Speaker identification using higher order spectral phase features and their effectiveness vis-a-vis Mel-Cepstral features

The effectiveness of higher-order spectral (HOS) phase features in speaker recognition is investigated by comparison with Mel Cepstral features on the same speech data. HOS phase features retain phase information from the Fourier spectrum unlikeMel–frequency Cepstral coefficients (MFCC). Gaussian mixture models are constructed from Mel– Cepstral features and HOS features, respectively, for the same data from various speakers in the Switchboard telephone Speech Corpus. Feature clusters, model parameters and classification performance are analyzed. HOS phase features on their own provide a correct identification rate of about 97% on the chosen subset of the corpus. This is the same level of accuracy as provided by MFCCs. Cluster plots and model parameters are compared to show that HOS phase features can provide complementary information to better discriminate between speakers.

Expression of Potato virus Y cytoplasmic inclusion protein in tobacco results in disorganization of parenchyma cells, distortion of epidermal cells, and induces mitochondrial and chloroplast abnormalities, formation of membrane whorls and atypical lipid accumulation

Infection of plant cells by potyviruses induces the formation of cytoplasmic inclusions ranging in size from 200 to 1000 nm. To determine if the ability to form these ordered, insoluble structures is intrinsic to the potyviral cytoplasmic inclusion protein, we have expressed the cytoplasmic inclusion protein from Potato virus Y in tobacco under the control of the chrysanthemum ribulose-1,5-bisphosphate carboxylase small subunit promoter, a highly active, green tissue promoter. No cytoplasmic inclusions were observed in the leaves of transgenic tobacco using transmission electron microscopy, despite being able to clearly visualize these inclusions in Potato virus Y infected tobacco leaves under the same conditions. However, we did observe a wide range of tissue and sub-cellular abnormalities associated with the expression of the Potato virus Y cytoplasmic inclusion protein. These changes included the disruption of normal cell morphology and organization in leaves, mitochondrial and chloroplast internal reorganization, and the formation of atypical lipid accumulations. Despite these significant structural changes, however, transgenic tobacco plants were viable and the results are discussed in the context of potyviral cytoplasmic inclusion protein function.

Crowd Counting Using Multiple Local Features

In public venues, crowd size is a key indicator of crowd safety and stability. Crowding levels can be detected using holistic image features, however this requires a large amount of training data to capture the wide variations in crowd distribution. If a crowd counting algorithm is to be deployed across a large number of cameras, such a large and burdensome training requirement is far from ideal. In this paper we propose an approach that uses local features to count the number of people in each foreground blob segment, so that the total crowd estimate is the sum of the group sizes. This results in an approach that is scalable to crowd volumes not seen in the training data, and can be trained on a very small data set. As a local approach is used, the proposed algorithm can easily be used to estimate crowd density throughout different regions of the scene and be used in a multi-camera environment. A unique localised approach to ground truth annotation reduces the required training data is also presented, as a localised approach to crowd counting has different training requirements to a holistic one. Testing on a large pedestrian database compares the proposed technique to existing holistic techniques and demonstrates improved accuracy, and superior performance when test conditions are unseen in the training set, or a minimal training set is used.

Tests of the elaborated intrusion theory of craving and desire : features of alcohol craving during treatment for an alcohol disorder

Objectives. We tested predictions from the elaborated intrusion (EI) theory of desire, which distinguishes intrusive thoughts and elaborations, and emphasizes the importance of imagery. Secondarily, we undertook preliminary evaluations of the Alcohol Craving Experience (ACE) questionnaire, a new measure based on EI Theory. Methods. Participants (N ¼ 232) were in correspondence-based treatment trials for alcohol abuse or dependence. The study used retrospective reports obtained early in treatment using the ACE, and daily self-monitoring of urges, craving, mood and alcohol consumption. Results. The ACE displayed high internal consistency and test – retest reliability and sound relationships with self-monitored craving, and was related to Baseline alcohol dependence, but not to consumption. Imagery during craving was experienced by 81%,with 2.3 senses involved on average. More frequent imagery was associated with longer episode durations and stronger craving. Transient intrusive thoughts were reported by 87% of respondents, and were more common if they frequently attempted to stop alcohol cognitions. Associations between average daily craving and weekly consumption were seen. Depression and negative mood were associated with more frequent, stronger and longer lasting desires for alcohol. Conclusions. Results supported the distinction of automatic and controlled processes in craving, together with the importance of craving imagery. They were also consistent with prediction of consumption from cross-situational averages of craving, and with positive associations between craving and negative mood. However, this study’s retrospective reporting and correlational design require that its results be interpreted cautiously. Research using ecological momentary measures and laboratory manipulations is needed before confident inferences about causality can be made.

