To further implement decentralized renewable energy resources, blockchain based peer-to-peer (P2P) energy trading is gaining attention and its architecture has been proposed with virtual demonstrations. In this paper, to further socially implement this concept, a blockchain based peer to peer energy trading system which could coordinate with energy control hardware was constructed, and a demonstration experiment was conducted. Previous work focused on virtually matching energy supply and demand via blockchain P2P energy markets, and our work pushes this forward by demonstrating the possibility of actual energy flow control. In this demonstration, Plug-in Hybrid Electrical Vehicles(PHEVs) and Home Energy Management Systems(HEMS) actually used in daily life were controlled in coordination with the blockchain system. In construction, the need of a multi-tagged continuous market was found and proposed. In the demonstration experiment, the proposed blockchain market and hardware control interface was proven capable of securing and stably transmitting energy within the P2P energy system. Also, by the implementation of multi-tagged energy markets, the number of transactions required to secure the required amount of electricity was reduced.

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In the coming Internet of Things (IoT) era, it is important to reduce the volume of data being output by sensors as well as their power consumption. Since conventional image sensor output data for photography are often redundant in AI applications, image sensors that can output lightweight data for use in AI are needed. In this work, we propose a complementary metal oxide semiconductor (CMOS) image sensor (CIS) pixel circuit that can extract intensity gradients without the use of analog memories. The gradients are available for histogram of oriented gradients (HoG) features that can reduce the amount of data used in image classification. We performed experiments to evaluate whether the HoG features calculated by the outputs from our image sensor pixels were suitable for image classification tasks. A support vector machine (SVM) classifier was trained with simulated sensor outputs to evaluate human detection accuracy. We also evaluated the accuracy when the sensor outputs were quantized using "low bit decimation" and "value clipping" to reduce the amount of data. Our experimental results indicated that the highest accuracy of 99.55% was achieved using the 2-bit-width quantized gradients by value clipping and HoG features calculated with 4 × 4 cells.