversial or unpopular opinions may hesitate to voice their opinion in public. In recent years, online social networks have played an important role in shaping public opinion. The prevalence of bots on such social networks is on the rise. Motivated by this, in this work, we study the effect of bots and the spiral of silence on the opinion dynamics of a network. We propose a variant of the voter model that captures the opinion dynamics in a network where individuals with the unpopular opinion hesitate to express their true opinion in public and bots are used to influence the opinion of individuals in the network toward the initially unpopular opinion. In the setting where bots can only be used for a limited period of time, we use the proposed model to characterize the optimal time to employ bots in the network to maximize the fraction of individuals that hold the initially unpopular opinion at the end of a finite time horizon.
Information Geometry Approach for Massive MIMO Channel Estimation

In this paper, we investigate the channel estimation for massive multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Using the sampled steering vectors in the space and frequency domains, we first establish a space-frequency (SF) beam based statistical channel model. Based on the channel model, the channel estimation is formulated as obtaining the a posteriori information of the beam domain channel. We solve this problem by calculating an approximation of the a posteriori distribution's marginals within the information geometry framework. Specifically, by viewing the set of Gaussian distributions and the set of the marginals as a manifold and its submanifold, respectively, we turn the calculation of the marginals into an iterative projection process between sub manifolds with different constraints. We derive the information geometry approach (IGA) for channel estimation by calculating the solutions of projections. We prove that the mean of the approximate marginals at the fixed point of IGA is equal to that of the a posteriori distribution. Simulations demonstrate that the proposed IGA can accurately estimate the beam domain channel within limited iterations.
Robust Orientation Estimation from Only A Plane and A Line in Manhattan World

The orientation estimation is an essential issue for indoor robotic tasks. Existing methods of orientation estimation in indoor environments suffer from poor accuracy, poor robustness and heavy computational burden due to drift error, textureless and clutter environment, and exhaustive search strategy. To overcome these deficiencies, a novel method of orientation estimation from only a plane and a line under the assumption of Manhattan World is proposed. Firstly, the framework of USAC is extended to estimate the dominant plane robustly. Then, the one plane and one line USAC is proposed to perform orientation estimation. Finally, we design a new cost function to refine the orientation estimation via nonlinear optimization. Extensive experiments show that the proposed method outperforms the state-of-the-art methods.
A Spatio-Temporal Traffic Flow Prediction Method Based on Dynamic Graph Convolution Network

A spatio-temporal traffic flow prediction model based on dynamic graph convolution network (DGCN) is proposed to solve the problem that using a fixed Laplacian matrix to model the spatial features of the road network in the existing traffic flow prediction process, which is unable to fully utilize the traffic data and leads to poor prediction performance. The model using global Laplacian matrix learning layer, spatial attention layer and gated cyclic unit layer to form a matrix learning module, extracts spatio-temporal features from the given traffic flow data and predicts the future Laplacian matrix, and then applies it to the spatio-temporal prediction module consisting of graph convolutional network (GCN), gated cyclic unit and Transformer layers. The experimental results show that the proposed model is not only capable of modeling spatial features in real time according to the given data, but also can effectively improve the accuracy of traffic flow prediction.
Deep learning optimizer performance analysis for pomegranate fruit quality gradation

Quality and safety are important factors in the food industry. In recent years automatic visual inspection technology has become more potential and important for fruit grading applications. This is because quality is an important factor for consumers and so essential for the market. This paper focuses on a comparative study of deep learning optimizers for pomegranate fruit quality grading. It plays an important role in maximizing the efficiency of the neural network model. Optimizers are mathematical functions or algorithms which are dependent on various parameters of the model i.e., weights and biases. This paper presents the performances of the various deep learning optimizers for pomegranate fruit quality grading. The dataset used for this study is named as Pomegranate Fruit dataset from the Kaggle dataset. Dataset has three grades G1, G2, and G3. Each grade is having four internal quality labels and has 90 images in it. Training is done using SGD, Adadelta, Adagrad, RMSprop, and Adam optimizers. This study helped in analyzing better optimizer and identifying the need for overall improvement in performance of the optimization.
A Markova-Chain Approach to Model Vehicles Traffic Behavior

