Miracle-dz/UniFAR-FTUs huggingface dataset:
Segmentation and classification of benign and malignant breast tumors via texture characterization from ultrasound images

The present paper deals with breast tumors classification from ultrasound images. The proposed procedure consists of four steps, namely preprocessing, segmentation, feature extraction and classification. To improve the quality of ultrasound images, the preprocessing step consists of anisotropic filtering and histogram equalization that are performed on the original images. The segmentation is performed on the preprocessed images using the Level Set method that allows to extract the region of interest (ROI) and to reduce its size at the same time. Two feature extraction methods are used in this work namely, the local binary pattern (LBP) method and the histogram of oriented gradients (HOG) method. The two methods (LBP and HOG) are techniques of textures analysis and allow to characterize the ROI. The extracted feature sets constitute the inputs for three classifiers namely, support vector machines (SVM), k-nearest neighbors (KNN) and decision trees (DT). In this work, the best results are obtained by the concatenation of the two feature vectors namely LBP and HOG associated to the SVM classifier. This allows to achieve an accuracy of 96%, a sensitivity of 97% and a specificity of 94%.
Monoplanar CT Reconstruction with GANs

Reconstructing Computed Tomography images (CT) from radiographs currently requires biplanar radiographs for accurate CT reconstruction due to the complementary information contained in the individual views. However, in many cases biplanar information is not available. In this work, we therefore propose a KNN and a PCA-based approach using biplanar radiographs only at the training stage while performing the final inference using only a single anterior-posterior radiograph, thereby increasing the applicability and usability of the model. The methods are quantitatively validated on a multiview database achieving 81% PSNR of biplanar inference and also qualitatively on a dataset of radiographs with no corresponding CT scans.
Outlier Rejection Approach for Direction of Arrival Estimation in Low SNR Conditions

This paper deals with the problem of Direction of Arrival (DoA) estimation through a Non-Uniform Linear Array (NULA) in low signal-to-noise ratio (SNR) conditions. By exploiting the spatial diversity offered by the different subarrays of a given NULA configuration, we propose a simple strategy that aims at identifying and discarding DoA estimates that are recognized to be outliers caused by the generally high level of the employed array beampattern sidelobes. The effectiveness of the proposed algorithm is shown on both simulated and experimental data for a drone surveillance application in the Wi-Fi band.
A Comparative Approach for Investigating an Efficient Method for Reduction of Search Space in Lung Image Analysis

Majority of the lung disorders are diagnosed and treated using chest radiographs and volumetric CT scans. The experts are examining mainly the lung parenchyma for disease predictions and treatment. Though the radiology experts could decipher the lung boundary manually, it is very lengthy and susceptible to observer variations. The effective and automated segmentation of the region of interest (ROI) guarantees the speed and accuracy of clinical examinations. This study mainly focuses on investigating an automated and efficient lung segmentation (LS) technique that will effectively reduce the search space and improve the accuracy of clinical investigations. This study reports the comparative analysis of the traditional LS methods viz. segmentation using image processing and Watershed technique with the supervised learning strategy (SLS) using modified U-net. SLS outperformed the existing frameworks and achieves the highest segmentation accuracy of 99.7%.
Machine Learning KNN Classification An Approach on Detecting Abnormality in Brain Tissues MRI-Graphic User Interface

In this study, we have investigated the k-nearest neighbors’ classifier based on magnetic resonance image to perform the diagnostic of pathology on the brain tumor. We have developed a graphic user interface application to perform the diagnostic. The system is implemented and simulated in Anaconda, and its performance is tested on the real dataset that contains six (06) features and two (02) classes. Each class, an abnormal class (patients have brain tumor) and a normal class (healthy patients) consists of 2764 instances(images), and 500 instances (images), respectively. For quality analysis, the performance metrics are examined and evaluated. When k changes from 1 to 20, a comparison of the performance metric characteristics under fixed test sizes (10%~50%) at constant random state 66 is carried out to predict the class status label. The simulation results achieved (93.2±6.8)% train accuracy, (87.0±3.2)% test accuracy, (66.3±17.6)% precision score, (42.6±25.8)% recall, (47.7+21.5)% F1-score, and (95.1±3.1)% specificity. The performance can be improved by adjusting k and the test size in the range of [2~10], and [15%~25%], respectively. The proposed method shows the effectiveness of identifying a normal class and an abnormal class (brain tumor tissues) from the statistical proprieties of the brain MRI images. This technique is faster with reduced diagnostic time and effective decisions. It will serve as a guideline to evaluate the performance metrics to tells us how much better the proposed model is then making prediction. It also helps programmers to select the optimal model that generates the best performance scenario in order to design a better graphic user interface.
Analysis and Design of a DRAM Cell for Low Leakage

