A Study on Clustering for Clustering Based Image De-Noising

In this paper, the problem of de-noising of an image contaminated with Additive White Gaussian Noise (AWGN) is studied. This subject is an open problem in signal processing for more than 50 years. Local methods suggested in recent years, have obtained better results than global methods. However by more intelligent training in such a way that first, important data is more effective for training, second, clustering in such way that training blocks lie in low-rank subspaces, we can design a dictionary applicable for image de-noising and obtain results near the state of the art local methods. In the present paper, we suggest a method based on global clustering of image constructing blocks. As the type of clustering plays an important role in clustering-based de-noising methods, we address two questions about the clustering. The first, which parts of the data should be considered for clustering? and the second, what data clustering method is suitable for de-noising.? Then clustering is exploited to learn an over complete dictionary. By obtaining sparse decomposition of the noisy image blocks in terms of the dictionary atoms, the de-noised version is achieved. In addition to our framework, 7 popular dictionary learning methods are simulated and compared. The results are compared based on two major factors: (1) de-noising performance and (2) execution time. Experimental results show that our dictionary learning framework outperforms its competitors in terms of both factors.Comment: 9 pages, 8 figures, Journal of Information Systems and Telecommunications (JIST

Architectural Enhancements for Data Transport in Datacenter Systems

Datacenter systems run myriad applications, which frequently communicate with each other and/or Input/Output (I/O) devices—including network adapters, storage devices, and accelerators. Due to the growing speed of I/O devices and the emergence of microservice-based programming models, the I/O software stacks have become a critical factor in end-to-end communication performance. As such, I/O software stacks have been evolving rapidly in recent years. Datacenters rely on fast, efficient “Software Data Planes”, which orchestrate data transfer between applications and I/O devices. The goal of this dissertation is to enhance the performance, efficiency, and scalability of software data planes by diagnosing their existing issues and addressing them through hardware-software solutions. In the first step, I characterize challenges of modern software data planes, which bypass the operating system kernel to avoid associated overheads. Since traditional interrupts and system calls cannot be delivered to user code without kernel assistance, kernel-bypass data planes use spinning cores on I/O queues to identify work/data arrival. Spin-polling obviously wastes CPU cycles on checking empty queues; however, I show that it entails even more drawbacks: (1) Full-tilt spinning cores perform more (useless) polling work when there is less work pending in the queues. (2) Spin-polling scales poorly with the number of polled queues due to processor cache capacity constraints, especially when traffic is unbalanced. (3) Spin-polling also scales poorly with the number of cores due to the overhead of polling and operation rate limits. (4) Whereas shared queues can mitigate load imbalance and head-of-line blocking, synchronization overheads of spinning on them limit their potential benefits. Next, I propose a notification accelerator, dubbed HyperPlane, which replaces spin-polling in software data planes. Design principles of HyperPlane are: (1) not iterating on empty I/O queues to find work/data in ready ones, (2) blocking/halting when all queues are empty rather than spinning fruitlessly, and (3) allowing multiple cores to efficiently monitor a shared set of queues. These principles lead to queue scalability, work proportionality, and enjoying theoretical merits of shared queues. HyperPlane is realized with a programming model front-end and a hardware microarchitecture back-end. Evaluation of HyperPlane shows its significant advantage in terms of throughput, average/tail latency, and energy efficiency over a state-of-the-art spin-polling-based software data plane, with very small power and area overheads. Finally, I focus on the data transfer aspect in software data planes. Cache misses incurred by accessing I/O data are a major bottleneck in software data planes. Despite considerable efforts put into delivering I/O data directly to the last-level cache, some access latency is still exposed. Cores cannot prefetch such data to nearer caches in today's systems because of the complex access pattern of data buffers and the lack of an appropriate notification mechanism that can trigger the prefetch operations. As such, I propose HyperData, a data transfer accelerator based on targeted prefetching. HyperData prefetches exact (rather than predicted) data buffers (or a required subset to avoid cache pollution) to the L1 cache of the consumer core at the right time. Prefetching can be done for both core-peripheral and core-core communications. HyperData's prefetcher is programmable and supports various queue formats—namely, direct (regular), indirect (Virtio), and multi-consumer queues. I show that with a minor overhead, HyperData effectively hides data access latency in software data planes, thereby improving both application- and system-level performance and efficiency.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169826/1/hosseing_1.pd

