In this paper, we present Hybrid MAC (H-MAC), a novel low power with minimal packet delay medium access control protocol for wireless sensor networks (WSNs). H-MAC achieves high energy efficiency under wide range of traffic load. It ensures high channel utilization during high traffic load without compromising energy efficiency. H-MAC does it by using the strength of CSMA and TDMA approach with intelligence. The novel idea behind the H-MAC is that, it uses both the broadcast scheduling and link scheduling. Depending on the network loads the H-MAC protocol dynamically switches from broadcast scheduling to link scheduling and vice-versa in order to achieve better efficiency. Furthermore, H-MAC uses Request-To-Send (RTS), Clear‒To-send (CTS) handshakes with methods for adapting the transmit power to the minimum level necessary to reach the intended neighbor with a given BER target or packet loss probability. Thus H-MAC reduces energy consumption by suitably varying the transmit power. The simulation results corroborate the theoretical idea, and show the efficiency of our proposed protocol.

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There have been no confirmed efficient fishery investigation methods for Laminaria beds in Nemuro, Hokkaido, Japan. Recently, using an underwater video camera has become an increasingly common way to investigate Laminaria beds. However, data obtained from the investigation need to be manually analyzed, which is inefficient. To develop an automated system for measuring the amount of fishery resources from images taken with an underwater camera for fishery investigation of Laminaria beds in Nemuro, Hokkaido, Japan. Ezo-wakame (a type of seaweed) extraction method using image analysis technology has been developed and evaluated in an experiment. In this paper, we explain about extraction method of Ezo-wakame and the experiment of applying the proposed method. This method uses the vein on the surface of Ezo-wakame to extract it because veins are specific characteristics and indicate the number of Ezo-wakames.

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Spatial pyramid matching (SPM) has been an important approach to image categorization. This method partitions the image into increasingly fine sub-regions and computes histograms of local features at each sub-region. Although SPM is an efficient extension of an unordered bag-of-features image representation, it still measures the similarity between sub-regions by application of the bag-of-features model. Therefore, it is limited in its capacity to achieve optimal matching between sets of unordered features. To overcome this limitation, we propose a hierarchical spatial matching kernel (HSMK) that uses a coarse-to-fine model for the sub-regions to obtain better optimal matching approximations. Our proposed kernel can deal robustly with unordered feature sets as well as various cardinalities. In experiments, results of HSMK outperformed those of SPM and led to state-of-the-art performance on several well-known databases of benchmarks in image categorization, even though we use only a single type of image feature.

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This paper proposes high-speed image matching method using a template autonomously optimized by learning of detection history. While many template matching methods have been proposed as one of object detection method, there are some methods that optimizes a template image by machine learning. However, those methods need a long time for preprocessing to optimize the template image. Moreover, acquisition of large amount of data for machine learning is not easy. To solve this problem, we propose a method that optimizes a template image by executing detection and learning history simultaneously. Through experiments, it is shown that the template image can be optimized even if large amount of data is not prepared in advance.

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This paper proposes a new method to estimate the distribution of some similar objects that have no specific feature like color and shape. In the field of animal behavior analysis, the estimation of the crowded swarm distribution with automated way of image processing is necessary. However, to trace the individual objects those may overlap each other in video sequence is quite difficult. This paper proposes to introduce a value function effective for the crowded swarm analysis, and shows the affirmative results acquired for the Soldier Crab behavior analysis.

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The purpose of this study is to reconstruct shapes of two human bodies even if human pose changes in mutually occluded regions. In this paper, we propose a shape estimation method based on Fast Level Set Method(FLSM).A conventional method based on FLSM is not able to reconstruct shapes of two human bodies precisely in case that human pose changes in mutually occluded regions, because the conventional method sets a constraint to FLSM in the mutual occluded regions. Therefore, in the proposed method, we firstly reconstruct approximate shape of each human body without setting a constraint to FLSM in case of occurring of mutual occlusion by excluding mutually occluded regions directly and extracting regions of human bodies. Secondly, we estimate pose of each human body from the reconstructed approximate shape of each human body. Finally, we estimate shape of each human body by reallocating shape parts of each human body, which were obtained before occurring of mutual occlusion by FLSM, based on the estimated pose of each human body.

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This paper proposes a low power video decoding with adaptive granularity in temporal scalability. This proposal can be applied to reduce the computational complexity of H.264/AVC decoder with acceptable loss of the video quality, and make the single layer bit stream sources much more flexible for various terminal devices. Proposed low power decoding process consists of four proposed algorithms, the reference frame index decision algorithm, motion vector composition algorithm, block-partition decision algorithm and the adaptive selecting algorithm for skipped frames. The experiment results show that the reduction rate of the decoding time decreases when the number of skipped frames increases, and the loss of the video quality increases at the same time. The PSNR loss in the B frame skipping is much smaller than the PSNR loss in the P frame skipping. In the fixed frame skipping cases, the 2/3 P frame is skipped with 60% decoding time reduction and 2.73 dB average PSNR loss in the all filling comparison or 1.59 dB average PSNR loss in the corresponding comparison. Analyzing the relation between motion vector information and the video quality loss of the corresponding frames in probability shows that the proposed adaptive skipping scheme reduces quality loss by skipping the frames with slight movements and keeping the frames with strong movements. Based on the adaptive skipping scheme, the average PSNR is improved 0.68 dB in the all filling comparison or 0.60 dB in the corresponding comparison compared with the fixed frame skipping scheme with almost the same reduction rate of the decoding time.

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