Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Sensory CNN Models for Close Proximity Satellite Operations

Breakthroughs in deep convolutional neural networks for new vision applications in image classification and object detection have pushed forward precision and speed performance indicators in both domains. The future of space exploration relies on the development of novel systems for autonomous operations and onboard data handling especially for computer vision and deep learning. However, previous works on object detection and image classification always operate on the rigid assumption that representative data is available and reliable while merely focusing on offline optimization of architectures for accuracy. This assumption cannot be extended to onboard processing especially in a space environment where unknown scene changes in the visual environment directly affect the performance of machine vision systems. The performance of a deep neural network is as dependent on the input data as it is on the network its self. We propose using a multi-sensory computer vision system that accounts for data reliability and availability using an adaptive input policy. We use custom datasets containing RGB and Depth images of a reference satellite mission for training and testing deep convolutional neural network models for object detection. Our simulation testbed generates our datasets which cover all poses, different ranges, lighting conditions and visual environments. The trained models use multi-sensory input data from both an optical sensor (RGB data) and a time of flight sensor (Depth data). The multi-sensory input data is passed through the adaptive input layer to complementarily provide the most reliable output in a harsh space environment that does not tolerate missing and unreliable data. For instance, the ToF sensor provides visual data that reliably cover close ranges and most importantly can operate regardless of ambient light. The optical sensor provides RGB data at farther ranges and, unlike ToF sensors, is not susceptible to saturation from Earth infra-red emissions. This selective multi-sensory input approach ensures that the CNN model receives reliable input data regardless of the changes in the visual environment to fit the strict operational requirements of space missions. Our work is validated using a sensory-data reliability assessment and object detection models based on the state of the art using Faster R-CNN and YOLO detection techniques. Average precision on the validation dataset saw a significant improvement using our approach. Average precision results went from 50% and 40% using RGB and Depth respectively to 080% using the input selective system.
A Symbolic Approach to the Self-Triggered Design for Networked Control Systems

In this letter, we investigate novel self-triggered controllers for nonlinear control systems with reachability and safety specifications. To synthesize the self-triggered controller, we leverage the notion of symbolic models, or abstractions, which represent abstracted expressions of control systems. The symbolic models will be constructed through the concepts of approximate alternating simulation relations, based on which, and by employing a reachability game, the self-triggered controller is synthesized. We illustrate the effectiveness of the proposed approach through numerical simulations.
Noise Figure of a Unidirectional Cloaking Circuit Based on Parity-Time Symmetry

One-way or unidirectional cloaking is realized in RF circuit design using active and passive components that are linked together in parity and time-reversed pairs otherwise known as parity-time symmetry. Such systems provide cloaking and reflectionless transmission properties at the exceptional point where loss and gain of the system are exactly balanced. Here, we estimate the noise figure of a two-port unidirectional cloaking circuit (unit cell) and the impact of noise figure on the radar cross section of a cloaked object.
Multi-Level Wavelet Convolutional Neural Networks

In computer vision, convolutional networks (CNNs) often adopt pooling to enlarge receptive field which has the advantage of low computational complexity. However, pooling can cause information loss and thus is detrimental to further operations such as features extraction and analysis. Recently, dilated filter has been proposed to tradeoff between receptive field size and efficiency. But the accompanying gridding effect can cause a sparse sampling of input images with checkerboard patterns. To address this problem, in this paper, we propose a novel multi-level wavelet CNN (MWCNN) model to achieve a better tradeoff between receptive field size and computational efficiency. The core idea is to embed wavelet transform into CNN architecture to reduce the resolution of feature maps while at the same time, increasing receptive field. Specifically, MWCNN for image restoration is based on U-Net architecture, and inverse wavelet transform (IWT) is deployed to reconstruct the high resolution (HR) feature maps. The proposed MWCNN can also be viewed as an improvement of dilated filter and a generalization of average pooling and can be applied to not only image restoration tasks, but also any CNNs requiring a pooling operation. The experimental results demonstrate the effectiveness of the proposed MWCNN for tasks, such as image denoising, single image super-resolution, JPEG image artifacts removal, and object classification.
Physical-Layer Security over Generalized SIMO Multipath Fading Channels

