Miracle-dz/UniFAR-FTUs huggingface dataset:
A Fuzzy PID-Controlled Iterative Calderon’s Method for Binary Distribution in Electrical Capacitance Tomography

Electrical capacitance tomography (ECT) utilizes measured mutual capacitances across a region of interest to visualize distributions inside. As typical two-phase flows can be roughly treated as binary-valued material distributions, in this article, a fuzzy PID-controlled iterative algorithm is proposed for image reconstruction in cases of binary distributions. A closed-loop control system includes a fuzzy PID controller, Calderon’s method, and fast calculation of the Dirichlet-to-Neumann map. Capacitances measured in an electrode array of the ECT sensor are compared with the feedback, and the difference is input to the controller. Fuzzy rules are used to automatically adjust the three parameters of the controller, i.e., <inline-formula> <tex-math notation="LaTeX">$K_{P}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$K_{I}$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$K_{D}$ </tex-math></inline-formula>. The controller passes the difference to Calderon’s method for reconstructing permittivity distribution. Reconstructed distribution is used to calculate a boundary map for feedback, by fast calculation of the Dirichlet-to-Neumann map, and serves as an updated reference for measured capacitances. A smooth segmentation method is also introduced to deal with the binary distribution and release the fluctuation in the tuning of the PID controller. Numerical simulations were done to verify the performance of the proposed iterative Calderon’s method for binary distributions. Experiments on real phantoms were also carried out using an ECT system to evaluate the proposed method. Several distributions were set up with solid particles and air. The results show that the proposed method can produce images with clear edges and shapes of binary distributions.
Cone-KG: A Semantic Knowledge Graph with News Content and Social Context for Studying Covid-19 News Articles on Social Media

Semantic knowledge graphs provide very significant benefits for structuring and analysing huge amounts of aggregated data across diverse heterogeneous sources. Beyond quick and efficient data query and analysis, they facilitate inference from data and generation of insights for several purposes. With the multi-faceted global challenges posed by the COVID-19 pandemic, this research focused on the use of a semantic knowledge graph to model, structure and store COVID-related news articles centrally and semantically towards knowledge discovery, knowledge acquisition and advanced data analytics for understanding varying metrics relating to the virus towards curbing its spread. The semantic knowledge graph provides a platform for researchers, data analysts and data scientists across societal sectors to investigate and recommend strategies towards addressing the challenges it poses to the global society.
Immunizer: A Scalable Loosely-Coupled Self-Protecting Software Framework using Adaptive Microagents and Parallelized Microservices

IT professionals are overwhelmed by rapidly-changing technology and growing complexity. Additional challenges are introduced by cyber-security. Self-protecting software tries to alleviate this situation by combining principles and techniques from both autonomic computing and software security. However, this combination creates scalability issues, as well as cross-cutting concerns. In this work, we present Immunizer: A Scalable Loosely-Coupled Self-Protecting Software Framework. Immunizer extends our Application-level Unsupervised Outlier-based Intrusion Detection and Prevention Framework by leveraging the architectural building blocks of autonomic computing, and adopting a microagent/microservice architectural model, augmented with distributed cluster computing, for maximum scalability and separation of concerns. More specifically, we design each of the Monitor, Analyze, Plan and Execute functions of the autonomic MAPE-K control loop as a parallelized microservice, while we model its Knowledge function as a data streaming, caching and storage infrastructure. Moreover, we design the Sensor and Effector touchpoint modules as adaptive lightweight runtime application instrumentation microagents.
FiLoc: Fine-Grained Indoor Localization Using a Single Access Point

This paper presents FiLoc, a single AP-based indoor localization system, which achieves sub-meter accuracy. With ToF measurements, we model the localization process into a quadratically constrained quadratic program problem and then adopt the Semi-definite Programming (SDP) to solve it. As a result, we can find a global optimum solution corresponding to a location without an accurate initial guess of the target location required in the traditional least-square based algorithm. We implement our system using USRP X310 software-defined radio platform and deploy it in various environments. The extensive experiments show that FiLoc can achieve sub-meter level localization accuracy at 100MHz signal bandwidth.
Generation of Virtual Reality Environments in which to Evaluate Swarm Adherence to Prescribed Control Rules

