In multiprocessor systems, the traffic on the bus does not solely originate from data transfers due to data dependencies between tasks, but is also affected by memory transfers as result of cache misses. This has a huge impact on worst-case execution time (WCET) analysis and, in general, on the predictability of real-time applications implemented on such systems. As opposed to the WCET analysis performed for a single processor system, where the cache miss penalty is considered constant, in a multiprocessor system each cache miss has a variable penalty, depending on the bus contention. This affects the tasks' WCET which, however, is needed in order to perform system scheduling. At the same time, the WCET depends on the system schedule due to the bus interference. In this paper we present an approach to worst-case execution time analysis and system scheduling for real-time applications implemented on multiprocessor SoC architectures. The emphasis of this paper is on the bus scheduling policy and its optimization, which is of huge importance for the performance of such a predictable multiprocessor application.
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Protein aggregation leads to several burdensome human maladies, but a molecular level understanding of how human proteome has tackled the threat of aggregation is currently lacking. In this work, we survey the human proteome for incidence of aggregation prone regions (APRs), by using sequences of experimentally validated amyloid-fibril forming peptides and via computational predictions. While approximately 30 human proteins are currently known to be amyloidogenic, we found that 260 proteins (∼1% of human proteome) contain at least one experimentally validated amyloid-fibril forming segment. Computer predictions suggest that more than 80% of the human proteins contain at least one potential APR and approximately two-thirds (65%) contain two or more APRs; spanning 3-5% of their sequences. Sequence randomizations show that this apparently high incidence of APRs has been actually significantly reduced by unique amino acid composition and sequence patterning of human proteins. The human proteome has utilized a wide repertoire of sequence-structural optimization strategies, most of them already known, to minimize deleterious consequences due to the presence of APRs while simultaneously taking advantage of their order promoting properties. This survey also found that APRs tend to be located near the active and ligand binding sites in human proteins, but not near the post translational modification sites. The APRs in human proteins are also preferentially found at heterotypic interfaces rather than homotypic ones. Interestingly, this survey reveals that APRs play multiple, often opposing, roles in the human protein sequence-structure-function relationships. Insights gained from this work have several interesting implications towards novel drug discovery and development. Proteins 2017; 85:1099-1118. © 2017 Wiley Periodicals, Inc.
We propose a novel learning algorithm to detect moving pedestrians from a stationary camera in real-time. The algorithm learns a discriminative model based on eigenflow, i.e. the eigenvectors derived from applying Principal Component Analysis to the optical flow of moving objects, to differentiate between human motion patterns from other kind of motions like cars etc. The learned model is a cascade of Adaboost classifiers of increasing complexity, with eigenflow vectors as the weak classifiers. Unlike some recent attempts to use motion for pedestrian detection, this system performs this task in realtime. The system is also robust to small camera jitter and illumination changes. Moreover, we are able to detect moving children using the same system even though the training data is mainly composed of adult pedestrians.
Human Activity Recognition (HAR) is a vast and exciting topic for researchers and students. HAR aims to recognize activities by observing the actions of subjects and surrounding conditions. This topic also has many significant and futuristic applications and a basis of many automated tasks like 24*7 security surveillance, healthcare, laws regulations, automatic vehicle controls, game controls by human motion detection, basically human-computer interaction. This survey paper focuses on reviewing other research papers on sensing technologies used in HAR. This paper has covered distinct research in which researchers collect data from smartphones; some use a surveillance camera system to get video clips. Most of the researchers used videos to train their systems to recognize human activities collected from YouTubes and other video sources. Several sensor-based approaches have also covered in this survey paper to study and predict human activities, such as accelerometer, gyroscope, and many more. Some of the papers also used technologies like a Convolutional neural network (CNN) with spatiotemporal three-dimensional (3D) kernels for model development and then using to integrate it with OpenCV. There are also work done for Alzheimer’s patient in the Healthcare sector, used for their better performance in day-to-day tasks. We will analyze the research using both classic and less commonly known classifiers on distinct datasets available on the UCI Machine Learning Repository. We describe each researcher’s approaches, compare the technologies used, and conclude the adequate technology for Human Activity Recognition. Every research will be discussed in detail in this survey paper to get a brief knowledge of activity recognition.
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