Semi-supervised learning (semi-SL) offers a promising solution for challenging scenarios in medical image analysis where acquiring sufficient labeled data is difficult. However, practical applications often violate the assumption that the unlabeled data distribution matches the labeled samples, especially in medical imaging. This leads to out-of-distribution (OOD) samples that hinder algorithm efficiency. Filtering methods commonly used for outlier samples may not be suitable for diverse anatomical structures and rare morphologies in medical images. To address these challenges in digital histology images, we propose a novel pipeline. Our approach utilizes self-supervised learning to estimate an OOD score for each unlabeled data point, enabling calibration of the subsequent semi-SL framework. By modulating sample selection based on the outlier score, we prioritize samples aligned with the labeled distribution during the semi-SL stage. Our framework is compatible with any semi-SL approach and has been validated on two digital pathology datasets. Extensive experiments on a colorectal histology dataset and a TCGA-BRCA whole slide image dataset demonstrate the superiority of our method compared to popular semi-SL techniques, particularly the widely used Mixmatch framework. Our approach enhances medical image analysis performance and shows promise in addressing challenges related to open-set supervised learning.

Watermarking is the advanced technology utilized to secure digital data by integrating ownership or copyright protection. Most of the traditional extracting processes in audio watermarking have some restrictions due to low reliability to various attacks. Hence, a deep learning-based audio watermarking system is proposed in this research to overcome the restriction in the traditional methods. The implication of the research relies on enhancing the performance of the watermarking system using the Discrete Wavelet Transform (DWT) and the optimized deep learning technique. The selection of optimal embedding location is the research contribution that is carried out by the deep convolutional neural network (DCNN). The hyperparameter tuning is performed by the so-called search location optimization, which minimizes the errors in the classifier. The experimental result reveals that the proposed digital audio watermarking system provides better robustness and performance in terms of Bit Error Rate (BER), Mean Square Error (MSE), and Signal-to-noise ratio. The BER, MSE, and SNR of the proposed audio watermarking model without the noise are 0.082, 0.099, and 45.363 respectively, which is found to be better performance than the existing watermarking models.

We propose an ultra-lightweight, compact, and low power block cipher BORON. BORON is a substitution and permutation based network, which operates on a 64-bit plain text and supports a key length of 128/80 bits. BORON has a compact structure which requires 1939 gate equivalents (GEs) for a 128-bit key and 1626 GEs for an 80-bit key. The BORON cipher includes shift operators, round permutation layers, and XOR operations. Its unique design helps generate a large number of active S-boxes in fewer rounds, which thwarts the linear and differential attacks on the cipher. BORON shows good performance on both hardware and software platforms. BORON consumes less power as compared to the lightweight cipher LED and it has a higher throughput as compared to other existing SP network ciphers. We also present the security analysis of BORON and its performance as an ultra-lightweight compact cipher. BORON is a well-suited cipher design for applications where both a small footprint area and low power dissipation play a crucial role.

Abstract This paper proposes an ultra lightweight cipher ANU. ANU is a balanced Feistel‐based network. ANU supports 64 bit plaintext and 128/80 bit key length, and it has total 25 rounds. It needs only 1015 gate equivalents for 128 bit key length that is less as compared with all existing lightweight ciphers. Its memory size is minimal, and power consumption is very less. It needs only 22 mW of dynamic power, while PRESENT cipher consumes 39 mW of power. This paper furnishes the complete security analysis of the ANU cipher design. Our security analysis shows that ANU can attain ample security level against linear and differential cryptanalysis, biclique attack, zero‐correlation attack, and algebraic attack. Biclique cryptanalysis provides maximal data complexity of 264. ANU cipher not only needs less gate equivalents but also it consumes very less power and has less memory requirement. ANU cipher is best suited for applications like Internet of Things. The design of ANU cipher will have a positive impact in the field of lightweight cryptography. Copyright © 2016 John Wiley & Sons, Ltd.

Recently, a method of finding the spectral samples of non-periodic-finite-rate-of-innovation (NP-FRI) signals using a sum-of-sincs (SoS) sampling kernel was proposed in the literature. In the SoS approach, the kernel is repeated at a rate dependent on the delays of the FRI signal. The number of repetitions depends on both the duration and the delays of pulses constituting the FRI signal. In this paper, we show that the kernel repetition can be avoided and perfect reconstruction can be obtained by working with the SoS kernel directly provided that certain sampling criteria are satisfied. We place a lower bound on the sampling rate to ensure that exact signal reconstruction is achieved using filtered samples. To suppress the effect of noise, we use Cadzow denoising technique. Reconstruction is achieved using the annihilating filter method. We report results on data simulated using Field II software as well as real cardiac ultrasound data. The experimental results show that, with nearly 10 times less data than that required by the standard technique, the proposed method gives a comparable quality of reconstruction. The reconstruction accuracy can be controlled by choosing the model order of the NP-FRI signal appropriately.