We report a unique concept to implement a high-order mode pass filter using mode converters. Our proposed design method implements a high-order mode pass filter of any order, uses different mode converters available, and applies to a variety of planar lightwave circuit material platforms. We fabricate a broadband fundamental mode filter device using a Mach–Zehnder interferometer and Y-junctions to demonstrate our idea. The performance of the fabricated device is demonstrated experimentally in the wavelength range of 1.530–1.565 μm (C-band). This filter exhibits a simulated extinction ratio of 37 dB with an excess loss of 0.52 dB for the first-order mode transmission.

We propose a novel design architecture to realize scalable selective mode filter based on the asymmetric directional coupler structure. In this structure, any arbitrary high-order mode can pass, whereas other unwanted modes are blocked. Furthermore, multiple optical modes can be blocked by only adjusting the structural parameters. As a proof of concept, we experimentally demonstrated a three-mode device and the scalability of the proposed structure is demonstrated by another design of four-mode filter. The proposed architecture offers scalability and high-design flexibility, and it has excellent potential to be used in advanced mode division multiplexing optical networks.

The intention of designing a robot is to facilitate the human beings through giving security and working as a helping hand through reprieving. The technology used in this rescue and security robot has a number of important features such as robotic car control by cell phone, automatically avoiding obstacles in its way. It can sense gas, smoke and provide an alert through text messaging. The robot can detect bombs, pick and place objects and also do distance measurements. A high quality wireless video camera equipped with a stepper motor for the omni-directional view is used for the spying purpose which is mounted on the robo car to send real video images and audio signals. These audio and video stream received by the receiver unit can be used to take proper steps according to the received signals. This robot can also be used to reach places where humans cannot reach like hidden places, small tunnels. The ultimate focus of this system is to offer far possible security for the human being. In this paper, we have illustrated our work by using the entire sub modules.

Mode division multiplexing (MDM) is a groundbreaking technology that meets future optical network capacity demand in conjunction with the wavelength multiplexing division (WDM). In our work, we propose a three-mode mux/demux device that comprises a three-arm unbalanced Mach-Zehnder interferometer (MZI) inserted between three Y-junctions. The device is compact and offers a high extinction ratio. Moreover, it has a simple structure. The maximum simulated excess loss is 0.37 dB with a minimum extinction ratio of 25.71 dB in the 1.550 µm wavelength, in different multiplexing and demultiplexing conditions. The device will find application in expanding the fiber transmission capacities in future MDM systems.

This paper introduces OVANet, a new deep learning framework for the diagnosis and classification of ovarian cancer subtypes from histopathological images. OVANet has been designed by integrating modified VGG19 and InceptionV3 blocks, incorporating dual attention mechanism (Squeeze-and-Excitation and custom spatial attention) to capture rich, multi-scale, and context-aware feature representations. This unique architecture combines intermediate layers from both base models, along with additional convolutional and max-pooling layers, dual attention mechanisms and global average pooling to extract diverse image features crucial for accurate subtype differentiation. OVANet is trained and evaluated on an original dataset of 508 histopathological images, augmented to approximately 13,024 images through preprocessing and augmentation techniques like rotations, shifts, zooms, and flips. This approach significantly enhances the generalization capabilities of the model, enabling it to achieve superior performance compared to well-known pretrained models and existing research outputs. The propose model shows accuracy, precision and AUC of 99.01% along with a recall of 99.87%. Using feature map visualization of all layers of the model and a model explainibility technique GRADCam, visual representation of classification have been shown which will help to establish transparency in clinical aspect. This architecture can be considered as a significant advancement in medical imaging, especially for the diagnosis and treatment planning of ovarian cancer. To reduce the death rate of cancer, proper detection is very essential and this model exhibits so promising results that can easily implemented clinically.

In a network topology, it is very usual to use various kinds of routing protocol for forwarding packets. A routing table is used in the memory of a router that keeps the track of routes to particular network destination and the most popular routing algorithms used to forward packets are Routing Information Protocol (RIPv2), Enhanced Interior Gateway Routing Protocol (EIGRP) and Open Shortest Path First (OSPF). The ultimate concentration of this research work is to depict the performance analysis comparison of these three dynamic routing protocols and redistribution among the protocols. Eight Cisco routers and a switch are used in our simulated network topology where four routers with different protocols directly connected with the switch take the responsibility for the redistribution algorithm.