Selecting, optimizing and fusing ‘salient’ Gabor features for facial expression recognition

This paper describes a novel framework for facial expression recognition from still images by selecting, optimizing and fusing ‘salient’ Gabor feature layers to recognize six universal facial expressions using the K nearest neighbor classifier. The recognition comparisons with all layer approach using JAFFE and Cohn-Kanade (CK) databases confirm that using ‘salient’ Gabor feature layers with optimized sizes can achieve better recognition performance and dramatically reduce computational time. Moreover, comparisons with the state of the art performances demonstrate the effectiveness of our approach.

Social networking : investigating the features of Facebook application

Research on social networking sites like Facebook is emerging but sparse. The exploratory study investigates the value users derive from self-described ‘cool’ Facebook applications, and explores the features that either encourage or discourage users to recommend application to their friends. Thus the concepts of value and cool are explored in a social networking setting. Our qualitative data shows that consumers derive a combination of functional value along with either social or emotional value from the applications. Female Facebook users indicated self-expression as important, while mates then to use Facebook application to socially compete. Three broad categories emerged for application features; symmetrical features can both encourage or discourage recommendation, asymmetrical features one encourage or discourage but not both, and polar features where different levels of the same feature encourage or discourage. Recommending or not recommending an application tends to be the result of a combination of features rather than one feature in isolation.

Human brain regions required for the dividing and switching of attention between two features of a single object

This combined PET and ERP study was designed to identify the brain regions activated in switching and divided attention between different features of a single object using matched sensory stimuli and motor response. The ERP data have previously been reported in this journal [64]. We now present the corresponding PET data. We identified partially overlapping neural networks with paradigms requiring the switching or dividing of attention between the elements of complex visual stimuli. Regions of activation were found in the prefrontal and temporal cortices and cerebellum. Each task resulted in different prefrontal cortical regions of activation lending support to the functional subspecialisation of the prefrontal and temporal cortices being based on the cognitive operations required rather than the stimuli themselves.

Modelling water droplet movement on a leaf surface

The central aim for the research undertaken in this PhD thesis is the development of a model for simulating water droplet movement on a leaf surface and to compare the model behavior with experimental observations. A series of five papers has been presented to explain systematically the way in which this droplet modelling work has been realised. Knowing the path of the droplet on the leaf surface is important for understanding how a droplet of water, pesticide, or nutrient will be absorbed through the leaf surface. An important aspect of the research is the generation of a leaf surface representation that acts as the foundation of the droplet model. Initially a laser scanner is used to capture the surface characteristics for two types of leaves in the form of a large scattered data set. After the identification of the leaf surface boundary, a set of internal points is chosen over which a triangulation of the surface is constructed. We present a novel hybrid approach for leaf surface fitting on this triangulation that combines Clough-Tocher (CT) and radial basis function (RBF) methods to achieve a surface with a continuously turning normal. The accuracy of the hybrid technique is assessed using numerical experimentation. The hybrid CT-RBF method is shown to give good representations of Frangipani and Anthurium leaves. Such leaf models facilitate an understanding of plant development and permit the modelling of the interaction of plants with their environment. The motion of a droplet traversing this virtual leaf surface is affected by various forces including gravity, friction and resistance between the surface and the droplet. The innovation of our model is the use of thin-film theory in the context of droplet movement to determine the thickness of the droplet as it moves on the surface. Experimental verification shows that the droplet model captures reality quite well and produces realistic droplet motion on the leaf surface. Most importantly, we observed that the simulated droplet motion follows the contours of the surface and spreads as a thin film. In the future, the model may be applied to determine the path of a droplet of pesticide along a leaf surface before it falls from or comes to a standstill on the surface. It will also be used to study the paths of many droplets of water or pesticide moving and colliding on the surface.

Cascading appearance-based features for visual speaker verification

The cascading appearance-based (CAB) feature extraction technique has established itself as the state of the art in extracting dynamic visual speech features for speech recognition. In this paper, we will focus on investigating the effectiveness of this technique for the related speaker verification application. By investigating the speaker verification ability of each stage of the cascade we will demonstrate that the same steps taken to reduce static speaker and environmental information for the speech recognition application also provide similar improvements for speaker recognition. These results suggest that visual speaker recognition can improve considerable when conducted solely through a consideration of the dynamic speech information rather than the static appearance of the speaker's mouth region.