Miracle-dz/UniFAR-FTUs huggingface dataset:
High Gain Antenna Using Dielectric Slabs and Electromagnetic Band Gap Feeding Structure

We propose a unique design perspective to reduce the size of the feeding network of a linear antenna array. The proposed technique provides a generalization of using either electric or magnetic excitations, with dielectric or magnetic rectangular slabs. This technique has a significant impact on reducing the complexity of the feeding network. The realized structure has a low profile, low cost, and compact size features.
Fault diagnosis of full-drive AUV based on graph theory and recurrent neural network

This paper proposes a fault diagnosis method for multi-thruster Autonomous Underwater vehicles (AUV) based on graph theory and Recurrent Neural Networks (RNN). In the absence of feedback information from the thruster, the fault diagnosis can be effectively realized. First, the RNN is used to identify the parameters of the AUV model with constraints. Next, the model output is compared with the measured value to generate the fault residual sequence. Then, the residual sequence is converted into a directed graph and then feature extraction is performed to generate feature vectors. Finally, the feature vector is input into the Support Vector Machine (SVM) to realize fault location and classification. The verification results of multiple sea trials show that the average accuracy of the experiment can reach 90.46%.
Chipless RFID Tag Detection with a Single Measurement in Presence of Multipath

This paper presents a chipless RFID tag detection method to extract the ID of a co-polarized tag with a single measurement in presence of high floor reflection multipath at a read range over 1 m. With one $S_{11}$ measurement, the components of the signal that do not contain information about the tag's ID are filtered out using a time-window whose start time and duration have been studied. This technique is robust as it can identify different tags' resonant frequencies with an offset below 3 % at a read range of at least 132 cm, no matter the tags' orientation on the plane perpendicular to the antenna.
Quantification of edge effects in capacitive biopotential sensing*

Knowledge of capacitance and factors contributing to its variation is important in capacitive biopotential sensing where the dimension of the electrode is much less than the dielectric (skin). As such, this study aimed to quantify the actual capacitance exhibited by an electrode, by accounting for fringe fields (edge effects). This study simulated two different dual-electrode configurations to calculate the capacitance of each electrode in the presence and absence of the other electrode. The results were compared with existing expressions from the literature to investigate their reliability in quantifying edge effects. It was found that the capacitance expression for electrode smaller than the body shows the least difference from simulated capacitance than other expressions. However, the difference increased with increasing airgap. Also, the concentric configuration identified that the outer electrode acts as a guard to the inner electrode and can be approximated with minimum error. This will help estimate the actual effective electrode capacitance in single and multi-electrode systems and allow for efficient measurement circuit design and optimal signal processing of the biopotential of interest.
Rethinking Watermark: Providing Proof of IP Ownership in Modern SoCs

—Intellectual property (IP) cores are essential to creating modern system-on-chips (SoCs). Protecting the IPs deployed in modern SoCs has become more difﬁcult as the IP houses have been established across the globe over the past three decades. The threat posed by IP piracy and overuse has been a topic of research for the past decade or so and has led to creation of a ﬁeld called watermarking. IP watermarking aims of detecting unauthorized IP usage by embedding excess, non-functional circuitry into the SoC. Unfortunately, prior work has been built upon assumptions that cannot be met within the modern SoC design and veriﬁcation processes. In this paper, we ﬁrst provide an extensive overview of the current state-of-the-art IP watermarking. Then, we challenge these dated assumptions and propose a new path for future effective IP watermarking approaches suitable for today’s complex SoCs in which IPs are deeply embedded.
3DRefTransformer: Fine-Grained Object Identification in Real-World Scenes Using Natural Language

