Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Spatial Information Based Semisupervised Classification of Remote Sensing Images

Motivation in the use of semisupervised learning method is because of its ability to strategically explore and use abundantly available unlabeled samples along with the limited number of labeled samples, as seen in the remote sensing (RS) imagery. In this direction, the present article proposes a semisupervised classification model with spatial information based self-learning methodology to classify land covers in RS images. The model uses granular neural network (GNN) as the base classifier because of its customizable network architecture that is functionally interpretable and costs less computational complexity. Architecture of GNN is governed by fuzzy if–then rules that are generated from fuzzy granulation of input feature space. We have used an improved spatial neighborhood learning method for better understanding of data distribution in a semisupervised framework. The method collects the information with collaborative opinions of two independent information extraction approaches, i.e., based on mutual neighborhood criteria and class map of unlabeled samples. Superiority of the proposed model with existing methods are established with different RS images in terms of various performance measurement indexes.
A Planar Circular Polarization Reconfigurable Trapezoid Monopole Antenna with Wide Axial-ratio Bandwidth for GNSS

A circular polarization (CP) reconfigurable monopole antenna with wide axial-ratio bandwidth (ARBW) for the Global Navigation Satellite System (GNSS) is presented. The antenna consists of two trapezoid monopoles placed symmetrically, a switchable feed network, and a right-angled isosceles triangular ground plane. The trapezoid monopoles are meandered to make the antenna more compact. The monopole radiator is fed asymmetrically with respect to the ground plane central line to generate CP. The two PIN diodes of the switchable feed network determine which monopole on the left or right side is fed by RF signal, thus generating right/left-handed CP (RHCP or LHCP). These two PIN diodes are controlled by the bias-T, so there is no need to use any additional biasing line. It eliminates the effect of the DC biasing lines on the RF performance. The simulated and measured return loss, axial-ratio, radiation patterns, realized boresight gains, and efficiencies are studied. Measured results show that the antenna can operate from 1.17 to 1.74 GHz (0.57 GHz, 39.2%) in RHCP and from 1.18 to 1.74 GHz (0.56 GHz, 38.4%) in LHCP covering the whole frequency bands of GNSS.
Detection of Counterfeit News Using Machine Learning

Counterfeit news or hoax news has been one of the prevalent problems that global society has faced. The differentiation between counterfeit news and real news has stayed one of the hardest problems to tackle. The proposed system comes up with models of different machine learning techniques for differentiating fake news and real news. This approach utilizes four different models of machine learning: Neural Network, Naive Bayesian, Support Vector Machine (SVM) and Long and Short-Term Memory(LSTM). The dataset that we are using is obtained from Kaggle(Fake News). We start with text normalization of the data(news) in the dataset. Text normalization includes stopping words removing, removing punctuations and other diacritics, converting all letters to lowercase, etc. The second step is feature extraction. Doc2vec model is used in order to represent the data as feature vectors. These vectors are the inputs of Naive Bayes, SVM, Neural network and LSTM. Every one of these algorithms will be assessed to determine which algorithm will yield the most accurate result. Among the four algorithms, LSTM proved to be the best with an accuracy of (92%).
A Reconfigurable Circular-Ring Patch Antenna with Pattern and Polarization Diversities

A reconfigurable circular-ring patch antenna with pattern and polarization diversities is proposed. The reconfigurable antenna has a simple two-layer structure with three PIN diodes mounted on it. The circular-ring patch is printed on the upper layer. An open-ended microstrip stub is introduced on the lower layer with the purpose of impedance matching. By switching on/off states of the PIN diodes, the antenna can be reconfigured to have conical radiation or broadside radiation. Besides, the antenna achieves vertical polarization (VP) for conical radiation mode and provides two orthogonal linear polarization for broadside radiation mode. The overlapped impedance bandwidth for the three modes is 2.35-2.64 GHz. The antenna shows the advantages of simple structure, easy fabrication and low cost, suitable to be used in wireless communication systems.
Combining Stereo Vision and Inertial Navigation for Inspection Spacecraft

This paper proposes a Kalman filtering scheme for relative navigation of inspection spacecraft. The proposed scheme incorporates multiple sensor measurements, such as stereo vision system, global navigation satellite system and inertial navigation system. The multiple sensor fusing feature makes the given method is applicable to both cooperative and non-cooperative relative navigation scenarios. To deal with unknown non-cooperative pose parameters and uncertainties, a detailed relative error dynamics and its linear form is carefully derived, and the presented filtering formulation can obtain both the target’s pose and attitude parameters that includes sensor and target’s body moment of inertia.
Fast Wide Angular Sweeping of Electromagnetic Scattering from Rough Surfaces

