Application of artificial intelligence techniques to probeless fault diagnosis of printed circuit boards

This thesis describes investigations which led to the development of a failure diagnosis expert system for printed circuit boards which exploits functional test data. The boards considered are highly complex mixed signal (analogue and digital) systems. The data is output from automatic test equipment which is used to test every board subsequent to manufacture.The use of a conventional machine learning technique produced only limited success due to the very large search space of failure reports. This also ruled out the use of some conventional knowledge-based approaches. In addition, there was a requirement to track changes m printed circuit board design and manufacture which also ruled out some techniques.Our investigations lead to the development of a system which tracks changes by learning in a more restricted search space derived from the original space of reports. The system performs a diagnosis by matching a failure report with information about previously seen reports. Both exact and inexact matching were investigated. The matching rules used are heuristic. The system also uses basic circuit connectivity information in conjunction with the matching procedure to improve diagnostic performance especially in cases where matching fails to identify a unique component

Parallel delay multiply and sum algorithm for microwave medical imaging using spark big data framework

Microwave imaging systems are currently being investigated for breast cancer, brain stroke and neurodegenerative disease detection due to their low cost, portable and wearable nature. At present, commonly used radar-based algorithms for microwave imaging are based on the delay and sum algorithm. These algorithms use ultra-wideband signals to reconstruct a 2D image of the targeted object or region. Delay multiply and sum is an extended version of the delay and sum algorithm. However, it is computationally expensive and time-consuming. In this paper, the delay multiply and sum algorithm is parallelised using a big data framework. The algorithm uses the Spark MapReduce programming model to improve its efficiency. The most computational part of the algorithm is pixel value calculation, where signals need to be multiplied in pairs and summed. The proposed algorithm broadcasts the input data and executes it in parallel in a distributed manner. The Spark-based parallel algorithm is compared with sequential and Python multiprocessing library implementation. The experimental results on both a standalone machine and a high-performance cluster show that Spark significantly accelerates the image reconstruction process without affecting its accuracy

Intelligent sensing technologies for the diagnosis, monitoring and therapy of alzheimer’s disease:A systematic review

Alzheimer’s disease is a lifelong progressive neurological disorder. It is associated with high disease management and caregiver costs. Intelligent sensing systems have the capability to provide context-aware adaptive feedback. These can assist Alzheimer’s patients with, continuous monitoring, functional support and timely therapeutic interventions for whom these are of paramount importance. This review aims to present a summary of such systems reported in the extant literature for the management of Alzheimer’s disease. Four databases were searched, and 253 English language articles were identified published between the years 2015 to 2020. Through a series of filtering mechanisms, 20 articles were found suitable to be included in this review. This study gives an overview of the depth and breadth of the efficacy as well as the limitations of these intelligent systems proposed for Alzheimer’s. Results indicate two broad categories of intelligent technologies, distributed systems and self-contained devices. Distributed systems base their outcomes mostly on long-term monitoring activity patterns of individuals whereas handheld devices give quick assessments through touch, vision and voice. The review concludes by discussing the potential of these intelligent technologies for clinical practice while highlighting future considerations for improvements in the design of these solutions for Alzheimer’s disease

Design, implementation, and measurement procedure of underwater and water surface antenna for lora communication

There is an increasing interest in water bodies, which make up more that seventy percent of our planet. It is thus imperative that the water environment should be remotely monitored. Radio frequency (RF) signals have higher bandwidth and lower latency compared to acoustic signals. However, water has high permittivity and conductivity which presents a challenge for the implementation of RF technology. In this work, we undertook a novel design, fabrication, measurement and implementation of an antenna for a sensor node with dual ability of underwater and water surface long range (LoRa) communication at 868 MHz. It was observed that the antenna’s performance deteriorated underwater without −10 dB effective bandwidth between 668 MHz and 1068 MHz. The introduction of an oil-impregnated paper buffer around the antenna resulted in an effective 400 MHz bandwidth within the same frequency span. The sensor node with the buffered antenna was able to achieve a distance of 6 m underwater and 160 m over water surface communication to a data gateway node. The sensor node without the buffered antenna was only able to achieve 80 m over water surface communication. These experimental results show the feasibility of using the LoRa 868 MHz frequency in underwater and water surface communication

System-Level Modelling of Heterogeneous System-on-Chip Architectures Targeting Multi-Standard Wireless Applications

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