The ability to accurately predict freeway traffic conditions in the near future has recently gained prominence as it plays a crucial role in the fundamental traffic management functions and trip decision making processes. This study proposes a stochastic approach, the Markov chain Model, for short-term freeway traffic prediction during 24 hours, taking into account the dynamic and stochastic nature of traffic flow by means of the Markov chain process’s transition matrix. The research utilized data collected by real-time traffic monitoring devices on the clockwise M25 highway in the United Kingdom between junction 13 and junction 14 for a full 24 hours. For the first method, a first order Markov chain transition probability matrix is used to characterize the subsequent traffic flow value based on the current and previous values. To estimate the unknown transition probabilities from the observed transition counts in a time series of the vehicles’ flow over time, a Discrete Time Markov Model was calibrated using the maximum likelihood estimate method. The method’s ability to preserve the expected flow’s statistical properties is evaluated by comparing the two sets of data. Mean, standard deviation, maximum, minimum, percentiles, and autocorrelations of traffic flow values are the most popular statistical properties used for this purpose. The modeled autocorrelation coefficients for a first order Markov chain are very close to the measured autocorrelations, with a root mean square error (RMSE) of (0.0065) for the first five autocorrelations’ coefficients. When actual traffic levels are compared to expects, it is clear that statistical characteristics have been maintained satisfactorily.
Cost Minimization Strategy for UAVs Offload Calculation in MEC Environment

To meet the cost minimization requirement of computational offloading for UAVs in Mobile Edge Computing(MEC) environment, this paper proposes a cost minimization strategy based on the improved DDQN optimization algorithm by time delays, energy consumption and computational offloading model. Aiming at the difficult problem of MEC server resource allocation in the model, this paper uses a dichotomous approximation solution strategy for optimal resource allocation, based on which an action screening strategy is adopted to avoid the dimensional disaster problem that may occur in the state space of DDQN, and finally priority is introduced on the empirical replay pool sampling, which is used to improve the convergence speed of the algorithm. The simulation experimental results show that the algorithm effectively reduces the overall system energy consumption and time delays, improves the task offloading success rate, and achieves good stability compared with several other classical algorithms.
Propagation and Focusing of Low-order Modes of a CW THz Waveguide Laser

Wave beams with different spatial polarizations have found a variety of practical applications. These beams are used in material processing, high resolution metrology, electron acceleration, spectroscopy and microellipsometry. However, at the moment, there are practically no theoretical or experimental results on the features of propagation and focusing of CW THz radiation. For a theoretical study of the propagation and focusing of THz laser beams excited by the modes of a waveguide quasi-optical resonator, the Rayleigh-Sommerfeld vector theory was applied in the work. Well - known methods of measuring in the THz range were used for experimental study. The obtained results supplement the ideas about the features of the propagation of terahertz laser beams, as well as their sharp and moderate focusing.
DDoS attack defence mechanism using sFlow

An upcoming communication network is software-defined networking (SDN). The idea of keeping the control plane and data plane separate has given researchers a significant amount of space to experiment with novel ideas. Strong security is needed since the central controller handles all network control operations. SDN security becomes more important since this dynamic network architecture encounters a variety of design complexity and frequent open-flow flaws, such as problems with a control system, in addition to having a bright future ahead of it. It is becoming the new standard in modern networks, and as part of our research, we looked at what impact they might have on security and privacy. The most common cyberattack, known as Distributed denial of service (DDoS) attacks is another of the major problems facing modern networks, and DDoS attacks can be very difficult to defend against and causes the system resources to be depleted, making the services unavailable to fulfil valid requests. Open Daylight (ODL) and Ryu, two of the most potent and well-known SDN controllers, are experimentally compared in this study. The Mininet emulator is used for the experimentation. Wireshark is used to record and analyze live packets. Additionally, early DDoS attack detection in the controller is a crucial area for research. There are numerous methods suggested to find DDoS attacks. In this study, we suggest a one-level protection DDoS attack detection method for SDN controllers. sFlow is used to initially identify attacks.
Features of the Application of Protective Devices in 5G Technology

The principle of construction of wide-band self-controlled power limiters mm - wavelength range is proposed in the work. The operating range, which includes the 28 GHz and 38 GHz frequency range used by 5G technology, is considered. Losses of the limiter in the small signal mode in this range do not exceed 2 dB. At the same time, the attenuation in the high-power input signal mode is about 30 dB in the entire range of operating frequencies. Methods of calculating the characteristics of self-controlled power limiters are provided. Ways to optimize their design are considered. A similar construction of the limiter can be used in the 60 GHz frequency range, which is also used by 5G technology.
Microwave Behavior of Partially Magnetized Ferrites

