Cancer identification and classification from histopathological images of the breast depends greatly on experts, and computer-aided diagnosis can play an important role in disagreement of experts. This automatic process has increased the accuracy of the classification at a reduced cost. The advancement in Convolution Neural Network (CNN) structure has outperformed the traditional approaches in biomedical imaging applications. One of the limiting factors of CNN is it uses spatial image features only for classification. The spectral features from the transform domain have equivalent importance in the complex image classification algorithm. This paper proposes a new CNN structure to classify the histopathological cancer images based on integrating the spectral features obtained using a multi-resolution wavelet transform with the spatial features of CNN. In addition, batch normalization process is used after every layer in the convolution network to improve the poor convergence problem of CNN and the deep layers of CNN are trained with spectral–spatial features. The proposed structure is tested on malignant histology images of the breast for both binary and multi-class classification of tissue using the BreaKHis Dataset and the Breast Cancer Classification Challenge 2015 Datasest. Experimental results show that the combination of spectral–spatial features improves classification accuracy of the CNN network and requires less training parameters in comparison with the well known models (i.e., VGG16 and ALEXNET). The proposed structure achieves an average accuracy of 97.58% and 97.45% with 7.6 million training parameters on both datasets, respectively.

Interconnected three-element and four-element wideband MIMO antennas have been proposed for millimeter-wave 5G applications by performing numerical computations and carrying out experimental measurements. The antenna structure is realized using Rogers 5880 substrate (ε_r = 2.2, tan δ = 0.0009), where the radiating element has the shape of an inverted L with a partial ground. The unit element is carefully designed and positioned (by orthogonally rotating the elements) to form three-element (case 1) and four-element (case 2) MIMO antennas. The interconnected ground for both cases is ascertained to increase the practical utilization of the resonator. The proposed MIMO antenna size is (0.95λ × 3λ) for case 1 and (2.01λ × 1.95λ) for case 2 (at the lowest functional frequency). Both the designs give an impedance bandwidth of approximately 26–40 GHz (43%). Moreover, they achieve greater than 15 dB isolation and more than 6 dBi gain with an ECC value lower than 0.02, which meets the MIMO diversity performance thus making the three-element and four-element MIMO antennas the best choice for millimeter-wave 5G applications.

The paper presents overmoded circular waveguide mode converters (TE₀₁ into HE₁₁ with TE₁₁ as an intermediate mode) used in a 42 ± 0.2 GHz, 200 kW gyrotron transmission line system for plasma heating in electron cyclotron resonance heating application. To convert mode from TE₀₁ to TE₁₁, we have used wriggle converter method with a deformation in the converter axis and for TE₁₁ to HE₁₁ mode converter the depth of corrugation decreases gradually from to . Both the converters are optimized in terms of design parameters using mode field matching technique. The TE₀₁ to TE₁₁ mode converter showed a conversion efficiency of 95% (predicted 99%) for a length of 1205 mm, while TE₁₁ to HE₁₁ mode converter gave conversion efficiency of 99% (predicted 99.98%) for a diameter of 32 mm. Cross-polarization isolation of 31.7 dB is obtained in TE₁₁ to HE₁₁ mode converter. Convergence of the theoretical values and the simulated results indicates that the converters meet the system requirements.

An experimental study on a physical model of memristor made from human blood (as material of resistor) is presented. The model manifests memristor characteristics as defined by Professor Leon Chua (1971), Strukov (2008) and William (2008). Speculative potential applications in human healthcare science and electronic memory circuit science/technology is envisaged.