Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel AESA Architecture for Earth Observation and Planetary Sciences

In this paper we present a description and evaluation of Gallium Nitride (GaN) Transceivers within AESA frontends for space-based Synthetic-aperture radar (SAR). The GaN power switches are evaluated in a voltage mode Class-D (VMCD) RF amplifier and as a DC-DC converter. The efficiency of the circuit when evaluated as a Class D power amplifier (PA) is 73% at 250 MHz and delivers an output power of 37.3 dBm. The measured DC-DC converter circuit has an efficiency of over 89.7% at 100 MHz, delivering 38.6 dBm. These are the highest reported efficiencies for the RF PA and the switching modulator performance is comparable to state of the art at its respective operating frequencies. A flat panel metasurface antenna topology that can be integrated into the RF front end to facilitate a variety of SAR modalities. This antenna structure can substantially simplify the deployment mechanism of airborne and spaceborne SAR instruments.
RayCloudTools: A Concise Interface for Analysis and Manipulation of Ray Clouds

We describe a new toolset for the manipulation and analysis of ray clouds (3D maps defined by a set of rays from a moving lidar to the scanned surfaces). Unlike point clouds, ray clouds contain information on free-space (air) as well as surface geometry. This allows the toolset to perform volumetric functions and analysis that cannot be done on point clouds alone. The presented toolset consists of seventeen command-line functions, with a C++ library available for those who require more control or tight integration. Our aim is that RayCloudTools is as useful and simple as possible, and we use this paper to demonstrate its utility, and to assess its ease of use, with comparison to established cloud processing libraries.
Investigating Performance Analysis of a Novel Low-power Efficient Area Carry-Look Ahead Adder

The Modified Gate Diffusion Input (M-GDI) is a technique of low-power logic and digital circuit design, it allows realizing low complex design of digital and logic circuits for enhancing the power efficiency. Performance of the implemented Carry-Look Ahead Adder (CLA)-GDI-based comparison with the various traditional logic design techniques with respect to the layout area is presented. This paper presents several logic circuits for the proposed design of the CLA that have been implemented in various design styles. Different metrics are utilized for evaluating the performance of the implemented CLA circuits. The simulation experiments results prove the superiority of the proposed FA implementation which consists of 23 transistors only compared to the previously CLAs circuits design by the various design style. The implementation of the presented CLA design achieves reducing the consumed power, optimizing the processing time, and area of the implemented CLA circuits.
Heterostructure Phototransistor Arrays for Free-Space Laser Communications

We explore using heterostructure phototransistors (HPT) to reduce the complexity and, hence, the size, weight, and power (SWaP) of a space borne laser communications system, by combining pointing/acquisition/tracking (PAT) and wideband communications functions in a single sensor assembly. Benefits of this approach include a reduced number of sensor elements, fewer receive optical paths requiring alignment over life, and subsequent SWaP improvements in interface electronics. Because of the uniform gain without excess noise, HPT arrays are expected to have superior performance compared to PIN and APD photodiodes for receive sensitivity or signal-to-noise ratio required for communication and/or PAT functions. The HPT also offers the potential to remove the point-ahead mirror, if the sensor is used in a look-behind mode, and, in the ultimate scenario, the sensor would be sampled at the data rate, if the FOV is sufficient to encompass the entire jitter spectrum, thus, negating the need for a fast steering mirror (FSM) for beam stabilization.
Non-technical losses in electricity distribution: a bibliometric analysis

This study aimed to provide an overview of the research on non-technical losses, presenting a bibliometric analysis of 392 studies in conference proceedings. The research was conducted using Scopus database from 1987 to 2019. The results showed an increasing trend in the number of publications on this subject, demonstrating the worldwide interest of the scientific community in non-technical losses. In addition, it was found that the predominant language in the writing of publications was English, the countries that most published articles in the area were India, Brazil and USA and the cumulative number of articles tends to reach 600 at the end of 2021. Developing countries are encouraging research in the area to reduce the damage caused by non-technical losses in electricity distribution, contributing to society as a large part of these costs are passed on to the consumer.
Cr-Prom: A Convolutional Neural Network-Based Model for the Prediction of Rice Promoters