In this paper, we study fine-grained 3D object identification in real-world scenes described by a textual query. The task aims to discriminatively understand an instance of a particular 3D object described by natural language utterances among other instances of 3D objects of the same class appearing in a visual scene. We introduce the 3DRefTransformer net, a transformer-based neural network that identifies 3D objects described by linguistic utterances in real-world scenes. The network’s input is 3D object segmented point cloud images representing a real-world scene and a language utterance that refers to one of the scene objects. The goal is to identify the referred object. Compared to the state-of-the-art models that are mostly based on graph convolutions and LSTMs, our 3DRefTrans-former net offers two key advantages. First, it is an end-to-end transformer model that operates both on language and 3D visual objects. Second, it has a natural ability to ground textual terms in the utterance to the learning representation of 3D objects in the scene. We further incorporate object pairwise spatial relation loss and contrastive learning during model training. We show in our experiments that our model improves the performance upon the current SOTA significantly on Referit3D Nr3D and Sr3D datasets. Code and Models will be made publicly available at https://vision-cair.github. io/3dreftransformer/.
Uma Proposta de Modelo para Avaliação da Qualidade da Tradução de Requisitos para Casos de Uso

Resumo. O documento de especificação de requisitos de software (Software Requirements Specification SRS) é decisivo para o desenvolvimento de um bom produto final. As métricas desempenham um papel essencial na detecção de defeitos dos requisitos, fornecendo meios para a visualização de discrepâncias e identificação de pontos fora de uma situação projetada. Assim, o objetivo deste trabalho é propor um modelo com a finalidade de avaliar a qualidade da tradução de requisitos para casos de uso.
Optimal Allocation of Distributed Generator placement: An Optimal Approach to Enhance the Reliability of Micro-Grid

Micro-grids have attracted much interest in the context of the vision of smart grids, which require methodical processes for their optimal assembly, the distribution of DG costs, and the increase of performance. The problem related to this issue defined in this work is a planning problem. Therefore, the general public and users of the proposed approach will be utility planners. The optimal micro-grid infrastructure must be determined through the optimization process. The micro-grid built using the proposed design, and the entire distribution system, has the reliability indicators optimized for this study. Utilities often use standard indexes to assess the reliability of distribution networks. A chain of algorithms is proposed for optimal allocation of DG taking into account system reliability. Load flow studies and reliability analysis are conducted on IEEE 14 Bus system to investigate the effect of reliability issues for optimal allocation of DG. A cost factor index is proposed.
Compressed Sensing MRI Reconstruction with Co-VeGAN: Complex-Valued Generative Adversarial Network

Compressed sensing (CS) is extensively used to reduce magnetic resonance imaging (MRI) acquisition time. State-of-the-art deep learning-based methods have proven effective in obtaining fast, high-quality reconstruction of CS-MR images. However, they treat the inherently complex-valued MRI data as real-valued entities by extracting the magnitude content or concatenating the complex-valued data as two real-valued channels for processing. In both cases, the phase content is discarded. To address the fundamental problem of real-valued deep networks, i.e. their inability to process complex-valued data, we propose a complex-valued generative adversarial network (Co-VeGAN) framework, which is the first-of-its-kind generative model exploring the use of complex-valued weights and operations. Further, since real-valued activation functions do not generalize well to the complex-valued space, we propose a novel complex-valued activation function that is sensitive to the input phase and has a learnable profile. Extensive evaluation of the proposed approach 1 on different datasets demonstrates that it significantly outperforms the existing CS-MRI reconstruction techniques.
Use of Machine Learning Algorithms for Android App Malware Detection

Malware attacks in mobile phones have become more frequent with time. One of the most common ways to infect a phone system with malware is through apps that look benign but are actually malicious. Machine Learning has been employed in the past to detect malware in phones. This has been achieved through supervised learning algorithms and Deep Learning. This paper analyses the performance of various classification algorithms to detect malware in android apps based on permissions requested from the user. The intention was to find as accurate a model as possible to make predictions based on the type of android package. Through implementation of multiple models, XGBoost classifier was found to yield the highest accuracy with 80.03%, followed by Cat Boost Classifier and Random Forest Classifier with 79.58% and 78.67% respectively. Even in most other metrics, XGBoost classifier gave the best results. The data was thoroughly cleaned and the relevant preprocessing steps were implemented, following which classification was performed along with detailed hyperparameter tuning. Various metrics were used to compare the results between the different algorithms, which were accuracy, precision, recall, F1 score and logistic loss function. The findings could help make people aware about the kind of permissions required of malicious applications.
Human-Robot Interaction System for Non-Expert Users in Convenience Stores using Behavior Trees