In Dynamic Random Access Memory, every cell experiences leakage current which consumes part of the stored charge. As the DRAM cell size is shrinking, the leakage is increasing. To maintain the desired data retention time, the leakage current must be kept within the acceptable limit. So, leakage reduction in memories is a topic of great challenge and interest in researchers. This book presents the analysis and design of a DRAM cell for low leakage. For the analysis, trench capacitor DRAM cell has been considered. For the design of trench capacitor DRAM cell, 0.18 μm submicron nMOSFET as access transistor and the conventional trench capacitor as storage device have been considered. Various DRAM cell structures, leakage mechanisms in a DRAM cell and process-level techniques for leakage reduction have been reviewed. Process simulation and device simulation of DRAM cell have been done using the ATHENA/ATLAS packages of SILVACO. This book will help the beginners as the book reviews the previous work done by many researchers and provides the trends in DRAM cell designs, theoretical knowledge of leakage mechanisms in DRAM cell and process/device simulation of DRAM cell.
Thermal Storage for Electric Vehicle Cabin Heating in Cold Weather Conditions

With global warming, an inevitable threat to humanity, significant efforts in all carbon emitting industries are required. Electric vehicles are a suitable alternative to the petroleum dominated automotive industry. However, obstacles like charging infrastructure and limited range still stand in the way of their continued acceptance. This limited driving range can be further reduced in cold weather due to decreased battery efficiency and increased heating load. The heating in most electric vehicles is provided by an electrical positive temperature coefficient resistor. This architecture can lead to reductions in range of over 50 %. A thermal storage system has been devised and presented in this thesis which can partially or fully offset the thermal requirements. This is accomplished by pre-heating a thermal storage tank which then uses sensible energy to provide the heat for the cabin and battery pack. The system has been shown to reduce consumption and improve driving range in low ambient temperature conditions. This system successfully offers a potential solution to the concern of large range fluctuations due to different ambient temperatures. After producing a representative electric vehicle model in AMESim, it was compared to the Nissan Leaf with acceptable errors. The range implications for this baseline electric vehicle are then presented. A coolant based, thermal storage tank is iv then added to the model and simulated across a variety of temperatures and thermal storage masses. The results show that a 80 kg, 80 ◦C coolant tank can provide all the heating requirements for a 36 km, one hour and 9 minute city drive cycle. Offering a calculated consumption reduction of up to 36 % at -30 ◦C as compared to the baseline electric vehicle model. Furthermore, a yearly analysis was performed based on this cycle and the results have shown that an optimal 30 kg thermal storage tank can decrease the yearly average consumption by up to 20 Wh/km or 12 %.
An ACTS Mobile Receiver Simulation

The continuing demand for mobile communication and the growing congestion of currently assigned frequency bands has precipitated the development of K/Ka band mobile-satellite technology. The Jet Propulsions Lab (JPL), using the Advanced Communications Technology Satellite (ACTS), has conducted prototype testing of a K/Ka band mobile-satellite link. The JPL system uses a narrow beam antenna which tracks the satellite signal. As the JPL vehicle experienced changes in yaw, pitch, and roll, the antenna experienced a pointing error. A model to describe the power losses caused by pointing error is developed. This model shows a received power loss on the order of 2.0 dB.
Compact, Broadband, and Highly Efficient Leaky-Wave Antenna in Air-Filled Substrate Integrated Waveguide Technology

A highly-efficient multilayer half-mode air-filled substrate integrated waveguide (HM-AFSIW) leaky-wave antenna (LWA) is demonstrated for millimeter-wave automotive radar systems. The antenna consists of two main parts: the antenna feed and a leaky-wave-based radiating part. The antenna feed is implemented in a single layer high-performance 0.5-mm-thick Rogers 4350B substrate. In the radiating part, two 1-mm-thick FR4 substrates sandwich a Rogers 4350B substrate, realizing an air-filled region. The antenna concept has been validated by measurements in the [22-26] GHz band, exhibiting a scanning capability over 9° in the elevation plane thanks to the frequency scanning behavior of the leaky-wave radiating part. The developed antenna is well-suited for fabrication by single-layer low-cost printed circuit board processes, and its low profile and compactness can be considered as a promising solution for automotive radar applications. The yz-plane radiation patterns measured in the [22-26]-GHz band show an average half-power beamwidth of about 12° and sidelobe levels better than 18 dB. The measured peak gain at 24 GHz equals 15 dBi, resulting in a total efficiency higher than 80%.
Normalized Attraction Travel Personality Representation for Improving Travel Recommender Systems

