Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI

Journal of Signal Processing Systems - Segmentation of thigh tissues (muscle, fat, inter-muscular adipose tissue (IMAT), bone, and bone marrow) from magnetic resonance imaging (MRI) scans is useful...     

Different Estimation Methods for Constant Stress Accelerated Life Test under the Family of the Exponentiated Distributions

The Accelerated Life Testing (ALT) has been used for a long time in several fields to obtain information on the reliability of product components and materials under operating conditions in a much shorter time. One of the main purposes of applying ALT is to estimate the failure time functions and reliability performance under normal conditions. This paper concentrates on the estimation procedures under ALT and how to select the best estimation method that gives accurate estimates for the reliability function. For this purpose, different estimation methods are used, such as maximum likelihood, least squares (LS), weighted LS, and probability weighted moment. Moreover, the reliability function under usual conditions is predicted. The estimation procedures are applied under the family of the exponentiated distributions in general, and for the exponentiated inverted Weibull (EIW) as a special case. Numerical analysis including simulated data and a real life data set is conducted to compare the performances between these four methods. It is found that the ML method gives the best results among other estimation methods. Finally, a comparison between the EIW and the Inverted Weibull (IW) distributions based on a real life data set is made using a likelihood ratio test. It is observed that the EIW distribution can provide better fitting than the IW in case of ALT. Copyright © 2015 John Wiley & Sons, Ltd.     

Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining

This research proposes a machine learning approach using fuzzy logic to build an information retrieval system for the next crop rotation. In case-based reasoning systems, case representation is critical, and thus, researchers have thoroughly investigated textual, attribute-value pair, and ontological representations. As big databases result in slow case retrieval, this research suggests a fast case retrieval strategy based on an associated representation, so that, cases are interrelated in both either similar or dissimilar cases. As soon as a new case is recorded, it is compared to prior data to find a relative match. The proposed method is worked on the number of cases and retrieval accuracy between the related case representation and conventional approaches. Hierarchical Long Short-Term Memory (HLSTM) is used to evaluate the efficiency, similarity of the models, and fuzzy rules are applied to predict the environmental condition and soil quality during a particular time of the year. Based on the results, the proposed approaches allows for rapid case retrieval with high accuracy.     

Royal Crown Shaped Polarization Insensitive Perfect Metamaterial Absorber for C-, X-, and Ku-Band Applications

This study proposed a new royal crown-shaped polarisation insensitive double negative triple band microwave range electromagnetic metamaterial absorber (MA). The primary purpose of this study is to utilise the exotic characteristics of this perfect metamaterial absorber (PMA) for microwave wireless communications. The fundamental unit cell of the proposed MA consists of two pentagonal-shaped resonators and two inverse C-shaped metallic components surrounded by a split ring resonator (SRR). The bottom thin copper deposit and upper metallic resonator surface are disjoined by an FR-4 dielectric substrate with 1.6 mm thickness. The CST MW studio, a high-frequency electromagnetic simulator has been deployed for numerical simulation of the unit cell in the frequency range of 4 to 14 GHz. In the TE mode, the offered MA structure demonstrated three different absorption peaks at 6.85 GHz (C-band), 8.87 GHz (X-band), and 12.03 GHz (Ku-band), with 96.82%, 99.24%, and 99.43% absorptivity, respectively. The electric field, magnetic field, and surface current distribution were analysed using Maxwell’s-Curl equations, whereas the angle sensitivity was investigated to comprehend the absorption mechanism of the proposed absorber. The numerical results were verified using the Ansys HFSS (high-frequency structure simulator) and ADS (advanced design system) for equivalent circuit models. Moreover, the proposed MA is polarisation and incident angle independent. Hence, the application of this MA can be extended to a great extent, including airborne radar applications, defence, and stealth-coating technology.     

An Efficient Path Planning and Control Algorithm for RUAV’s in Unknown and Cluttered Environments

This paper presents an efficient planning and execution algorithm for the navigation of an autonomous rotary wing UAV (RUAV) manoeuvering in an unknown and cluttered environment. A Rapidly-exploring Random Tree (RRT) variant is used for the generation of a collision free path and linear Model Predictive Control(MPC) is applied to follow this path. The guidance errors are mapped to the states of the linear MPC structure by using the nonlinear kinematic equations. The proposed path planning algorithm considers the run time of the planning stage explicitly and generates a continuous curvature path whenever replanning occurs. Simulation results show that the RUAV with the proposed methodology successfully achieves autonomous navigation regardless of its lack of prior information about the environment.     

A novel composite model approach to improve software quality prediction

Context:How can quality of software systems be predicted before deployment? In attempting to answer this question, prediction models are advocated in several studies. The performance of such models drops dramatically, with very low accuracy, when they are used in new software development environments or in new circumstances.ObjectiveThe main objective of this work is to circumvent the model generalizability problem. We propose a new approach that substitutes traditional ways of building prediction models which use historical data and machine learning techniques.MethodIn this paper, existing models are decision trees built to predict module fault-proneness within the NASA Critical Mission Software. A genetic algorithm is developed to combine and adapt expertise extracted from existing models in order to derive a “composite” model that performs accurately in a given context of software development. Experimental evaluation of the approach is carried out in three different software development circumstances.ResultsThe results show that derived prediction models work more accurately not only for a particular state of a software organization but also for evolving and modified ones.ConclusionOur approach is considered suitable for software data nature and at the same time superior to model selection and data combination approaches. It is then concluded that learning from existing software models (i.e., software expertise) has two immediate advantages; circumventing model generalizability and alleviating the lack of data in software-engineering.     

