Titlebook: Advances in VLSI, Communication, and Signal Processing; Select Proceedings o Debashis Dutta,Haranath Kar,Vijaya Bhadauria Conference procee

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書目名稱Advances in VLSI, Communication, and Signal Processing影響因子(影響力)




書目名稱Advances in VLSI, Communication, and Signal Processing影響因子(影響力)學(xué)科排名




書目名稱Advances in VLSI, Communication, and Signal Processing網(wǎng)絡(luò)公開度




書目名稱Advances in VLSI, Communication, and Signal Processing網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Advances in VLSI, Communication, and Signal Processing被引頻次




書目名稱Advances in VLSI, Communication, and Signal Processing被引頻次學(xué)科排名




書目名稱Advances in VLSI, Communication, and Signal Processing年度引用




書目名稱Advances in VLSI, Communication, and Signal Processing年度引用學(xué)科排名




書目名稱Advances in VLSI, Communication, and Signal Processing讀者反饋




書目名稱Advances in VLSI, Communication, and Signal Processing讀者反饋學(xué)科排名

瑣碎

4.2.16 Allenyl (propargyl) radicals,uster head with minimum velocity for maximum connectivity among the cluster members. Selection of reliable forwarder improves network performance. Extensive simulation study is carried out to evaluate the performance of the proposed routing protocol with respect to delay, throughput, PDR, and total energy consumption.

enterprise

規(guī)范就好

4.2.15 Bicyclic allyl radicals,upper layer zone coordinator. The comparison has been made of the proposed framework with static model, LEACH and EESAA routing protocols. It identified that the proposed technique performs well in terms of energy consumption and network lifetime.

爆米花

Alienated

DFT Precoder Technique Combined with μ-Law Companding for PAPR Reduction in OFDM Systemtion results show how the DFT precoder-based μ-law companding technique realizes reduced PAPR and improved Bit Error Rate (BER) performance in comparison to Discrete Hartley Transform (DHT) precoding with a μ-law companded-based OFDM system.

spinal-stenosis

Narrative

Comparative Study of Anomaly Detection in Wireless Sensor Networks Using Different Kernel Functionsnomalous. SVM suffers from computational complexity while handling large datasets, so sequential minimal optimization SVM (SMO-SVM) is used to reduce the complexity. In this paper, a comparative study is made on anomaly detection using SMO-SVM classifier utilizing different kernel functions.

容易懂得

巨碩