The high‐frequency permeability of partially magnetized ferrites is calculated for some simple domain configurations, comprising only ``up''‐ and ``down''‐domains. The method used is based upon the magnetostatic approximation and neglects exchange effects, but is otherwise substantially rigorous. The components of the effective permeability tensor (ratio of average induction to average magnetic field) in general depends upon details of the domain configuration in addition to the net dc magnetization. When the dc magnetization is cycled between the two states of complete magnetization the high frequency permeability, considered as a function of the dc magnetization, in general shows hysteresis. Detailed calculations of the high‐frequency permeability have been carried out for the case in which the domain configuration is cylindrically symmetric, i.e., invariant under rotation around the direction of magnetization. For any such domain configuration the two relevant components μeff and κeff of the effective .
Generalized TLM Block Meshing Scheme Based on N-port Network Representation

This article revisits the ideal transformer model used at the interface between different sub-grids in non-structured TLM meshes. The derivation of the voltage-exchange equations between two subdomains with an arbitrary meshing ratio and connection topology is presented. The voltage-exchange process is then generalized as a scattering-matrix of an N-port network that processes all desired properties for a perfect interface. This S-matrix is derived directly for its properties and no concrete physical representation is required. Moreover, the necessary conditions that ensure stability of such connection will be presented in the final submission.
Exploring Cancer Biomarker Genes from Gene Expression Data via Natureinspired Multiobjective Optimization

The identification of high-dimensional cancer genesis particularly important for cancer detection and early prevention. Many studies have been proposed to address cancer sub-type diagnosis, however, these methods often suffer from low diagnostic power and poor generalization performance. In this paper, we investigate a TLBO-based multi-objective hybrid algorithm (MOTLBO) to optimize six objectives including the number of features, the accuracy, the precision, two entropy based measures: sensitivity, specificity and comprehensive index Kappa index. First, the data are dimensionalzed by a mutual information-based approach, and then we employ a fractional encoding mapping to select a subset of informative genes to optimize these target functions. Secondly, different TF values are used based on the roles in the evolutionary process to seek Pareto solutions. Finally, a perturbation-based local search method is proposed to explore neighborhood regions with sparse high-quality solutions. To demonstrate the effectiveness of the algorithm, we apply it on cancer gene expression data and compare it ten classification algorithms.
Adaptive Pinning Control of Nonlinear Heterogeneous Networked Multi-Agent Systems with Delayed Communication Topology

The global bounded consensus problem of Nonlinear Heterogeneous Networked Multi-Agent Systems (NHNMAS)is investigated in this paper. Based on the methods of the linear feedback pinning control and the adaptive pinning control, globally bounded controlled consensus criteria is derived to relax the stringent conditions of the results before. The proposed consensus criteria ensures all agent dynamics approach to the desired dynamic trajectories in the sense of estimable boundary. The results extend the case of linear identical agent dynamics to the situation of nonlinear heterogeneous, and a large set of existing results can be obtained via less complicated computation. Finally, simulation study is performed to demonstrate the effectiveness of the proposed theoretical results.
IoT Based Agricultural Drones for Pest Control

Drones, also known as unmanned aerial vehicles (UAVs), have become more ubiquitous as a result of their capacity to work swiftly and have a wide range of applications in many situations in real life, use of UAVs for precise navigation. The scientific community has recently paid a lot of attention to farming. This study examines the use of drones to help with area of precision agriculture. This study significantly advances the field by examining communication protocols and using them. The agricultural process can be supported by the Internet of Things (IoTs). , which can scale from small to large farmers. The application of IoT in several aspects of agriculture has been examined in this research. Farmers are unable to stand in front of the enormous markets. Increased yield costs result in lower returns on investment for farmers. Drone proponents have long promoted precision agriculture, or crop management that makes use of Big data & GPS, as a method to boost agricultural productivity which is to be addressing the world's food and water issues.
Security Challenges in 5G Network: A technical features survey and analysis

