Miracle-dz/UniFAR-FTUs huggingface dataset:
SVM-based Detection of False Data Injection in Intelligent Transportation System

Vehicular Ad-Hoc Network (VANET) is a subcategory of Intelligent Transportation Systems (ITS) that allows vehicles to communicate with other vehicles and static roadside infrastructure. However, the integration of cyber and physical systems introduce many possible points of attack that make VANET vulnerable to cyber attacks. In this paper, we implemented a machine learning-based intrusion detection system that identifies False Data Injection (FDI) attacks on a vehicular network. A co-simulation framework between MATLAB and NS-3 is used to simulate the system. The intrusion detection system is installed in every vehicle and processes the information obtained from the packets sent by other vehicles. The packet is classified into either trusted or malicious using Support Vector Machines (SVM). The comparison of the performance of the system is evaluated in different scenarios using the following metrics: classification rate, attack detection rate, false positive rate, and detection speed. Simulation results show that the SVM-based IDS is able to provide high accuracy detection, low false positive rate, consequently improving the traffic congestion in the simulated highway.
Design of a Compact Power Amplifier with 18.6 dBm 60 GHz 20.5% PAE in 22 nm FD-SOI

This paper presents the design of a 60 GHz power amplifier (PA) in a 22 nm FD-SOI CMOS technology. To improve the performance at millimeter-wave frequencies by minimizing the parasitics around transistors, a compact gain cell layout is proposed, which also integrates neutralization capacitors. By utilizing two cascode stages the gain has achieved 30 dB. The PA delivers a saturated output power of 18.6 dBm at 60 GHz, while drawing only 351mW from a supply voltage of 1.8 V, corresponding to a peak power added efficiency (PAE) of 20.5 %. Benefit from the compact transformers for impedance matching the active circuit area is reduced to 166 um x 424 um = 0.07 mm2, which gives this PA one of the highest output power to area ratio $(\frac{Psat}{Area})$ among the state-of-the-art.
Pentacoordinate silicon in high‐pressure crystalline and glassy phases of calcium disilicate (CaSi2O5)

We report 29Si MAS NMR spectra for the monoclinic and triclinic crystalline phases of CaSi2O5. The latter, which is now known from single crystal x‐ray diffraction to contain pentacoordinate silicon (SiO5 or [5]Si) units, has an NMR peak for this group near to −150 ppm, for the first time definitively confirming previous peak assignments in the spectra of ambient and high‐pressure glasses. This result, and new data on a CaSi2O5 glass quenched from a liquid at 2300°C, 10 GPa, demonstrate that [5]Si groups, along with SiO6 ([6]Si) units, form at high pressures in silicate liquids with abundant non‐bridging oxygens, and are thus likely to be important for the properties such as viscosity and density of mafic to ultramafic magmas in nature.
A 0.4-1.7GHz Wide Range Fractional-N PLL Using a Transition-Detection DAC for Jitter Reduction

This paper proposes a fractional-N phase-locked loop (PLL) with a transition-detection DAC (TD-DAC) for jitter reduction. By introducing a TD-DAC, the sigma-delta modulator (SDM) quantization error of the PLL is compensated, and its overall jitter performance is improved. In order for the output frequency to cover the output frequency range of voltage controlled oscillator (VCO), the division ratio of the multi-modulus divider (MMD) is extended. Implemented in a 110nm CMOS process, the proposed fractional-N PLL dissipates 7.9mA from a 1.5V and 3.3V supply while occupying an area of 0.284mm2. It operates over a frequency range of 0.4-1.7GHz, and a division range 4-4048. The prototype chip achieves an integrated RMS jitter of 3.78ps (100Hz to 40MHz), and the long-term jitter of it is 3.52psrms and 22.1pspp.
An Iterative Approach to Automate the Tuning of Continuous Controller Parameters

Cyber-physical systems evolving in uncertain environment endure fluctuating dynamics during their lifetime. In such a variable context, controlling systems towards safety and system performances is challenging. In particular, controller tuning (finding optimal control parameters) is a challenging process due to the multiplicity of contexts to be considered. In this paper, we use a combination of model-driven simulation, dimensionality reduction, clustering and prediction techniques to define adequate control parameter settings. First, we propose to explore the controller behavior by simulating different configurations, a configuration is defined by a context (controlled process, environment, sensors, actuators) and a control parameters setting. From simulation results, a discretization is performed by binning the evaluation of quality of control. Then, we apply feature selection algorithms to identify contextual parameters that have a significant impact on performances of the controller. Considering only selected parameters, we finally carry out a clustering aiming at identifying for context domains an optimal control parameter setting. The approach is iterative to define the boundaries of the controller for a given context domain. For non simulated contexts, we propose a prediction module based on regression techniques.To evaluate the proposed approach, we compare it with classical control theory and we apply it to a proportional controller used for a leader/follower application. The experiment shows effectiveness in the identification of control parameters setting for different contexts.
A Miniaturized Bandpass Filter with Wideband and High Stopband Rejection Using LTCC Technology

