1,145 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    Identifying therapeutic targets against viral hepatitis and liver cancer

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    Development of standardised sizing system and size charts for the production of ready-to-wear clothing for Ghanaian children aged 6-11

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    Children experience rapid growth rate and often indulge in various physical and motion related activities in education and play spaces. Ill-fitting clothing such as very tight or unproportionally balanced clothes can cause movement restrictions, psychological challenges, and other undesirable health related issues. This makes appropriate clothing sizing crucial to address, as it gives children the right fit that allows room for movement and growth. Effectiveness of fit is based on a sizing system and size charts that have been developed using current and accurate body measurements of a specific population since differences exist among populations. Currently, established size charts and academic publications on sizing systems in Ghana have focused on women. No national database or anthropometric study has been developed exclusively for Ghanaian children. Practitioners either take measurements on ad-hoc basis for made-to-measure outfits; or use adapted versions of the British sizing system for manufacturing ready-to-wear garments such as uniforms. This research has therefore been undertaken to develop a standard clothing sizing system and size charts for Ghanaian children between the (school) ages of 6 and 11. This will sustain the general production of reliably sized garments for Ghanaian children whiles providing appropriate fit. It will further enhance mass production of ready-to-wear garments for the apparel market in Ghana. The study involved both secondary and primary data collection methods. An extensive review of literature was conducted focusing on relevant topics in anthropometry and anthropometric surveys for sizing creation, sizing systems, growth of children and garment fit. A comprehensive set of body measurements including height and weight of the sample population of school children were collected. A critical measurement procedural guide and two instructional videos in English and Twi (dominant Ghanaian language) were developed by the study taking into account efficacy, ethical and sustainable considerations for good practice. These were made available and guided parents/legal guardians and participants in the data collection process during fieldwork. The population consisted of primary school pupils in Ghana. A sample of 776 usable data was used for the analysis. With the IBM SPSS analytics software, appropriate statistical procedures such as means, t-test and analysis of variance tests (ANOVA) were conducted to ascertain the relationships among the variables and to obtain statistical data for the development of the sizing system. Principal component analysis (PCA) and cluster analysis were also used to aid the development of the sizing system. Centred on the PCA technique, three key dimensions (height, chest, and waist girths) were selected based on the factor loading and practicality. The study found and established significant differences between the body measurements of Ghanaian children aged 6-11 along gender lines. Using the cluster analysis technique, the selected dimensions were used to categorize the study sample into homogenous subgroups according to upper and lower body separately for both males and females. Four or five sizes were created for each cluster group, and size charts were established based on percentile values. This study presents theoretical and empirical contributions to the body of knowledge in anthropometrics. It has modelled a guide that demonstrates the capability of remote and safe body measuring practices on children, which is particularly useful, economical, and reliable for clothing related practices that seek to employ consistent traditional manual measuring techniques. The study has created an original up-to-date anthropometric database for Ghanaian children between 6-11 years; and developed a comprehensive sizing system for wider clothing practices. In addition to providing a framework for procedures in creating children’s sizing system and size chart, it establishes new size charts for both males and females aged 6-11, based on the Ghanaian population. These developments stand to increase productivity, consistency, and economic efficiency for the Ghanaian apparel industry. The study makes recommendations for extending this work to other segments of the population

    Improving Classification in Single and Multi-View Images

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    Image classification is a sub-field of computer vision that focuses on identifying objects within digital images. In order to improve image classification we must address the following areas of improvement: 1) Single and Multi-View data quality using data pre-processing techniques. 2) Enhancing deep feature learning to extract alternative representation of the data. 3) Improving decision or prediction of labels. This dissertation presents a series of four published papers that explore different improvements of image classification. In our first paper, we explore the Siamese network architecture to create a Convolution Neural Network based similarity metric. We learn the priority features that differentiate two given input images. The metric proposed achieves state-of-the-art Fβ measure. In our second paper, we explore multi-view data classification. We investigate the application of Generative Adversarial Networks GANs on Multi-view data image classification and few-shot learning. Experimental results show that our method outperforms state-of-the-art research. In our third paper, we take on the challenge of improving ResNet backbone model. For this task, we focus on improving channel attention mechanisms. We utilize Discrete Wavelet Transform compression to address the channel representation problem. Experimental results on ImageNet shows that our method outperforms baseline SENet-34 and SOTA FcaNet-34 at no extra computational cost. In our fourth paper, we investigate further the potential of orthogonalization of filters for extraction of diverse information for channel attention. We prove that using only random constant orthogonal filters is sufficient enough to achieve good channel attention. We test our proposed method using ImageNet, Places365, and Birds datasets for image classification, MS-COCO for object detection, and instance segmentation tasks. Our method outperforms FcaNet, and WaveNet and achieves the state-of-the-art results

    Comprehensive Survey and Analysis of Techniques, Advancements, and Challenges in Video-Based Traffic Surveillance Systems

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    The challenges inherent in video surveillance are compounded by a several factors, like dynamic lighting conditions, the coordination of object matching, diverse environmental scenarios, the tracking of heterogeneous objects, and coping with fluctuations in object poses, occlusions, and motion blur. This research endeavor aims to undertake a rigorous and in-depth analysis of deep learning- oriented models utilized for object identification and tracking. Emphasizing the development of effective model design methodologies, this study intends to furnish a exhaustive and in-depth analysis of object tracking and identification models within the specific domain of video surveillance

    Science and Innovations for Food Systems Transformation

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    This Open Access book compiles the findings of the Scientific Group of the United Nations Food Systems Summit 2021 and its research partners. The Scientific Group was an independent group of 28 food systems scientists from all over the world with a mandate from the Deputy Secretary-General of the United Nations. The chapters provide science- and research-based, state-of-the-art, solution-oriented knowledge and evidence to inform the transformation of contemporary food systems in order to achieve more sustainable, equitable and resilient systems
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