Effects of resistance exercise type on cortisol and androgen cross talk in resistance-trained women

The current study aimed to compare the effect of hypertrophy-, strength-, and power-type resistance exercise in resistance-trained women with considering cortisol and androgen cross talk. After one-repetition maximum (1-RM) estimation, ten resistance-trained women (age: 26.30 ± 4.95 years; body mass index: 22.07 ± 2.02 kg/m2; body fat: 24.64 ± 4.98%) conducted hypertrophy- (70% of 1-RM), strength- (90% of 1-RM), and power-type (45% of 1-RM) resistance exercise for three consecutive weeks. The movements included lever leg extension, reverse-grip lat pull-down, horizontal leg press, standing biceps cable curl, lying leg curl, machines bench press, standing cable triceps extension, and seated calf raises. Fasting blood was taken before and immediately after each trial. Statistical analyses were performed at a significance level of

Effect of Prophylactic Vasopressin on Hemodynamic Parameters after Coronary Artery Bypass Graft Surgery

Background: As common complications of Coronary artery bypass grafting (CABG), low vascular resistance and hypotension could be life threatening . The aim of present study was to investigate the effect of low-dose vasopressin on hemodynamics in CABG patients.Material &Methods: In this randomized double-blinded clinical trial, 80 patients undergoing selective CABG were randomly divided into two equal case and control groups (n=40). Case group was received vasopressin 0.03 IU/min 30 minute before the end of cardio-pulmonary bypass (CPB) until one hour after that. Control group was received normal saline in the same manner. Dopamine requirement, ICU stay, heart rate (HR), mean arterial blood pressure (MAP), central venues pressure (CVP) and atrial blood acidity (PH) were recorded and compared between groups  in 5 phases ( 0,30,60,90,120 min) after separation of CPB.Results: There was no significant difference between two groups in number of patients with severe hypotension (11 vs. 12 patients in case and control group respectively). CVP was corrected and then dopamine administration was compared in both group. In vasopressin and placebo group, 3 vs 11 patients need to dopamine administration immediately after separation from CPB (p= 0.018) and 4 vs 12 patients later in ICU (p=0.024) respectively. The mean needed dose of dopamine in vasopressin and placebo group immediately after separation from CPB were 7.63±3.42 vs 9.21±2.08 µg/kg/min (p=0.031) and later in ICU were 7.42±2.02 vs 8.66±4.08 µg/kg/min (p=0.045) respectively, which was significantly lower in vasopressin group in comparison with placebo group.Conclusion: Based on our results low-dose vasopressin administration significantly reduced the mean needed dose of required dopamine, 24 hours urinary output, Duration of mechanical ventilation and patient’s heart rate

Minimisation of image watermarking side effects through subjective optimisation

This study investigates the use of structural similarity index (SSIM) on the minimised side effect to image watermarking. For the fast implementation and more compatibility with the standard discrete cosine transform (DCT)-based codecs, watermark insertion is carried out on the DCT coefficients and hence an SSIM model for DCT-based watermarking is developed. For faster implementation, the SSIM index is maximised over independent 4 × 4 non-overlapped blocks, but the disparity between the adjacent blocks reduces the overall image quality. This problem is resolved through optimisation of overlapped blocks, but, the higher image quality is achieved at a cost of high computational complexity. To reduce the computational complexity while preserving the good quality, optimisation of semi-overlapped blocks is introduced. The authors show that while SSIM-based optimisation over overlapped blocks has as high as 64 times the complexity of the 4 × 4 non-overlapped method, with semi-overlapped optimisation the high quality of overlapped method is preserved only at a cost of less than 8 times the non-overlapped method

3D Models in Motion Compensation

Presentation at the SVCP 201