Mihai Gavrilescu

Context-aware reconfigurable interoperability for vertical handover in wireless communications

In this paper we describe a context-aware reconfigurable system, which implements the IEEE 802.21 Media Independent Handover (MIH) standard for interoperability between wireless hybrid access networks. The proposed approach uses vertical handover prediction algorithms to improve the resource management decisions, through access to our Web Service implementation of the Media Independent Information Service (MIIS), which stores previously acquired information about surrounding hybrid access networks. Various prediction alternatives are discussed, and two proposed algorithms are evaluated, on our prototype implementation of the system.

A streaming application for vertical handover testing in wireless hybrid access networks

In this paper we describe a media streaming application build to test a system which implements the IEEE 802.21 Media Independent Handover (MIH) standard, for interoperability between wireless hybrid access networks. We evaluated the real-time performance of our vertical handover prototype system by means of the audio streaming continuity assessment. The current results proved only moderately performances, but they also showed us which components have to be improved. Further developments will address these improvements, and will extend the application with video streaming capabilities.

Recognizing emotions from videos by studying facial expressions, body postures and hand gestures

A system for recognizing emotions from videos by studying facial expressions, hand gestures and body postures is presented. A stochastic context-free grammar (SCFG) containing 8 combinations of hand gestures and body postures for each emotion is used and we show that increasing the number of combinations in SCFG improves the systems generalization for new hand gesture and body posture combinations. We show that hand gestures and body postures contribute to improving the emotion recognition rate with up to 5% for Anger, Sadness and Fear compared to the standard facial emotion recognition system, while for Happiness, Surprise and Disgust no significant improvement was noticed.

Considerations over implementing IEEE 802.21 on a device powered by a mobile operating system

During the last years, software applications performing scenarios related to vertical handovers were revealed. However, the great majority of these demonstrators were designed and optimized for multimode notebooks, having a fair amount of processing power. This article aims to describe on how to implement and optimize the main components of the IEEE 802.21 standard on a device like a smartphone powered by a mobile operating system. Performance evaluations were made in this purpose showing reasonable delays, taking us one step closer to achieving a seamless vertical handover on a mobile terminal.

Noise robust Automatic Speech Recognition system by integrating Robust Principal Component Analysis (RPCA) and Exemplar-based Sparse Representation

An enhanced Automatic Speech Recognition (ASR) system based on Hidden Markov Models (HMM) is presented. The system makes use of two sparse algorithms in order to remove the noise from the speech signal and improve the overall ASR recognition rate: Robust Principal Component Analysis (RPCA) and Exemplar-based Sparse Representation. We start with the premise that RPCA offers better results at lower Signal-to-noise ratios (SNRs) while Exemplar-based Sparse Representation offers good results for SNRs lower than 15 dB, and therefore we envisage architecture able to select between the two algorithms depending on the SNR detected in the speech signal. We present the architecture of our proposed model, as well as the experimental results in different scenarios and the improvements that can be brought in future researches.

Study on determining the Big-Five personality traits of an individual based on facial expressions

Previous studies revealed an increasing interest in determining the personality and behavior of individuals in areas such as career development and counseling, personalized health assistance, mental disorder diagnosis as well as detection of physical diseases with personality shift symptoms. Current ways of determining the Big-Five personality types involve completing a questionnaire, that takes an impractical amount of time and it cannot be used often. Our research aims building a novel non-invasive system to determine Big-Five personality traits based on facial features acquired using Facial Action Coding System. Results show links between the FACS action units present at maximum intensities in facial features and the personality traits of the individual. Moreover, the system built offers over 75% accuracy in predicting openness to experience, as well as neuroticism and extraversion and proves practical, offering results in no more than 3 minutes compared to the amount of time taken to complete a questionnaire.

Video streaming for evaluation of predictive VHO in wireless hybrid access networks

The paper describes a video streaming application build to evaluate session continuity over wireless hybrid access networks in vertical handover (VHO) conditions. The results showed us which components have to be improved. Further developments will address these improvements.

Using Off-Line Handwriting to Predict Blood Pressure Level: A Neural-Network-Based Approach

We propose a novel, non-invasive, neural-network based, three-layered architecture for determining blood pressure levels of individuals solely based on their handwriting. We employ four handwriting features (baseline, lowercase letter “f”, connecting strokes, writing pressure) and the result is computed as low, normal or high blood pressure. We create our own database to correlate handwriting with blood pressure levels and we show that it is important to use a predefined text for the handwritten sample used for training the system in order to have high prediction accuracy, while for further tests any random text can be used, keeping the accuracy at similar levels. We obtained over 84% accuracy in intra-subject tests and over 78% accuracy in inter-subject tests. We also show there is a link between several handwriting features and blood pressure level prediction with high accuracy which can be further exploited to improve the accuracy of the proposed approach.

Improved Automatic Speech Recognition system by using compressed sensing signal reconstruction based on L0 and L1 estimation algorithms

This paper presents a way of improving the recognition rate of a typical Hidden Markov Model (HMM)-based Automatic Speech Recognition (ASR) system by integrating the l1 - least absolute deviation (LAD) algorithm and the l0 - least square (LS) algorithm in a framework designed to selectively use them based on the level of impulse noise present in speech signal. We present the overall architecture of the model, as well as experimental results and compare our enhanced noise-robust HMM-based ASR system with state-of-the-art proving the improvements brought by this approach as well as future directions of research.

Study on determining the Myers-Briggs personality type based on individual's handwriting

Studies in psychology showed a close link between handwriting and personality, but this was never formally analyzed. In the context of career development there is a need to determine the personality type in a more efficient manner than the classic questionnaire. Moreover, in the fields of psychology and medicine, constant monitoring the patients personality can provide information regarding his mental health status, if he suffers from mental disorders or show psychological symptoms for common physical diseases. We analyze the link between personality types and handwriting, by correlating the handwriting features with the personality primitives in a neural-network 3-level architecture. Results show an accuracy of 86.7% in determining the personality type, with highest accuracies for Extravert vs. Introvert and Thinking vs. Feeling personality primitives. The system computes the personality type in less than 1 minute, proving to be more efficient than a questionnaire and suitable for real-life use.