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Although many carpooling systems have been proposed, most of them lack various levels of automation, functionality, practicality, and solution quality. While Genetic Algorithms (GAs) have been successfully adopted for solving combinatorial optimization problems, their use is still rare in carpooling problems. Motivated to propose a solution for the many to many carpooling scenario, we present in this...
In this paper, we propose and investigate a hybrid positioning data fusion technique for heterogeneous networks in critical transmission scenarios. The focus is on two scenarios: the small indoor scenario combining Wi-Fi and cellular systems and the small-to-mid-scale scenario composed of one located Mobile Terminal (MT) and one anchor node (AN). More specifically, we investigate the effect of the...
We study the performance and energy characteristics of a reconfigurable active solid-state drive (RASSD) consisting of a tightly-coupled FPGA/SSD pair. The FPGA implements a compute node that uses partial dynamic reconfiguration to implement hardware accelerators that process data streaming from the SSD. Using K-means clustering as a representative data analytics workload, we show that a RASSD node...
Because existing public transportation infrastructure cannot be adapted in a timely manner to address the daunting traffic and parking congestion in urban environments, researchers are investigating social solutions, such as carpooling, where a driver and one or more passengers having semi-common routes share a private vehicle. Although many carpooling systems have been proposed, most of them lack...
Cortical algorithms (CA) inspired by and modeled after the human cortex, have shown superior accuracy in few machine learning applications. However, CA have not been extensively implemented for speech recognition applications, in particular the Arabic language. Motivated to apply CA to Arabic speech recognition, we present in this paper an improved CA that is efficiently trained using an entropy-based...
Class imbalance (CI) is common in most non synthetic datasets, which presents a major challenge for many classification algorithms geared towards optimized overall accuracy whenever the minority class risk loss is often higher than the majority class one. Support vector machine (SVM), a machine learning (ML) technique deeply rooted in statistics, maximizes linear margins between classes and generalizes...
Despite support vector machines' (SVM) robustness and optimality, SVM do not scale well computationally. Suffering from slow training convergence on large datasets, SVM online testing time can be suboptimal because SVM write the classifier hyper-plane model as a sum of support vectors that could total as much as half the datasets. Motivated to speed up SVM real time testing by reducing the number...
A novel and rapidly growing area of research concerns data-intensive applications and the technical challenges that accompany it. One of those challenges is developing approaches and mechanisms that render high performance in processing and storing data. We joined this research effort by proposing a reconfigurable active solid state drives (RASSD) system that deals with such applications, through...
This paper investigates and presents the results of hybrid localization techniques in heterogeneous networks with lack of hearability scenarios. In this contribution, we generalize the work presented in [1] and [2] by investigating the limits of their combination but also the limits of the minimal number of anchor nodes (AN) used for hybrid positioning. The focus is on two scenarios: the small indoor...
Because knowing information about the currently running workload and the thermal status of the processor is of importance for more adequate planning and allocating resources in microprocessor environments, we propose in this paper using support vector regression (SVR) to predict future processor thermal status as well as the currently running workload. We build two generalized SVR models trained with...
Load forecasting is a critical necessity in the electricity industry since any unanticipated demand could cause possible grid instability and blackouts. Ideally, the capacity should be kept slightly above the current demand to avoid any undesired outages and suboptimal last minute power purchase. Motivated to develop an intelligent and efficient forecasting approach, we propose investigating in this...
Different intelligent techniques have been proposed to solve the downlink resource allocation in orthogonal frequency division multiple access (OFDMA)-based networks. These include mathematical optimization, game theory and heuristic algorithms. In an attempt to improve the performance of traditional genetic algorithm (GA), we propose a novel improved GA (IGA) which uses a new mutation operator as...
Since isolated letter handwriting recognition is an essential step for online hand writing recognition, we present in this paper an efficient and writer independent isolated letter handwriting recognition system using pen trajectory modeling for feature extraction and a multi-stage Support Vector Machines (SVM) for classification. Inheriting the good discriminating ability of SVM while modeling sequential...
We have recently proposed a Distributed Reconfigurable Active SSD computation platform (RASSD) for processing data-intensive applications at the storage node itself, without having to move data over slow networks. In this paper, we present the design of an operating system (OS) for the RASSD node. RASSD OS is a multitasking real-time operating system that runs on the 32-bit MicroBlaze® soft processor...
In an energy aware environment, designers frequently turn to advanced power reduction techniques such as power shutoff and multi-supply-voltage architectures. In order to implement these techniques, it is important that power estimates be made. Power prediction is a critical necessity as chip sizes continually decrease and the desire for low power consumption is a foremost design objective. For such...
Power optimization and power control are challenging issues for server computer systems. To obtain power optimization in an enterprise server, one needs to observe temporal behavior of workloads, and how they contribute to relative variations in power drawn by different server components. This depth of analysis helps to validate and quantify various energy/performance trends important for power modeling...
Efficiently exploring the microarchitectural design space is crucial in order to find promising design subspaces satisfying better power constraints. Based on our previous work on Guided Search Space Genetic Programming (GSS-GP), we introduce a new fitness function based on Fisher Linear Discriminant, in addition to the weighted fitness function designed to improve unbalanced classification accuracy...
Given the exploding number of mobile subscribers and the quality of service requirements of emerging mobile services specially the multimedia ones, efficient use of the limited network resources has become so important that it is driving development of intelligent resource allocation (RA) algorithms. Whereas link specific RA problems have been widely investigated in the literature targeting either...
In this paper, we propose to combine active solid state drives and reconfigurable FPGAs into a storage-compute node to use as a building block in a distributed, high performance computation platform for data intensive applications. We propose a complete framework for middleware functionality through an API abstraction layer that hides the complexity of accessing and processing data stored on these...
Wireless networks' evolutionary roadmap has always been evolving towards achieving increased network capacity and higher data rates. Load balancing, by handing off mobiles present at cell borders to neighboring ones, would accommodate additional users thus it would reduce congestion and improve cell capacity. Motivated to autonomously achieve load balancing, we propose in this paper a game theoretic...
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