A fast method to study the angular response of the random rough surfaces is proposed in this paper based on a randomized low-rank matrix decomposition. The stochastic characteristics of the scattering from the random rough surfaces under tapered wave incidence are studied numerically. The rule of selecting the number of incidents to obtain a high spatial resolution is investigated. Numerical examples demonstrate the performance of the proposed angular sweeping scheme.
A 20MHz Supply Modulator Designed for Envelope Tracking Power Amplifier at 42GHz

The work presented in this paper is to demonstrate a method that improves the efficiency of a 42GHz monolithic microwave integrated circuit (MMIC) power amplifier using envelope tracking (ET) techniques. A linear amplifier with wide bandwidth is used as the supply modulator for the MMIC power amplifier. And the MMIC power amplifier is tested under fixed DC supply as well as ET modes using a 5.8dB PAPR 20 MHz 16QAM signal. The experimental results showed that the MMIC power amplifier achieves 6% improvement in efficiency. Digital pre-distortion (DPD) is used to linearize the MMIC power amplifier. After DPD adjacent channel leakage ratio (ACLR) can reach −44.5dBc with an improvement above 6dB.
Dual Clutch Transmission Plant Modeling in AVL Cruise

It is aimed to develop a vehicle plant model with a detailed dual clutch transmission (DCT) model. AVL Cruise and Matlab/ Simulink is used as simulation environment. AVL Cruise is a powertrain simulation software in which it is possible the model different powertrains with a special library contains modules. It is used to develop the plant model and simulate it with different driving cycles. Then the model is implemented to Matlab/ Simulink as an S-function. Matlab/ Simulink is used to test the plant model with a transmission control unit (TCU) software. The simulation results in Matlab/ Simulink is compared with the real vehicle data to validate the model in AVL Cruise.
Experimental Demonstration of Conversion-Based SNNs with 1T1R Mott Neurons for Neuromorphic Inference

SNNs using the conversion-based approach could benefit the energy efficiency of inference and retain high accuracy of DLNs. However, transistor-based spiking neurons and synapses are not scalable and inefficient. In this work, a Mott neuron with 1T1R structure is designed to meet the requirement of the conversion-based approach, whose spiking rates dependence on voltage naturally implements the rectified linear unit (ReLU). Based on the 1T1R Mott neuron, we experimentally demonstrated a one-layer SNN (320 ×10), which consists of RRAM synaptic weight elements and Mott-type output neurons, for the first time. Attributes to the rectified linear voltage-rates relationship of the 1T1R neuron and its inherent stochasticity, 95.7% converting accuracy of the neurons and 85.7% recognition accuracy in MNIST datasets are obtained. At last, a neuron X-bar architecture is proposed for parallel multi-tasking and better system integration.
Forecasting of Electric Energy Consumption for Housing Cooperative with a Grid Connected PV System

Our research aims to develop an adaptive control system for photovoltaic systems with energy storage that adapts after changing different kinds of conditions. In particular, for efficient controlling of battery storage, the precise prediction of electricity consumption is required. Due to the complexity of the proposed research, in this paper, we proposed the simplification of the complexity of the long short-term memory model for the forecasting of the electric energy consumption from a house cooperative in Karlstad, Sweden. Based on the experimental results, there is a 1.233 kWh of mean absolute error and 1.859 kWh of root-mean square error for the predicted energy consumption (validated from testing data collected 7 days after the collected training data for the selected deep learning model).
Research on Obstacle Detection and Tracking Method of Unmanned Sailboat Based on Millimeter Wave Radar

Unmanned sailboats provide a new observation platform for marine environment monitoring. Obstacle detection and tracking is the basis for the independent navigation of unmanned sailboats. Based on the comparison of common obstacle tracking sensors, this paper focuses on the detection and tracking method of unmanned sailing obstacles based on millimeter wave radar. This method can identify obstacles in the detection area and track them. Tracking can provide a basis for autonomous obstacle avoidance planning. The millimeter wave radar detection system is constructed and experiments are carried out. The results show that the method has high recognition accuracy and good tracking effect, which can provide basic information for unmanned sailing obstacle avoidance path planning.
A Survey on Preventing Crypto Ransomware Using Machine Learning

Because of the changing behavior of ransomware, ordinary sort and identification systems do not effectively discover new variations of ransomware. Here the use device getting to know classification to perceive modified editions of ransomware primarily based on their conduct. To conduct the take a look at, here used behavioral reports of a hundred and fifty ransomware samples from 10 exceptional ransomware families. Our data set includes some of the most up-to-date ransomware samples available, providing an assessment of the category accuracy of device studying algorithms on the present day evolving repute of ransomware. Two primary parts of this observe are identity of the behavioral attributes swhich can be used for choicest class accuracy and type of ransomware the use of machine learning algorithms. After classifying the ransomware editions, a prevention mechanism is also completed to the cryptographic ransomware variants.
A narrow bandwidth detection method for inter-harmonics in Tokamak power supply

