Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of Remotely Sensed Data by Texture Features with the Nature-Inspire Optimization Algorithm

: At regular intervals, the satellite remote sensing technologies collect images/data and volume of information gathered is substantial and it is developing exponentially as the innovation is developing at a quick speed. Remote sensing technique has been extensively utilized for recognition of land use and land cover structures. In this paper, we have used four classes of images which are enhanced by Prewitt filter and features are extracted by texture approaches, which increases the pattern visibility. The proposed methodology here includes an efficient feature extraction and further classification of the satellite images on the basis of these features using Particle Swarm Optimization, hybrid (PSO+ACO). For the training Convolution Neural Network (CNN) is used. The various performance metrics have been determined and contrasted according to the recovered satellite images from the dataset.
OmniTrack: Orientation-Aware RFID Tracking With Centimeter-Level Accuracy

RFID tracking attracts a lot of research efforts in recent years. Most of the existing approaches, however, adopt an orientation-oblivious model. When tracking a target whose orientation changes, those approaches suffer from serious accuracy degradation. In order to achieve target tracking with pervasive applicability in various scenarios, in this article, we propose OmniTrack, an orientation-aware RFID tracking approach. Our study presents a generic model that discribes the linear relationship between the tag orientation and the phase change of the backscattered signals. Based on this finding, we propose an orientation-aware phase model to explicitly quantify the respective impact of the read-tag distance and the tag's orientation. OmniTrack addresses practical challenges in tracking the location and orientation of a mobile tag. Our experimental results demonstrate that OmniTrack achieves centimeter-level location accuracy and has significant advantages in tracking targets with varing orientations, compared to the state-of-the-art approaches.
Fault-Tolerant Function of DC-Bus Power Source in A Dual Inverter Drive System and Its Operation Characteristics

The dual inverter drive system feeding an open-end winding permanent magnet (PM) motor has been studied for developing autopilot technologies of hybrid vehicles. Autopilot systems require fault-tolerant functions, which enable it to continue to drive the motor even if some failure occurs in the motor drive system. The fault-tolerant function of a dual inverter drive system, which assumes that the DC-bus battery power source of the dual inverter drive system has failed, is discussed in this paper. In the dual inverter drive system that is considered, both the inverters have a capacitor in parallel with a battery across each DC-bus. The capacitor drives the motor continuously even if the DC-bus battery has failed. The inverter, in which the DC-bus battery has failed, is operated with the capacitor instead of the failed battery. It is required to both control the capacitor voltage at a constant value and simultaneously generate multilevel voltage waveforms across the motor windings with the space vector modulation (SVM). In this paper, the fault-tolerant function of the DC-bus battery in a dual inverter drive system is proposed, and its operation characteristics are examined through several experiments and compared with those of a normal system.
A 12.5-MHz Bandwidth 77-dB SNDR SAR-Assisted Noise Shaping Pipeline ADC

This article presents a successive approximation register (SAR)-assisted noise-shaping (NS) pipeline architecture which breaks the speed bottleneck of the existing SAR or SAR-assisted-type NS analog-to-digital converters (ADCs). Rather than only for residue amplification and pipeline operation, the multiplying digital-to-analog converter (MDAC) is also reused as unity buffer and analog adder to realize the NS with error feedback (EF) structure in this design. While incorporating the proposed alternative loading capacitor (ALC) technique, an ideal first-order noise transfer function (NTF) is realized without additional feedback phase and only with a small analog circuit overhead. Unlike other NS SAR ADCs that involved amplification, the inter-stage gain attenuates the noise from the second-stage comparator, thus leading to both high speed and resolution. Fabricated in a 65-nm CMOS process, the prototype achieves a signal-to-noise-and-distortion ratio (SNDR) of 77.1 dB over 12.5-MHz bandwidth (BW) with only over-sampling ratio (OSR) of 8. Under a 1.2-V supply voltage, the ADC consumes 4.5 mW and exhibits a Scherier figure of merit (FoM) of 171.5 dB.
Migrating GWT to Angular 6 using MDE

In the context of a collaboration with Berger-Levrault, a major IT company, we are working on the migration of a GWT application to Angular. We focus on the GUI aspect of this migration which, even if both are web frameworks, is made difficult because they use different programming languages (Java for one, Typescript for the other) and different organization schemas (e.g. different XML files). Moreover, the new application must mimic closely the visual aspect of the old one so that the users of the application are not disturbed. We propose an approach in three steps that uses a meta-model to represent the GUI at a high abstraction level. We evaluated this approach on an application comprising 470 Java (GWT) classes representing 56 screens. We are able to model all the web pages of the application and 93% of the wid-gets they contain, and we successfully migrated (i.e., the result is visually equal to the original) 26 out of 39 pages (66%). We give examples of the migrated pages, both successful and not.
Statistical Methods to Measure Reading Progression Using Eye-Gaze Fixation Points

