Mobile-based Telemedicine Application using SVD and F-XoR Watermarking for Medical Images

منصة الخدمات الطبية عبارة عن تطبيق متنقل يتم من خلاله تزويد المرضى بتشخيصات الأطباء بناءً على المعلومات المستقاة من الصور الطبية. يجب ألا يتم تبديل محتوى هذه النتائج التشخيصية بشكل غير قانوني أثناء النقل ويجب إعادته إلى المريض الصحيح. في هذه المقالة، نقدم حلاً لهذه المشكلات باستخدام علامة مائية عمياء وقابلة للانعكاس وهشة استنادًا إلى مصادقة صورة المضيف. في الخوارزمية المقترحة، يتم استخدام الإصدار الثنائي من ترميز بوس_شوهوري _هوكوينجهام (BCH) للتقرير الطبي للمريض (PMR) والصورة الطبية الثنائية للمريض (PMI) بعد استخدام الغامض الحصري أو (F-XoR) لإنتاج العلامة الفريدة للمريض باستخدام مخطط المشاركة السرية (SSS). يتم استخدامه لاحقًا كعلامة مائية ليتم تضمينها في مضيف (PMI) باستخدام خوارزمية تحليل القيمة المفرد (SVD) العمياء القائمة على العلامة المائية. وهو حل جديد اقترحناه أيضًا بتطبيق SVD على صورة العلامة المائية العمياء. تحافظ الخوارزمية الخاصة بنا على مصادقة محتوى (PMI) أثناء النقل وملكية (PMR) للمريض لنقل التشخيص المصاحب فيما بعد إلى المريض الصحيح عبر تطبيق التطبيب عن بعد المحمول. يستخدم تقييم الخوارزمية لدينا علامات مائية مسترجعة توضح النتائج الواعدة لمقاييس الأداء العالية مقارنتا مع نتائج الاعمال السابقة في مقاييس الكشف عن التزوير وإمكانية الاسترداد الذاتي، مع قيمة 30NB PSNR، قيمة NC هي 0.99.A medical- service platform is a mobile application through which patients are provided with doctor’s diagnoses based on information gleaned from medical images. The content of these diagnostic results must not be illegitimately altered during transmission and must be returned to the correct patient. In this paper, we present a solution to these problems using blind, reversible, and fragile watermarking based on authentication of the host image. In our proposed algorithm, the binary version of the Bose_Chaudhuri_Hocquengham (BCH) code for patient medical report (PMR) and binary patient medical image (PMI) after fuzzy exclusive or (F-XoR) are used to produce the patient's unique mark using secret sharing schema (SSS). The patient’s unique mark is used later as a watermark to be embedded into host PMI using blind watermarking-based singular value decomposition (SVD) algorithm. This is a new solution that we also proposed to applying SVD into a blind watermarking image. Our algorithm preserves PMI content authentication during the transmission and PMR ownership to the patient for subsequently transmitting associated diagnosis to the correct patient via a mobile telemedicine application. The performance of experimental results is high compare to previous results, uses recovered watermarks demonstrating promising results in the tamper detection metrics and self-recovery capability, with 30db PSNR, NC value is 0.99

Measurements, Models, Systems and Design

531 s.

Deep Space Network information system architecture study

The purpose of this article is to describe an architecture for the Deep Space Network (DSN) information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990s. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies, such as the following: computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control