Travel recommender systems (TRSs) aim to reduce travel-related search overload. A significant part of a TRS is representing attractions in a way that reflect the explicit and implicit features of attractions. However, traditional attraction representation methods may not provide a complete image of attractions. Building on the notions of user travel styles (UTSs) and the wisdom of crowds, we propose a method derived from topic-model-based models to represent travel attractions, called the Normalized Attraction Travel Personality (NATP) representation. This approach attempts to leverage the semantics of attraction reviews to model user travel personalities (UTPs), which collectively can construct the attraction travel personality (ATP) representation. Furthermore, we regularize and normalize the ATP representation to obtain our proposed representation. This NATP-based attraction representation could capture implicit characteristics of attractions revealed by the wisdom of crowds. Our experiments show that our representation method gained better results when evaluated against comparative approaches in terms of rating prediction and recommendation ranking quality, indicating the effectiveness of the proposed attraction representation. Lastly, we qualitatively investigate how our attraction representation surpasses the state-of-the-art representation methods.
A Survey of Spatio-Temporal Big Data Indexing Methods in Distributed Environment

With the widespread use of mobile and sensing devices, and the popularity of online map-based services, such as navigation services, the volume of spatio-temporal data is growing rapidly. Conventional big data technologies in existing distributed systems cannot effectively process spatio-temporal big data with temporal continuity and spatial proximity. How to construct an effective index for the application requirements of spatio-temporal data in a distributed environment has become one of the hotspots of spatio-temporal big data research. Many spatio-temporal indexing methods have been proposed to support efficient query processing of spatio-temporal data. In this article, the various spatio-temporal big data indexing methods proposed by domestic and foreign researchers from 2010 to 2020 are classified and summarized according to the distributed environment and application background, and the hot issues that need to be paid attention to in the future are proposed according to the changes in application requirements
Variations in strategy perception among business and military managers

The paper aims to identify the similarities and differences in the emphases and patterns that military and civilian managers attribute to strategy. Two different analysis methods were employed in order to achieve abovementioned research objective. In the first method, the aim was to reveal and compare strategy understanding of the sample groups. Towards that end, ten different strategy definitions and nine different necessity statements compiled from various definitions/statements in the existing strategic management literature were provided to the samples. Then, the samples were requested to consider how strategy and necessity of strategy is formed in their minds, and then to choose top three definitions out of 9/10, and finally rank them in preference order, from one to three. Lastly, we counted the ranks/perceived importance of the various strategy definitions and necessity statements that which statement is ranked to which place. Using mean rank assessment by nonparametric comparison method the study shows differences between military and civilian managers in following aspect: the comparison of the emphases that are attributed to approach to strategy identified significant differences among business and military managers. Concisely, the paper reveals the varying perceptions of business and military managers on strategy based on their personal perceptions.
Coupled Rectangular Waveguide Resonator Filter Using 3D-HFSS Based Admittance Extraction

This research work presents an accurate method of dimensioning geometric parameters of a microwave multi-ports filter design by 3D-HFSS (High Frequency Structure Simulator) simulations based on admittance (Y) matrix extraction technique. The filter's resonant frequency, coupling coefficients and external quality factor can be directly tuned via the multiport Y matrix in a way it achieves the target filter synthesis datasheet set before. A four pole Chebyshev coaxial bandpass filter design is developed showing a good electromagnetic response that presents the availability of the adjustment technique which can be easily adapted to other filter structures.
Comparative Study of Fairness and Fixed Power allocation Algorithms : In Non-Orthogonal Multiple Access system

Non-orthogonal multiple accesses (NOMA) is one of advanced technologies which is used to improve the quality of service in fifth generation systems. Furthermore, it increases the capacity, massive connectivity, enhanced data rate, reduced latency, and minimizing the required spectrum bandwidth. On the other hand, NOMA is characterized by some disadvantages specially the problem of consuming high power allocation from the users. For this reason, the community of the researchers in the wireless communication domain has been developed two techniques such as: the fix and the fair power allocation. In this paper, we compare the sum rate of the fair Power Allocation with the fixed Power allocation algorithm in downlink NOMA systems. Preliminary simulation results have been achieved to confirm that the dynamic fairness power allocation can be useful to perform high sum rate than the fixed power allocation.
Antenna Mutual-Coupling Mitigation With Analogue Compensation Network

This work presents a systematic approach supported by theory, simulation, and measurements that demonstrates antenna mutual-coupling reduction by means of an analogue compensation network. This compensation network is realized with two couplers and a passive network based on a 2nd-order band-pass filter. A 3.5 GHz two-element patch antenna is built and used for comparison with the same antenna and compensation network. After a theoretical analysis highlighting the relevant design constraints, simulated and measured results demonstrate a reduction in the antenna $\vert S_{A12}\vert$ from − 27 dBs to −50 dBs, corresponding to an improvement up to 23 dB over the whole antenna −10dB bandwidth, while the antenna $S_{A11}$ and $S_{A22}$ remain unchanged. This $\vert S_{A12}\vert$ improvement is also shown to improve the far-field radiation pattern for the considered antenna array.
Toxic Speech Classification using Machine Learning Algorithms

