Histopathology images are very distinctive, and one image may contain thousands of objects. Transferring features from natural images to histopathology images may not provide impressive outcomes. In this study, we have proposed a novel modality-specific CBAM-VGGNet model for classifying H and E stained breast histopathology images. Instead of using pre-trained models on ImageNet, we have trained VGG16 and VGG19 models on the same domain cancerous histopathology datasets which are then used as fixed feature extractors. We have added GAP layer and Convolutional block attention module (CBAM) after the first convolutional layer of convolutional blocks. CBAM is an effective module for neural networks to focus on relevant features. We have implemented the VGG16 and VGG19 in a novel way following the configuration of state-of-the-art models with our own concatenated layers. The addition of the GAP layer in VGGNet has reduced the number of parameters and hence requiring less computational power. Both models are ensembled using the averaging ensemble technique. Features are extracted from the final ensembled model then passed to the feed-forward neural network. A hybrid pre-processing technique is proposed that first use median filter and afterwards contrast limited adaptive histogram equalization (CLAHE). Median filter is utilized to remove the highly significant noise and is directly related to image quality. CLAHE improve the local contrast present in an image and boost the weak boundary edges in each image pixel. The proposed CBAM ensemble model has outperformed state-of-the-art models with an accuracy of 98.96% and 97.95% F1-score on 400X data of BreakHis dataset.

Low Power Wide Area Network (LPWAN) technologies have been exponentially growing because of the tremendous growth of the Internet of Things (IoT) devices across the globe. Several LPWAN technologies have been utilized by the researchers to address certain issues like increased number of collisions, retransmissions, delay, and energy consumption. However, Long Range Wide Area Network (LoRaWAN) is the most suitable and attractive technology in terms of delay optimization, low cost and efficient energy consumption. The main issue which arises in LoRaWAN is because of its high packet drop rate due to collision. The reason behind this packet drop rate is the MAC scheme known as Pure Aloha used by LoRaWAN for the transmission of the frames. Long Range (LoRa) End Devices (EDs) initiate communication with Pure Aloha that leads to a high number of retransmissions. These retransmissions further enhance the delay in LoRa networks. This paper aims to optimize the delay in LoRaWAN by using an Adaptive Scheduling Algorithm (ASA) with an unsupervised probabilistic approach called Gaussian Mixture Model (GMM). By using ASA with GMM, the retransmissions are reduced which optimizes the delay in LoRaWAN. The results show that in our approach, Packet Collision Rate (PCR) is reduced by 39% as compared to conventional LoRaWAN. In addition, the Packet Success Ratio (PSR) is also increased by 39% as compared to the conventional LoRaWAN and Dynamic Priority Scheduling Technique (PST). Further, the delay is optimized by 91% and 79%. This research could be effective for the environments where the critical data of patients need to be sent with optimised retransmissions and minimum delay towards gateways.

In optical communication systems, the Q-factor is an important performance metric to evaluate the performance of an optical link. In this paper, a deep learning-based eye diagram analyzer is proposed to estimate the Q-factor. CNN architectures such as LeNet, Wide ResNet, and Inception-v4 are used for ON-Off Keying (OOK) and Pulse Amplitude Modulation (PAM) formats’ eye diagrams. The performance of these architectures is evaluated in terms of accuracy, Mean Squared Error (MSE), and error tolerance. This work shows that Wide ResNet demonstrates better performance in both OOK and PAM4 transmission schemes, achieving MSE values of 0.00188 and 0.00036, respectively. Additionally, it attains a high R-squared (R2) value of 0.9998. This deep learning-based eye diagram analyzer may be a promising approach for analyzing and optimizing optical communication systems without extensive human intervention.

<!-- *** Custom HTML *** --> We demonstrate a high data rate and low-cost OM4 multimode fiber (MMF) and free-space optics (FSO)-based self-restorable 2 × 100 Gbps per wavelength intra data center interconnect between two pairs of servers. This transmission system is based on two vertical cavity surface-emitting lasers that are directly modulated with PAM-4 data at the rate of 100 Gbps and multiplexed using mode division multiplexing of two linearly polarized modes. In case a fault occurs in the MMF-based primary path, the traffic is shifted to FSO-based protection path through a specially designed triggering and switching mechanism. Offline digital signal processing is used at the receiver to process the received PAM-4 signal for compensating the effects of channel impairments. The performance of the proposed architecture is evaluated at link lengths of 35 and 70 m under normal and faulty scenarios. The effect of varying the refractive index structure parameter of the log-normal channel model and the effect of mode coupling between linearly polarized (LP) modes on the bit-error-rate performance is also analyzed under different conditions. We observe that the performance of the FSO-based protection path is better than the OM4-based primary path at a forward error correction limit of 3.8 × ^{10  −  3} bit-error-rate.