Service robot in convenience stores is an emerging domain of a human-robot interaction system (HRI). To utilize the full efficiency of the robot and meet users’ needs in HRI, the robot has to recognize human action to decide suitable robot reactions. However, it is difficult for non-expert users (e.g., store staff) who do not have experience with robot programming to create robot behaviors and make sure that those behaviors work properly in the real robot. To solve these difficulties, we provide (i) a graphical interface to facilitate the robot behaviors creation by using Behavior Trees and (ii) a robot simulation environment to test and debug the created robot behaviors before deploying them to the real robot. By using our system, non-expert users can easily create the robot behaviors by making pairs of the detected human action to their desired robot reaction. Non-expert users also can make sure that the created robot behaviors are working without any issues with the surrounding environment via the robot simulation. We evaluate our proposed system by using the System Usability Scale (SUS) and found that the system has good usability but still has points for improvement as reported during our user study.
SCA Assisted Reduced-Order Modelling of Interval Modelled Doha Water Treatment Plant

The performance of the system is affected by parameter variations. The parameter variations can effectively be analysed by interval system. In this work, Doha water treatment plant is modeled as interval system. After this, reduced-order modeling is accomplished for such interval modeled Doha water treatment plant. Initially, uncertainty is considered in each coefficient of the transfer function to obtain the interval model of Doha water treatment plant. Later, time moments and Markov parameters are utilized to determine the reduced-order model of obtained interval modeled Doha water treatment plant. An error function is formed in between time moments and Markov parameters for the determination of reduced-order model. Sine-cosine algorithm (SCA) is used for minimization of thus obtained error function. SCA is population-based stochastic optimization technique which uses sine and cosine functions for the exploration and exploitation of search space to find the solutions. The analysis of results shows that reduced-order model is effectively describing and approaching the response of the system under consideration.
Recognition of isolated characters across different input interfaces using 2D DCNN

Recognition of the characters has gained much attention due to its potential applications like document analysis, license plate detection, house number detection, virtual text entry system, etc., in pattern recognition. However, it is very challenging to recognize the characters under the variations in pattern, style, translation, scale, rotation. This work develops a computationally efficient deep learning model to recognize handwritten, printable, and gesticulated characters. For gesture, the NITS gesticulated database having 60 characters (10 digits, 26 English uppercase alphabets, 4 operations, 18 special symbols) is proposed with the variation in pattern, style, scale in this work. To evaluate the ability and robustness of the proposed model, the handwritten characters (MNIST, EMNIST), printable characters (SVHN, Chars74) databases are considered. This network achieves 94.55%, 89.54%, 87.33, and 93.90% recognition accuracy on NITS gesticulated, EMNIST merge (balanced), SVHN, and Chars74 databases.
ConvNet Based Detection and Segmentation of Brain Tumor from MR Images

One of the diseases that affects humans is brain tumor. It is a type of malignancy disease. A brain tumor is aberrant brain cells that has grown out of control in the brain. This sickness affects many people, and it might be difficult to survive in large groups. When allowing people for early detection of brain tumor, it will help to survive and reduce the death rate of people. Detection of aberrant cells formation in brain is very difficult in medical imaging. The Detection is done by using magnetic resonance imaging (MRI). In this paper, ConvNet architecture is proposed with transfer learning to detect tumor and it aims to differentiate the tumor area by using ROI and non-ROI. The data set is taken from open source Kaggle repository. This model obtained 98.1% accuracy on test data set. This model performed state of the art work.
A Kernelized Support Tensor-Ring Machine for High-Dimensional Data Classification