This paper shares experiences in the development and generation of Virtual Reality Environments in which to evaluate swarm adherence to prescribed control rules. It describes the attributes of locations in the environments which allow swarms of robotic devices to cooperatively converge on desired emergent behaviour. In addition, the work describes how robotic devices perceive the meanings of the information held on the proposed locations of the virtual environments. Results indicate that extra care should be taken when designing simulated environments in order to guarantee usefulness of those environments to robotic devices in swarms. The environments should possess features which would further inform the robotic devices of the geometry and map of the simulated environments. In addition, good designs are the hopes to the fourth industrial revolution. Without proper design of the testbeds, the envisioned usefulness of robotics to the fourth industrial revolution would fail.
Suppression of Frequency Fluctuation of Power System by Output Control of PMSG Wind Generator

In recent years, due to the aggravation of global environmental problems, wind power generation, which has less impact on the environment, has been attracting attention and being introduced. However, such renewable energy power generation systems are affected by weather conditions, which can lead to unstable output and fluctuations in the frequency and voltage of the power system. Therefore, it must be smoothed before being supplied to the power system. In this paper, a control system that suppresses frequency fluctuations in the power system is proposed which is based on using kinetic energy to control the output of PMSG based variable-speed wind power generators by using dead band technology. To confirm that the suggested method is effective, simulation analysis using PSCAD/EMTDC software has been performed under various conditions.
An Approach to Indoor Occupant Counting Method Based on Zenithal Video Analysis

For many applications in buildings, such as heating, ventilation and air conditioning (HVAC), lighting control under normal conditions and fast evacuation under emergency conditions, the real-time and reliable knowledge of the number of occupants in each area plays an important role. This paper works on a naive and ideal occupant estimation model by detecting the enter and leave occupants of boundary detection area, and the zenithal video analysis is utilized to reduce the cumulative error of the naive estimation model. Specifically, the Kalman Filter is utilized to track the targets that through the border of each area, and two key tunable pareamters that are the matching distance threshold and scaling factor are designed and tested elaborately to reduce the estimation error. Experimental results show that the proposed zenithal video analysis based indoor occupant counting approach with the careful tuning of matching distance threshold and the scaling factor,can effectively reduce the discriminant error of occupants entering and exiting, achieving the estimation of the indoor occupant number with above 92% accuracy, the occupied or unoccupied state estimation for an area with more than 99% accuracy, and the staying time of indoor occupant with more than 99% accuracy.
A Novel Scheme to Map Convolutional Networks to Network-on-Chip with Computing-In-Memory Nodes

Computing-In Memory (CIM) has been widely used to accelerate the inferencing speed of deep learning. Network-on-Chips (NoCs) are usually used together with CIM to enable the versatile ability of the hardware. This paper proposes a bandwidth aware mapping scheme to minimize both hops and bandwidth requirement. The simulation results show that the proposed scheme could reduce the hops and bandwidth requirements by more than 33.57% and 46.13%, respectively.
Optimal Virtual Network Function Placement in Chains Using Backups with Availability Schedule

A suitable virtual network function (VNF) placement considering a node availability schedule extends service continuous serviceable time by suppressing service interruption caused by function reallocation and node unavailabilities. However, function placement cannot avoid service interruption caused by node unavailabilities. This paper considers a primary and backup VNF placement model to avoid service interruption caused by node unavailabilities by using backup functions. The considered backup functions have a period of startup time for preparation before they can be used and the number of them is limited. The proposed model is formulated as an integer linear programming problem to place the primary and backup VNFs based on the availability schedule at continuous time slots. We aim to maximize the minimum number of continuously available time slots in all service function chains (SFCs) over the deterministic availability schedule. We obverse that the proposed model considering the limited number of backup functions outperforms baseline models in terms of the minimum number of longest continuous available time slots in all SFCs. We introduce an algorithm to estimate the number of key unavailabilities at each time slot, which can find the unavailable nodes which are the bottlenecks to increase the service continuous available time at each time slot.
A CMOS 60-GHz Asymmetrical SPDT Switch with Enhanced Power Handling Capability