Low-frequency harmonics which are close to the fundamental have characteristics of small amplitude and non-stationary state, and contain complex side-lobe inter-harmonics. To ensure the precise and real-time extraction of the whole spectrum band, the compensation measure is difficult. In order to solve this detection problem, a hybrid detection algorithm is proposed based on elliptic digital bandpass filter with boundary characteristics and single-frequency notch filter with optimal Q-factors, which can effectively extract the according band. Single-frequency notch filter has excellent third-order harmonic suppression ability and dynamic robustness characteristics. Elliptic digital bandpass filter can extract harmonics of target frequency band and ensure the phase offset, so as to achieve effective spectrum separation. Simulation with MATLAB is adopted to improve the design process. The validity of the detection algorithm is verified by experiments with digital analysis system based on DSP.
Where are You? Localising Stationary Nodes with Limited Information

Low-Power wireless technology has allowed the free and flexible deployment of embedded systems in a variety of scenarios. As more and more systems get deployed and an increasing number of devices are introduced in scenarios with already existing infrastructures, it becomes increasingly important to understand system properties with limited information and intervention from the user. In this paper, we focus on understanding the positioning of wireless devices in an indoor environment with use of connectivity information and a physical map of the scenario as well as optional anchor devices to further increase accuracy. With limited information, we explore how the location of devices can be estimated through the use of force-directed graphs augmented with specific domain knowledge. By looking at different indoor testbeds and challenging simulated scenarios, we investigate to what extent we can infer the correct positioning. We discuss the assumptions that needed to be made, thus exploring to what extent our approach can be employed in practice. In conclusion, this paper shows that it is possible to position devices correctly with only limited user intervention, having the network identify the displacement of its own components and supporting further reasoning about system properties.
State of the Art in Simulation Software for Health Monitoring through Vehicular Ad-Hoc Network

Communication Technology has made considerable progress which was previously wired, now it has been wireless. In today’s time, in every country, roads of every city are full of vehicles. Traffic jam problems are everywhere. Lack of road travel safety has imposed a threat to human lives and our environment as well. Providing road safety is becoming a serious challenge. A vehicular ad-hoc network is an important application of wireless technology that has attracted the attention of academicians and researchers. Intelligent Transportation System(ITS) cannot be conceived without Vehicular ad-hoc networks. VANETs provide the safety on the highway network by sharing beacons which contains vehicles information like direction, position and its speed. VANETs can provide warning messages to driver about risky situations such as traffic jams, accidents, road conditions. Traffic signal violation, Blind spot warning, Pedestrian crossing information, Lane change warning, Forward collision warning, Curve speed, Wrong way and Emergency vehicle service are some of the useful safety application of VANETs. Even more VANETs can provide Travelling Comfort services like automatic toll collection, information about roadside restaurant or parking zone, downloading routing map and movies, playing games and sending driver’s physical health information via wireless body sensor network (WBSN) to nearby ambulance or hospitals. Making, deploying and testing of VANET on real ground for experimental purpose requires lot of budgets and efforts. Therefore simulation is preferred a good alternative before to actual execution. Academicians and Researchers have developed many VANET simulators to study and examine various kinds of emergency warning, data dissemination and routing protocols. When simulating VANETs we should consider specials textbf{characteristics of VANETs VANETs simulation requires different approaches than Mobile Ad-hocnetworks (MANET’s) simulations because of drivers’ behavior, unstable vehicular speed and mobility, restricted vehicular movements, multi-path fading, traffic lights and traffic congestion. Therefore VANETs requires traffic flow simulator and network simulator both for better and close to reality as possible results. Many traffic flow simulator and network simulator are now available easily but because of compatibility issues some of them have integration problem. Currently there are VANET simulators that provide GUI in which traffic flow simulator and network simulator can be integrated. In this paper authors presents licenses based and open accessed traffic flow simulator, network simulator and VANETs simulators.
Development and Applications of a Four-Volt Josephson Arbitrary Waveform Synthesizer

We have recently created a 4 V rms cryocooled JAWS (Josephson Arbitrary Waveform Synthesizer) using 204,960 nearly identical Josephson junctions (JJs) that are embedded in coplanar-wave guides. The JJs are pulse-biased at repetition rates up to 16×109 pulses per second to create quantum-accurate, calculable AC waveforms at frequencies from DC to greater than 1 MHz. This system has metrological applications including in precision ac voltage calibrations, comparisons of arbitrary impedances, and ac power measurements.
RL-Sketch: Scaling Reinforcement Learning for Adaptive and Automate Anomaly Detection in Network Data Streams

