Digital signal processing implementation for near real-time wavelet transformation system for binary images

Communication has become an essential function in our civilization. With the increasing demand for communication channels, it is now necessary to find ways to optimize the use of their bandwidth. One way to achieve this is by transforming the information before it is transmitted. This transformation can be performed by several techniques. One of the newest of these techniques is the use of wavelets. Wavelet transformation refers to the act of breaking down a signal into components called details and trends by using small waveforms that have a zero average in the time domain. After this transformation the data can be compressed by discarding the details, transmitting the trends. In the receiving end, the trends are used to reconstruct the image. In this work, the wavelet used for the transformation of an image will be selected from a library of available bases. The accuracy of the reconstruction, after the details are discarded, is dependent on the wavelets chosen from the wavelet basis library. The system developed in this thesis takes a 2-D image and decomposes it using a wavelet bank. A digital signal processor is used to achieve near real-time performance in this transformation task. A contribution of this thesis project is the development of DSP-based test bed for the future development of new real-time wavelet transformation algorithms.

A Digital Signal Processing Approach for Affective Sensing of a Computer User through Pupil Diameter Monitoring

Recent research has indicated that the pupil diameter (PD) in humans varies with their affective states. However, this signal has not been fully investigated for affective sensing purposes in human-computer interaction systems. This may be due to the dominant separate effect of the pupillary light reflex (PLR), which shrinks the pupil when light intensity increases. In this dissertation, an adaptive interference canceller (AIC) system using the H∞ time-varying (HITV) adaptive algorithm was developed to minimize the impact of the PLR on the measured pupil diameter signal. The modified pupil diameter (MPD) signal, obtained from the AIC was expected to reflect primarily the pupillary affective responses (PAR) of the subject. Additional manipulations of the AIC output resulted in a processed MPD (PMPD) signal, from which a classification feature, PMPDmean, was extracted. This feature was used to train and test a support vector machine (SVM), for the identification of stress states in the subject from whom the pupil diameter signal was recorded, achieving an accuracy rate of 77.78%. The advantages of affective recognition through the PD signal were verified by comparatively investigating the classification of stress and relaxation states through features derived from the simultaneously recorded galvanic skin response (GSR) and blood volume pulse (BVP) signals, with and without the PD feature. The discriminating potential of each individual feature extracted from GSR, BVP and PD was studied by analysis of its receiver operating characteristic (ROC) curve. The ROC curve found for the PMPDmean feature encompassed the largest area (0.8546) of all the single-feature ROCs investigated. The encouraging results seen in affective sensing based on pupil diameter monitoring were obtained in spite of intermittent illumination increases purposely introduced during the experiments. Therefore, these results confirmed the benefits of using the AIC implementation with the HITV adaptive algorithm to isolate the PAR and the potential of using PD monitoring to sense the evolving affective states of a computer user.

Signal processing techniques for reducing the impact of fiber nonlinearities on system performance

Signal processing techniques for mitigating intra-channel and inter-channel fiber nonlinearities are reviewed. More detailed descriptions of three specific examples highlight the diversity of the electronic and optical approaches that have been investigated.

Child-to-parent violence: the role of exposure to violence and its relationship to social-cognitive processing

Research suggests that child-to-parent violence (CPV) is related to a previous history of violence within the family setting. The current study was aimed to explore the exposure to violence in different settings (school, community, home, and TV) and its relationship to some variables of the social-cognitive processing (hostile social perception, impulsivity, ability to anticipate the consequences of social behaviors and to select the appropriate means to achieve the goals of social behaviors) in a group of juveniles who assaulted their parents. It is also examined how they differ from other young offenders and non-offender adolescents. The sample included 90 adolescents from Jaén (Spain). Thirty of them were juveniles who had been reported by their parents for being violent towards them and 30 were juveniles who had committed other types of offences. The third group was made up of 30 adolescents without any criminal charge. Adolescents answered measures of exposure to violence, perception of criticism/rejection from parents, hostile social perception, and social problem- solving skills. Results revealed that juveniles who abused their parents reported higher levels of exposure to violence at home when comparing to the other groups. In addition, exposure to violence at home was significantly correlated to the hostile social perception of adolescents in CPV cases. Implications for prevention and treatment are discussed

An Event Driven Fusion Approach for Enjoyment Recognition in Real-time

Social signals and interpretation of carried information is of high importance in Human Computer Interaction. Often used for affect recognition, the cues within these signals are displayed in various modalities. Fusion of multi-modal signals is a natural and interesting way to improve automatic classification of emotions transported in social signals. Throughout most present studies, uni-modal affect recognition as well as multi-modal fusion, decisions are forced for fixed annotation segments across all modalities. In this paper, we investigate the less prevalent approach of event driven fusion, which indirectly accumulates asynchronous events in all modalities for final predictions. We present a fusion approach, handling short-timed events in a vector space, which is of special interest for real-time applications. We compare results of segmentation based uni-modal classification and fusion schemes to the event driven fusion approach. The evaluation is carried out via detection of enjoyment-episodes within the audiovisual Belfast Story-Telling Corpus.

Characterising an ECG signal using statistical modelling : a feasibility study

For clinical use, in electrocardiogram (ECG) signal analysis it is important to detect not only the centre of the P wave, the QRS complex and the T wave, but also the time intervals, such as the ST segment. Much research focused entirely on qrs complex detection, via methods such as wavelet transforms, spline fitting and neural networks. However, drawbacks include the false classification of a severe noise spike as a QRS complex, possibly requiring manual editing, or the omission of information contained in other regions of the ECG signal. While some attempts were made to develop algorithms to detect additional signal characteristics, such as P and T waves, the reported success rates are subject to change from person-to-person and beat-to-beat. To address this variability we propose the use of Markov-chain Monte Carlo statistical modelling to extract the key features of an ECG signal and we report on a feasibility study to investigate the utility of the approach. The modelling approach is examined with reference to a realistic computer generated ECG signal, where details such as wave morphology and noise levels are variable.

Politeness and social signals

In the literature, politeness has been researched within many disciplines. Although Brown and Levinson’s theory of politeness (1978, 1987) is often cited, it is primarily a linguistic theory and has been criticized for its lack of generalizability to all cultures. Consequently, there is a need for a more comprehensive approach to understand and explain politeness. We suggest applying a social signal framework that considers politeness as a communicative state. By doing so, we aim to unify and explain politeness and its corresponding research and identify further research needed in this area.

Processing of faces and emotional expressions in infants at risk of social phobia

Individuals with social phobia display social information processing biases yet their aetiological significance is unclear. Infants of mothers with social phobia and control infants' responses were assessed at 10 days, 10 and 16 weeks, and 10 months to faces versus non-faces, variations in intensity of emotional expressions, and gaze direction. Infant temperament and maternal behaviours were also assessed. Both groups showed a preference for faces over non-faces at 10 days and 10 weeks, and full faces over profiles at 16 weeks; they also looked more to high vs. low intensity angry faces at 10 weeks, and fearful faces at 10 months; however, index infants' initial orientation and overall looking to high-intensity fear faces was relatively less than controls at 10 weeks. This was not explained by infant temperament or maternal behaviours. The findings suggest that offspring of mothers with social phobia show processing biases to emotional expressions in infancy.