The promoter is a regulatory region of the DNA typically located upstream of a gene and plays a key role in regulating gene transcription. Accurate prediction of promoters is crucial for the analysis of gene expression patterns and for the development and understanding of genetic regulatory networks. Genomes of several species have been sequenced, and their gene content has been established to a large extent. Some bioinformatics algorithms have been developed for predicting promoters with high universality for all kinds of plants; however, few studies have been conducted to identify promoters in rice, which might affect the practical applications. Here, we present a rice promoter prediction tool, Cr-Prom. This predictor has been established using a series of sequence-based features and datasets extracted from the PlantProm and RAP-DB databases. We applied a convolutional neural network (CNN)-based strategy to construct a predictor with robust classification performance. To demonstrate our dominance, we ran experiments on a benchmark dataset using 5-fold cross-validation and compared our results with existing techniques using four figure of merits. In addition, CR-Prom was analyzed on an independent dataset. Based on the results, Cr-Prom outperformed the existing rice-specific promoter predictors. The Cr-Prom tool can be freely accessed at: http://nsclbio.jbnu. ac.kr/tools/Cr-Prom/
An Energy-efficient Localization System for Imprecisely Positioned Sensor Nodes with Flying UAVs

This work investigates the capability of unmanned aerial vehicles (UAVs) to find and communicate with wireless sensor nodes positioned at unknown locations. In this scenario, the UAV acts as a mobile gateway that estimates the sensor node position using multiple ultra-wideband (UWB) range measurements, before flying in its vicinity to perform energy-efficient data acquisition. In addition to UWB, we use wake-up radio (WUR) to improve the sensor node's energy efficiency, keeping it in the always-on “low-activity” state when the drone is not nearby. The paper proposes a localization algorithm that consists of an iterative, noise-robust and computationally lightweight approach based on multi-lateration. Experimental evaluations performed on synthetic data demonstrate that our approach achieves a submeter localization accuracy using only three range measurements. We confirm this with an extensive in-field evaluation. The multilateration algorithm runs in 4 ms, in low power microcontrollers such as the ARM Cortex-M4F. The WUR and our energy-efficient algorithm enable the sensor node to consume only 31 mJ during the whole localization-acquisition process. Our solution can be introduced in many other industrial applications where a mobile robot needs to estimate the location of imprecisely positioned objects.
RF signal fluctuation based passive UHF RFID tag localization

RF signals are vulnerable to multipath, noise and interference. Most localization algorithms are trying to create a similarity based relationship between RF signal measurements and propagation distance or tag location. So it's a huge challenge to realize precise localization for real applications. In this paper, we propose a novel passive UHF RFID tag localization algorithm based on RF signal fluctuation, FBL algorithm. Not considering similarity, we use signal fluctuation as the neighboring criteria. According to our experimental results, with a metal shelf covered with multiple fixed tags, FBL algorithm can estimate 15 grid-level locations with an average error of about 32 cm. As the grid spacing distance was about 35 cm and the accuracy of traditional k nearest neighbor (KNN) algorithm was about 72 cm, the results showed that FBL algorithm significantly reduced the location estimation error.
Electro-Microfluidic Viscometer with Integrated Microcontroller and Pumping System for Point-of-Care Biosensing Applications

Viscometers are extensively utilized for a wide variety of sensing and monitoring applications like biomedical diagnostics, biochemical optimization, fluid adulteration detection and quality control in pharmaceuticals. Realizing the viscometer in the lab-on-chip/microfluidic domain increases its potential to be a point-of-care device by making it completely automated and robust. This work presents the development of a simplified, cost-effective and time efficient 3D Printed (3DP) Electro-Microfluidic Viscometer (EMV), with microchannel and intersecting electrodes, in a single-step process. A completely automated, integrated and handheld EMV was realized and comprised an in-house made 3DP peristaltic pump and motherboard of a microcontroller, along with a display unit and the microcontroller to estimate viscosity. Here, the viscosity values of bovine serum albumin (BSA), human serum albumin (HSA), and lysozyme enzyme were measured with respect to the reference fluid before and after denaturation. The developed device is capable of measuring viscosity values ranging from 0.5 cP to 10 cP. These values are within a ±6% limit when compared with a benchtop viscometer, showing the commercial potential of the present device with innumerable add-on features.
Omnidirectional Free-Degree Wireless Power Transfer System Based on Magnetic Dipole Coils for Multiple Receivers

Wireless power transfer (WPT) technology offers a potential solution for the energy-supply problem. In this paper, we propose a novel magnetic coupling mechanism for omnidirectional and multiple-pickup energy transfer based on the magnetic dipole coils. Firstly, a cubic transmitter structure capable of generating three-dimension (3D) homogeneous magnetic field is fabricated to weaken directional sensitivity of receiving coil. Furthermore, the direction of current flowing through each dipole coil is also studied in detail. Numerical and simulated analysis is implemented to verify the omnidirectionality of the transmitter. Secondly, the equivalent circuit model for WPT systems with ferrite is analyzed. In addition, optimal loads, power distribution and transfer efficiency for multiple receivers are discussed and used to achieve the proper system design. Finally, experimental prototype is set up to validate the transmission performance of the proposed WPT system. The results have showed that genuine 3D high degree of freedom (DoF) can be achieved. Meantime, above 60% efficiency at least 30 W of total output power can be obtained for WPT systems with eight pick-ups. We believe the proposed system will give a new guideline for future low power electronic applications, such as monitoring sensors, miniature robots and household devices.
Dynamic optimisation Toolbox dynopt 5.0

