Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances.

In recent years, indoor positioning has emerged as a critical function in many end-user applications; including military, civilian, disaster relief and peacekeeping missions. In comparison with outdoor environments, sensing location information in indoor environments requires a higher precision and is a more challenging task in part because various objects reflect and disperse signals. Ultra WideBand (UWB) is an emerging technology in the field of indoor positioning that has shown better performance compared to others. In order to set the stage for this work, we provide a survey of the state-of-the-art technologies in indoor positioning, followed by a detailed comparative analysis of UWB positioning technologies. We also provide an analysis of strengths, weaknesses, opportunities, and threats (SWOT) to analyze the present state of UWB positioning technologies. While SWOT is not a quantitative approach, it helps in assessing the real status and in revealing the potential of UWB positioning to effectively address the indoor positioning problem. Unlike previous studies, this paper presents new taxonomies, reviews some major recent advances, and argues for further exploration by the research community of this challenging problem space.

Fly-LeNet: A deep learning-based framework for converting multilingual braille images.

For many years, braille-assistive technologies have aided blind individuals in reading, writing, learning, and communicating with sighted individuals. These technologies have been instrumental in promoting inclusivity and breaking down communication barriers in the lives of blind people. One of these technologies is the Optical Braille Recognition (OBR) system, which facilitates communication between sighted and blind individuals. However, current OBR systems have a gap in their ability to convert braille documents into multilingual texts, making it challenging for sighted individuals to learn braille for self-learning-based uses. To address this gap, we recommend a segmentation and deep learning-based approach named Fly-LeNet that converts braille images into multilingual texts. The approach includes image acquisition, preprocessing, and segmentation using the Mayfly optimization approach with a thresholding method and a braille multilingual mapping step. It uses a deep learning model, LeNet-5, that recognizes braille cells. We evaluated the performance of the Fly-LeNet through several experiments on two datasets of braille images. Dataset-1 consists of 1404 labeled samples of 27 braille signs demonstrating the alphabet letters, while Dataset-2 comprises 5420 labeled samples of 37 braille symbols representing alphabets, numbers, and punctuations, among which we used 2000 samples for cross-validation. The suggested model achieved a high classification accuracy of 99.77% and 99.80% on the test sets of the first and second datasets, respectively. The results demonstrate the potential of Fly-LeNet for multilingual braille transformation, enabling effective communication with sighted individuals.

Handling Big Data Scalability in Biological Domain Using Parallel and Distributed Processing: A Case of Three Biological Semantic Similarity Measures.

In the field of biology, researchers need to compare genes or gene products using semantic similarity measures (SSM). Continuous data growth and diversity in data characteristics comprise what is called big data; current biological SSMs cannot handle big data. Therefore, these measures need the ability to control the size of big data. We used parallel and distributed processing by splitting data into multiple partitions and applied SSM measures to each partition; this approach helped manage big data scalability and computational problems. Our solution involves three steps: split gene ontology (GO), data clustering, and semantic similarity calculation. To test this method, split GO and data clustering algorithms were defined and assessed for performance in the first two steps. Three of the best SSMs in biology [Resnik, Shortest Semantic Differentiation Distance (SSDD), and SORA] are enhanced by introducing threaded parallel processing, which is used in the third step. Our results demonstrate that introducing threads in SSMs reduced the time of calculating semantic similarity between gene pairs and improved performance of the three SSMs. Average time was reduced by 24.51% for Resnik, 22.93%, for SSDD, and 33.68% for SORA. Total time was reduced by 8.88% for Resnik, 23.14% for SSDD, and 39.27% for SORA. Using these threaded measures in the distributed system, combined with using split GO and data clustering algorithms to split input data based on their similarity, reduced the average time more than did the approach of equally dividing input data. Time reduction increased with increasing number of splits. Time reduction percentage was 24.1%, 39.2%, and 66.6% for Threaded SSDD; 33.0%, 78.2%, and 93.1% for Threaded SORA in the case of 2, 3, and 4 slaves, respectively; and 92.04% for Threaded Resnik in the case of four slaves.