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This paper aims to present an intelligent system for parking space detection based on image processing technique. The proposed system captures and processes the rounded image drawn at parking lot and produces the information of the empty car parking spaces. In this work, a camera is used as a sensor to take photos to show the occupancy of car parks. The reason why a camera is used is because with an image it can detect the presence of many cars at once. Also, the camera can be easily moved to detect different car parking lots. By having this image, the particular car parks vacant can be known and then the processed information was used to guide a driver to an available car park rather than wasting time to find one. The proposed system has been developed in both software and hardware platform. An automatic parking system is used to make the whole process of parking cars more efficient and less complex for both drivers and administrators.

Background: Human gut microbiota can provide lifelong health information and even influence mood and behaviour. We currently lack the tools to obtain a microbial sample, directly from the small intestine, without contamination. Methods: Shape memory alloy springs are used in concentric configuration to develop an axial actuator. A novel design of sampling mechanism is fabricated for collecting the sample from the gut. Storage chamber (500 µl) is used to protect the sample from downstream contamination. Results: The developed actuator occupies a small space (5 � Ø5.75 mm) and produces sufficient output force (1.75 N) to operate the sampling mechanism. A noninvasive capsule robot was tested ex vivo on the animal intestine, and it captured an average of 134 µl content which was sufficient for microbiome assessment. Conclusions: Laboratory testing revealed that the collected sample had an amino acid signature indicative of microbiota, mucus and digesta, which provided a proof of concept for the proposed design. K E Y W O R D S capsule robot, GI tract, gut microbiota, intestine, micro robot 1 | INTRODUCTION A significant population of microorganisms (bacteria, archaea and fungi) live inside the gastrointestinal (GI) tract, play a major role in fibre fermentation and the synthesis of short-chain fatty acids, and some nutrients (e.g., vitamins), and are collectively known as microbiota. 1 Human intestinal microbiota consist of 10 13-10 14 microorganisms which can act as biomarkers. 2 The microbiota, present in the GI tract, contain lifelong information on the health of an individual and can assist in early diagnosis of diseases such as cancer, diabetes and obesity. 3-5 Several researchers believe that analysis of microbiota could be helpful in predicting obesity, diabetes and inflammatory bowel disease. 3-6 Microbiota can also help to study the relationship or interaction between nutrition and human health. 3 Given the significance of gut microbiota, it is critical to explore its ecology. Microbiota colonise the mucous layer which covers the columnar epithelium of the GI tract and the digesta within the intestinal lumen. 7 The population of microorganisms (microbiota) inside the intestine has been studied using various methods. The most common samples used as a proxy for intestinal microbiota are fecal samples; however, they do not categorise spatial inhabitants, and it is not possible to localise them. Similarly, they lack temporal information and cannot reciprocate real-time gut environment as they are examined after travelling through the entire GI tract, which exposes the sample to contamination. 8 Some efforts have been made in obtaining samples from the human gut with biopsy; however, it is a tethered method which largely restricts its use to the large intestine. 9,10 Furthermore, tethered methods involve a high risk of gut perforation, bleeding and require sedation, and the procedure is also invasive and unpleasant for a patient in terms of comfort. 10 Most importantly, the sample obtained by biopsy is a tissue s...

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The intermittent nature of wind energy raised multiple challenges to the power systems and is the biggest challenge to declare wind energy a reliable source. One solution to overcome this problem is wind energy forecasting. A precise forecast can help to develop appropriate incentives and wellfunctioning electric markets. The paper presents a comprehensive review of existing research and current developments in deterministic wind speed and power forecasting. Firstly, we categorize wind forecasting methods into four broader classifications: input data, timescales , power output, and forecasting method. Secondly, the performance of wind speed and power forecasting models is evaluated based on 634 accuracy tests reported in twenty-eight published articles covering fifty locations of ten countries. From the analysis, the most significant errors were witnessed for the physical models, whereas the hybrid models showed the best performance. Although, the physical models have a large normalized root mean square error values but have small volatility. The hybrid models perform best for every time horizon. However, the errors almost doubled at the medium-term forecast from its initial value. The statistical models showed better performance than artificial intelligence models only in the very short term forecast. Overall, we observed the increase in the performance of forecasting models during the last ten years such that the normalized mean absolute error and normalized root mean square error values reduced to about half the initial values.

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