The present work investigates the physical layer security of wireless communication systems over non-homogeneous fading environments, which are typically encountered in realistic wireless transmission scenarios in the context of conventional and emerging communication systems. This is carried out in the context of a single-input multiple-output system that consists of a single-antenna transmitter, a multi-antenna legitimate receiver, and a multi-antenna active eavesdropper. To this end, novel exact analytic expressions are derived for the corresponding average secrecy capacity, which are corroborated by respective results from computer simulations that verify their validity. Capitalizing on the offered results, the physical layer security is quantified in terms of different parameters. This leads to the development of useful insights on the impact of non-homogeneous fading environment and the number of employed antennas on the achieved physical layer security levels of the considered set up.
Frequency Tunable SIW Cavity Resonator Integrated With Schottky Diode

A Frequency Tunable Substrate Integrated Waveguide (SIW) Cavity Resonator Integrated with Schottky Diode has been reported in this paper. Frequency tunability between 8.5 GHz to 11.9 GHz (X band) has been achieved by varying the biasing voltage of the Schottky diode. Further we have also presented a physical model describing the phenomenon by the help of an equivalent circuit. The design of the SIW is done by the HFSS based on finite Element Method (FEM) and the integration of Schottky diode in the structure is done by ADS based on Method of Moment (MoM). Frequency tunability by integrating schottky diode in the SIW structure has shown better improvement in voltage requirement, temperature range and conversion efficiency. The structure implementation is quite simple and thus will be appropriate in the application of low cost microwave and millimeter wave devices.
Energy Modeling of VTOL Aircraft for Titan Aerial Daughtercraft (TAD)Concepts

Aerial vehicles are of considerable interest for exploration of Saturn's icy moon Titan. The concept of Titan Aerial Daughtercraft (TAD)was introduced in 2014, where small-scale (e.g. < 10 kg)VTOL aircraft would conduct multiple sorties from a mothership (lander or balloon), recharging batteries from a radioisotope power source (RPS)on the mothership between sorties. More recently, NASA has funded a study of a much larger Titan rotorcraft ($\sim$ 450 kg)that would carry its own RPS. The TAD concept is still worth further analysis as a low-cost element of mission architectures where the mothership is a lander, balloon, or other large mobility system. To compare the potential performance of daughtercraft that are rotary-wing only or fixed/rotary-wing hybrids, this study examines potential flight durations and flight radius for small-scale (4 to 10 kg)quadcopters and tailsitters. For notional system parameters, a payload mass fraction of 25%, and a conservative battery model, energy estimation modeling for lander-based scenarios shows maximum flight endurance at Titan's surface for 10 kg quad-copter and tailsitter as 9.3 hours and 12.4 hours, respectively, and maximum sortie radius from a lander as 89 km and 125 km, respectively. Modeling of balloon-based scenarios estimated the mass potentially available for payload as a function of the total TAD mass and the float altitude of the balloon. For a balloon floating at 10 km above the surface and a 10 kg TAD, the model gave available mass of about 2.8 kg for a quadcopter and 3.5 kg for a tailsitter. Non-aerodynamic power dominates aerodynamic power at the lowest mass, but becomes a less significant fraction of the total as mass increases. The tailsitter has little or no advantage over the quadcopter at the lowest mass, but increasing advantage as mass increases.
The Segmentation Problem in Arabic Character Recognition The State Of The Art

Arabic characters are used in several languages other than Arabic, despite to this fact; Arabic Character Recognition (ACR) has not received enough interests by researchers. Little researchprogress has been achieved comparing to the one done on the Latin or Chinese and the solutions available in the market are still far from being perfect. However, recent years have shown a considerable increase in the number of research papers. The cursive nature of Arabic writing makes the process of recognition a very challenging one. Several methods to segment the Arabic words into characters have been proposed in the past two decades. This paper seeks to provide a comprehensive review of the methods proposed by researchers to segment. There is a room for research in this area; hence, the speech aims at promoting the research among Muslim researchers to work on ACR by addressing the challenges posed by the nature of the characters. The segmentation methods are categorized in nine different methods based on the techniques used. The advantages and drawback of each one are discussed.
Stereovision Based Generic Obstacle Detection and Motion Estimation Using V-stxiel Algorithm

