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Cognitive radio network (CRN) is the latest paradigm of techniques for enhancing the utility and quality of radio communication systems by the efficient utilization of frequency spectrum. It is classified by overlay and underlay cognitive radio. Antennas that designed for the overlay scheme must have the capability to sense the channel and provide communication over a portion of it. Overlay antennas can be designed as two-port, where one port supports ultra wideband, and the other supports narrowband and are frequency reconfigurable. Moreover, they can be implemented as a one-port antenna, in which the same port is used for both sensing as well as communicating, and thus it must switch between wideband and narrowband modes. Evolutionary computation techniques like the genetic algorithm (GA) are efficient in designing new kinds of antennas with challenging designs. A binary genetic algorithm can be utilized to optimize the shape of an antenna to achieve maximum possible frequency reconfigurability with minimum number of switches for obtaining a wideband performance from a single cognitive radio antenna. General TermsUltrawideband and narrowband antenna, Evolutionary algorithms.

Artificial neural networks and image processing are the error tolerant applications that require massive workloads to be executed within certain power limits. Energy efficient multipliers on an embedded processor are crucial for such applications. In this paper, a highly diminished yet accurate design of an approximate truncated multiplier is presented. A compact error compensation circuit is presented that aims to mitigate the truncation error. To reduce the energy consumption further, the voltage overscaling method is used. The performance of the circuit is optimized by adjusting the two approximation knobs of circuit pruning and voltage scaling. Simulation results for a truncation factor of 8 with an MRED of 0.01 demonstrate an 11.4% reduction in energy consumption compared to that of the 16‐bit radix‐4 exact multiplier. Application based evaluation is performed by using the approximate multipliers on image multiplication application and a high value of PSNR (43.17 dB) is obtained. When evaluated using a handwritten digit recognition application based on convolutional neural networks (CNN), an accuracy of 97.85% is obtained.

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