Mariette Awad

Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications

The availability of location information has become a key factor in today’s communications systems allowing location based services. In outdoor scenarios, the mobile terminal position is obtained with high accuracy thanks to the global positioning system (GPS) or to the standalone cellular systems. However, the main problem of GPS and cellular systems resides in the indoor environment and in scenarios with deep shadowing effects where the satellite or cellular signals are broken. In this paper, we survey different technologies and methodologies for indoor and outdoor localization with an emphasis on indoor methodologies and concepts. Additionally, we discuss in this review different localization-based applications, where the location information is critical to estimate. Finally, a comprehensive discussion of the challenges in terms of accuracy, cost, complexity, security, scalability, etc. is given. The aim of this survey is to provide a comprehensive overview of existing efforts as well as auspicious and anticipated dimensions for future work in indoor localization techniques and applications.

Support Vector Machines for Classification

This chapter covers details of the support vector machine (SVM) technique, a sparse kernel decision machine that avoids computing posterior probabilities when building its learning model. SVM offers a principled approach to problems because of its mathematical foundation in statistical learning theory. SVM constructs its solution in terms of a subset of the training input. SVM has been extensively used for classification, regression, novelty detection tasks, and feature reduction. This chapter focuses on SVM for supervised classification tasks only, providing SVM formulations for when the input space is linearly separable or linearly nonseparable and when the data are unbalanced, along with examples. The chapter also presents recent improvements to and extensions of the original SVM formulation. A case study concludes the chapter.

Incremental support vector machine framework for visual sensor networks

Motivated by the emerging requirements of surveillance networks, we present in this paper an incremental multiclassification support vector machine (SVM) technique as a new framework for action classification based on real-time multivideo collected by homogeneous sites. The technique is based on an adaptation of least square SVM (LS-SVM) formulation but extends beyond the static image-based learning of current SVM methodologies. In applying the technique, an initial supervised offline learning phase is followed by a visual behavior data acquisition and an online learning phase during which the cluster head performs an ensemble of model aggregations based on the sensor nodes inputs. The cluster head then selectively switches on designated sensor nodes for future incremental learning. Combining sensor data offers an improvement over single camera sensing especially when the latter has an occluded view of the target object. The optimization involved alleviates the burdens of power consumption and communication bandwidth requirements. The resulting misclassification error rate, the iterative error reduction rate of the proposed incremental learning, and the decision fusion technique prove its validity when applied to visual sensor networks. Furthermore, the enabled online learning allows an adaptive domain knowledge insertion and offers the advantage of reducing both the model training time and the information storage requirements of the overall system which makes it even more attractive for distributed sensor networks communication.

FPGA supercomputing platforms: A survey

Field Programmable Gate Arrays (FPGAs) inherent reconfigurable nature and their low power consumption have made them so complementary to microprocessors that many are advocating their inclusion in all supercomputing clusters. Today FPGAs are included in few mainstream computer systems for accelerating application specific performance. Among the numerous areas in reconfigurable computing FPGA have been encroaching into, we focus our literature review mainly on the area of high performance computing. Moving from FPGA general features to the evolution of FPGA supercomputing architecture, its roadmap, we reference selected applications lately developed for accelerating large simulation tasks using FPGA based supercomputers before presenting concluding remarks on challenges yet to be overcome.

A Distributed Reconfigurable Active SSD Platform for Data Intensive Applications

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 distributed nodes, thus allowing programmers to focus on the application, and not the underlying specialized architecture. The application in turn is re-architected to maximize its performance by delegating selected computations down to the storage-compute node. We present preliminary results measured on a real hardware prototype of a single-node. These results show that our proposed architecture provides more than a 2x improvement in performance over non-reconfigurable active-disk architectures that use electromechanical disks for storage and a 6x improvement in performance over a platform that performs the computation on the middle server.