When network is undergoing problems, such as DDoS attack, component failures, etc., the detection of heavy flows (e.g. heavy hitters and heavy changers) is much more critical. However, it has been increasing challenging to ensure the accurate detection of heavy flows while dealing with massive network traffic volume, diversified traffic distribution and the stringent memory requirement. Although recent research efforts like LD-Sketch are scalable for diverse network traffic, they depend on excessive memory to maintain high accuracy, such that they fail to work well when the memory is limited. We propose RL-Sketch, a adaptive sketch using reinforcement learning in detecting heavy flows. It predicts potential heavy flows based on the statistics of network traffic, to achieve both high accuracy and scalability with minor memory. Trace-driven evaluation shows that RL-Sketch achieves higher accuracy than state-of-the-art sketch-based technologies with up to 17.79× accuracy gain, while maintaining high robustness in extreme conditions.
Challenges and Issues in Securing Data Privacy in IoT and Connected Devices

In today’s world, we are surrounded by many Internet of Things devices that are continuously transmitting data from one to another devices and vice-versa. This data may be used to analyze a pattern of something the intruder may be interested of. As the IoT devices uses low power protocols to transfer or exchange data between devices, they are vulnerable to the hackers more than any other devices. In this paper we’ve examine the different scenarios in a normal day to day life with the devices that are common in India like Smart Bands, Smart Watches, Home Automations Devices like Google Home, Amazon Alexa etc. which also includes the privacy related challenges that may affect the user privacy or how the data may be manipulated for other causes. This paper concludes the reports and suggestions related to Internet of Things and Data privacy in India which may help shaping the future of internet of things without compromising the privacy of the user data.
Energy Efficient Mac and Routing Protocols – A Survey

Energy is one of the important constraints in the wireless sensor networks. Now a days the booming technology is Internet of things (IoT). According to IoT experts’ group all the things can communicate to themselves with or without human intervention. IoT means communication of everything. Hence energy plays a very important role. The communication and routing protocols has to be energy efficient. Hence the paper briefs about the energy aware MAC protocol and their working towards consumption of energy. The IoT is emerging technology which requires powerful protocols for reducing energy consumption. This paper describes the taxonomy of the Routing protocols and explains the different routing protocols according to the classification. It briefly gives the working of protocols and draws the advantages and drawback MAC and Routing protocols.
Design of a Filtering Dielectric Resonator Antenna with Two Radiation Nulls

A high-gain filtering dielectric resonator (DR) antenna (DRA) with two radiation nulls is proposed. It is fed by a microstrip-couple slot from the bottom. Two radiation nulls in stopband which realize filtering function are generated by a shorting via which needs a separation in the slot and a pair of transverse stubs. For realizing high gain, an additional piece of dielectric is placed under the dielectric resonator. Consequently, a high-gain filtering dielectric resonator antenna is obtained with no filtering circuits. As the results of simulation, the impedance bandwidth $(\vert \mathrm{S}_{11}\vert < -10\ \text{dB})$ of the DRA is from 5.21 to 6.66 GHz. The simulated average gain is 9.5 dBi and the peak gain of 10.2 dBi within passband.
Electromagnetic energy harvester

Energy harvesting is the technology which gathers and converts external energies such as light, vibration and heat which are disposed into reusable electrical energy and uses such electrical energy. The vibration energy harvesting is very interesting technology because it produces very high density of energy and unaffected by the climate.This paper presents an approach to linear magnetic generators for use in environments that present high amplitude, low frequency vibrations. A prototype has been developed and several efficiency improvement ideas have been investigated. Experimental vibration shaker test is conducted to assess the performance of the energy harvester.
A Review on Beamforming Techniques for Full Duplex MIMO Relaying Networks

The tremendous growth of wireless networks using wideband services brought significant improvement in channel capacity and data rate of communication systems. Signal quality and interference free transmission and reception is necessary for wireless networks employing relay based communications. Beamforming plays a vital role in placing an antenna towards the signal source so as to suppress the interferences and enhance desired signal quality. Many fundamental problems in multi user communication through relay nodes can be mitigated by using proper beamforming algorithms. This paper deals with recent research contributions on different beamforming algorithms, architectures, performance metrics and joint beamforming approaches. Furthermore, the paper provides a comparative analysis of recent beamforming techniques used in MIMO relay networks and their respective benefits and drawbacks. Finally, some future directions to improve the beamforming performance in terms of some optimisation targets are also discussed.
Patellar complications in total knee arthroplasty. A literature review.

: Increased implant longevity, improved range of motion, and the use of total knee arthroplasty in younger and more active patients have placed greater demands on the patella component. Commonly observed complications of the patellofemoral joint are crepitation, subluxation, excessive wear, loosening, and persistent patient discomfort. These complications have been attributed to inadequate prosthetic design, fixation techniques, anatomic abnormalities, and surgical error. This paper reviews the most prevalent complications and discusses their causes and prevention.