In this work, a novel single-pole double-throw (SPDT) switch with high power handling capability along with a good isolation is presented. To achieve both excellent 1-dB compression point (P1dB) and isolation simultaneously, an asymmetrical SPDT structure is used. By carefully layout the design, this SPDT switch achieves a simulated P0.5 dB of 26 dBm, a minimum insertion loss of 1.7 dB and an isolation of 40 dB at 60 GHz in transmitter mode; a minimum insertion loss of 1.8 dB and isolation of 25 dB at 60 GHz in receiver mode. The circuit is designed in 0.13-¼m CMOS process. The area of this design, excluding pads, is 1010 ¼m × 186¼m.
Empowering GaN-Si HEMT Nonlinear Modelling for Doherty Power Amplifier Design

New system architectures oriented to more and more challenging performance for the next generations of mobile devices demand for an accurate design of integrated circuits. The power amplifier is one of the most critical components in an RF system and the need for high performance has focused the designer attention to complex architectures, such as the Doherty power amplifier (DPA). In its most common implementations, the design requires transistor models showing high-accuracy levels under different classes of operation. In this work, we investigate the possibility of achieving the required level of accuracy for the transistor current-generator model using a set of measurements performed under the different classes of operation that mimic realistic device operation and use them for the model optimization. The developed approach is fully validated on a 28-GHz MMIC DPA, showing good agreement with the measured results.
A Survey on Indoor Vehicle Localization Through RFID Technology

This paper presents a state-of-the-art analysis on the methods suitable for vehicle indoor localization and exploiting the RFID (Radio Frequency IDentification) technology. The survey describes three main categories of vehicle localization systems: (i) solutions exploiting only the RFID technology, (ii) sensor-fusion techniques combining data from RFID systems and proprioceptive sensors, and (iii) sensor-fusion techniques combing RFID data with those of other exteroceptive sensors in addition to the RFID system itself. For each method, implementation and methodological details are discussed, by highlighting the applied RFID technology, namely passive HF-RFID, passive UHF-RFID, or any other RFID system. Also, the employed RFID parameters, i.e., tag EPC, RSSI or backscattered phase, are discussed. The survey focuses on the achievable localization performance, also accounting for infrastructure-deployment costs together with complexity and maintenance overhead. Positioning, tracking, navigation and simultaneous localization and mapping (SLAM) issues are here considered. The analysis highlights pros and cons of each method, together with the main challenges and perspectives of RFID-based solutions for vehicle localization.
MIMO-ISAR-Based UWB Imaging System for Non-Destructive Testing

A MIMO-ISAR-based UWB imaging system for non-destructive testing is proposed. The scans are realized with a multi-modular MIMO array and a one-dimensional scan. Experimental studies of focusing characteristics of the system are performed. Imaging enhancement is accomplished with a method based-on a virtual element filter and verified by experimental results. A possible application of the system is demonstrated by imaging a wood structure and visualizing the moisture distribution in 3D.
Fractal-based Approach for Evaluating the Shielding Design of High Voltage Substations Against Direct Lightning Strikes

A fractal-based model for the simulation of lightning incidence to high voltage substations is introduced. The proposed model takes into account the stochastic nature of lightning attachment by considering the lightning discharge branching and tortuous behavior and integrates up-to-date knowledge on leader inception and progression criteria. Fractal-based simulations are performed for the case of a typical 69 kV substation of the IEEE Std 998-2012. Simulation results show that the basic shielding design parameter, the striking distance, is stochastic in nature and that shielding failures may occur even for substations with shielding design in accordance with IEEE Std 998-2012. This work may shed light to unexpected failures of modern substations reported by utilities in a lightning protection survey held by IEEE.
Spatial Mapping of Material Properties utilizing FMCW Near Field Radar Scans

