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In Systems-on-Chip (SoCs) based on Networks-on-Chip (NoCs), the timing requirements of target applications can be met by using virtual channels and traffic differentiation mechanisms to prioritize the most urgent communication streams. However, the use of virtual channels in NoCs results in silicon and power overheads as they are usually implemented by means of additional buffers and multiplexers...
We describe and evaluate the development of mission planners in intralogistics for a commercial unmanned aerial vehicle equipped with a robotic gripper in an industrial environment, which consists of an input warehouse, production lines, and a product depot. In this particular study, the planner produces the needed commands for carrying out a given mission, which includes the delivery of inputs picked...
The existence of non-line-of-sight (NLOS) errors will considerably degrade the localization accuracy. Therefore, the NLOS node localization is investigated. In this article, we propose a NLOS node localization algorithm that utilizes the firefly algorithm. The objective function is established by the probability of propagation of NLOS and LOS according to the approximate maximum likelihood method...
In the past few years, wireless sensor networks (WSNs) have been increasingly gaining impact in the real world with with various applications such as healthcare, condition monitoring, control networks, etc. Anomaly detection in WSNs is an important aspect of data analysis in order to identify data items which does not conform to an expected pattern or other items in a dataset. This paper describes...
Model-based software estimation uses algorithms and past project data to make predictions for new projects. This paper presents a comparative assessment of four modeling approaches, including the original COCOMO, COCOMO calibration, k-Nearest Neighbors, and a combination of COCOMO calibration and k-Nearest Neighbors. Our results indicate that using kNN to select the nearest projects and calibrating...
Background. Often motivated by optimization objectives, software products are characterized by different subsequent releases and deployed through different strategies. The impact of these two aspects of software on energy consumption has still to be completely understood and can be improved by carrying out ad-hoc analyses for specific software products. Aims. In this research we report on an industrial...
We consider tracking of a target with elliptical nonlinear constraints on its motion dynamics. The state estimates are generated by sensors and sent over long-haul links to a remote fusion center for fusion. We show that the constraints can be projected onto the known ellipse and hence incorporated into the estimation and fusion process. In particular, two methods based on (i) direct connection to...
Mimicking the collaborative behavior of biological swarms, such as bird flocks and ant colonies, Swarm Intelligence algorithms provide efficient solutions for various optimization problems. On the other hand, a computational model of the human brain, spiking neural networks, has been showing great promise in recognition, inference, and learning, due to recent emergence of neuromorphic hardware for...
Heart failure (HF) has a highly variable annual mortality rate and there is an urgent need of determining patient prognosis to enable informed decision-making about heart failure treatment strategies. Existing survival risk prediction models either require features that limit their applicability or pose difficulties for parameter estimation as physicians have to use a limited set of variables with...
Post-database searching is a key procedure for peptide spectrum matches (PSMs) in protein identification with mass spectrometry-based strategies. Although many machine learning-based approaches have been developed to improve the accuracy of peptide identification, the challenge remains for improvement due to the poor quality of data samples. CRanker has shown its effectiveness and efficiency in terms...
The rapid development of high-throughput sequencing technology provides unique opportunities for studies of transcription factor binding, while also bringing new computational challenges. Recently, a series of discriminative motif discovery (DMD) methods have been proposed and offer promising solutions for addressing these challenges. However, because of the huge computational cost, most of them have...
As the blooming development of data mining in social computing systems (e.g., crowdsourcing system), statistical inference from crowdsourced data severs as a powerful tool to provide diversified services. To support critical applications (e.g., recommendation), in this paper, we shall focus on the collaborative ranking problems and construct a system of which the input is crowdsourced pairwise comparisons...
It is well recognized that air quality inference is of great importance for environmental protection. However, due to the limited monitoring stations and various impact factors, e.g., meteorology, traffic volume and human mobility, inference of air quality index (AQI) could be a difficult task. Recently, with the development of new ways for collecting and integrating urban, mobile, and public service...
One of the most current challenging problems in Gaussian process regression (GPR) is to handle large-scale datasets and to accommodate an online learning setting where data arrive irregularly on the fly. In this paper, we introduce a novel online Gaussian process model that could scale with massive datasets. Our approach is formulated based on alternative representation of the Gaussian process under...
Bayesian optimization (BO) has recently emerged as a powerful and flexible tool for hyper-parameter tuning and more generally for the efficient global optimization of expensive black-box functions. Systems implementing BO has successfully solved difficult problems in automatic design choices and machine learning hyper-parameters tunings. Many recent advances in the methodologies and theories underlying...
Given the soaring amount of data being generated daily, graph mining tasks are becoming increasingly challenging, leading to tremendous demand for summarization techniques. Feature selection is a representative approach that simplifies a dataset by choosing features that are relevant to a specific task, such as classification, prediction, and anomaly detection. Although it can be viewed as a way to...
Clustering results are often affected by covariates that are independent of the clusters one would like to discover. Traditionally, Alternative Clustering algorithms can be used to solve such a problem. However, these suffer from at least one of the following problems: i) continuous covariates or non-linearly separable clusters cannot be handled; ii) assumptions are made about the distribution of...
Visible spectrum video based fire detection using non-stationary cameras has been an overlooked research problem. While many authors have successfully developed algorithms to identify and measure the proportions of uncontrolled fire using thermal or stationary surveillance cameras, the development of non-stationary systems provides a much larger application scope. We present a deep learning based...
This paper presents a new method for designing the weights used for development of robust controllers for a quadrotor model with parameter uncertainty. The weights alongside the controllers are developed for attitude and altitude tracking by resolving a constrained non-linear minimization problem formulated over the conventional mixed sensitivity optimization S over T method. The optimization routine...
In this paper, we first propose a Quality of Experience (QoE) evaluation model for dynamic adaptive streaming over HTTP (DASH) services. The proposed model predicts the perceived quality of user based on segment media quality, playback continuity and perceptual quality fluctuations caused by bitrate switching. Large quantities of subjective mean-opinion-score (MOS) tests demonstrate that our QoE evaluation...
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