Denoising Diffusion MRI via Graph Total Variance in Spatioangular Domain

Diffusion MRI (DMRI) plays an essential role in diagnosing brain disorders related to white matter abnormalities. However, it suffers from heavy noise, which restricts its quantitative analysis. The total variance (TV) regularization is an effective noise reduction technique that penalizes noise-induced variances. However, existing TV-based denoising methods only focus on the spatial domain, overlooking that DMRI data lives in a combined spatioangular domain. It eventually results in an unsatisfactory noise reduction effect. To resolve this issue, we propose to remove the noise in DMRI using graph total variance (GTV) in the spatioangular domain. Expressly, we first represent the DMRI data using a graph, which encodes the geometric information of sampling points in the spatioangular domain. We then perform effective noise reduction using the powerful GTV regularization, which penalizes the noise-induced variances on the graph. GTV effectively resolves the limitation in existing methods, which only rely on spatial information for removing the noise. Extensive experiments on synthetic and real DMRI data demonstrate that GTV can remove the noise effectively and outperforms state-of-the-art methods.

Fully Homomorphic Encryption Without Noise

In this paper, the authors present a novel fully homomorphic encryption scheme operating between ZN and capable of arbitrarily performing additions and multiplications. The new scheme is compact and each operation (addition or multiplication) performed on any two ciphertexts produces a fresh ciphertext without any associated noise. Thus, the scheme does not need any bootstrapping procedure or noise reduction technique to refresh ciphertexts. In the absence, to the best of the knowledge of the authors, of any existing fully, partially or leveled homomorphic encryption scheme using ZN as the set of plaintexts, the new cryptosystem has been implemented and has had its performance compared to the identity encoding.

Increasing Sensitivity of Optical Receivers using a Divide and Conquer Technique

This paper presents a noise-reduction technique to increase the sensitivity of optical receivers with large integrated photodiodes (PD). It consists of manufacturing the PD in several pieces and connecting each piece to a dedicated transimpedance amplifier (TIA). The output signals are combined achieving a better sensitivity and a higher transimpedance.