Selective determination of urea using urease immobilized on ZnO nanowires

Well-aligned zinc oxide (ZnO) nanowire arrays were fabricated on gold-coated plastic substrates using a low-temperature aqueous chemical growth (ACG) method. The ZnO nanowire arrays with 50–130 nm diameters and ∼1 μm in lengths were used in an enzyme-based urea sensor through immobilization of the enzyme urease that was found to be sensitive to urea concentrations from 0.1 mM to 100 mM. Two linear sensitivity regions were observed when the electrochemical responses (EMF) of the sensors were plotted vs. the logarithmic concentration range of urea from 0.1 mM to 100 mM. The proposed sensor showed a sensitivity of 52.8 mV/decade for 0.1–40 mM urea and a fast response time less than 4 s was achieved with good selectivity, reproducibility and negligible response to common interferents such as ascorbic acid and uric acid, glucose, K⁺ and Na⁺ ions.     

Implementation and experimental performance evaluation of a hybrid interrupt-handling scheme

The performance of network hosts can be severely degraded when subjected to heavy traffic of today’s Gigabit networks. This degradation occurs as a result of the interrupt overhead associated with the high rate of packet arrivals. NAPI, a packet reception mechanism integrated into the latest version of Linux networking subsystem, was designed to improve Linux performance to suit today’s Gigabit traffic. NAPI is definitely a major step up from earlier reception mechanisms; however, NAPI has shortcomings and its performance can be further enhanced. A hybrid interrupt-handling scheme, which was recently proposed in Salah et al. [K. Salah, K. El-Badawi, F. Haidari, Performance Analysis and Comparison of Interrupt-Handling Schemes in Gigabit Networks, International Journal of Computer Communications, Elsevier, Amsterdam 30 (17) (2007) 3425–3441], can better improve the performance of Gigabit network hosts. The hybrid scheme switches between interrupt disabling–enabling (DE) and polling (NAPI). In this paper, we present and discuss major changes required to implement such a hybrid scheme in the latest version of Linux kernel 2.6.15. We prove experimentally that the hybrid scheme can significantly improve the performance of general-purpose network desktops or servers running network I/O-bound applications, when subjecting such network hosts to both light and heavy traffic load conditions. The performance is measured and analyzed in terms of throughput, packet loss, latency, and CPU availability.     

Assessing readiness of IP networks to support desktop videoconferencing using OPNET

OPNET is a powerful network design and simulation tool that has gained popularity in industry and academia. However, there exists no known simulation approach on how to deploy a popular real-time network service such as videoconferencing. This paper demonstrates how OPNET can be leveraged to assess the readiness of existing IP networks to support desktop videoconference. To date, OPNET does not have built-in features to support videoconferencing or its deployment. The paper offers remarkable details on how to model and configure OPNET for such a purpose. The paper considers two types of video traffic (viz. fixed and empirical video packet sizes). Empirical video packet sizes are collected from well-known Internet traffic traces. The paper presents in-depth analysis and interpretation of simulation results and shows how to draw proper engineering conclusions.     

Real-Time Natural Disasters Detection and Monitoring from Smart Earth Observation Satellite

Current remote sensing satellites with multispectral sensors capture high-resolution images and produce vast quantities of data. The size and volume of this information has dramatically increased in the last decade as sensor resolution and capabilities have significantly improved, without a similar improvement on the satellite system capacity to accommodate these changes. Remote sensing satellites currently operate on a “store and forward” paradigm, where data is stored on the satellite until the satellite is in view of the ground station. Low Earth orbit satellites may only see a ground station for a 10–15 min window per pass, in which time all the collected information must be telemetered to the ground. This process requires large and expensive onboard storage resources and places tremendous stress on communication channels. Hence, a complete image may not be successfully telemetered in one pass causing a significant delay between capture and analysis and limiting the benefits of these images. Smart satellites are more technologically advanced, require less ground station support and data storage, and are capable of transmitting required information quickly and easily to ground stations. With onboard reconfigurable data processing, these satellites have faster data product turnaround, less communication requirements, and provide more useful information. The high performance computing (HPC-I) payload on board the Australian satellite FedSat, launched in December 2002, is a demonstration device of the feasibility of reconfigurable computing technology in space. This device is small in size, requires low power, and has the processing capacity to handle large data volumes. Using this device in conjunction with a high-resolution imaging sensor, such as the bispectral infrared detection (BIRD) sensor, smart dedicated satellites become a feasible and cost effective solution to remote sensing needs. This paper elaborates on the system level design of a real-time fire observation system in the context of a smart satellite mission for detecting and monitoring natural disasters. The proposed system is built upon flight tested field programmable gate arrays based HPC-I technology, and would be capable of producing useful information about natural disasters directly broadcasted to interested parties within rapid timeframes. The algorithms for onboard real-time detection of direction, intensity, and location of fires are discussed, and reliable algorithms for detecting and verifying these fires using smoke plume detection are presented. Further work is described including fire-front analysis and the tracking of fire movement.