Most real-life data is high-dimensional tensor format, which traditional machine learning methods based on vector and matrix cannot deal with directly and result in the curse of dimensionality problems. For the sake of addressing those issues, we develop a kernelized support tensor-ring machine (KSTRM) for high-dimensional tensorial data. Specifically, tensor-ring (TR) and kernel methods are utilized to the support vector machine (SVM). And we construct a TR decomposition based kernel function. Experiments are made on real-life tensorial datasets, which confirms the superiority of an STRM over the SVM, STuM and STTM.
MC-FGSM: Black-box Adversarial Attack for Deep Learning System

Deep learning (DL) technology has been widely applied in the safety-critical area, for instance, autopilot system in which the misbehavior will have a huge influence. Hence the reliability of DL system should be tested thoroughly. DL reliability testing is mainly achieved via adversarial attack, however, the existing attack methods lack mathematical proof whether the convergence of the attack can be guaranteed. This paper proposes a novel adversarial attack method, i.e., Monte Carlo-Fast Gradient Sign Method (MC-FGSM) to test the DL robustness. This method does not require any knowledge of the victim DL system. Specifically, this method first approximates the gradient of the input variable via Monte Carlo sampling technique, and then the gradient-based method is applied to generate adversarial attacks. Moreover, a strict mathematical proof has shown the gradient estimation is unbiased and the time complexity is $\boldsymbol{O}(1)$, while the existing method is $\boldsymbol{O}(N)$. The effectiveness of the proposed method is demonstrated by numerical experiments. This method can work as the reliability evaluation tool of the autopilot system.
Evaluation of cyber security in agile manufacturing: Maturity of Technologies and Applications

Utilizing Industry 4.0 technologies offers SMEs a possibility to increase productivity. Utilizing new technologies requires connectivity between production equipment, which raises Cyber Security (CS) issues that need to be addressed. In this work we analyse 18 demonstrators that are representative for different technologies and applications, and are of interest to the digitization of factories and agile production. CS analysis was performed by CS questionnaires to evaluate the current level of CS. As small and medium sized companies (SMEs), and researchers, may be unaware of CS issues, we provide general recommendations and measures to secure production systems from CS attacks.
Website Performance Analysis and Evaluation using Automated Tools

The upcoming improvisations to the websites are so much complex and critical that it contains lots of valuable information and data that is needed to be analysed and evaluated. The qualities that are desired for a perfect website is high performance, better accessibility and usability. The user interface of the website should acquire mobile-friendliness also must be linked with social media and has to be purely secure. The need for the performance testing is needed for better evaluation of the websites and web applications. In this paper we have analysed many websites which contains text based information which is valuable for the people and the data included in the webpages are needed to be properly managed and to assess the websites we have taken some website evaluation techniques and tools in to consideration which are SEOptimer, Qualidator, Website Grader, Webometrics rating criteria, Evaluation by the cloud and various Attribute selection methods are basically applied to the websites and proper evaluation of those websites are done and presented in this paper.
"CHIRON" A PROPOSED REMOTE SENSING PROMPT GAMMA RAY ACTIVATION ANALYSIS INSTRUMENT FOR A NUCLEAR POWERED PROMETHEUS MISSION. Samuel R. Floyd

Prompt Gamma Ray Activation Analysis (PGAA) from neutron capture is an important experimental method that yields information on the elemental abundance of target materials. Gamma ray analysis has been used in planetary exploration missions by taking advantage of the production of neutrons as a result of Galactic Cosmic Ray interaction within the planetary surfaces. The . gamma ray signal that can be obtained from the GCR production of neutrons is very low, so we seek a superior neutron source. NASA s Project Prometheus and the Dept. of Energy aim to develop a nuclear power system for planetary exploration. This provides us with a tremendous opportunity to harness the reactor as a source of neutrons that can be used for PGAA. We envision a narrow stream of neutrons from the reactor directed toward the surface of an asteroid or comet producing the prompt gamma ray signal for analysis. Under ideal conditions of neutron flux and spacecraft orbit, both the signal strength and the spatial resolution will improved by several orders of magnitude over previously missions.
A Dual-Band Hollow Dielectric Resonator Antenna for GPS Applications