The toolbox dynopt is a collection of scripts and functions for dynamic optimisation of processes described by ordinary differential and algebraic equations (ODE/DAE). The original problem is transformed into nonlinear programming (NLP) using orthogonal collocation on finite elements and it is implemented in MATLAB. The new version 5.0 adds automatic differentiation feature which greatly simplifies problem definition and reduces possibility of user errors. The usage of the toolbox is explained in tutorial example and in selected case studies that demonstrate its capabilities to solve dynamic optimisation problems of various size.
Supervised and Unsupervised Learning for Fraud and Money Laundering Detection using Behavior Measuring Distance

Money laundering is the process of making large amounts of fund obtained from criminal activities appear to originate from a legitimate source. Fraud occurs when a person or business intentionally deceives another with promises of services or financial benefits that do not exist or were misrepresented. Fraud and Money laundering detections require to analyze abnormal behavioral patterns. To develop a detection model, we present a machine learning-based model which incorporates risk scoring and statistical clustering approaches. Given a customer represented by its values in a set of attributes, we define its Customer Behavior Score based on its percentile rank in each attribute, which measures the behavior of the customer against the median or “normal” customers in the group. The Customer Behavior Score induces a distance, called Behavior Measuring Distance, between any two customers. The k-medoids clustering technique based on the Behavior Measuring Distance is then applied iteratively to classify customers. The key features of the model are that the abnormality of customers' behaviors are measured based on their percentile ranks in their respective classes and that such measurement is dynamically updated based on the reclassification after each iteration during the training. Finally, the model is tested using the country risk data collected from public and internal sources, and the model outcomes are compared against a benchmark model. The experimental results show convergence and effectiveness of the model.
Intelligent Fault Diagnosis of Rotary Machines: Conditional Auxiliary Classifier GAN Coupled With Meta Learning Using Limited Data

The industrial advancement has promoted the development of deep learning (DL)-based intelligent fault diagnosis methods for condition-based maintenance (CBM). Though these methods rely on large dataset for training, the collection of large number of fault samples is not practically feasible. For this purpose, generative adversarial networks (GANs) are capable to generate high-quality synthetic samples. However, the problem still persists with the training of GAN using limited fault samples that are present in practical conditions. This article proposes a novel conditional auxiliary classifier GAN framework coupled with model agnostic meta learning (MAML) to resolve this problem. The objective is to initialize and update the network parameters using MAML instead of regular stochastic gradient learning. This modification enables GAN to learn the task of synthetic sample generation using the limited training dataset. The effectiveness of the proposed framework has been compared with several famous state-of-the-art intelligent fault diagnosis methods existing in the literature. The comparative performance has been validated on benchmarked datasets, i.e., air compressor and bearing datasets collected from a single-stage reciprocating air compressor. The proposed framework is able to achieve the classification accuracy of 99.26% and 98.55% for bearing and air compressor datasets, respectively, with only ten samples per class. Moreover, a real-time case study is performed to validate the proposed method in real time.
SGuard: machine learning-based Distrbuted Denial-of-Service Detection Scheme for Software Defined Network

A Software Defined Networking (SDN) is an advanced network design that presents central control for a complete network. It is a dynamic, easy-to-manage, cost-efficient, and adaptive advanced architecture, making it utilitarian for dynamic nature and high-bandwidth of the present applications. Distributed Denial-of-Service (DDoS) attacks specific to SDN networks to deplete the control plane bandwidth and overload the buffer memory of OpenFlow switch. In this research, a design and implementation of secure guard to assist in solving the issue of DDoS attacks on pox controller is presented, this guard is named SGuard. A novel Five-tuple as feature vector is utilized for classifying traffic flow using Support Vector Machine (SVM). A Mininet is utilized to evaluate SGuard in a software environment. The introduced system is evaluated by measuring the system’s performance in terms of delay, bandwidth, traffic flow and accuracy.
Predicting Student Performance in an Embodied Learning Environment

Implementing machine learning techniques is receiving considerable attention in the educational technology research field. Different systems and techniques were proposed to predict student performance and gain insights regarding their learning needs. However, to the best of our knowledge, no previous research studies explored predicting student performance based on their interaction with different modalities in a real classroom context In this paper, we have proposed an adaptive learning engine to recommend embodied activities and interaction modalities according to students learning profile, prior knowledge, and academic performance. We have firstly conducted a comparative study by analyzing two educational datasets. Among five classification algorithms, Random Forest outperformed with 84% as accuracy. Thus, in a second phase, we have explored predicting students’ performance with our dataset generated from embodied learning environment. The accuracy of the proposed model using student profile, academic, and behavioral features reached an accuracy of 62%. These results highlighted the potential of implementing machine learning techniques to classify and recommend embodied interaction.
On Performance of Two-Way Full-Duplex Communication System With Reconfigurable Intelligent Surface

