Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploiting Markov Random Field Sparsity for Wideband Channel Estimation in Massive MIMO Systems

This paper studies the wideband channel estimation (CE) problem in massive multiple-input multiple-output (MIMO) systems employing orthogonal frequency division multiplexing (OFDM). We first develop an angle-delay domain channel model with the sampled steering vectors. Then we propose a Markov random field (MRF) prior to flexibly model the angle-delay domain channel sparsity. The constrained Bethe free energy minimization (CBFEM) framework is introduced to design a message passing algorithm for the CE problem. Finally, simulation results validate the superior performance of our proposed algorithm.
Design of Battery Charger with IoT Capabilities for Electric Bikes

This paper describes the design process of a microcontroller-based portable battery charger that has an Internet of Things (IoT) capability to monitor the charging process. A standard DC-DC buck converter is used as power converter to give a DC charging current to a battery, which is controlled by ESP32. At various points of operation, the micro controller can also send various data to a remote server for monitoring purposes via the Global System for Mobile Communications (GSM) network. It can also send its current location to the server. A rudimentary web dashboard is also made to monitor the charger's state of operation at any given time. The designed system is able to utilize a single microprocessor to simultaneously control the charging process, and also to communicate with the remote server. The charger itself has a maximum output of 650 watts (65.5V, lOA). Experimental works have been done to verify the proposed design
Towards adoption of secure communication protocol in Software Defined Networks

Software Defined Networking (SDN) decouples the forwarding data plane from the network control plane to provide centralized control and programmability of the data plane elements like switches and routers. Traditionally, this commu-nication between control plane and the data plane (southbound communication) for e.g., using OpenFlow were based on the non-secure protocol like transmission control protocol (TCP), which over-the-years resulted in several security incidents. In order to facilitate secure data communication, the adoption of transport layer security (TLS) has become unavoidable. To this extent, we first present the key qualitative aspects and suitability of using TLS 1.2 and the newer TLS 1.3 for southbound communication. Further, we present extensive quantitative evaluation on Mininet emulator testbed to assess the performance impact of using the TLS 1.2 and TLS 1.3 (for most widely used cipher suites) over TCP to secure the controller-switch communication. Our work shows that the adoption of secure communication channel TLS incurs significant overheads (2 - 6 ×) when compared to baseline TCP (unsecure channel), while TLS 1.3 adds marginal overheads in terms of latency and throughput (≈ 5 %) in comparison to TLS 1.2. Also, we observed that the memory and processing (computational cost) overheads with TLS 1.2 and TLS 1.3 to be negligible, even when supporting a large number of flows. Further, we also discuss the potential adoption of QUIC protocol as an alternative to provide high performance secure communication for the southbound interface.
A new solution for the least squares problem

This paper presents a new iterative method to obtain the generalized inverse of a continuous linear mapping. This method gives the solution of the least squares problem and, from numerical point of view, it is a very efficient one. Our method generalizes the results from [3] and [6] and it is not based on the QR or SVD decompositions ([1, 5]).
Point-BLS: 3D Point Cloud Classification Combining Deep Learning and Broad Learning System

3D object recognition and detection based on point clouds is an important research topic in computer vision and autonomous navigation. Nowadays, deep learning algorithms have significantly improved the accuracy and robustness of 3D point cloud classification. However, deep learning networks usually suffer from complex network structures and time-consuming training process. In this paper, we proposed a 3D point cloud classification network Point-BLS, which combines deep learning and broad learning system together. Specifically, we first extract point cloud features through a deep learning-based feature extraction network, and then classifies them with the broad learning system. Experiments on the ModelNet40 dataset showed that our proposed network can achieve high 3D point cloud recognition accuracy of over 87%, which is better than that of a pure deep learning network with an identical backbone. In addition, the shortest training time of Point-BLS is 10.31 seconds in our experiments.
The Internet of Things Security Issues and Countermeasures in Network Layer: A Systematic Literature Review

The Internet of Things (IoT) is a known term in recent communications where a system of physical objects connected to the internet is capable of collecting, creating, and transferring data easily without human assistance or intervention. IoT contributes to facilitate the communication that can contain sensitive data, therefore, it becomes a desirable target for adversaries to establish various attacks. Therefore, IoT security should be maintained and enhanced in the physical, Perception, Network, and Application layers. Many researches were conducted to investigate IoT security issues and formulate proper solutions and countermeasures that can address these issues in general. This study was conducted using the Systematic Literature Review methodology to provide an overview of conducted research about IoT security issues in the network layer specifically and proposed proper countermeasures and solutions to address these issues as well as providing future direction for further investigation and research in IoT security.
VIBRATIONAL PROPERTIES CHARACTERIZATION OF MOUSE EMBRYO DURING MICROINJECTION

