Investigation into scalable energy and performance models for many-core systems

PhD ThesisIt is likely that many-core processor systems will continue to penetrate emerging embedded and high-performance applications. Scalable energy and performance models are two critical aspects that provide insights into the conflicting trade-offs between them with growing hardware and software complexity. Traditional performance models, such as Amdahl’s Law, Gustafson’s and Sun-Ni’s, have helped the research community and industry to better understand the system performance bounds with given processing resources, which is otherwise known as speedup. However, these models and their existing extensions have limited applicability for energy and/or performance-driven system optimization in practical systems. For instance, these are typically based on software characteristics, assuming ideal and homogeneous hardware platforms or limited forms of processor heterogeneity. In addition, the measurement of speedup and parallelization factors of an application running on a specific hardware platform require instrumenting the original software codes. Indeed, practical speedup and parallelizability models of application workloads running on modern heterogeneous hardware are critical for energy and performance models, as they can be used to inform design and control decisions with an aim to improve system throughput and energy efficiency. This thesis addresses the limitations by firstly developing novel and scalable speedup and energy consumption models based on a more general representation of heterogeneity, referred to as the normal form heterogeneity. A method is developed whereby standard performance counters found in modern many-core platforms can be used to derive speedup, and therefore the parallelizability of the software, without instrumenting applications. This extends the usability of the new models to scenarios where the parallelizability of software is unknown, leading to potentially Run-Time Management (RTM) speedup and/or energy efficiency optimization. The models and optimization methods presented in this thesis are validated through extensive experimentation, by running a number of different applications in wide-ranging concurrency scenarios on a number of different homogeneous and heterogeneous Multi/Many Core Processor (M/MCP) systems. These include homogeneous and heterogeneous architectures and viii range from existing off-the-shelf platforms to potential future system extensions. The practical use of these models and methods is demonstrated through real examples such as studying the effectiveness of the system load balancer. The models and methodologies proposed in this thesis provide guidance to a new opportunities for improving the energy efficiency of M/MCP systemsHigher Committee of Education Development (HCED) in Ira

A VHDL Model for Implementation of MD5 Hash Algorithm

With the increase of the amount of data and users in the information systems, the requirement of data integrity is needed to be improved as well, so the work has become necessary independently. One important element in the information system is a key of authentication schemes, which is used as a message authentication code (MAC). One technique to produce a MAC is based on using a hash function and is referred to as a HMAC.MD5 represents one efficient algorithms for hashing the data, then, the purpose of implementation and used this algorithm is to give them some privacy in the application. Where they become independent work accessories as much as possible, but what is necessary, such as RAM and the pulse generator. Therefore, we focus on the application of VHDL for implement and computing to MD5 for data integrity checking method and to ensure that the data of an information system is in a correct state. The implementation of MD5 algorithm by using Xilinx-spartan-3A XCS1400AFPGA, with 50 MHz internal clock is helping for satisfies the above requirements

Detection and segmentation the affected brain using ThingSpeak platform based on IoT cloud analysis

The world has accelerated around a new industrial revolution called the Internet of Things, as this technology is expected to enter all aspects of industrial life, commercial and civil applications. The Internet of Things stands for highly important applications in the world of medical applications, which is the access to linking all medical clinics in the world into a single network capable of analyzing patient data and presenting it to medical professionals anywhere in the world. One of the medical applications in the Internet of Things is the discovery of a healthy human brain. This work proposes a health care system based on medical image analysis processes in the programmable ThingSpeak platform using MATLAB built into the platform within the cloud. The analysis is done using the MATLAB program within the Windows operating system and then the analysis is performed within ThingSpeak platform. The analysis includes classification process by using SVM classifier linear kernel in which we achieved 99.4% classification rate as well as using RBF kernel, which achieved 98.6% classification accuracy in classifying infected brains from healthy ones and the work was supported by cross validation technology to ensure effective classification accuracy. The patient brain is segmented then the tumor segment is isolated, its area is calculated, and the tumor boundaries are found, based on the k-mean technique, to support the specialist doctor when performing the analysis process in the cloud environment. Through this work we achieved a match in the analysis processes within the local environment, and ThingSpeak platform environment by 100%, and in order to support our work, we have automated the analysis, visualization and data transfer processes within the cloud and MATLAB environment

Intelligent and secure real-time auto-stop car system using deep-learning models

In this study, we introduce an innovative auto-stop car system empowered by deep learning technology, specifically employing two Convolutional Neural Networks (CNNs) for face recognition and travel drowsiness detection. Implemented on a Raspberry Pi 4, our system is designed to cater exclusively to certified drivers, ensuring enhanced safety through intelligent features. The face recognition CNN model accurately identifies authorized drivers, employing deep learning techniques to verify their identity before granting access to vehicle functions. This first model demonstrates a remarkable accuracy rate of 99.1%, surpassing existing solutions in secure driver authentication. Simultaneously, our second CNN focuses on real-time detecting+ of driver drowsiness, monitoring eye movements, and utilizing a touch sensor on the steering wheel. Upon detecting signs of drowsiness, the system issues an immediate alert through a speaker, initiating an emergency park and sending a distress message via Global Positioning System (GPS). The successful implementation of our proposed system on the Raspberry Pi 4, integrated with a real-time monitoring camera, attains an impressive accuracy of 99.1% for both deep learning models. This performance surpasses current industry benchmarks, showcasing the efficacy and reliability of our solution. Our auto-stop car system advances user convenience and establishes unparalleled safety standards, marking a significant stride in autonomous vehicle technology

