Industrial application of fuzzy systems; adaptive fuzzy control of solder paste stencil printing

This paper presents an adaptive fuzzy control algorithm for the control of the solder paste stencil printing stage of surface mount printed circuit board assembly. The proposed method of automatic solder paste stencil printing consist of four blocks; fuzzy feature extraction, defect classifcation of paste deposits, adaptive fuzzy rule-based model identifcation and subsequently an optimal control action for the stencil printing process. Experimental results are presented to illustrate the capability of the algorithm

Noninteractive fuzzy rule-based systems

In this paper, we have introduced a noninteractive model for fuzzy rule-based systems. A critical aspect of this noninteractive model is the introduction of a new set of rules with fewer parameters and without considering the interaction between the functionality of inputs. The new noninteractive model of the fuzzy rule-based system represents the output as a linear combination of the nonlinear function of individual inputs

Human Movement Recognition Based on the Stochastic Characterisation of Acceleration Data

Human activity recognition algorithms based on information obtained from wearable sensors are successfully applied in detecting many basic activities. Identified activities with time-stationary features are characterised inside a predefined temporal window by using different machine learning algorithms on extracted features from the measured data. Better accuracy, precision and recall levels could be achieved by combining the information from different sensors. However, detecting short and sporadic human movements, gestures and actions is still a challenging task. In this paper, a novel algorithm to detect human basic movements from wearable measured data is proposed and evaluated. The proposed algorithm is designed to minimise computational requirements while achieving acceptable accuracy levels based on characterising some particular points in the temporal series obtained from a single sensor. The underlying idea is that this algorithm would be implemented in the sensor device in order to pre-process the sensed data stream before sending the information to a central point combining the information from different sensors to improve accuracy levels. Intra- and inter-person validation is used for two particular cases: single step detection and fall detection and classification using a single tri-axial accelerometer. Relevant results for the above cases and pertinent conclusions are also presented

Semantic-based decision support for remote care of dementia patients

This paper investigates the challenges in developing a semantic-based Dementia Care Decision Support System based on the non-intrusive monitoring of the patient's behaviour. Semantic-based approaches are well suited for modelling context-aware scenarios similar to Dementia care systems, where the patient's dynamic behaviour observations (occupants movement, equipment use) need to be analysed against the semantic knowledge about the patient's condition (illness history, medical advice, known symptoms) in an integrated knowledgebase. However, our research findings establish that the ability of semantic technologies to reason upon the complex interrelated events emanating from the behaviour monitoring sensors to infer knowledge assisting medical advice represents a major challenge. We attempt to address this problem by introducing a new approach that relies on propositional calculus modelling to segregate complex events that are amenable for semantic reasoning from events that require pre-processing outside the semantic engine before they can be reasoned upon. The event pre-processing activity also controls the timing of triggering the reasoning process in order to further improve the efficiency of the inference process. Using regression analysis, we evaluate the response-time as the number of monitored patients increases and conclude that the incurred overhead on the response time of the prototype decision support systems remains tolerable

THE PREVALENCE OF CUTANEOUS LEISHMANIASIS IN EAST OF AHVAZ COUNTY, SOUTH-WESTERN IRAN

Objectives: Cutaneous Leishmaniasis (CL) is a zoonotic parasitological disease. This disease cause always important health challenges for the human communities. It is common in many parts of the globe. This research was designed to determine the epidemiology of CL in East of Ahvaz County during 2003- 2013. Methods: This was a descriptive cross-sectional study. The disease was diagnosed based on clinical examination and microscopic observation of the parasite in the ulcer site. The patient's Information such as age, gender, number and sites of ulcer (s) on the body, month and residence area were recorded. Data analysis was performed using SPSS software. Results: Totally, 2287 cases were detected during 2003 2013. About 53.4 patients were male and 46.4 female. The highest frequency infected age groups were observed in 10-19 years old (n=550 ,24). Nearly 37 of the patients had one and 38.1 had three ulcers. The most common location of ulcers were on hands (n=1022, 44.7) and then on feet (n=501, 21.9). Totally 1877 of the patients were infected in rural areas. Based on the appearance of the lesion it was found that 410 cases (17.9) were of the dry type and 1877 cases (82.1) were wet type. Concluaions: Such high prevalence and incidence rates are alarming and require control and prevention measures. Further epidemiological studies of CL are suggested