A compact low-temperature co-fire ceramic (LTCC) bandpass filter based on quarter-wavelength steppedimpedance resonators (SIRs) and lowpass loading cells with improved wideband and high stopband rejection level is proposed and demonstrated in this work. In the implementation, two low impedance SIRs are placed on different layers, they generate strong capacitive coupling and form a wide passband. Further, the width of the stopband is improved by loading lowpass cells, while two extra poles formed to improve the passband response simultaneously. The final simulation proves the proposed filter has a passband bandwidth of 67.5%, while the circuit size is only 1.68mm×0.8mm×0.48mm, which has clear advantage in bandwidth and size to other similar LTCC based filters.
A 119 GHz Bandwidth Distributed Amplifier with a ±2 ps Group Delay Variation

This paper presents an analysis on the bandwidth and group delay variation of the artificial transmission line structures that are inherent to distributed amplifiers. An Indium-Phosphide (InP) distributed amplifier consisting of four buffered cascode cells is presented, which reaches a bandwidth of 119 GHz. The gain is 18.2 dB and the average in-band output 1 dB compression point is 10.9 dBm. The group delay has a variation of ±2 ps between 14 GHz and 119 GHz. To the authors knowledge this is the smallest reported group delay variation for amplifiers with a bandwidth beyond 50 GHz.
Low Cost IoT Based Weather Station for Real-Time Monitoring

The Internet of Things (IoT) is a modern innovation in today's world of the Internet, establishing an interconnection between devices and individuals and exchanging information through a shared platform. This paper proposes a low-cost water station based on IoT that can provide critical water information in real time, such as wind speeds and direction, temperature, humidity, and amount of rainfall. The use of low-cost but reliable controllers from Node MCU and Arduino UNO makes it accessible for a wide range of clients. The availability of information on a ThingSpeak web server guarantees the authorized person's access to data from anywhere in the world. A 3 cups anemometer and wind vane are used for wind speed detection and wind direction measurement, respectively. The DHT22 digital temperature and humidity sensor are used for the measurement of temperature and humidity. A custom acrylic glass rain gauge is designed for rainfall measurement. With the aid of a circuit, all weather data was de-bounced, and this de-bounced data was uploaded directly to the ThingSpeak server. This proposed design will solve the problem of the unavailability of weather information at different locations. Low-cost design, while ensuring reliable and accurate measurements, will encourage the use of the proposed design in many research and educational institutions as well.
Research and Application on Power Generation Safety Monitoring and Cloud Platform

Aiming at the problem that the traditional monitoring mode of power generation can’t monitor the normative behavior of operators as well as realize the comprehensive monitoring and intelligent decision-making analysis of power generation safety, this paper applies cloud platform, real-time database, OpenPose and TensorFlow to the power generation safety management system to monitor and manage the operation process of staff. The research shows that the application of cloud computing and deep learning technology in power generation safety monitoring system can significantly improve the compatibility, real-time performance and supervision ability of the system, and ensure the accuracy and recognition rate of data. And the application of human posture recognition can improve the safety of staff daily operations.
Circular Polarization Microstrip Antenna using Truncated Square Patches for IR-UWB Localization System

The design of circular polarization microstrip antenna using truncated square patch has been discussed, which is working in the frequency bandwidth ranging from 4.35 GHz to 4.84 GHz. Moreover, the 2×2 antenna array with a distance of about 0. 56λ (λ is the electromagnetic wavelength) is presented to get a good port isolation in order to fulfill the application in an impulse radio ultra-wideband (IR-UWB) localization system based on the angle of arrival (AOA) method.
Design of Indoor Fall Detection System for the Elderly Based on ZYNQ

Aiming at the problems of prevention of elderly fall damage, a kind of intelligent video surveillance system is designed in this research. CMOS camera transmits the collected video to the video processing module based on ZYNQ(ARM+FPGA). CNN (Convolutional Neural Network) image classification algorithm and hardware acceleration technology are used to process images in real time and identify whether the elderly have fallen. When the elderly falls, the system immediately transmit message about the elderly to the guardian via 5th generation mobile networks(5G). The results of experiments demonstrate the accuracy of this fall detection system is about 92%, response time for alarming is about 0.413s, system satisfices the requirement of real-time.
Microstrip Phased Array Antenna With Small Element Space for 5G Millimeter-Wave Applications