Driving environment perception is a critical and fundamental task of Advanced Driver Assistance System (ADAS) and self-driving technology. In object detection, one of the major challenges is how to achieve an effective model from real scene. Although A medium-level representation named stixel-world has achieve accurate detection results for the task of generic obstacle detection, robustness is still a problem and hard for application. To address this issue, this paper proposes a variant of approach named V-stixel that need no free space computation. Compared with the detection method of monocular image, a variety of obstacle types can be detected without the specific object modeling. It is a generic obstacle detection method and needs no previous training step. Finally, we compare this new method with baseline methods on the KITTI stereo dataset. The experimental results show that the accuracy of the detection results is good, the measurement error is robust in the long video sequences and gain a significant speed-wise.
Supporting Source Code Feature Analysis Using Execution Trace Mining

Software maintenance is a significant phase of a software life-cycle. Once a system is developed the main focus shifts to maintenance to keep the system up to date. A system may be changed for various reasons such as fulfilling customer requirements, fixing bugs or optimizing existing code. Code needs to be studied and understood before any modification is done to it. Understanding code is a time intensive and often complicated part of software maintenance that is supported by documentation and various tools such as profilers, debuggers and source code analysis techniques. However, most of the tools fail to assist in locating the portions of the code that implement the functionality the software developer is focusing. Mining execution traces can help developers identify parts of the source code specific to the functionality of interest and at the same time help them understand the behaviour of the code. We propose a use-driven hybrid framework of static and dynamic analyses to mine and manage execution traces to support software developers in understanding how the system’s functionality is implemented through feature analysis. We express a system’s use as a set of tests. In our approach, we develop a set of uses that represents how a system is used or how a user uses some specific functionality. Each use set describes a user’s interaction with the system. To manage large and complex traces we organize them by system use and segment them by user interface events. The segmented traces are also clustered based on internal and external method types. The clusters are further categorized into groups based on application programming interfaces and active clones. To further support comprehension we propose a taxonomy of metrics which are used to quantify the trace. To validate the framework we built a tool called TrAM that implements trace mining and provides visualization features. It can quantify the trace method information, mine similar code fragments called active clones, cluster methods based on types, categorise them based on groups and quantify their behavioural aspects using a set of metrics. The tool also lets the users visualize the design and implementation of a system using images, filtering, grouping, event and system use, and present them with values calculated using trace, group, clone and method metrics. We also conducted a case study on five different subject systems using the tool to determine the dynamic properties of the source code clones at runtime and answer three research questions using our findings. We compared our tool with trace mining tools and profilers in terms of features, and scenarios. Finally, we evaluated TrAM by conducting a user study on its effectiveness, usability and information management.
Text Mining For Expert Witness

Expert witness is a person who is specialist in a subject, often technical, who may present his/her expert opinion without having been witness to any occurrence relating to the lawsuit. Where text mining is as a process of exploratory data analysis that leads to heretofore unknown information, or to answers for questions for which the answer is not currently known. In the publication an application using text mining for attorney is given, if an attorney who need expert advice from an oncologist to determine whether the client was a victim of medical malpractice. Traditionally, I’ll have to rely on word-of-mouth recommendations, small directories of selfselected experts on the web or time-consuming research conducted over multiple text collections. Text mining can overcome these problems using Bayesian Belief Network algorithm concept which merge the link entities and records for efficient result to take a decision. Keywords— Text mining, Information Extraction, Record Linkage, Expert Witness
Speech/nonspeech discrimination for speech recognition system under real life noise environments

A method to identify speech segments in noisy environments is presented. It consists of two subsystems: one is for segmentation of a nonstationary signal corrupted by a stationary background noise, and the other is for discrimination of the nonstationary segment, whether it is speech or not. The proposed segmentation method is sensitive enough to detect weak consonants at word initials correctly. Feature parameters effective for speech/nonspeech discrimination are presented. <<ETX>>
A vision system for an autonomous underwater vehicle

A vision system for the PISCIS project is described. This is a proposed project for the use of an untethered autonomous underwater vehicle (AUV) for pipeline inspection. The vision system is termed PVS (PISCIS vision system). It will assist the AUV in finding and following the pipeline. Robust image interpretation is important as humans can not interact with the vision system and correct errors. The image quality is reduced by backscatter, light absorption, and marine material on the pipelines. The PVS is fixed to a moving vehicle and must rely on a heading sensor and an altitude sonar to match images features with pipeline models. The models are retrieved from a map based on the vehicle's position. <<ETX>>
Use of hyperspectral imagery for mapping grape varieties in the Barossa Valley, South Australia

