Safety and Security: Smart Cameras for Intelligent Buildings

In any remote surveillance scenario, smart cameras have to take intelligent decisions to generate summary frames to minimize communication and processing overhead. Video summary generation, in the context of smart camera, is the process of merging the information from multiple frames. A summary generation scheme based on clustering based change detection algorithm has been implemented in our smart camera system for generating frames to deliver requisite information. In this paper we propose an embedded platform based framework for implementing summary generation scheme using HW-SW Co-Design based methodology. The complete system is implemented on Xilinx XUP Virtex-II Pro FPGA board. The overall algorithm is running on PowerPC405 and some of the blocks which are computationally intensive and more frequently called are implemented in hardware using VHDL. The system is designed using Xilinx Embedded Design Kit (EDK).

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Heat-electricity joint bidding strategies for intelligent buildings in intelligent building cluster

International Journal of Electrical Power & Energy Systems ◽

10.1016/j.ijepes.2021.106891 ◽

2021 ◽

Vol 129 ◽

pp. 106891

Author(s):

Xun Dou ◽

Yunfan Shao ◽

Jun Wang ◽

Qinran Hu

Keyword(s):

Intelligent Building ◽

Bidding Strategies ◽

Intelligent Buildings

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Thermodynamics Energy for Both Supervised and Unsupervised Learning Neural Nets at a Constant Temperature

International Journal of Neural Systems ◽

10.1142/s0129065799000162 ◽

1999 ◽

Vol 09 (03) ◽

pp. 175-186 ◽

Cited By ~ 6

Author(s):

HAROLD SZU

Keyword(s):

Free Energy ◽

Constant Temperature ◽

Helmholtz Free Energy ◽

Principal Component ◽

Neural Nets ◽

Mixing Condition ◽

Smart Cameras ◽

Minimization Principle ◽

Output Entropy ◽

Artificial Neural Network Ann

Unified Lyaponov function is given for the first time to prove the learning methodologies convergence of artificial neural network (ANN), both supervised and unsupervised, from the viewpoint of the minimization of the Helmholtz free energy at the constant temperature. Early in 1982, Hopfield has proven the supervised learning by the energy minimization principle. Recently in 1996, Bell & Sejnowski has algorithmically demonstrated. Independent Component Analyses (ICA) generalizing the Principal Component Analyses (PCA) that the continuing reduction of early vision redundancy happens towards the "sparse edge maps" by maximization of the ANN output entropy. We explore the combination of both as Lyaponov function of which the proven convergence gives both learning methodologies. The unification is possible because of the thermodynamics Helmholtz free energy at a constant temperature. The blind de-mixing condition for more than two objects using two sensor measurement. We design two smart cameras with short term working memory to do better image de-mixing of more than two objects. We consider channel communication application that we can efficiently mix four images using matrices [AO] and [Al] to send through two channels.

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Efficient Ontology Integration Model for Better Inference in Context Aware Computing

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.268-270.841 ◽

2011 ◽

Vol 268-270 ◽

pp. 841-846

Author(s):

Soo Mi Yang

Keyword(s):

Video Data ◽

Smart Devices ◽

Collaborative Network ◽

Context Aware ◽

Integration Model ◽

Smart Cameras ◽

Ontology Integration ◽

Context Aware Computing ◽

Heterogeneous Devices ◽

Distributed Ontology

In this paper, we describe efficient ontology integration model for better context inference based on distributed ontology framework. Context aware computing with inference based on ontology is widely used in distributed surveillance environment. In such a distributed surveillance environment, surveillance devices such as smart cameras may carry heterogeneous video data with different transmission ranges, latency, and formats. However even smart devices, they generally have small memory and power which can manage only part of ontology data. In our efficient ontology integration model, each of agents built in such devices get services not only from a region server, but also peer servers. For such a collaborative network, an effective cache framework that can handle heterogeneous devices is required for the efficient ontology integration. In this paper, we propose a efficient ontology integration model which is adaptive to the actual device demands and that of its neighbors. Our scheme shows the efficiency of model resulted in better context inference.

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