In this thesis, we investigate methods to accurately track reading progression by analyzing eye-gaze fixation points, using commercially available eye tracking devices and without the imposition of unnatural movement constraints. In order to obtain the most accurate eye-gaze fixation point data possible, the current state of the art relies on expensive, cumbersome apparatuses. Eye-gaze tracking using less expensive hardware, and without constraints imposed on the individual whose gaze is being tracked, results in less reliable, noise-corrupt data which proves difficult to interpret. Extending the accessibility of accurate reading progression tracking beyond its current limits and enabling its feasibility in a real-world, constraint-free environment will enable a multitude of futuristic functionalities for educational, enterprise, and consumer technologies. We first discuss the “Line Detection System” (LDS), a Kalman filter and hidden Markov model based algorithm designed to infer from noisy data the line of text associated with each eye-gaze fixation point reported every few milliseconds during reading. This system is shown to yield an average line detection accuracy of 88.1%. Next, we discuss a “Horizontal Saccade Tracking System” (HSTS) which aims to track horizontal progression within each line, using a least squares approach to filter out noise. Finally, we discuss a novel “Slip-Kalman” filter which is custom designed to track the progression of reading. This method improves upon the original LDS, performing at an average line detection accuracy of 97.8%, and offers advanced capability in horizontal tracking compared to the HSTS. The performance of each method is demonstrated using 25 pages worth of data collected during reading.
A dynamic thermal management in buildings for comfort enhancement and energy conservation : from sensor networking to user participation

Nowadays, buildings occupy a significant portion of global energy consumption. Sustainable efforts and measures have been carried out to reduce the energy consumption in buildings. Yet, buildings are not only a shelter for people, they also serve to provide people with comfort and safety; otherwise, turning off every power consuming equipment can maximize the energy conservation. Balancing the energy consumption and human comfort is challenging since it is hard to evaluate the satisfaction of occupants. This thesis presents a user-participatory framework, and models the thermal comfort of individuals by using mobile devices with minimal manual input. The framework connects with the building management system (BMS) to adjust the temperature setting; the mobility of underlying controllers is thus important to the framework applicability. An asynchronous-response design is developed to dewiring existing controllers into wireless while keeping the communication protocols intact. A modular design for wireless data plane is used to selectively prioritize and schedule data transmission in case of link quality and throughput variations. The feasibility of the design is tested by comprehensive experiments and field deployments in the University. A temperature-comfort correlation model is developed to estimate the individual comfort based on a collection of parameters and sensors data. In addition, a setpoint optimization algorithm is developed to find out the temperature that maximizes the group comfort. Field experiments in the University and a commercial office show the improvement of thermal comfort by 63% and 33.8% respectively, while reducing 18% of energy consumption in the experiment of commercial office.
A Super-Resolution Mixed-Signal Doherty Power Amplifier for Simultaneous Linearity and Efficiency Enhancement

This article presents a mixed-signal Doherty power amplifier (MSDPA) architecture for simultaneous linearity and efficiency enhancement. The MSDPA comprises one analog power amplifier (PA) as the main PA and one binary-weighted digital PA as the auxiliary PA. The MSDPA input is a generic envelope-varying complex-modulated signal. Based on the real-time amplitude-modulated (AM) envelope, auxiliary digital PA weightings are dynamically turned-on to perform optimum Doherty load modulation for superior linearity and back-off efficiency. Moreover, quantization noise is largely suppressed by the mixed-signal Doherty operation and nonuniform quantization (NUQ), while spectral images are substantially reduced by the quasi-first-order hold (quasi-FOH) operation, which together achieves super-resolution over conventional digital PAs; this makes the MSDPA conducive to millimeter-wave (mm-Wave) or compound PA designs by obviating the need for a large effective number of bits (ENOB) on AM digital controls. As a proof of concept, a 3-bit MSDPA is implemented at 27 GHz in a 45-nm silicon-on-insulator (SOI) CMOS process. The prototype PA achieves 40.1% peak power-added efficiency (PAE), 23.3-dBm saturated output power (<inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {sat}}$ </tex-math></inline-formula>), and 39.4% PAE for 22.4-dBm <inline-formula> <tex-math notation="LaTeX">$P_{1\mathrm {dB}}$ </tex-math></inline-formula> at 27 GHz in continuous-wave (CW) measurements. The PAE at 6-dB power back-off (PBO) is 33.1%, which corresponds to a 1.68<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> improvement over a normalized Class-B PA. With only three control bits, the MSDPA PA supports a 12-Gb/s 64-QAM signal at −24.5-dB rms error vector magnitude (EVM) and average <inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {out}}$ </tex-math></inline-formula>/PAE of +15.6 dBm/27.8% without digital pre-distortion (DPD).
Content-aware image resizing for faster object detection on aerial imagery

