Image coding based on maximum entropy partitioning for identifying improbable intensities related to facial expressions

In this paper we investigate information-theoretic image coding techniques that assign longer codes to improbable, imprecise and non-distinct intensities in the image. The variable length coding techniques when applied to cropped facial images of subjects with different facial expressions, highlight the set of low probability intensities that characterize the facial expression such as the creases in the forehead, the widening of the eyes and the opening and closing of the mouth. A new coding scheme based on maximum entropy partitioning is proposed in our work, particularly to identify the improbable intensities related to different emotions. The improbable intensities when used as a mask decode the facial expression correctly, providing an effective platform for future emotion categorization experiments.     

Smart ultrasonic sensors systems: potential of aluminum nitride thin films for the excitation of the ultrasound at high frequencies

We investigated the potential of the aluminum nitride films to excite ultrasonic waves at frequencies >50?MHz. The deposition process of the aluminum nitride thin film layers on silicon substrates was investigated and optimized regarding their piezoelectric behavior. Large single element transducers were deposited on silicon substrates with aluminum electrodes, under different parameters for the magnetron sputter process, like pressure and bias voltage. Special test setup and a measuring station were created to characterize the sensors. Acoustical measurements were carried out in pulse echo mode up to 500?MHz and the values of piezoelectric charge constant (d₃₃) were determined. As a result, two parameter sets were found for the sputtering process to obtain an excellent piezoelectric charge constant of about 7.2?pC/N maximum. Then the sputtering process with these parameters was used to deposit sensors on various substrate materials and with different electrode sizes.     

Relationships between glucose, energy intake and dietary composition in obese adults with type 2 diabetes receiving the cannabinoid 1 (CB1) receptor antagonist, rimonabant

ABSTRACT: BACKGROUND: Weight loss is often difficult to achieve in individuals with type 2 diabetes and anti-obesity drugs are often advocated to support dietary intervention. Despite the extensive use of centrally acting anti-obesity drugs, there is little evidence of how they affect dietary composition. We investigated changes in energy intake and dietary composition of macro- and micronutrients following therapy with the endocannabinoid receptor blocker, rimonabant. METHODS: 20 obese patients with type 2 diabetes were studied before and after 6 months dietary intervention with rimonabant. Dietary intervention was supervised by a diabetes dietician. Five-day food diaries were completed at baseline and at 6 months and dietary analysis was performed using computer software (Dietplan 6). RESULTS: After 6 months, (compared with baseline) there were reductions in weight (107+/-21Kg versus 112+/-21, p<0.001, 4% body weight reduction), and improvements in HbA1c (7.4+/-1.7 versus 8.0+/-1.6%, p<0.05) and HDL cholesterol. Intake of energy (1589+/-384 versus 2225+/-1109kcal, p<0.01), carbohydrate (199+/-74 versus 273+/-194g, p<0.05), protein (78+/-23 versus 98+/-36g, p<0.05), fats (55+/-18 versus 84+/-39g, p<0.01) and several micronutrients were reduced. However, relative macronutrient composition of the diet was unchanged. Improvement in blood glucose was strongly correlated with a reduction in carbohydrate intake (r = 0.76, p<0.001). CONCLUSIONS: In obese patients with type 2 diabetes, rimonabant in combination with dietary intervention led to reduced intake of energy and most macronutrients. Despite this, macronutrient composition of the diet was unaltered. These dietary changes (especially carbohydrate restriction) were associated with weight loss and favourable metabolic effects.     

An exploratory study of the relationship between construction workforce physical strain and task level productivity

The monitoring of construction workforce physical strain can be a valuable management strategy in improving workforce productivity, safety, health, and quality of work. Nevertheless, clear relationships between workforce performance and physical strain have yet to be established. An exploratory investigation of the relationship between task level productivity and physical strain was conducted. Nine participants individually performed a four-hour simulated construction task while a wearable physiological status monitor continuously assessed their physiological condition. Heart rate, relative heart rate, and breathing rate were utilized as predictors of physical strain, and task level–single factor productivity was used as an index of productivity. Numerous regression models were generated using the collected data. This investigation initially unsuccessfully attempted to establish a relationship between physiological condition and productivity at the individual worker level. However, an analysis of the regression models showed that there is a relationship between productivity and either heart rate or relative heart rate at the group level, and that this relationship is parabolic. Breathing rate was proved to not be a significant predictor of productivity. Research results significantly improve understanding of the relationship between work physiology and task productivity. Researchers and practitioners may use the tested monitoring devices, analysis methods, and results to design further applied studies and to improve workforce productivity.     

Unpacking the temporal advantage of distributing complex visual displays

Spatial arrangement of information can have large effects on problem solving. Although such effects have been observed in various domains (e.g., instruction and interface designs), little is known about the cognitive processing mechanisms underlying these effects, nor its applicability to complex visual problem solving. In three experiments, we showed that the impact of spatial arrangement of information on problem solving time can be surprisingly large for complex real world tasks. It was also found that the effect can be caused by large increases in slow, external information searches (Experiment 1), that the spatial arrangement itself is the critical factor and the effect is domain-general (Experiment 2a), and that the underlying mechanism can involve micro-strategy selection for information encoding in a response to differing information access cost (Experiment 2b). Overall, these studies show a large slowdown effect (i.e., approximately 30%) that stacking information produces over spatially distributed information, and multiple paths by which this effect can be produced.     

Situation identification techniques in pervasive computing: A review

Pervasive systems must offer an open, extensible, and evolving portfolio of services which integrate sensor data from a diverse range of sources. The core challenge is to provide appropriate and consistent adaptive behaviours for these services in the face of huge volumes of sensor data exhibiting varying degrees of precision, accuracy and dynamism. Situation identification is an enabling technology that resolves noisy sensor data and abstracts it into higher-level concepts that are interesting to applications. We provide a comprehensive analysis of the nature and characteristics of situations, discuss the complexities of situation identification, and review the techniques that are most popularly used in modelling and inferring situations from sensor data. We compare and contrast these techniques, and conclude by identifying some of the open research opportunities in the area.     

Emotions in autonomous agents: comparative analysis of mechanisms and functions

Emotion mechanisms are often used in artificial agents as a method of improving action selection. Comparisons between agents are difficult due to a lack of unity between the theories of emotion, tasks of agents and types of action selection utilised. A set of architectural qualities is proposed as a basis for making comparisons between agents. An analysis of existing agent architectures that include an emotion mechanism can help to triangulate design possibilities within the space outlined by these qualities. With this in mind, twelve autonomous agents incorporating an emotion mechanism into action selection are selected for analysis. Each agent is dissected using these architectural qualities (the agent architecture, the action selection mechanism, the emotion mechanism and emotion state representation, along with the emotion model it is based on). This helps to place the agents within an architectural space, highlights contrasting methods of implementing similar theoretical components, and suggests which architectural aspects are important to performance of tasks. An initial framework is introduced, consisting of a series of recommendations for designing emotion mechanisms within artificial agents, based on correlations between emotion roles performed and the aspects of emotion mechanisms used to perform those roles. The conclusion discusses how problems with this type of research can be resolved and to what extent development of a framework can aid future research.