A signal processing method for event detection have been developed with low power, parallel architectures that optimize performance for unique sensor system includements [25]. Signal processing architectures for sensor networks are also provided in [24, 26, 30].

           <p>Implementation of a parallel data paths with shared arithmetic elements enabling             high throughput at low clock rate [25] is presented. This method has been used to implement             a micro sensor spectrum analyzer for a 200 sample/s measurement system [25].                A new object-relational data type is introduced termed as GADT (Gaussian ADT).              GADT models physical data as gaussian pdfs [28] and shows that existing ../index structures             can be used as fast access methods for GADT data. it also presents a measuretheoretic               model of probabilistic data and evaluate GADT in its light [28].</p>                <p>A physical layer driven approach to designing protocols and algorithms is proposed               [31] first presents a hardware model for the wireless sensor node and then introduces               the design of physical layer aware protocols, algorithms, and applications that             minimize energy consumption of the system [31]. The approach prescribes methods             that can be used at all levels of the hierarchy to take advantage of the underlying             hardware [31]. It also shows how to reduce energy consumption of non-ideal hardware             through physical layer aware algorithms and protocols [31]. Micro power data converter              is discussed in [24]. Digital signal processing systems, weak inversion CMOS                RF circuits are also designed in [24]. This RF circuit utilizes low power [24]. Some                physical layer principles are presented in [30]. In [27] it is identified the opportunities             and challenges for distributed signal processing for sensor networks.</p>                                               <h2 class="left">References</h2>                [24] G. Asada, M. Dong, T. S. Lin, F. Newberg, G. Pottie, and W. J. Kaiser. Wireless                integrated network sensors: Low power systems on a chip. European Solid State               Circuits Conference, Oct 1998.<br /><br />              [25] M. J. Dong, G. Yung, and W. J. Kaiser. Low power signal processing architectures               for network microsensors. International Symposium on Low Power Electronics              and Design, Digest of Technical Papers, pages 173-177, Aug 1997.<br /><br />                [26] J. Elson, L. Girod, and D. Estrin. A wireless time syncronized cots sensor part1:              System architecture. IEEE CAS Workshop On Wireless Communications and               Networking, Sep 2002.<br /><br />               [27] D. Estrin, L. Girod, G. Pottie, and M. Srivastava. Instrumenting the world with                wireless sensor networks. ICASSP, pages 2675-2678, May 2001.<br /><br />                [28] A. Faradjian, J. E. Gehrke, and P. Bonnet. Gadt: A probability space adt for               representing and querying the physical world. In Proc. of the 18th International                Conference on Data Engineering (ICDE 2002), Feb 2002.<br /><br />               [29] F. Lorenzelli and K.Yao. Arrays of randomly spaced sensors. proc. SPIE, pages              122-133, 1996.<br /><br />              [30] G.J. Pottie and W.J. Kaiser. Wireless integrated network sensors. Comm. ACM,               43:51-58, May 2000.<br /><br />             [31] E. Shih, S. Cho, N. Ickes, R. Min, A. Sinha, A. Wang, and A. Chandrakasan.             Physical layer driven protocol and algorithm design for energy efficient wireless               sensor networks. The 7th annual international conference on Mobile computing                and networking, pages 272-287,  2001.<br /><br /> From: http://sitestree.com/?p=5313<br /> Categories:111<br />Tags:<br /> Post Data:2011-09-22 12:11:53<br />

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