This paper presents an innovative method for the design of a triple band meta-mode antenna. This unique design of antenna finds application in a particular frequency band of WLAN and WiMAX. This antenna comprises of a square complimentary split ring resonator (SCSRR), a coaxial feed, and two symmetrical comb shaped split ring resonators (CSSRR). The metamaterial unit cell SCSRR independently gains control in the band range 3.15–3.25 GHz (WiMAX), whereas two symmetrical CSSRR unit cell controls the band in the ranges 3.91–4.01 GHz and 5.79–5.94 GHz (WLAN). This design methodology and the study of the suggested unit cells structure are reviewed in classical waveguide medium theory. The antenna has a miniaturized size of only 0.213λ0 × 0.192λ0 × 0.0271λ0 (20 × 18 × 2.54 mm3, where λ0 is the free space wavelength at 3.2 GHz). The detailed dimension analysis of the proposed antenna and its radiation efficiency are also presented in this paper. All the necessary simulations are carried out in High Frequency Structure Simulator (HFSS) 13.0 tool.

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A brief theoretical foundation has been provided that expands upon QAM and FSK modulation in the application of Frequency Hopped spread spectrum systems. This paper mainly focuses on the Bit Error Estimation in Frequency Hop Spread Spectrum System using Quadrature Amplitude modulation and Frequency shift keying which are used in defense applications. The main aim here is to calculate the bit error performance of a frequency hopping spread spectrum model in the presence of AWGN channel. This paper provides a systematic approach for evaluating the performance of FHSS operating with coherent M-ary FSK demodulation. There have been investigations into the frequency hop spread spectrum systems employing different modulation schemes to decrease the bit error ratios. There has been much work done on computing BER of FHSS systems with error control coding using industry standard convolutional coding.

This study aims at developing a clinically oriented automated diagnostic tool for distinguishing malignant melanocytic lesions from benign melanocytic nevi in diverse image databases. Due to the presence of artifacts, smooth lesion boundaries, and subtlety in diagnostic features, the accuracy of such systems gets hampered. Thus, the proposed framework improves the accuracy of melanoma detection by combining the clinical aspects of dermoscopy. Two methods have been adopted for achieving the aforementioned objective. Firstly, artifact removal and lesion localization are performed. In the second step, various clinically significant features such as shape, color, texture, and pigment network are detected. Features are further reduced by checking their individual significance (i.e., hypothesis testing). These reduced feature vectors are then classified using SVM classifier. Features specific to the domain have been used for this design as opposed to features of the abstract images. The domain knowledge of an expert gets enhanced by this methodology. The proposed approach is implemented on a multi-source dataset (PH2 + ISBI 2016 and 2017) of 515 annotated images, thereby resulting in sensitivity, specificity and accuracy of 83.8%, 88.3%, and 86%, respectively. The experimental results are promising, and can be applied to detect asymmetry, pigment network, colors, and texture of the lesions.