To combat poaching or perform game counts nature conservationists need to inspect areas that are very large and hard to reach by car or foot. Recently, nature conservationists have been able to inspect these areas more easily by using UAVs equipped with cameras. Despite of the ease with which these systems can be deployed, the recorded imagery still needs to be analyzed manually. Automatic object detection algorithms could greatly reduce the time spent looking for the object of interest, beneﬁting the conservation work. resolution im-ages in are much lower than for nature conservation tasks. Object proposal methods are signiﬁcantly slower when applied to high resolution images, which aﬀects the detection rate. To maintain a good object detection rate, we apply a content-aware image resizing method that resizes the image without compromising on the content.
An Efficient Technique for Network Intrusion Detection Using Feature selection

Network Intrusion Detection System (NIDS) is one of the most significant parts of network security that can make secure transactions over a network. Despite many efforts in the field, we can observe increased sophistication and variety of attacks on networks. In such situation Machine learning (ML) based methods have emerged some of the most effective as well as popular methods to detect the attacks. One of the complexities involved in the ML-based method is that they are mostly of the black-box nature, so their inner working phenomena are very often quite complex to understand and interpret. Moreover, high-dimensional features and an inadequate number of training records have caused some problems in the classifications, such as over fitting of the results, noise sensitiveness, overload computation and lack of significant physical interoperability. In this paper, we propose a discriminative features selection and network intrusion classification by applying sparse modeling with Lasso and SVMs with two kernel functions. SVMs are standard ML techniques which can provide reasonable performance however it can have some shortcomings such as interpretability and huge computational cost. On the other hand, sparse modeling has been considered as an advanced technique for data analysis and processing via regularization. Sparse modeling can be used to simultaneously select discriminative features from the repository of the dataset. Moreover, it also determines the coefficient of the linear classifier where prior information about features structure can be mapped into various sparsity-inducing regularization such as Lasso. Furthermore, we apply sparse modeling for the multiclass-classification purpose; in this way, we can identify and select the features yielded by the network attacks that are the most significant ones. Our experimental in this correspondence suggest that the proposed techniques have better performance than most of the state-of-the-art methods.
A decompiler for Objective-C

Objective-C is a popular programming language primarily used on the OS X and iOS platforms. We present a practical approach to decompilation of programs written in Objective-C and compiled for the x86 and AArch64 architectures using LLVM. Based on already-known generic reverse engineering techniques and compiler theory, this thesis analyzes new challenges and opportunities that occur in Objective-C binaries. We then offer solutions and algorithms that allow a decompiler to better recognize the high-level structures commonly used in Objective-C source codes. The thesis introduces an implementation of a new decompiler called “Cricket”, an interactive GUI application for OS X, which uses the described algorithms and pattern matching methods to reconstruct source code in Objective-C. The decompiler tries to maximize readability of the output and allows user interaction to further modify the generated source code. The implemented software is then evaluated on a popular open-source framework and the results are compared to a competing product.
Distributed Green Offloading and Power Optimization in Virtualized Small Cell Networks With Mobile Edge Computing

Virtualized small cell networks (SCNs) integrated with mobile edge computing (MEC) is a promising paradigm to provide both wideband access and intensive computation economically for user equipments (UEs) in the scenario of multiple mobile virtual network operators and infrastructure providers. However, the model of offloading in this case is often of high complexity and lacks effective solution. In this paper, by jointly considering offloading, time slice and power allocation, we formulate the energy consumption reduction of the UEs in virtualized SCNs with MEC as a mixed integer nonlinear programming. Our aim is to minimize the total energy consumption of the UEs subject to minimum overall throughput of the network. To solve the problem efficiently, we convert it into a biconvex problem by adding auxiliary variable, which enables the derivation of an efficient iterative algorithm by two subproblems. Towards the first subproblem, we introduce local variables to handle the coupling constraint, and propose an alternating direction method of multipliers (ADMM)-based distributed algorithm, where the closed-form expressions of the optimal solutions in variables updating are derived. For the second subproblem, the closed-form expressions of the optimal solution is also derived. Finally, the effectiveness of the proposed algorithm is demonstrated by extensive simulations with different system configurations.
Building Firmly Nonexpansive Convolutional Neural Networks

Building nonexpansive Convolutional Neural Networks (CNNs) is a challenging problem that has recently gained a lot of attention from the image processing community. In particular, it appears to be the key to obtain convergent Plugand-Play algorithms. This problem, which relies on an accurate control of the the Lipschitz constant of the convolutional layers, has also been investigated for Generative Adversarial Networks to improve robustness to adversarial perturbations. However, to the best of our knowledge, no efficient method has been developed yet to build nonexpansive CNNs. In this paper, we develop an optimization algorithm that can be incorporated in the training of a network to ensure the nonexpansiveness of its convolutional layers. This is shown to allow us to build firmly nonexpansive CNNs. We apply the proposed approach to train a CNN for an image denoising task and show its effectiveness through simulations.
Transfer Learning Tuning Utilizing Grey Wolf Optimizer for Identification of Brain Hemorrhage from Head CT Images

