Football or Soccer is one of the most popular games in the world. “Handball” event in the game is one of the most controversial and important decisions by a single referee. This paper proposes a method to define a “handball event” in a football game by recognizing the hand, ball, and their interaction from a single camera. We trained a model to identify hands and balls using detectron2, then used the vertices of two objects to find out the overlapping situation between these two objects to determine the handball event in a football game. The train and test result of detection was satisfactory with 96% for hand and 100% for the ball respectively.

Deep learning models require large amounts of data to be trained to fulfill their potential. To solve this problem, we propose a novel method for creating high-quality photorealistic synthetic training data and compare its performance to real data for object detection. We introduce the RealStreet and SynthStreet datasets, which were designed to enhance a safety analysis for road object detection. The objective of the project is to provide a useful synthetic environment for learning and building a road traffic experience by imitating the real environment as nearly as possible. This will improve the safety of road users and enable testing and planning before actual, dangerous events arise. The RealStreet data-set was collected in real-world scenarios of an urban city, while the SynthStreet closely recreates the RealStreet scenes: field of view, road objects, such as pedestrians, cyclists, vehicles, and background information buildings are matched. We study the performance and behavior of a network model trained on real and synthetic data-sets and both with various ratio mixes. Our approach is to evaluate data-set performances using a state-of-the-art method for object detection tasks in learning from synthetic data. We also compare the performance of each dataset, which is evaluated on real-world data, to determine the possibility of synthetic data-set benefits and analyze the effect of limited real-world data. However, it is important to note that synthetic data-sets may not always accurately represent the variability and complexity of real-world environments and may not generalize well to real-world scenarios. Therefore, it is important to carefully evaluate the performance of deep learning models trained on synthetic data and validate their results using real-world data.

In this paper, we propose a method for recognizing human actions indoors using fuzzy rules and multi cameras. To recognize the human actions, initially, we use the background difference method to extract human area candidates. We then extract HOG features and learn to detect humans using the features and AdaBoost. Fuzzy rules are then used of detect the human actions. The detected human is determined to be stationary or not using the distance between the detected areas in consecutive frames. We also estimate the direction the human is facing using the width of detection, and finally recognize the standard action using the height of the detected region. In addition, we recognize suspicious action using duration of detection and presence of abandoned object. After experiments, recognition accuracy achieved for “walking” and “stop” actions is about 87%, for “running” action about 54%, for “sitting” about 96%, for “desk working” about 83%, and “falling” about 88%.

An analysis of the Electroencephalogram (EEG) signals while performing a monotonous task and drinking alcohol using principal component analysis (PCA), linear discriminant analysis (LDA) for feature extraction and Neural Networks (NNs) for classification is proposed. The EEG is captured while performing a monotonous task that can adversely affect the brain and possibly cause stress. Moreover, we investigate the effects of alcohol on the brain by capturing the data continuously after consumption of equal amounts of alcohol. We hope that our work will shed more light on the relationship between such actions and EEG, and investigate if there is any relation between the tasks and mental stress. EEG signals offers a rare look at brain activity, while, monotonous activities are well known to cause irritation which may contribute to mental stress. We apply PCA and LDA to characterize the change in each component, extract it and discriminate using a NN. After experiments, it was found that PCA and LDA are effective analysis methods in EEG signal analysis.

The number of smartphone users and mobile applications are growing rapidly. Though smartphones are expected to have PC-like functionality, hardware resources such as CPUs, memory and batteries are still limited. To solve this resource problem, many researches have proposed architectures to use server resources in the cloud for mobile devices. We propose a conceptual architecture of Android as a Server Platform, which enables multiple user Android applications on cloud server via network. Though Android is mainly designed for physical smartphone, Android's two other features are useful to construct a server platform. Android is open-source product and runs on an ×86 CPU. We show three types of multi-tenant architecture for an Android server platform and discuss the direction to take to it reality.

The numbers of smart phone users and related applications are growing rapidly, and applications continue to become more data-intensive. In the cloud based service for smart phone, if user demand on virtual machines exceeds the hardware capacity of the server, the server incurs an overload and bottleneck, network delay, latency, and packet loss rate are increased in 3G and Wi-Fi connections. Therefore, it is important to predict user demand and to use this information for resource allocation methods such as network virtualization and load balancing. We present a novel user demand prediction method that uses analysis results of application usage patterns. By analysis of log data and using the proposed method, we can predict execution time and average volume of transmitted application data. The proposed method is mainly considered for adoption in our virtual smart phone system. We show results from an experiment performed in an implemented test-bed, including prediction results and performance of wireless media.

This paper presents a new ascending-price multi-unit auction protocol. As far as the authors are aware, this is the first protocol that has an open format, and in which sincere bidding is an equilibrium strategy, even if the marginal utilities of each agent can increase and agents can submit bids. As ever-increasing numbers of companies and consumers are trading on Internet auctions, a new type of cheating called false-name has been noticed. Specifically, there may be some agents with fictitious names such as multiple e-mail addresses. The VCG is not an open format, and truth-telling is no longer a dominant strategy if agents can submit bids and the marginal utilities of each agent can increase. The Iterative Reducing (IR) protocol with a sealed-bid format is robust against bids, although it requires the auctioneer to carefully pre-determine a reservation price for one unit. Open format protocols, such as the Ausubel auction, outperform sealed-bid format protocols in terms of the simplicity and privacy-preservation. These two advantages are said to encourage more agents to bid sincerely and to provide the seller with higher revenue. We extend the Ausubel auction to our proposed protocol which can handle the cases where the marginal utilities of each agent can increase. Moreover, it is robust against bids and does not require the auctioneer to set a reservation price. Our simulation result indicates that our protocol herein obtains a social surplus close to Pareto efficient and that it outperforms the IR with respect to the social surplus and the seller's revenue.

The attempts to count the passing people by the image processing have been made some time ago. But the conventional methods could not count the passing people accurately unless there were very few of the passing people through the gate at one time. In this paper, we propose that passing people be accurately counted using images obtained by a stereo camera. In proposed method, the stereo camera is hung from the ceiling of the gate and the optical axis of the camera is set up so that the passing people could be observed from just overhead. In this system arrangement, if there are crowd people in the gate, then the data of the passing people are not overlapped each other on the obtained images. In addition, by using the stereo camera, the human region and road region on the obtained images are able to be segmented accurately, In this paper, we show some experimental results obtained by using a simple experimental system to verify effectiveness of the proposed method.