Balakrishnan Prabhakaran

Bio: Balakrishnan Prabhakaran is an academic researcher from University of Texas at Dallas. The author has contributed to research in topics: Motion estimation & Digital watermarking. The author has an hindex of 26, co-authored 234 publications receiving 2786 citations. Previous affiliations of Balakrishnan Prabhakaran include Indian Institute of Technology Madras & University of Texas at Austin.

A survey of quality of service in IEEE 802.11 networks

Abstract: Developed as a simple and cost-effective wireless technology for best effort services, IEEE 802.11 has gained popularity at an unprecedented rate. However, due to the lack of built-in quality of service support, IEEE 802.11 experiences serious challenges in meeting the demands of multimedia services and applications. This article surveys 802.11 QoS schemes, including service differentiation in the MAC layer, admission control and bandwidth reservation in MAC and higher layers, and link adaptation in the physical layer, designed to meet these challenges by providing the necessary enhancements for the required QoS. Furthermore, the article addresses issues that arise when end-to-end QoS has to be guaranteed in today's pervasive heterogeneous wired-cum-wireless networks. Among these challenges, protocol interoperability, multihop scheduling, full mobility support, and seamless vertical handoff among multiple mobile/wireless interfaces are specifically addressed.

A Body Sensor Network With Electromyogram and Inertial Sensors: Multimodal Interpretation of Muscular Activities

Abstract: The evaluation of the postural control system (PCS) has applications in rehabilitation, sports medicine, gait analysis, fall detection, and diagnosis of many diseases associated with a reduction in balance ability. Standing involves significant muscle use to maintain balance, making standing balance a good indicator of the health of the PCS. Inertial sensor systems have been used to quantify standing balance by assessing displacement of the center of mass, resulting in several standardized measures. Electromyogram (EMG) sensors directly measure the muscle control signals. Despite strong evidence of the potential of muscle activity for balance evaluation, less study has been done on extracting unique features from EMG data that express balance abnormalities. In this paper, we present machine learning and statistical techniques to extract parameters from EMG sensors placed on the tibialis anterior and gastrocnemius muscles, which show a strong correlation to the standard parameters extracted from accelerometer data. This novel interpretation of the neuromuscular system provides a unique method of assessing human balance based on EMG signals. In order to verify the effectiveness of the introduced features in measuring postural sway, we conduct several classification tests that operate on the EMG features and predict significance of different balance measures.

Real-Time Mobile Facial Expression Recognition System -- A Case Study

Abstract: This paper presents a mobile application for real time facial expression recognition running on a smart phone with a camera. The proposed system uses a set of Support Vector Machines (SVMs) for classifying 6 basic emotions and neutral expression along with checking mouth status. The facial expression features for emotion recognition are extracted by Active Shape Model (ASM) fitting landmarks on a face and then dynamic features are generated by the displacement between neutral and expression features. We show experimental results with 86% of accuracy with 10 folds cross validation in 309 video samples of the extended Cohn-Kanade (CK+) dataset. Using the same SVM models, the mobile app is running on Samsung Galaxy S3 with 2.4 fps. The accuracy of real-time mobile emotion recognition is about 72% for 6 posed basic emotions and neutral expression by 7 subjects who are not professional actors.

CHIMP: a framework for supporting distributed multimedia document authoring and presentation

Abstract: A multimedia document consists of different media objects that are to be sequenced and presented according to temporal and spatial specifications. Collaborative authoring helps in simultaneous editing and viewing of a multimedia document by multiple authors. However, it may cause the objects composing a multimedia document to be distributed over a computer network. In this paper, we propose a framework for distributed multimedia document authoring and presentation. The salient features of this framework are: flexible temporal specification based on difference constraints, system and user defined access filters, local editing, format conversions of media objects, and flexible object retrieval schedules for handling variations in system parameters such as network throughput and buffer resources. We propose shortestpath based algorithms for solving difference constraints. We show how the proposed algorithms can handle local editing and access filtering of multimedia documents. We also describe how the difference constraints based temporal specifications can help in deriving a flexible object retrieval schedule.

Synchronization models for multimedia presentation with user participation

Abstract: This paper addresses the key issue of providingflexible multimedia presentation with user participation and suggests synchronization models that can specify the user participation during the presentation. We study models like the Petrinet-based hypertext model and the object composition Petri nets (OCPN). We suggest adynamic timed Petri nets structure that can model pre-emptions and modifications to the temporal characteristics of the net. This structure can be adopted by the OCPN to facilitate modeling of multimedia synchronization characteristics with dynamic user participation. We show that the suggested enhancements for the dynamic timed Petri nets satisfy all the properties of the Petri net theory. We use the suggested enhancements to model typical scenarios in a multimedia presentation with user inputs.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Big Data: A Survey

Abstract: In this paper, we review the background and state-of-the-art of big data. We first introduce the general background of big data and review related technologies, such as could computing, Internet of Things, data centers, and Hadoop. We then focus on the four phases of the value chain of big data, i.e., data generation, data acquisition, data storage, and data analysis. For each phase, we introduce the general background, discuss the technical challenges, and review the latest advances. We finally examine the several representative applications of big data, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid. These discussions aim to provide a comprehensive overview and big-picture to readers of this exciting area. This survey is concluded with a discussion of open problems and future directions.

Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition

Abstract: Machine learning is enabling a myriad innovations, including new algorithms for cancer diagnosis and self-driving cars. The broad use of machine learning makes it important to understand the extent to which machine-learning algorithms are subject to attack, particularly when used in applications where physical security or safety is at risk. In this paper, we focus on facial biometric systems, which are widely used in surveillance and access control. We define and investigate a novel class of attacks: attacks that are physically realizable and inconspicuous, and allow an attacker to evade recognition or impersonate another individual. We develop a systematic method to automatically generate such attacks, which are realized through printing a pair of eyeglass frames. When worn by the attacker whose image is supplied to a state-of-the-art face-recognition algorithm, the eyeglasses allow her to evade being recognized or to impersonate another individual. Our investigation focuses on white-box face-recognition systems, but we also demonstrate how similar techniques can be used in black-box scenarios, as well as to avoid face detection.

Body Area Networks: A Survey

Abstract: Advances in wireless communication technologies, such as wearable and implantable biosensors, along with recent developments in the embedded computing area are enabling the design, development, and implementation of body area networks. This class of networks is paving the way for the deployment of innovative healthcare monitoring applications. In the past few years, much of the research in the area of body area networks has focused on issues related to wireless sensor designs, sensor miniaturization, low-power sensor circuitry, signal processing, and communications protocols. In this paper, we present an overview of body area networks, and a discussion of BAN communications types and their related issues. We provide a detailed investigation of sensor devices, physical layer, data link layer, and radio technology aspects of BAN research. We also present a taxonomy of BAN projects that have been introduced/proposed to date. Finally, we highlight some of the design challenges and open issues that still need to be addressed to make BANs truly ubiquitous for a wide range of applications.