In this paper, a compact dual-layer wide-angle scanning 1×4 microstrip array operating at 28 GHz is proposed for 5G applications. The microstrip element is loaded with capacitive via fences to reduce the size of the element and widen the beamwidth. The element space of the array is merely 0. 34λ0 (λ0is the wavelength at 28 GHz in free space). U-shaped decoupling structure is introduced between the adjacent element antennas to reduce mutual coupling. The simulated beam scanning ranges from −50° to +50° within 3 dB gain variation, which is suitable for wide-angle scanning applications.
Additive Manufacturing of E-Plane Cut Extracted Pole Waveguide Filters With Frequency-Dependent Coupling Apertures

This paper presents additive manufactured extracted pole waveguide filters. The filters are realized with an E-plane cut, which allows the manufacturing with low cost fused deposition modeling (FDM) techniques. Classical extracted pole waveguide filters consist of at least one non-resonating node which couples the extracted pole cavity usually in the H-plane. However, this cutting plane introduces high losses especially if low cost manufacturing techniques are used for the fabrication. The design proposed here stacks the extracted pole cavity above (or even below) the non-resonating node. Advantageously, an extra transmission zero (TZ) apart from the one generated by each extracted pole section can be observed. Additive manufacturing techniques furthermore allow the realization of complex structures. The filters proposed here utilize up to two frequency-dependent coupling apertures, while each frequency-dependent coupling introduces one further TZ nearby the passband.
Road Accident Prevention by Detecting Drowsiness & Ensure Safety Issues

Driver’s drowsiness detection during driving is a car safety technology that helps to prevent accidents caused by the driver getting drowsy. Drinking alcohol and seat-belts not being fastened are also other reasons for road accidents. In this paper, the design of a system that can alert the drivers and stopping them from driving while being in a drowsy state to mitigate road accidents have been presented. The system is also focused on ensuring safety issues like fastening the seatbelt, preventing unauthorized access, preventing driving while being in a drunk state, etc. The implemented system uses Artificial Intelligence-based advanced algorithms to detect the expressions of the driver and can calculate the rate of the driver’s drowsiness. The system detects drowsiness by extracting the ‘Eye Aspect Ratio’ (EAR) calculation of facial landmark localization and the authentication is verified by incorporating with the facial recognition technology. The outcome of this paper can be stated that the system ensures the safety and security of both the driver and the vehicle by preventing the driver from falling asleep while driving. In addition, sending the location of the car at the time of any kind occurrence of accidents is another outcome of this research work.
A trust attack detection algorithm based on improved K-means clustering

In the current unsupervised attack detection, the classification attribute of proxy attack has the problem that the resolution of attack users and ordinary users is not high, which affects the recommendation accuracy. This paper proposes a trust attack detection algorithm based on K-means clustering and user rating interval weighted RDMA (SRDMA). In this algorithm, the user reputation IGR classification feature attribute is introduced, and the two classification features with the highest discrimination are extracted by using the distinction coefficient, and then the traditional K-means clustering algorithm is improved through the user reputation IGR. The improved K-means clustering algorithm can cluster most attackers together. Finally, in view of the fact that there are misjudged real users in the attack user set after clustering, this paper uses user rating interval weighted RDMA algorithm (SRDMA) for secondary classification, so as to gather as few real users as possible, and reduce the impact of trust attack on the recommendation system. The experimental results of real MovieLens show that the proposed attack detection algorithm based on improved K-means clustering and SRDMA can effectively detect the random proxy attack profile model, and the detection effect is obviously better than the comparison algorithm.
Bit-Serial multiplier based Neural Processing Element with Approximate adder tree

Deep learning algorithms are computationally intensive and require dedicated hardware accelerators. Deep learning algorithms repeat multiply-accumulate (MAC) operations. This process produces a large number of partial sums that account for about 60% of the total logic. Therefore, optimizing multi-operand adders (MOA) that add these partial sums can reduce the high resource utilization of deep learning accelerators. This study designed a neural processing element with approximate adders that reduces resource utilization without changing the accuracy of deep learning algorithms by using the fault tolerance property of deep learning algorithms. As a result, the accuracy dropped by only 0.04% with 4.7% less resource usage.
Validation and testing of the decentralized architecture for the occupancy grid filtering pipeline

A reliable and precise model of the environment is of the highest importance for autonomous vehicles. Occupancy grids are a well-known approach for environment modeling and are a crucial part of multiple autonomous driving functionalities. The standard method is to use a single 2D occupancy grid to model the environment using nonground points. In this paper, we propose a decentralized occupancy grid filtering chain (pipeline) where a high-density 64-layer LiDAR provided the input to our pipeline. Our approach enables us to obtain detailed 2D and 3D models of the environment simultaneously. The pipeline was validated on different scenarios in both simulation and real world. The performance of the designed occupancy grid pipeline was evaluated by the proposed key performance indicators (KPIs) based on accuracy. The results have shown that the approach was able to extract free space information with a high degree of accuracy, while reducing the size of the unobserved area in the grid compared to the standard methods and achieving real-time performance.
CNN-SC Decoder for Polar Codes under Correlated Noise Channels