This paper presents a method for spatial mapping of material properties utilizing a 2D synthetic aperture radar scanner. The method is based on a time domain approach resulting in the determination of the surface permittivity and geometrical length of the material. Further, the impact of diffraction and surface roughnesses on the calculation of the surface permittivity is discussed. In Addition, a smoothing approach is present to compensate diffraction effects leading to a more precisely determination of material properties.
Gabor Transforms for Compressing RESM Data

This paper describes the advantages of using a Gabor transform to represent the signals seen by Radar Electronic Support Measure (RESM) receivers. It shows how a compact, invertible Gabor Transform can be created. It further shows how the RESM data can be sub-sampled and then reconstructed via an undersampled Gabor Transform, effecting a substantial compression of the data rate. A subsampling factor of better than 7.5, with practically perfect reconstruction, is demonstrated with a real recorded radar pulse.
Design Of The Automatic Control System For Restaurant Food Delivery Based On PLC

The paper designs an automatic control system for restaurant food delivery based on PLC, including the mechanical structure and automatic control system design. The mechanical structure of the system includes horizontal delivery subsystems and a vertical delivery subsystem. The automatic control system includes PLC control and the human-machine interface, which realizes the entire system's automation. At the end of the paper, we analyze the whole system's reliability and economy to reflect the characteristics and practicability of the automatic control system.
Loop Antenna Array System with Simultaneous Operation of OAM Multiplex Communication and Wireless Power Transfer

A circular loop antenna array system has been successfully developed for short-range large-capacity communication and power charging, where both orbital angular momentum (OAM) four-value multiplexing communication at the 5-GHz band and wireless power transfer (WPT) at the megahertz band are simultaneously realized. To improve WPT efficiency, a spiral reflector was applied instead of a normal reflector plane. In this fabricated system, a WPT efficiency of 82.6% was achieved at 41 MHz, while keeping four-value multiplexing OAM communication at 5.23 GHz.
Analysis and Verification of a 60-GHz Single-Antenna Doppler Radar for Vital Sign Detection

An analysis of a 60-GHz Doppler radar for noncontact respiration and heart rate detection is presented. The smaller wavelength provides a possibility of using a more compact circuit. The system parameters of Doppler radar at 60GHz are discussed and verified. The single-antenna structure can further reduce the area. The TX-to-RX leakage and clutter noise can be reduced by the clutter-canceller circuit. And null detection point issue and nonlinear phase modulation can be solved by complex signal demodulation (CSD) relying on quadrature generation. The verified results show robust human vital-sign detection by using the proposed structure and system parameters indicating the instructional meaning for Doppler radar design.
DDoS attack detection and defense in SDN based on machine learning

Distributed Denial of Service (DDoS) attack is one of the most dangerous threats in computer networks. Hence, DDoS attack detection is one of the key defense mechanisms. In this paper, we propose a DDoS detection and defense approach in Software Defined Network (SDN) systems based on machine learning (ML) and deep neural network (DNN) models. The combination of ML and DNN classifiers with the centralized factors of SDN can efficiently mitigate the harmful effect of DDoS to the network system. Besides, we conducted two types of attack scenarios, one is from inside and one is from outside of the network system.
Circularly Polarized Pattern Reconfigurable Flexible Antenna for 5G-sub-6-GHz Applications

This paper presents the design and characterization of circularly polarized (CP) pattern reconfigurable flexible antenna for 5G-sub-6GHz applications. The antenna consists of CPW fed rectangular stub loaded semicircular monopole antenna designed for wideband applications. Afterword, two rectangular stubs along with pin diodes were utilized to achieve pattern reconfigurability. The antenna offers compact size, CP, pattern reconfigurability along with flexibility to be used in flexible devices operating in 5G mid-band.
A Compact Dual-Polarized Differentially-Fed Antenna with High Gain for 5G Microcells