In today's era of online social media platforms, there has been a massive surge in the propagation of toxic content speech. They provide many betterments. However, persons with considerable differences in their viewpoints have contributed to an increase in lethality of people in internet posts and debates. With the outbreak of the pandemic, corporations, educational institutions, students, and the general public have all increased their usage in web sites. For a long time, the growing popularity of internet platforms like Twitter and Facebook has been a major cause of anxiety. These platforms not only allow for improved communication, but they also allow the users to express their thoughts, which are quickly shared with the rest of the world. Furthermore, given the diversity of these platforms' users' histories, beliefs, race, and customs, many of them choose to use disparaging, abusive, and antagonistic language while interacting with those who do not share their background. This online toxicity has been increasing exponentially by advancements provided by these social media platforms in this emerging world under the cloud of anonymity. Unlike manually, this problem can be solved using Machine Learning. Phrases like “Obscene”,”Toxic”, “Severe Toxic”,”Threat”, “Insult”,”Identity Hate” are used mutually and hence have been incorporated under “Toxic” speech content. As a result, it is vital to recognise and eliminate toxic speech from internet - based social media networks naturally. The numerous varieties of Machine Learning approaches, such as traditional Machine Learning, ensemble approach are explored in this paper. We use a corpus collected from online platform twitter to do binary and multi-class classification and investigate two techniques. : (a) a method which consists in extracting of word embeddings and then generating the model; (b)Improving the existing models- RF, DT, VC, LR, KNN. Any other sort of social media comment can be analyzed using the proposed methods. By this, we developed a model that can classify given comments into different categories of toxicity with greater precision, recall, and accuracy score.
Non-contact estimation of breathing parameters based on CW radar

The present paper addresses the problem of contactless respiration monitoring system realization based on continuous wave radar (CWR). The developed system is composed of two parts, hardware and software parts. In hardware part, the main purpose is to realize a CWR by using low cost components. The software part is dedicated to develop algorithms for drawing the breathing rhythm and estimating the respiration frequency. Firstly, the received signal is processed and its phase is exploited in the estimation of the respiratory rhythm. Secondly, the respiratory frequency is estimated based on either the periodogram method, the autocorrelation method or on the combination of both methods. Numerical simulation, together with an experimental study have shown the efficiency of the proposed solution in estimating the respiratory rhythm and frequency.
An Efficient Key Agreement And Anonymous Mutual Authentication Protocols For Secure Communication In VANETs

In recent years, the technology of Vehicular Ad Hoc Networks (VANETs) has turned into a significant field of research. A VANET is a form of network that is built on the notion of constructing a network of vehicles for a given scenario, and it is ad-hoc in nature. Data can be transmitted via vehicle-to-vehicle (V2V) and vehicle-to-roadside (V2R) connections. A VANET is a type of network that is essential for a variety of traffic safety applications and accident prevention. A VANET is a wireless network that connects a group of moving or static vehicles. In vehicle contexts, VANETs play a critical role in delivering safety and comfort to drivers. These include intelligent traffic control, real-time data, and activity management. The data is transmitted through a wireless network. This can lead to security and privacy problems. We proposed an authentication protocol for secure communication in VANETs. Vehicle authentication has been identified as one of the security techniques in vehicular networks for preventing unauthorized access by attackers. Furthermore, the majority of solutions are focused on authentication between V2V communications only. However, V2R authentication is also very much essential for securing VANETs. According to an evaluation of its performance, our suggested system is more efficient in minimizing computation and storage overheads.
Weakly Supervised Semantic Segmentation for Range-Doppler Maps

Deep convolutional neural networks (DCNNs) have been successfully applied for object detection and semantic segmentation of radar range-Doppler (RD) maps. However, training a DCNN requires many annotated examples that are costly and difficult to create. In this work we present a method that reduces significantly the manual effort involved in the annotation of RD maps to train a DCNN for segmentation. A 40 times reduction in manual labelling effort is achieved because the annotation of each RD map includes only the class of the objects instead of drawing a polygon around the corresponding cells. The localization of the objects is performed by tracing back from the output to the input of a classification neural network. Experimental results show that our approach achieves robust localization performance in complex real-world urban scenarios as observed with a low-cost automotive radar. Furthermore, we show that our approach performs similarly to DCNNs that are trained with a publicly available dataset in which localization information is provided.