Ordinal Optimization for Dynamic Network Reconfiguration

Abstract Motivated by the challenge of efficiently reconfiguring distribution networks for power loss reduction, this study presents an approach for finding a minimum loss radial configuration for a power network using ordinal optimization. Ordinal optimization relies on order comparison and goal softening to make the problem solution easier and the computation more efficient. The successful application of ordinal optimization to such a complex optimization problem required the investigation of several algorithmic parameters. The solution algorithm was implemented in a software package, where an acceptable solution is considered good enough if it is in the top m% of the solutions with a probability P. Testing it on 33- and 136-bus systems, minimal power loss results were obtained on the 33-bus system that are in the top 0.03% of the search space. Comparing the experimental results with other recently published methods showed the effectiveness of ordinal optimization for minimum loss calculations and motivated further studies in smart-grid-like scenarios, where the results obtained for different load levels were in the top 0.13% of the search space.

A Clinical Decision Support Framework for Incremental Polyps Classification in Virtual Colonoscopy

Abstract: We present in this paper a novel dynamic learning method for classifying polyp candidate detections in Computed Tomographic Colonography (CTC) using an adaptation of the Least Square Support Vector Machine (LS-SVM). The proposed technique, called Weighted Proximal Support Vector Machines ( WP-SVM ), extends the offline capabilities of the SVM scheme to address practical CTC applications. Incremental data are incorporated in the WP-SVM as a weighted vector space, and the only storage requirements are the hyper-plane parameters. WP-SVM performance evaluation based on 169 clinical CTC cases using a 3D computer-aided diagnosis (CAD) scheme for feature reduction comparable favorably with previously published CTC CAD studies that have however involved only binary and offline classification schemes. The experimental results obtained from iteratively applying WP-SVM to improve detection sensitivity demonstrate its viability for incremental learning, thereby motivating further follow on research to address a wider range of true positive subclasses such as pedunculated, sessile, and flat polyps, and over a wider range of false positive subclasses such as folds, stool, and tagged materials.

A 3-D visualization method for image-guided brain surgery

This paper deals with a 3D methodology for brain tumor image-guided surgery. The methodology is based on development of a visualization process that mimics the human surgeon behavior and decision-making. In particular, it originally constructs a 3D representation of a tumor by using the segmented version of the 2D MRI images. Then it develops an optimal path for the tumor extraction based on minimizing the surgical effort and penetration area. A cost function, incorporated in this process, minimizes the damage surrounding healthy tissues taking into consideration the constraints of a new snake-like surgical tool proposed here. The tumor extraction method presented in this paper is compared with the ordinary method used on brain surgery, which is based on a straight-line based surgical tool. Illustrative examples based on real simulations present the advantages of the 3D methodology proposed here.

Support Vector Regression

Rooted in statistical learning or Vapnik-Chervonenkis (VC) theory, support vector machines (SVMs) are well positioned to generalize on yet-to-be-seen data. The SVM concepts presented in Chapter 3 can be generalized to become applicable to regression problems. As in classification, support vector regression (SVR) is characterized by the use of kernels, sparse solution, and VC control of the margin and the number of support vectors. Although less popular than SVM, SVR has been proven to be an effective tool in real-value function estimation. As a supervised-learning approach, SVR trains using a symmetrical loss function, which equally penalizes high and low misestimates. Using Vapnik’s -insensitive approach, a flexible tube of minimal radius is formed symmetrically around the estimated function, such that the absolute values of errors less than a certain threshold are ignored both above and below the estimate. In this manner, points outside the tube are penalized, but those within the tube, either above or below the function, receive no penalty. One of the main advantages of SVR is that its computational complexity does not depend on the dimensionality of the input space. Additionally, it has excellent generalization capability, with high prediction accuracy.

Ham or spam? A comparative study for some content-based classification algorithms for email filtering

Spam emails are widely spreading to constitute a significant share of everyones daily inbox. Being a source of financial loss and inconvenience for the recipients, spam emails have to be filtered and separated from legitimate ones. This paper presents a survey of some popular filtering algorithms that rely on text classification to decide whether an email is unsolicited or not. A comparison among them is performed on the SpamBase dataset to identify the best classification algorithm in terms of accuracy, computational time, and precision/recall rates.