To determine the vibration characteristics (natural frequencies and mode shapes) of a mouse embryo during microinjection the modal analysis is used. The spherical mouse embryo 60 µm in diameter is modeled as elastic finite elements biostructure consisting of 6µm thick micromembrane and 38 µm in diameter nucleus. Embryo modeling and modal analysis were based on the use of the finite elements method in the modal analysis system of ANSYS software. The modal analysis was carried out for first six modes of embryo natural frequencies. The numerical analysis of dependence of embryo own frequencies on the boundary conditions and external loads are presented. The relevant illustrations of the typical variations of the shape, deformation and particle velocities of vibrating embryo are given.
A Visual Identification Method of Analog Instrument Panel Based on Faster R-CNN

Intelligent inspection robots are being widely used in unmanned gas stations. However, the complicated background environment is a disturbance for most of the current visual identification method of analog instrument panel. It is challenging to extract pointer centerline and surface area. The existing methods cannot satisfy the actual demand of gas stations to get the current problems solved. In this work, we presents an object detection algorithm based on faster R-CNN method and designed a two-stage automatic identification method of analog instrument panel combined with conventional computer vision algorithms. First, the improved faster R-CNN is utilized for the detection of the target dashboard area. Then, the image is preprocessed based on some image processing methods. Moreover, the pointer’s centerline is detected by the algorithm based on Hough transformation. Finally, the meter identification is calculated by the angle calculation method. The performance verification and analysis of the algorithm demonstrates that the method proposed in this work is reliable and efficient for automatic identification of instrument panel in the working condition of gas stations and provides a quite practical method for target detection and identification of instrument panel.
Reflectarray Antenna Direct Optimization Using Surrogate Models with Several Geometrical Degrees of Freedom per Polarization

In this work, surrogate models based on support vector regression (SVR) of a multi-resonant unit cell with several degrees of freedom (DoF) per polarization are trained and used in a reflectarray antenna design and optimization. Since the unit cell has multiple sharp resonances when considering several DoF, the training process is carried out in a hyper-rectangle around a plane of stability. Results of SVR models with four geometrical DoF are shown to provide highly accurate results for the design and analysis of a very large contoured-beam reflectarray for space applications. The direct optimization layout with the surrogate models allows to improve the cross-polarization figures of merit several dB.
Substitute Tool Select Method using Contact Force Data Trained Neural Network Model

In this study, we propose a method for selecting substitute tools using force data. The majority of investigations in this area have employed geometric data. However, it is possible to select tools with the same function but different shapes if the substitute tools can be estimated using force data. For example, if a robot intends to hit something with a hammer, it can find a substitute tool, such as a stone instead of a hammer. Our approach first measures the force between the original tool and manipulated objects via tele-operated simulation and then evaluates candidate substitute objects based on their ability to match the demonstrated force profile. The force data at each contact point on a three-dimensional tool was projected onto a two-dimensional force map, and then the force map was input to a classification head using a multilayer perceptron to find a substitute tool as a classification problem. Considering the high dimensionality of the force map, we also investigated the application of a force map encoder using an auto-encoder or convolutional neural network. These three types of substitute tool estimation were performed using a striking motion with nine candidate objects, resulting in a recall value of over 78%.
TRAM: An Open-Source Template-based Reconfigurable Architecture Modeling Framework

Coarse-grained reconfigurable architecture (CGRA) is a promising accelerator design choice due to its high performance and power efficiency in the computation or data-intensive application domains, such as security, multimedia, digital signal processing, machine learning, and high-performance computing. CGRA consists of coarse-grained processing elements (PEs) and interconnects that determine the architecture flexibility to support different applications and also affect the performance and power efficiency significantly. Although multiple types of interconnects have been proposed, a parameterized unified model is still lacking. In this paper, we propose a flexible and scalable CGRA template with a novel interconnect model that can unify the typical neighbor-to-neighbor, switch-based, and FPGA-like interconnects. Furthermore, we present TRAM, an open-source template-based reconfigurable architecture modeling framework that integrates the Chisel-based CGRA modeling, architecture intermediate representation (IR) and Verilog generation, dataflow graph (DFG) mapping, simulation, and evaluation. The mapping flow contains graph-based placement and routing, critical-path-driven data synchronization, and simulated-annealing-based optimization. We evaluate the impacts of the rich design parameters, which demonstrate the significance of such a flexible template to facilitate architecture optimization. Compared with the related work, TRAM can achieve a 4.1× smaller DFG latency and a faster mapping speed for both the 8×8 and 16×16 CGRAs. Moreover, TRAM is able to attain an extremely high PE utilization of 94.4 % on average by architecture tuning.
Design and modeling of a waiter robot

