General form of almost instantaneous fixed-to-variable-length codes

A general class of the almost instantaneous fixed-to-variable-length (AIFV) codes is proposed, which contains every possible binary code we can make when allowing finite bits of decoding delay. The contribution of the paper lies in the following. (i) Introducing $N$-bit-delay AIFV codes, constructed by multiple code trees with higher flexibility than the conventional AIFV codes. (ii) Proving that the proposed codes can represent any uniquely-encodable and uniquely-decodable variable-to-variable length codes. (iii) Showing how to express codes as multiple code trees with minimum decoding delay. (iv) Formulating the constraints of decodability as the comparison of intervals in the real number line. The theoretical results in this paper are expected to be useful for further study on AIFV codes.Comment: submitted to IEEE Transactions on Information Theory. arXiv admin note: text overlap with arXiv:1607.07247 by other author

Deep sound-field denoiser: optically-measured sound-field denoising using deep neural network

This paper proposes a deep sound-field denoiser, a deep neural network (DNN) based denoising of optically measured sound-field images. Sound-field imaging using optical methods has gained considerable attention due to its ability to achieve high-spatial-resolution imaging of acoustic phenomena that conventional acoustic sensors cannot accomplish. However, the optically measured sound-field images are often heavily contaminated by noise because of the low sensitivity of optical interferometric measurements to airborne sound. Here, we propose a DNN-based sound-field denoising method. Time-varying sound-field image sequences are decomposed into harmonic complex-amplitude images by using a time-directional Fourier transform. The complex images are converted into two-channel images consisting of real and imaginary parts and denoised by a nonlinear-activation-free network. The network is trained on a sound-field dataset obtained from numerical acoustic simulations with randomized parameters. We compared the method with conventional ones, such as image filters and a spatiotemporal filter, on numerical and experimental data. The experimental data were measured by parallel phase-shifting interferometry and holographic speckle interferometry. The proposed deep sound-field denoiser significantly outperformed the conventional methods on both the numerical and experimental data.Comment: 13 pages, 8 figures, 2 table

A Probabilistic Shaping Approach for Optical Region-of-Interest Signaling

We propose a probabilistic shaping approach for region-of-interest signaling, where a low-rate signal controls the desired probabilistic ranges of a high-rate data stream using a flexible distribution controller. In addition, we introduce run-length-aware values for frozen bit indices in systematic polar code to minimize the run-length without using run-length-limited code. Our compact system can support soft-decision forward-error-correction decoding with excellent spectral efficiency compared with related work based on hybrid modulation schemes.Comment: Cite to this paper as: Nguyen, Duc-Phuc, Yoshifumi Shiraki, Jun Muramatsu, and Takehiro Moriya. "A Probabilistic Shaping Approach for Optical Region-of-Interest Signaling." IEEE Photonics Technology Letters 34, no. 6 (2022): 309-31