The wine industry is important to Australia's economy. With the advances in remote sensing there has been increasing interest in its potential application for vineyard varietal, condition and health mapping. CASI (Compact Airborne Spectrographic Imager) data was obtained over a vineyard in the Barossa Valley, South Australia in an attempt to discriminate between the grape cultivars (Vitis vinifera), Cabernet Sauvignon and Shiraz. Statistical analysis of sample spectra from the two varieties in the CASI imagery showed that the significant differences in the visible region. Maximum likelihood classification was employed to map the two grape varieties present on the site. Classification was performed using 12 visible and near infrared CASI bands and repeated using a spectral subset of seven bands shown to be most significant in separating the varieties. Discrimination between Cabernet Sauvignon and Shiraz was successful with 91.5% of vine rows correctly classified. Spectral subsetting did not improve classification and led to under classification of vine pixels.
A robust digitally programmable and reconfigurable monolithic filter structure

A completely programmable and reconfigurable digitally controllable active structure is presented. A specific implementation, which has over three decades of adjustment range and is capable of realizing any even-order filter function of up to sixth-order, was fabricated and tested. The filter operates over the 1-kHz to 1-MHz frequency range. Experimental results that confirm the reconfigurability and programmable characteristics of this structure are presented. <<ETX>>
Object-Oriented Software Engineering Using UML, Patterns, and Java

This widely used book teaches practical object-oriented software engineering with the key real world tools UML, design patterns and Java. This step-by-step approach allows the reader to address complex and changing problems with practical and state-of-the-art solutions. This book uses examples from real systems and examines the interaction between such techniques as UML, Java-based technologies, design patterns, rationale, configuration management, and quality control. It also discusses project management related issues and their impacts. A valuable book for development engineers, software engineers, consulting engineers, software architects, product managers, project leaders, and knowledge managers.
This item is the archived peer-reviewed author-version of: Empirical conceptualisation of integrative learning : a focus on theory-practice integration in technical vocational education and training Reference:

The integrative learning of theory and practice has been widely recognised as a cornerstone of today’s technical vocational education and training (T-VET). Considerable uncertainty persists regarding how to construe such integrative learning, let alone regarding how it proceeds or what it generates. This article reports an in-depth qualitative study designed to clarify the concept of integrative learning by advancing current understanding of what constitutes the integrative learning of theory and practice (ILTP) in terms of both its process and its outcome aspects. In all, 48 key actors in dual T-VET (students, tutors and mentors) participated in serial focus groups, class observations and apprenticeship observations. The constant comparison method was used to generate a description of both the learning process and the learning outcome based on descriptive axial dimensions along which learning and knowledge were positioned. More specifically, we distinguished three process dimensions (intentionality, time of the prompt and locus of learning) and three outcome dimensions (purpose, logic and locus of integrated knowledge). All in all, the findings can be understood only in consideration of co-existing perspectives on integration according to which the separation of theory and practice is more or less marked. The article discusses expected implications for practitioners and future research.
Target image search using fMRI signals

Recent neural signal decoding studies based on functional magnetic resonance imaging (fMRI) have identified the specific image presenting to the subject from a set of potential images, and some studies extend neural decoding into image reconstruction, i.e. image contents that the subject perceived were decoded from the fMRI signals recorded during the subject looking at images. In this paper, we decoded the target images using fMRI signals and described a target image searching method based on the relationship between target image stimuli and fMRI activity. We recorded fMRI data during a serial visual stimuli image presentation task, some of the stimuli images were target images and the rest images were non-target ones. Our fMRI data analysis results showed that in the serial visual presentation task, target images elicited a stereotypical response in the fMRI, which can be detected by multi-voxel pattern analysis (MVPA). Classifiers designed with support vector machine (SVM) used this response to decipher target images from non-target images. The leave-one-run-out cross-validation showed that we can pick out the target images with a possibility far above the chance level, which indicate that there’s a neural signatures correlated with the target image recognition process in the human systems.