Design and Development of a Twisted String Exoskeleton Robot for the Upper Limb

High-intensity and task-specific upper-limb treatment of active, highly repetitive movements are the effective approaches for patients with motor disorders. However, with the severe shortage of medical service in the United States and the fact that post-stroke survivors can continue to incur significant financial costs, patients often choose not to return to the hospital or clinic for complete recovery. Therefore, robot-assisted therapy can be considered as an alternative rehabilitation approach because the similar or better results as the patients who receive intensive conventional therapy offered by professional physicians.;The primary objective of this study was to design and fabricate an effective mobile assistive robotic system that can provide stroke patients shoulder and elbow assistance. To reduce the size of actuators and to minimize the weight that needs to be carried by users, two sets of dual twisted-string actuators, each with 7 strands (1 neutral and 6 effective) were used to extend/contract the adopted strings to drive the rotational movements of shoulder and elbow joints through a Bowden cable mechanism. Furthermore, movements of non-disabled people were captured as templates of training trajectories to provide effective rehabilitation.;The specific aims of this study included the development of a two-degree-of-freedom prototype for the elbow and shoulder joints, an adaptive robust control algorithm with cross-coupling dynamics that can compensate for both nonlinear factors of the system and asynchronization between individual actuators as well as an approach for extracting the reference trajectories for the assistive robotic from non-disabled people based on Microsoft Kinect sensor and Dynamic time warping algorithm. Finally, the data acquisition and control system of the robot was implemented by Intel Galileo and XILINX FPGA embedded system

Artificial Intelligence and Cognitive Computing

Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that

Using Fuzzy Cognitive Maps (FCMs) to Evaluate the Vulnerabilities with ICT Assets Disposal Policies

Abstract-- This paper evaluates the possible vulnerabilities of ICT assets disposal policies and the associated impact that can affect the SMEs. A poorly implemented policy or unenforced policy is “potentially the weakest link ” in the cyber-security chain. Do SMEs have an idea of vulnerabilities or threats due to assets disposal? In the event of breaches, the SMEs pay for the cost of notifying the concerned stakeholders, compensate affected parties, invest in improved mitigation technologies and also may be subjected to unwarranted public scrutiny. ICT assets at the end-of-useful life span usually have data left on the hard disk drives or storage media, which is a source of data confidentiality vulnerability. SMEs were surveyed in developing economies on their assets disposal policies. The perceived correlations were analyzed using fuzzy cognitive maps (FCMs) to ascertain if any cyber-security vulnerabilities inherent in a particular policy have implications on others. The study endeavored to show that, SMEs ought to have appropriate assets disposal policies in place. Then, these policies ought to be signed off by all stakeholders as a matter of responsibility. By employing the FCM approach with fuzzy matrix operations, the results indicate positive correlations exist amongst the policy constructs. Thus, vulnerabilities with one policy have implications on others

Machine Learning And Image Processing For Noise Removal And Robust Edge Detection In The Presence Of Mixed Noise

The central goal of this dissertation is to design and model a smoothing filter based on the random single and mixed noise distribution that would attenuate the effect of noise while preserving edge details. Only then could robust, integrated and resilient edge detection methods be deployed to overcome the ubiquitous presence of random noise in images. Random noise effects are modeled as those that could emanate from impulse noise, Gaussian noise and speckle noise. In the first step, evaluation of methods is performed based on an exhaustive review on the different types of denoising methods which focus on impulse noise, Gaussian noise and their related denoising filters. These include spatial filters (linear, non-linear and a combination of them), transform domain filters, neural network-based filters, numerical-based filters, fuzzy based filters, morphological filters, statistical filters, and supervised learning-based filters. In the second step, switching adaptive median and fixed weighted mean filter (SAMFWMF) which is a combination of linear and non-linear filters, is introduced in order to detect and remove impulse noise. Then, a robust edge detection method is applied which relies on an integrated process including non-maximum suppression, maximum sequence, thresholding and morphological operations. The results are obtained on MRI and natural images. In the third step, a combination of transform domain-based filter which is a combination of dual tree – complex wavelet transform (DT-CWT) and total variation, is introduced in order to detect and remove Gaussian noise as well as mixed Gaussian and Speckle noise. Then, a robust edge detection is applied in order to track the true edges. The results are obtained on medical ultrasound and natural images. In the fourth step, a smoothing filter, which is a feed-forward convolutional network (CNN) is introduced to assume a deep architecture, and supported through a specific learning algorithm, l2 loss function minimization, a regularization method, and batch normalization all integrated in order to detect and remove impulse noise as well as mixed impulse and Gaussian noise. Then, a robust edge detection is applied in order to track the true edges. The results are obtained on natural images for both specific and non-specific noise-level