Most commonly, diagnosing the brain hemorrhage a condition caused by a brain artery busting and causing bleeding is done by medical experts identifying such pathologies from the computer tomography (CT) images. With great advancements in the domain of deep learning, utilizing deep convolutional neural networks (CNN) for such tasks has already proven to achieve encouraging results. One of the major problems of using such an approach is the need for big labeled datasets to train such deep architectures. One of the efficient techniques for training CNNs with smaller datasets is transfer learning. For the efficient use of transfer learning, many parameters are needed to be set, which are having a great impact on the classification performance of the CNN. Most of those parameters are commonly set based on our previous experience or by trial and error. The proposed method addresses the problem of tuning the transfer learning technique utilizing the nature-inspired, population-based metaheuristic Grey Wolf Optimizer (GWO). The proposed method was tested on a small head CT medical imaging dataset. The results obtained from the conducted experiments show that the proposed method outperforms the conventional approach of parameter settings for transfer learning.
Versatile Binary-level Concolic Testing

Computing systems are experiencing an explosive growth, both in complexities and diversities, ushered in by the proliferation of cloud computing, mobile computing, and Internet of Things. This growth has also exposed the consequences of unsafe, insecure, and unreliable computing systems. These all point to the great needs of sophisticated system validation techniques. Recent advances in research on symbolic execution has shown great promises for automated software analysis, e.g., generating test cases, finding bugs, and detecting security vulnerabilities. However, symbolic execution is mostly adopted to analyze user applications, while modern computing systems in practice consist of many components shipped by various vendors, besides user applications, e.g., operating systems, firmware and hardware devices. In this dissertation, we propose versatile binary-level concolic testing, which defines a standard execution-trace format, and features an open and highly extensible architecture. It allows easy integration of multiple concrete execution frontends and symbolic execution backends, which significantly improves the applicability and flexibility of symbolic execution, especially to modern computing systems with various components. First, we present the design and implementation of CRETE, the infrastructure of versatile binary-level concolic testing. CRETE provides an open and highly extensible architecture allowing easy integration of multiple concrete and symbolic execution environments, which communicate with each other only by exchanging standardized
ACCREDITATION OF THE FACULTY OF MEDICINE UNIVERSITY OF GEZIRA (FMUG), PILOT STUDY

The Faculty of Medicine University of Khartoum was founded in 1924 and has graduated the majority of doctors in the Sudan. In recent years the government invested heavily in higher education and 26 new medical schools have been established and are now functioning and graduating doctors. Those new schools adopted different models of curricula, however most of them benefited from the Gezira medical school experience and hence their curricula are innovative. The large number of schools and the diversity in their curricular models fueled interest in the Sudan General Medical Council and the Ministry of Higher Education to establish a national mechanism for evaluating those schools and ensuring the society that they graduate doctors with optimum standard.
A High-Resolution Imaging Method for Strip-Map SAR With Missing Data

Due to the long aperture, the high-resolution imaging for strip-map SAR with missing data is a challenge, in which the range migration correction and phase error correction are challenging. In this paper, a high-resolution imaging method of this type of data based on compressed sensing (CS) is proposed, which divides the strip-map data into several sub-apertures restored by CS and recombined to the strip-map data. The basis matrix and the measurement matrix for CS are deduced. The sub-aperture data is autofocused by the Projection Approximation Subspace Tracking (PAST) algorithm to meet the sparse requirement for the reconstructed image and the intact phase error data is restored by CS in order to combine the sub-apertures. A high-resolution image of the restored data can be obtained by conventional imaging method which performs range migration and autofocus.
A time efficient and accurate retrieval of range aggregate queries using fuzzy clustering means (FCM) approach

Massive growth in the big data makes difficult to analyse and retrieve the useful information from the set of available data’s. Statistical analysis: Existing approaches cannot guarantee an efficient retrieval of data from the database. In the existing work stratified sampling is used to partition the tables in terms of static variables. However k means clustering algorithm cannot guarantees an efficient retrieval where the choosing centroid in the large volume of data would be difficult. And less knowledge about the static variable might leads to the less efficient partitioning of tables. Findings: This problem is overcome in the proposed methodology by introducing the FCM clustering instead of k means clustering which can cluster the large volume of data which are similar in nature. Stratification problem is overcome by introducing the post stratification approach which will leads to efficient selection of static variable. Improvements: This methodology leads to an efficient retrieval process in terms of user query within less time and more accuracy.