Technical Report on Deploying a highly secured OpenStack Cloud Infrastructure using BradStack as a Case Study

Cloud computing has emerged as a popular paradigm and an attractive model for providing a reliable distributed computing model.it is increasing attracting huge attention both in academic research and industrial initiatives. Cloud deployments are paramount for institution and organizations of all scales. The availability of a flexible, free open source cloud platform designed with no propriety software and the ability of its integration with legacy systems and third-party applications are fundamental. Open stack is a free and opensource software released under the terms of Apache license with a fragmented and distributed architecture making it highly flexible. This project was initiated and aimed at designing a secured cloud infrastructure called BradStack, which is built on OpenStack in the Computing Laboratory at the University of Bradford. In this report, we present and discuss the steps required in deploying a secured BradStack Multi-node cloud infrastructure and conducting Penetration testing on OpenStack Services to validate the effectiveness of the security controls on the BradStack platform. This report serves as a practical guideline, focusing on security and practical infrastructure related issues. It also serves as a reference for institutions looking at the possibilities of implementing a secured cloud solution.Comment: 38 pages, 19 figures

Saudi-KAU Coupled Global Climate Model: Description and Performance

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Strengthening Behavior of Rectangular Stainless Steel Tube Beams Filled with Recycled Concrete Using Flat CFRP Sheets

Recently, the adoption of recycled concrete instead of normal concrete as infill material in tubular stainless steel members has received great attention from researchers regarding environmental improvement. However, the flexural behavior of recycled concrete-filled stainless steel tube (RCFSST) beams that have been repaired/strengthened using carbon fiber-reinforced polymer (CFRP) sheets via a partial-wrapping scheme has not yet been investigated, and is required for a variety of reasons, as with any conventional structural member. Therefore, this study experimentally tested six specimens for investigating the effects of using varied recycled aggregate content (0%, 50%, and 100%) in infill concrete material of stainless steel tube beams strengthened with CFRP sheets. Additionally, several finite element RCFSST models were built and analyzed to numerically investigate the effects of further parameters, such as the varied width-to-thickness ratios and yield strengths. Generally, the results showed that using 100% recycled aggregates in infill concrete material reduced the RCFSST beam’s bending capacity by about 15% when compared to the corresponding control specimen (0% recycled aggregate), with little difference in the failure mode behavior. Pre-damaged RCFSST beam capacity showed significant improvement (43.6%) when strengthened with three CFRP layers. The RCFST model with a lower w/t ratio showed better-strengthening performance than those with a higher ratio, where, the models with w/t ratios equal to 15 and 48 achieved a bending capacity improvement equal to about 18% and 35%, respectively, as an example. Furthermore, the results obtained from the current study are well compared by those predicted using the existing analytical methods

The transmutation of Escherichia coli ATCC 25922 to small colony variants (SCVs) E. coli strain as a result of exposure to gentamicin

Background: Small colony variants (SCVs) are biotypes of bacteria that have a size of approximately one-tenth or less of the wild types and has distinct characteristics comparing to the wild type strains. Clinical SCVs are usually associated with persistent infection and require a long-term treatment program with antibiotics. In Saudi Arabia, there are few studies about SCVs Escherichia coli for this reason, this study is aimed to investigate the ability of gentamicin to mutate E. coli ATCC 25922 to produce small SCVs and investigate the genotypes and phenotypes changes and stress tolerance comparing to clinical SCVs E. coli and normal clinical E. coli Isolated from blood samples. Methods: In this investigation, four clinical blood samples were collected ted from patients and the cultivation and isolation were carried out in KFMC between December 2019 and February 2021. The identification of positive blood culture samples was done using phoenix MD. Non-SCV E. coli ATCC25922 were mutated to SCV using exposure to increasing gradual concentrations of gentamicin at 100-generation intervals. Biochemical features and susceptibility to standard antibiotics using automated Phoenix MD 50 and. The survival assays were done using several stresses including heat shock, low pH, high osmotic pressure, and oxidative pressure. Virulence genes screening included the detection of genes that encoded to α-haemolysin, CS12 fimbriae, F17-like fimbrial adhesion, P-related fimbriae, yersiniabactin siderophore system, P-fimbriae, aerobactin, iron-regulated genes using PCR and gel electrophoresis. Results: The data from the mutating E. coli ATCC 25922 small colony test with gentamicin revealed that the first emergence of the multidrug resistance (MDR) SCV E. coli strain occurred at generation number 250, corresponding to a gentamicin concentration of 57 g/ml. Pathogenicity islands detection revealed that all tested E. coli strains have PAI IV 536 genes on their chromosomes furthermore, mutated SCV E. coli ATCC 25922 acquired PAII CFT073 and PAI IV 536. Survival tests showed no significant differences changes in tolerance of mutated SCVs comparing to parental strain. Conclusion: The present work concluded that gentamicin sub-MIC concentration gradual exposure can induce mutation responsible for SCV formation and evolving of MDR E. coli strains. The mutated SCVs evolved high-level aminoglycoside resistance for gentamicin and resistance to amikacin, it also developed resistance to 2 cephalosporin antibiotics cefuroxime, and cephalothin and a resistance to aztreonam