5G networks control new technical concepts like ultra-low latency, ultra-high bandwidth, ultra-reliability, ultra-massive device access and manage the continually rising demands of diverse applications. The architecture and hierarchical framework of 5G networks need to understand and introduced. Security has been a primary focus for many telecommunications businesses since risks might have serious consequences for their front-line applications. A new class of security issues will be raised by network softwarization and new technologies including software-defined networking, network function virtualization, mobile-access edge computing and network slicing. This paper emphasizes the security concerns that 5G will raise and demands urgent security fixes. We also discuss the future of safe 5G systems and security fixes for these problems. To increase network security, DDOS attacks are detected and mitigated using a variety of SDN approaches.
Characteristics of plasma turbulence and radio emission from an interplanetary shock wave

Aims. We investigate the characteristics of energetic electron beams, plasma turbulence and radio emission from interplanetary shock waves. Methods. Numerical calculations of spectra and Landau damping of hot plasma eigen oscillations in the magnetic field are used. Results. It is shown that the longitudinal wave spectrum, excited in the solar wind plasma, extends with the increase of the refractive index n over range of values n >10. This result allows us to explain the broad band of emission, the constant value of the average ratio of frequency-band to radio emission frequency from interplanetary shock wave fronts, and to estimate the electron beam density and amplitude of Langmuir waves at the shock. It is shown that a spectrum of radio emission is determined by the spectrum of Langmuir waves excited upstream of the interplanetary shock wave by heated electrons escaping from the shock wave front.
Development of an Open-Source Software Tool to Analyze Closed-Loop Control Systems

This paper presents an interactive software tool developed to be used in an entry level course of control system. This software tool is open-source and it was programmed using Python3 language. It was created with the purpose of offering students an alternative software tool with a low computational cost. It was made thinking in providing a great user experience to the students when they are learning about process dynamics and closed-loop system behavior based on proportional-integral and derivative controllers. In particular, the real-time simulation mode, provides to the user an interactive way to understand the meaning of each parameter in classical second order process models or in PID controllers and, hence it could become in a useful tool in careers as electrical engineering.
Use of periodic approximants in a dynamical LEED study of the quasicrystalline tenfold surface of decagonal AL-Ni-Co

The determination of quasicrystal (QC) surface structures is a challenge to current surface structure techniques. Low-energy electron diffraction (LEED) is the primary technique for the determination of periodic surface structures, but application of dynamical LEED to quasicrystals requires the use of many approximations. In this study, two different approaches were used to apply dynamical LEED to the structure of the tenfold surface of decagonal Al{sub 73}Ni{sub 10}Co{sub 17}. One method (method 1) involves the use of a quasicrystalline model along with approximations that average over the composition and local geometries. The other method (method 2) uses periodic models that approximate the actual local QC structure (approximants) in more exact, atomistic calculations. Although the results using the two methods were consistent, the results of the approximant analysis (method 2) suggested a different way to apply the approximations in method 1, resulting in a better fit between experimental and calculated beams. Thus, periodic approximant structure models can provide a simpler and more efficient method for the determination of local geometries in QC surfaces, and may also facilitate analyses using quasicrystal models.
A High Accuracy and Recall Rate 6D Pose Estimation Method Using Point Pair Features for Bin-picking

Bin-picking is one of the most common tasks for robot applications in industry, while point cloud registration is the most important part of it. Although much research has been done in the related field and obtained considerable achievement, it is still a difficult challenge since multiple target instances locate in the scene. In addition to higher recognition accuracy, it is also necessary to recall as many target instances as possible. In this paper, we improve Point Pair Feature (PPF) matching method and propose a new 6D target pose estimation method for point cloud registration. We adjust the computation of feature and the voting scheme of the original PPF method. Besides, we add a post-processing step after pose clustering to remove the wrong and overlapped poses. Furthermore, we test our method on sileane dataset and real data. The final experiments show that our method greatly improve the recall rate and accuracy, and can effectively remove the poses which overlap with each other or are mismatched.
Estimation of Road Surface Shape and Object Height Focusing on the Division Scale in Disparity Image Using Fisheye Stereo Camera

In this paper, we propose a novel algorithm to estimate road surface shape and object height using a fisheye stereo camera. Environment recognition is an important task for Advanced Driver-Assistance Systems (ADAS). However, previous studies use sensors with a narrow measurement range. They also use the constraint that the road is flat and cannot deal with changes in the road slope. We use a fisheye stereo camera, which can measure wide and dense 3D information, and design a novel algorithm focusing on the scale of division on the disparity image to overcome these defects. Experiments show that our method can detect an object. Furthermore it is shown that measurement accuracy of obstacle height and distance to obstacles strongly depends on the 3D measurement accuracy.