A compact dual-polarized differentially-fed antenna with high gain is proposed for 5G microcells. The antenna mainly consists of two parts: a radiating element with metasurface and a differentially-fed network. The metasurface with four square cells is etched on the top layer, while the radiating octagonal patch etched with four fan-shaped slots is placed on the second layer. The profile of the antenna is 0.125 λ0 (where λ0 is the starting wavelength). Simulation results show that the overlapped bandwidths are 3.30-3.61 GHz and 4.72-5.07 GHz, and their corresponding gains vary from 8.30 to 9.65 dBi.
COVID-19 Detection using Transfer Learning with Convolutional Neural Network

The Novel Coronavirus disease 2019 (COVID-19) is a fatal infectious disease, first recognized in December 2019 in Wuhan, Hubei, China, and has gone on an epidemic situation. Under these circumstances, it became more important to detect COVID-19 in infected people. Nowadays, the testing kits are gradually lessening in number compared to the number of infected population. Under recent prevailing conditions, the diagnosis of lung disease by analyzing chest CT (Computed Tomography) images has become an important tool for both diagnosis and prophecy of COVID-19 patients. In this study, a Transfer learning strategy (CNN) for detecting COVID-19 infection from CT images has been proposed. In the proposed model, a multilayer Convolutional neural network (CNN) with Transfer learning model Inception V3 has been designed. Similar to CNN, it uses convolution and pooling to extract features, but this transfer learning model contains weights of dataset Imagenet. Thus it can detect features very effectively which gives it an upper hand for achieving better accuracy.
Gait Recognition Based on Feature Selection of sEMG Signals Using PCA-RELM Method

To improve the accuracy and real-time performance of gait recognition, this paper studies the gait recognition based on multiple features of surface electromyography (sEMG) signals. Firstly, three types of features, i.e., time domain, frequency domain, and wavelet features, were extracted from the denoised sEMG signals. Then the principal component analysis (PCA) is employed to reduce the dimensionality of the sample features. Finally, three algorithms, i.e., support vector machine (SVM), extreme learning machine (ELM), and regularized extreme learning machine (RELM), are presented in gait recognition, respectively. The results show that the PCA-RELM method can get the higher classification accuracy and recognition efficiency.
A Polymorphic Advanced Encryption Standard – A Novel Approach

To secure various forms of data, a polymorphic variant of the Advanced Encryption Standard (P-AES) has been introduced. In the P-AES, the AES parameters’ values will change with every new key. The exact values will be available only to legitimate communicating parties during execution time. To achieve these objectives, the basic AES transformations, SubBytes, ShiftRows, and MixColumns, have been made key-dependent in the proposed P-AES. Hence, with every new key, these transformations will operate differently. The receiver can retrieve the operations’ details from the encryption key. Consequently, polymorphism has been achieved and interoperability remains intact. P-AES has been implemented seamlessly using the existing AES modules, and the performance was more or less equal to the AES performance (71 and 70 milliseconds to encrypt 500 bytes using the P-AES and the AES respectively). From a security standpoint, the proposed P-AES fully complies with Kerckhoff’s principle. This means the cipher has an open design, and the security provided by the P-AES depends only on the secrecy of the encryption key. The cipher resistance to differential and linear attacks has been proved. Moreover, the resulting proposed cipher can operate in 128 different ways, which will significantly reduce the capabilities of any sophisticated attacker. Furthermore, the proposed P-AES’s scores of the key avalanche and the plaintext avalanche were 0.496 and 0.504 respectively. Finally, the Statistical Test Suite (STS) recommended by the NIST has been used to ensure the randomness of the cipher output, and the cipher has passed all the STS tests.