This work presents a dual-band hollow rectangular dielectric resonator antenna (DRA) fed by a single coaxial probe, which is designed to operate at the L5 and L1 bands for Global Positioning System (GPS) applications. The dimensions of the dielectric resonator are designed so that each one of its fundamental modes $\mathbf{TE}_{\delta 11}^{x}$ and $\mathbf{TE}_{1\delta 1}^{y}$ resonates at the L5 and L1 bands, respectively. However, the higher-mode $\mathbf{TE}_{\delta 21}^{x}$ may appear at the upper band and, thus, disturbing the radiation characteristics of the $\mathbf{TE}_{\delta 11}^{x}$ mode. To overcome this situation, an air gap is introduced into the DRA to shift this mode away from the L1 band. Also, the reflection coefficient and radiation patterns are simulated and the results show the efficiency of the proposed model.
Design Of A Compact Ultra-Wideband Microstrip Antenna for Millimeter-Wave Communication

In this paper, a planar compact ultra-wideband microstrip antenna is proposed for millimeter-wave communication. The proposed single antenna element has 93.78% fractional bandwidth (covered band of 23.5 GHz to 65 GHz). The simulated radiation efficiency is greater than 93% and a peak gain of 5.6 dBi, with less than 3 dB variation in the whole band. The return loss of the single element antenna is well below −10 dB in the proposed band. The unique contribution of the proposed antenna design lies in its huge operation bandwidth (41.5 GHz) with a very simple structure. The proposed ultra-wideband antenna is suitable for many mmWave applications such as ISM band at 24 GHz, the upper region of K-band, Ka-band (27–40 GHz), mmWave 5G communication (26 GHz, 28 GHz, 38 GHz), as well as 60 GHz high-speed wireless communication (IEEE 802.11ad).
Prediction of transformer top oil temperature based on improved weighted support vector regression based on particle swarm optimization

A support vector regression (SVR) based on particle swarm optimization (PSO) is proposed to estimate the top oil temperature of transformer. This model establishes SVR model based on sample data such as ambient temperature, transformer load, and top oil temperature of transformer. The model analyzes the relationship between the top oil temperature of transformer and other factors, establishes the support vector hyperplane according to different influencing factors, and limits the prediction of the top oil temperature of transformer to a reasonable interval. According to the choice of penalty factor and relaxation factor of support vector machine, the error between this area and the actual oil temperature of the top layer of transformer is minimized, and the top-oil temperature prediction model has the highest prediction accuracy. PSO is used to optimize the penalty factor and relaxation factor in SVR model. The kernel function is improved by principal component analysis to optimize the support vector regression model. Compared with particle swarm optimization(pso) support vector machine(SVM), which considers the weight of data feature quantity, the prediction accuracy is higher.This model uses the advantages of support vector regression method, such as not requiring a large number of samples, not involving probability measure, and being able to deal with multi-dimensional influencing factors, etc.It can provide accurate top-oil temperature prediction results in case of insufficient short-term prediction data of transformer oil temperature or more dimensions of oil temperature related data collected.
Design of stub loaded transmission line matching circuit for series fed patch array

A stub loaded transmission line based matching circuit is designed for the series fed patch array antenna with Dolph-Tschebyshev amplitude tapering at 78.675 GHz. The short-circuited stub loaded transmission line is designed to replace the quarter wave transformer with unrealizably small width. The designed antenna array is matched well at 78.675 GHz. The 10 dB bandwidth is from 78.12 GHz to 79.2 GHz and the co-polarized gain is 9.277 dB.