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As a wide used protocol in industry, BMS and T&D system, I2C bus with isolation features stands out to provide additional safety protection and noise immunity. This paper introduces an implementation method of the isolated I2C interface device with high reliability and low delay. This device can meet the I2C ultra-high speed working mode with communication frequency of 1.7MHz as well as prevent the occurrence of interlock in bi-directional communication. The device is fabricated in 0.18um CMOS process.

We propose SpectroStream, a full-band multi-channel neural audio codec. Successor to the well-established SoundStream, SpectroStream extends its capability beyond 24 kHz monophonic audio and enables high-quality reconstruction of 48 kHz stereo music at bit rates of 4--16 kbps. This is accomplished with a new neural architecture that leverages audio representation in the time-frequency domain, which leads to better audio quality especially at higher sample rate. The model also uses a delayed-fusion strategy to handle multi-channel audio, which is crucial in balancing per-channel acoustic quality and cross-channel phase consistency.

Asymmetric visual evoked potentials (aVEPs) have shown potential in brain-computer interfaces (BCIs). However, its codec design is limited by the effect of repetition suppression, which is characterized by the attenuation of neural response after repeated stimulation. For now, there is still a lack of targeted explanation on the main mechanism of the repetition suppression effect in aVEP. To meet this end, a spatially multiple repeated visual stimulation paradigm under two different attentional states is proposed. Results revealed that repetition suppression effect in aVEPs is mainly driven by a bottom-up fatigue mechanism. Additionally, the response to a single repetition at the same location remains relatively stable, indicating potential usage in future coding paradigms for weak-stimulus brain-computer interface systems.

ABSTRACT Blosc2 is a high‐performance compression library and data format designed for binary data such as numerical arrays, tensors, and other structured types. In this proposal, we aim to develop two new codecs for Blosc2 by leveraging its extensible plugin‐based codec framework. Specifically, we propose integrating archetypal analysis (AA) and fuzzy clustering as novel codecs within Blosc2. Our proposal has been assessed by using the Olivetti faces dataset and results have demonstrated that AA outperforms fuzzy clustering in preserving fine‐grained data details, achieving substantially higher Structural Similarity Index. These results underscore AA's capability to capture the complete data distribution, including its extreme values, which is an essential property for achieving high‐fidelity compression.

Multi-stage network uses stacked networks to enhance the feature extraction capability, and can gradually refine the keypoints with the information of previous stages’ output. Obviously, multi-stage networks are more suitable for human pose estimation. However, most current multi-stage networks use a codec structure as the backbone in which downsample will cause information loss. HRNet maintains high-resolution features to supply the information which is lost in down-sampling stage. In this regard, we propose a novel two-stage network with HRNet as the backbone and stacked codec structure. HRNet has more efficient feature extraction capability, and the stacked codec network can utilize the multi-scale features generated by HRNet more effectively. This method obtains a 1.2AP improvement compared to HRNet and a significant improvement compared to other two-stage networks.

Recent state-of-the-art neural audio compression models have progressively adopted residual vector quantization (RVQ). Despite this success, these models employ a fixed number of codebooks per frame, which can be suboptimal in terms of rate-distortion tradeoff, particularly in scenarios with simple input audio, such as silence. To address this limitation, we propose variable bitrate RVQ (VRVQ) for audio codecs, which allows for more efficient coding by adapting the number of codebooks used per frame. Furthermore, we propose a gradient estimation method for the non-differentiable masking operation that transforms from the importance map to the binary importance mask, improving model training via a straight-through estimator. We demonstrate that the proposed training framework achieves superior results compared to the baseline method and shows further improvement when applied to the current state-of-the-art codec.

This paper proposes a transform domain audio coding method based on deep complex networks. In the proposed codec, the time-frequency spectrum of the audio signal is fed to the encoder which consists of complex convolutional blocks and a frequency-temporal modeling module to obtain the extracted features which are then quantized with a target bitrate by the vector quantizer. The structure of the decoder which reconstruct the time-frequency spectrum of the audio from quantized features is symmetrical to the encoder. In this paper, a structure combining the complex multi-head self-attention module and the complex long short-term memory is proposed to capture both frequency and temporal dependencies. Subjective and objective evaluation tests show the advantage of the proposed method.

Although Shannon introduced the concept of a rate distortion function in 1948, only in the last decade has the methodology for developing rate distortion function lower bounds for real-world sources been established. However, these recent results have not been fully exploited due to some confusion about how these new rate distortion bounds, once they are obtained, should be interpreted and should be used in source codec performance analysis and design. We present the relevant rate distortion theory and show how this theory can be used for practical codec design and performance prediction and evaluation. Examples for speech and video indicate exactly how the new rate distortion functions can be calculated, interpreted, and extended. These examples illustrate the interplay between source models for rate distortion theoretic studies and the source models underlying video and speech codec design. Key concepts include the development of composite source models per source realization and the application of conditional rate distortion theory.

DNA is an emerging medium for digital data and its adoption can be accelerated by synthesis processes specialized for storage applications. Here, we describe a de novo enzymatic synthesis strategy designed for data storage which harnesses the template-independent polymerase terminal deoxynucleotidyl transferase (TdT) in kinetically controlled conditions. Information is stored in transitions between non-identical nucleotides of DNA strands. To produce strands representing user-defined content, nucleotide substrates are added iteratively, yielding short homopolymeric extensions whose lengths are controlled by apyrase-mediated substrate degradation. With this scheme, we synthesize DNA strands carrying 144 bits, including addressing, and demonstrate retrieval with streaming nanopore sequencing. We further devise a digital codec to reduce requirements for synthesis accuracy and sequencing coverage, and experimentally show robust data retrieval from imperfectly synthesized strands. This work provides distributive enzymatic synthesis and information-theoretic approaches to advance digital information storage in DNA. Adoption of DNA as a data storage medium could be accelerated with specialized synthesis processes and codecs. The authors describe TdT-mediated DNA synthesis in which data is stored in transitions between non-identical nucleotides and the use of synchronization markers to provide error tolerance.

Here, the authors propose an energy-efficient codec design using a rate-0.91 systematic quasi-cyclic-low-density parity-check (QC-LDPC) code. A cost-effective early termination (ET) scheme is presented for efficiently terminating the decoding iterations and maintaining desirable correcting performance. Compared with no ET scheme, the cost-effective ET scheme achieves 54.6% energy reduction with 1.7% area overhead. Finally, the proposed QC-LDPC codec employing the cost-effective ET scheme is implemented in a prototyping chip of 9.86 mm2 core area using the TSMC 90 nm CMOS technology. Compared with the other decoder chips, the prototyping codec operating at 278 MHz achieves the best decoding energy efficiency of 156 pJ/bit with a high decoding throughput of 4.3 Gbps. The prototyping codec also achieves a high encoding throughput of 4.4 Gbps.