Image processing technique to count the number of logs in a timber truck

This paper summarises the results of using image processing technique to get information about the load of timber trucks before their arrival using digital images or geo tagged images. Once the images are captured and sent to sawmill by drivers from forest, we can predict their arrival time using geo tagged coordinates, count the number of (timber) logs piled up in a truck, identify their type and calculate their diameter. With this information we can schedule and prioritise the inflow and unloading of trucks in the light of production schedules and raw material stocks available at the sawmill yard. It is important to keep all the actors in a supply chain integrated coordinated, so that optimal working routines can be reached in the sawmill yard.   

Convex Hulls in Image Processing : A Scoping Review

The demands of image processing related systems are robustness, high recognition rates, capability to handle incomplete digital information, and magnanimous flexibility in capturing shape of an object in an image. It is exactly here that, the role of convex hulls comes to play. The objective of this paper is twofold. First, we summarize the state of the art in computational convex hull development for researchers interested in using convex hull image processing to build their intuition, or generate nontrivial models. Secondly, we present several applications involving convex hulls in image processing related tasks. By this, we have striven to show researchers the rich and varied set of applications they can contribute to. This paper also makes a humble effort to enthuse prospective researchers in this area. We hope that the resulting awareness will result in new advances for specific image recognition applications.

On the EA-style integrated processing of self-contained mathematical texts

A sound and complete first-order goal-oriented sequent-type calculus is developed with ``large-block'' inference rules. In particular, the calculus contains formal analogues of such natural proof-search techniques as handling definitions and applying auxiliary propositions.

Evidence Algorithm and Processing Formalized Mathematical Texts

The goal of a research programme Evidence Algorithm is a development of an open system of automated proving that is able to accumulate mathematical knowledge and to prove theorems in a context of a self-contained mathematical text. By now, the first version of such a system called a System for Automated Deduction, SAD, is implemented in software. The system SAD possesses the following main features: mathematical texts are formalized using a specific formal language that is close to a natural language of mathematical publications; a proof search is based on special sequent-type calculi formalizing natural reasoning style, such as application of definitions and auxiliary propositions. These calculi also admit a separation of equality handling from deduction that gives an opportunity to integrate logical reasoning with symbolic calculation.

The Effects of Eating Behaviors and Exercise Patterns on the Processing of Food and Exercise Related Stimuli

The effectiveness of Cognitive Behavioral Therapy (CBT) for eating disorders has established a link between cognitive processes and unhealthy eating behaviors. However, the relationship between individual differences in unhealthy eating behaviors that are not related to clinical eating disorders, such as overeating and restrained eating, and the processing of food related verbal stimuli remains undetermined. Furthermore, the cognitive processes that promote unhealthy and healthy exercise patterns remain virtually unexplored by previous research. The present study compared individual differences in attitudes and behaviors around eating and exercise to responses to food and exercise-related words using a Lexical Decision Task (LDT). Participants were recruited from Colby (n = 61) and the greater Waterville community (n = 16). The results indicate the following trends in the data: Individuals who scored high in “thin ideal” responded faster to food-related words than individuals with low “thin Ideal” scores did. Regarding the exercise-related data, individuals who engage in more “low intensity exercise” responded faster to exercise-related words than individuals who engage in less “low intensity exercise” did. These findings suggest that cognitive schemata about food and exercise might mediate individual’s eating and exercise patterns.