Dorababu Sudarsa

As technology has advanced, the internet of medical things (IoMT) has become incredibly useful. It is used to transmit a wide variety of medical images. Sensitive patient data may be altered during transmission or subject to illegal access. To overcome all of these challenges and preserve the integrity of medical images while transmission over IoMT, a blind region-based data concealing approach called medical image watermarking (MIW) is suggested. The region of interest (ROI) and region of non-interest (RONI) are the two sections that make up the medical image. The aim of the suggested MIW technique is to prevent transmission-related manipulation of medical image ROI. To provide high imperceptibility and resilience, confined integrity verification and recovery bits (CIVRB) bits are embedded in the RONI using hybrid integer wavelet transform–singular value decomposition (IWT-SVD). According to the experimental results, the suggested system is highly imperceptible (average peak signal-to-noise ratio (PSNR)=56dB), robust (average NC=0.99), and exhibits integrity verification accuracy of over 98% against a variety of image processing attacks. In terms of several watermarking properties, the proposed technique performs over state-of-the-art schemes. This method offers a dependable framework for protecting medical images in real-time IoMT applications and is suitable for smart healthcare environments.

The recognition of handwritten digits holds a significant place in the field of information processing. Recognizing such characters accurately from images is a complex task because of the vast differences in people's writing styles. Furthermore, the presence of various image artifacts such as blurring, intensity variations, and noise adds to the complexity of this process. The existing algorithm, convolution neural network (CNN) is one of the prominent algorithms in deep learning to handle the above problems. But there is a difficulty in handling input data that differs significantly from the training data, leading to decreased accuracy and performance. In this work, a method is proposed to overcome the aforementioned limitations by incorporating a quantum convolutional neural network algorithm (QCNN). QCNN is capable of performing more complex operations than classical CNNs. It can achieve higher levels of accuracy than classical CNNs, especially when working with noisy or incomplete data. It has the potential to scale more efficiently and effectively than classical CNNs, making them better suited for large-scale applications. The effectiveness of the proposed model is demonstrated on the modified national institute of standards and technology (MNIST) dataset and achieved an average accuracy of 91.08%.

The increasing demand for faster, robust, and efficient device development of enabling technology to mass production of industrial research in circuit design deals with challenges like size, efficiency, power, and scalability. This paper, presents a design and analysis of low power high speed full adder using negative capacitance field effecting transistors. A comprehensive study is performed with adiabatic logic and reversable logic. The performance of full adder is studied with metal oxide field effect transistor (MOSFET) and negative capacitance field effecting (NCFET). The NCFET based full adder offers a low power and high speed compared with conventional MOSFET. The complete design and analysis are performed using cadence virtuoso. The adiabatic logic offering low delay of 0.023 ns and reversable logic is offering low power of 7.19 mw.