In this paper, we apply a reconfigurable intelligent surface (RIS) to a two-way (TW) full-duplex (FD) wireless communication system, where a RIS has <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> reflecting elements and operates as a relay station forwarding signals between two FD terminals. To demonstrate the advantages of RIS compared with traditional relay, we also present the system and signal models of the TW-FD amplify-and-forward relay (AFR) system. We mathematically analyze the performance of the TW-FD-RIS system by deriving the exact closed-form expressions of the average outage probability (AOP) and the average symbol error rate (ASER) in the case <inline-formula> <tex-math notation="LaTeX">$N=1$ </tex-math></inline-formula>, and approximate expressions of the AOP and the ASER in the case <inline-formula> <tex-math notation="LaTeX">$N \ge 2$ </tex-math></inline-formula>. Besides, we provide the exact closed-form expression of the AOP and approximate expression of the ASER of the TW-FD-AFR system. Monte-Carlo simulations validate all derived mathematical expressions. Numerical results demonstrate that using the RIS greatly improves the TW-FD communication system’s performance compared with using amplify-and-forward (AF) relay. Specifically, even the RIS with one element (<inline-formula> <tex-math notation="LaTeX">$N=1$ </tex-math></inline-formula>), the AOP and ASER of the TW-FD-RIS system are still lower than those of the TW-FD-AFR system in the low signal-to-noise ratio (SNR) regime. However, in the high SNR regime, the AOP and ASER of the TW-FD-RIS system are higher. In the case <inline-formula> <tex-math notation="LaTeX">$N \ge 2$ </tex-math></inline-formula>, the AOP and ASER of the TW-FD-RIS system are significantly reduced. On the other hand, the impact of residual self-interference (SI) due to FD transmission mode on the performance of TW-FD-RIS and TW-FD-AFR systems is remarkable, making the AOP and ASER of these two systems reach the error floor in the high SNR regime. Fortunately, the usage of RIS with <inline-formula> <tex-math notation="LaTeX">$N \ge 2$ </tex-math></inline-formula> can greatly reduce this impact and avoid the error floor for the TW-FD-RIS system.
Diverse and Adjustable Versatile Image Enhancer

Enhancing the quality of photographs is a highly subjective process and depends on users’ preferences. Hence, it is often more desired to let users choose their own best from a set of diverse and adjustable enhanced images with astounding quality. However, a system that can satisfy this requirement has not yet been established. While classical algorithms blindly enhance an image by filtering, recent intelligent enhancement systems can only do it with limited styles through learning from a set of single expert-retouched (ER) images. To fill this void, we propose a novel framework, Diverse and adjustable Versatile Image Enhancer (DaVIE), that learns from multiple ER images simultaneously. Thereby, it can output diverse results without being bound to a specific enhancement style while allowing users to freely adjust the level of enhancement. For ease of diversity, we adopt a variational auto-encoder (VAE) that learns stochastic distribution of enhancement styles. By using the VAE, the proposed model provides diversely enhanced images. To establish better control in terms of enhancement level, we propose a more general form of adaptive instance normalization and loss functions, which can afford even extreme image editing. Through rigorous experiments, we demonstrate that the proposed DaVIE framework yields visually pleasing and diverse results. We also show the proposed model quantitatively outperforms existing methods on the MIT-Adobe-5K dataset. Furthermore, through a strict user-study, we show that the users consider the qualities of ER images and machine-retouched images to be similar, with about 35% selection probability for DaVIE enhanced images.
Frequency Analysis of Particulate Matter in Urban Environments under Low-cost Sensors

Air pollution comes to be a research relevance due to the over pollution generated by urban environments and their human health impact. The growing interest has impulsed the development of low-cost ultra-fine particulate matter (PM) sensors, and numerous projects involving WSN devices. However, monitoring ultra-fine particles present different challenges to define an optimal estimation rate. There is no defined estimation rate to different PM events of interest, or involving frequency behavior of PM signals sensed by light-scattering based sensors at higher rates than professional PM metering stations. This study presents a meticulous analysis of PM signals’ frequency behavior from an open-source dataset to correctly determine the maximum frequency needed to digitalize air pollution signals. The results obtained from the analysis allow setting a period rate for estimating the concentration of particulate material with less resolution affectation and reducing the generation of data and energy consumption.