In 5G mobile communication standard, polar codes have been used as coding on control channels. Although the polar code has good performance, the performance of its short code is still facing huge challenges. Moreover, the size limitation of using the deep learning model as the decoder of the polar code has always been the focus of researchers seeking a breakthrough. In this paper, the convolutional neural network (CNN) model in deep learning is cascaded with a successive cancellation (SC) decoder to achieve the decoding of polar codes under additive correlated Gaussian noise (ACGN) channels, thereby improving the decoding performance of short polar codes under ACGN and alleviating the code length limitation of a single deep learning model as a decoder. The cascaded decoding structure is represented as a CNN-SC decoder, which can effectively estimate colored noise. The simulation shows that the CNN-SC decoder improves the performance of polar codes under ACGN channels significantly.
Noise Reduction Algorithm for Communication based on Improved Generative Adversarial Network

There are many researches on noise suppression of traditional noise suppression algorithms, and the suppression effect has reached the bottleneck. Now the widely studied artificial intelligence algorithm provides a new idea for very low frequency (VLF) communication noise reduction in ocean navigation. The generative adversarial network is introduced into the noise interference suppression algorithm.Based on the improved generative adversarial network model, the VLF noise interference suppression algorithm is studied. The generation model with strong learning ability is used to improve the accuracy of expected signal frequency estimation in the spectrum of noisy signal, and further improve the signal-to-noise (SNR) ratio in the signal bandwidth. The experimental results show that, by training the generative adversarial network, the received signals in the direction of - 45° to 45° of the receiving antenna show good noise reduction effect, and the accuracy rate can reach above 90%.
Research and innovation in behavioral decision-making, image recognition, and action control of humanoid robot

This paper talks about some research and innovation in behavioral decision-making, image recognition and action control of a humanoid robot. At first, we show our ideas on the humanoid robot’s behavioral decision-making which is mainly about the advantage of crossing the minefield. Then for image recognition, we introduce the image processing for kicking a ball and present the processing effect of each stage. At last, we show particularly our action control for setting up and explain the meaning of each frame of this action. In conclusion, our robot can quickly and stably pass most of the terrains and adapt to various environmental conditions.
Methods for effective and reliable resource allocation in service-oriented systems

Many life and infrastructure critical service-oriented systems require rigid compliance of their execution parameters values with the defined Service Level Agreement (SLA). In such systems reliable and proper execution of provided functionalities is an important part of their security assurance procedures. To meet these demands, the majority of system architects overprovision systems’ infrastructures to ensure proper operability even during high load scenarios. In this article, a proposition of methods for effective and reliable resource allocation methods for service-oriented systems is proposed. The main foundation of this research lays in the user request processing procedure. This paper proposes a just-intime resource provisioning procedure for composite service with optimization methods ensuring proper resource allocation, compliance with the defined SLA and optimization of composite service delivery cost. The paper describes the concept of such request processing procedure, proposition of resource allocation methods and their experimental verification results.
Monitoring Air Quality of Dhaka using IoT: Effects of COVID-19

Air pollution and COVID-19 both are the most provocative issue nowadays. Air pollution holds a dangerous impact on the COVID-19 issue as well as human health. The urban cities like Dhaka are under stress to remain habitable. With the huge density of transportation and population, air quality index is to be monitored minutely and the impact of the COVID-19 pandemic is to be observed. It is required to develop an Internet of Things based remote monitoring system to observe the air particularity in the different areas of the Dhaka city and make a comparison between the before and during the COVID-19 Pandemic. The platform aims to track out the concentration of gases in the Dhaka City like carbon monoxide (CO), nitrogen di-oxides (NO2) on real-time that provides air quality index (AQI). Using Arduino based Node MCU and the sensors are to detect substantive conditions of gases. ESP-32 Wi-Fi module is used to send the data to the server so that it can be accessed from anywhere. The data is taken before and during COVID-19 period with the developed IoT platform. It shows the difference between the CO and NO2 emission on the impact of the pandemic with the measured value. This study will help us to make further decisions and action regarding air pollution.
An Efficient Method to Collect Statistics in SDN Using Curvature Based Sampling

Network management involves measurements and control. Software Defined Networking (SDN) provides a higher level of control on network devices. In SDN, the controller can poll the statistics at different granularity with a constant or dynamic polling rate. There is always a trade-off between the accuracy and polling overhead. This paper proposes a new method for dynamic polling, which provides the best trade-off between the accuracy and polling overhead. We use curvature based sampling as the basis for our method and compute the change in the rate of change to decide the polling frequency. We use real traffic traces for the experiments. The experimental results demonstrate that the proposed method provides better accuracy compared to MoMon [1] and CeMon [2]. The proposed method achieves 23% accuracy over MoMon [1] and roughly three to four times better accuracy than CeMon [2].