The science of robotics is considered one of the most practical sciences in all fields. The application of this science is visible in all kinds of work fields and related fields, from construction activities to activities in the fields of medicine or even social services. One of the social services that are very widely used, is delivering items and orders to customers. This work is the duty of people who are called waiters. This job has very few benefits for people working in this field. Also, things like illness can cause some delay in the employer's work or not complete his work in some cases, also in situations such as when contagious diseases have spread, the direct communication of people within a short distance can cause more spread of the disease. The devices ordered by the customers could increase the speed of work and have a low-risk connection, the costs of the employer could be reduced, perfect service could be given to the customers, and the workforce could be employed for more useful work. This robot is specifically designed to use for reception in the conference hall of the growth center of Kharazmi University to receive the people present in this conference hall, but as mentioned above, these robots can be used in other places such as hospitals for delivering medicines to patients, also can be used in restaurants to deliver customer's orders to them. With this replacement, the speed of catering increases, at the same time, there is no lack of accuracy, and the issue that becomes more important with the spread of the contagious disease Covid-19 is hygiene, which can achieve several important goals in this field with this replacement. Specifically, during the reception, the distance between the host and the guest is less than one meter and is unsafe. Also, there is a possibility that each of the parties is a carrier of contagious diseases, and these problems are solved by this replacement.
Design of WSNs Sensor for River Water Quality Monitoring System for Neolissochillus thienemanni sumateranus Lifes

In order to raise fish and provide them with healthy habitat, proper water quality is essential. This article outlines the creation of a river flow monitoring system that uses wireless sensor networks to track the number of important characteristics (WSNs). The WSNs-MS devices are deployed at three distinct sites in the same river, far from the monitoring station, and use solar panels as a source of recharging the battery. Each WSNs-MS will gather information, store it, and send it to the WSNs-MS sink node, which is a web server running on a Raspberry Pi and acts as a local database for information and monitoring systems. These WSNs-MS were developed and tested, and the outcomes demonstrate that each WSNs-MS is reliable.
Localization of Mobile Robot by Particle Filter Considering Shape Information of Environment and Location of Water Puddles

In this paper, we propose a method to estimate robot pose accurately by considering environmental features for the particle filter. Particle filters have been widely used for pose estimation of mobile robots. However, the accuracy of the self-positioning estimation is degraded in environments where the shape features are scarce. Therefore, a novel likelihood function is designed by taking into account the environmental features for the particle filter. We improve the accuracy of the pose estimation by fusing the likelihood of the shape information and the likelihood of the binary attribute information in the water puddle for the particle filter.
Classification of Covid19 Vaccine-Related Tweets Using Deep Learning

In recent years, the majority of the world's population has been impacted by the COVID-19 pandemic, but owing to the invention of vaccinations, the epidemic has been brought under control. Most people are hesitant to share their experiences on official platforms after being vaccinated. As a result, information about vaccine-related adverse effects other than clinical trial results is challenging to identify. However, most people have shared their opinions about vaccines on social media since the COVID-19 immunization program began worldwide. This study aims to assess, using social media, the adverse effects of the COVID-19 vaccination as perceived by the general population. The authors of the previous studies did not categorize tweets on the COVID-19 vaccine adverse effects as personal experience, informative, or advice-seeking. The authors of this study aim to classify tweets in the manner described above to fill a research gap and increase public awareness of the COVID-19 vaccine's side effects. The Kaggle repository collected tweets pertaining to COVID-19 vaccinations for this investigation. The authors manually classified collected tweets into two categories: those connected to COVID-19 vaccinations' adverse effects and those unrelated to COVID-19 vaccines' adverse effects. Then, valid tweets were further classified into three categories: personal experience, informative, and seeking advice. The authors then used the data to train four ML models. There are also SVM, Logistic Regression, LSTM, and ANN. The LSTM algorithm generated the most outstanding results, with an accuracy of 97.64&. In addition, the researchers conclude that the SVM may not be suitable for planned research since it gave the lowest degree of accuracy, 80%.
A Transport Simulation-Based Analysis of Charging Station Occupation for Long-Distance Route Planning of Battery Electric Vehicles

Battery electric vehicles (BEVs) are on the advance in many countries worldwide. A major concern of potential customers is battery electric range. This is tackled strongly by car manufacturers with technological progress in batteries and efficiency to increase range and decrease charging times. The next challenge to overcome is charging station occupation, which can lead to long queues and waiting times. This is already experienced on heavy traffic routes or busy travel days like during holiday season. Furthermore, in Germany the amount of BEVs grows faster than the amount of high performance charging stations (HPCs). In this work a new simulation framework based on MATSim is introduced to analyze charging station occupation for long distance route planning of BEVs within Germany. In a scenario analysis it is shown, that combined with egoistic decentralized route planning, long queues can occur. The simulator provides the ability to develop, test and benchmark fleet based route planning algorithms to reduce waiting times.
Robust sliding model predictive control with disturbance compensation in application to AUV pitch-axis motion

Constraints on the control input and system states as well as uncertain disturbances are the practical challenges in many motion control systems. In this paper, a disturbance observer (DO) with two-step disturbance compensation algorithm is proposed to deal with disturbances and enhance the robustness of the controller, while a discrete sliding mode predictive control (DSMPC) scheme is designed to handle saturation constraints on the control input and system states with the application to an autonomous underwater vehicle (AUV). The DSMPC combines the model predictive control (MPC) with the discrete sliding mode control (DSMC) using linear matrix inequalities approach. The stability of the proposed DSMPC are proved via Lyapunov stability theorem. Finally, simulation results are presented to illustrate the effectiveness of the whole control scheme.
Recycling of Rubber Scrap Tyres and Its Processes of the Utilization

Solid waste treatment is becoming a critical issue for the environment. According to estimates, the amount of garbage produced globally would more than quadruple by 2050 and triple by 2100 compared to 2016. Tyres is an integral component of transportation facilities. Tyre production accounts for 70% of the production of natural and synthetic rubber. The requirement and production of rubber is ever increasing. India exported rubber scrap products worth 28.5 USD Million to the overseas market. Use of rubber waste and recycling practice can reduce environmental footprints. The amount of carbon dioxide is reduced by 323 pounds as a result of recycling. Numerous physical and chemical procedures that aim to recover materials, energy, or chemicals from used tyres can be used to recycle them and convert them into useful products. Various techniques are available and are being explored for tyre recycling. Rapidly increasing rubber demand and consumption calls for effective and flexible methods for recycling of rubber waste including tyres. Recycling can reduce 10 to 30 % virgin rubber demand. Improper dumping and storage of rubber waste adversely affects the hygienic condition of the area. Waste tyres serve as a breeding ground for more mosquitoes, which can spread disease. Large tyre stacks can pose fire risks, produce toxic acid fumes that are dangerous to human health, and leave behind a risky oily residue. One tyre has a lifespan of 50–60 years in atmospheric conditions and 1500–2000 years in water. If a tyre is burnt, soot and hazardous gasses such biphenyl, anthracene, and pyrene are released into the atmosphere. These gasses catalyse soil, air and water pollution. Recycling helps in reducing all these menaces serving twin purposes of reducing solid waste and synthesizing the products from waste material at low cost.
Real-time Optimal Allocation for Uncertain Time Coupled Resource Based on Reinforcement Learning

The allocation of the time coupled resource plays an important role in many applications, such as the charging/discharging control of electric vehicles, utilization of electromagnetic spectrum, etc. The availability of time coupled resource is usually uncertain and has limited working time and budget which makes its allocation non-trivial to be solved. In this paper, we use the electromagnetic spectrum allocation as an illustrative example to make better use of time coupled resource among different frequency demand while reducing the electromagnetic interference. However, the frequency demand is usually uncertain and the allocation is usually required to be made in real-time which makes this allocation problem difficult to solve. Therefore, we consider this important problem in this paper and make the following contributions. First, this problem is formulated as a Markov decision process (MDP) model which considers the uncertainty of the frequency demand. Second, in order to meet the real-time decision making of the frequency demand, a reinforcement learning approach with Gaussian process regression is proposed to solve the model. The Gaussian process regression is used to learn the optimal Q-factor in reinforcement learning. Third, the numerical results demonstrate the proposed method can improve the occupancy efficiency of the electromagnetic spectrum.