Research on SLM Algorithm for PAPR reduction in MB-OFDM UWB Systems

AbstractMultiband orthogonal frequency division multiplexing (MB-OFDM) is one of the key techniques of ultra wideband (UWB) systems. A major drawback of MB-OFDM technique is the high peak-to-average power ratio (PAPR) of the transmit signal. In this paper, a novel phase sequence of selected mapping algorithm which makes the side information not needed is designed to lower the PAPR of MB-OFDM UWB signals. It is also shown that comparable PAPR reduction performance with the original SLM algorithm can be achieved with a small increase in signal power. Simulation results show that there must be equilibriums between SLM computational complexity and PAPR performance. The objective of the new algorithm is to lower PAPR close to ordinary SLM technique with reduced computational complexity with little performance degradation and achieves better system resource utilization

The Error Performance and Fairness of CUWB Correlated Channels

AbstractThe symbol period becomes smaller compared to the channel delay in multiband orthogonal frequency division multiplexing (MB-OFDM) cognitive ultra wideband (CUWB) wireless communications, the transmitted signals experiences frequency-selective fading and leads to performance degradation. In this paper, a new design method for space-time trellis codes in MB-OFDM systems with correlated Rayleigh fading channels is introduced. This method converts the single output code symbol into several STTC code symbols, which are to be transmitted simultaneously from multiple transmitter-antennas. By using Viterbi optimal soft decision decoding algorithm, we investigate both quasi-static and interleaved channels and demonstrate how the spatial fading correlation affects the performance of space–time codes over these two different MB-OFDM wireless channel models. Simulation results show that the performance of space–time code is to be robust to spatial correlation. When the system bandwidth increases, the long term fairness quality will gradually become better and finally converges to 1

Multimodal Fish Feeding Intensity Assessment in Aquaculture

Fish feeding intensity assessment (FFIA) aims to evaluate the intensity change of fish appetite during the feeding process, which is vital in industrial aquaculture applications. The main challenges surrounding FFIA are two-fold. 1) robustness: existing work has mainly leveraged single-modality (e.g., vision, audio) methods, which have a high sensitivity to input noise. 2) efficiency: FFIA models are generally expected to be employed on devices. This presents a challenge in terms of computational efficiency. In this work, we first introduce an audio-visual dataset, called AV-FFIA. AV-FFIA consists of 27,000 labeled audio and video clips that capture different levels of fish feeding intensity. To our knowledge, AV-FFIA is the first large-scale multimodal dataset for FFIA research. Then, we introduce a multi-modal approach for FFIA by leveraging single-modality pre-trained models and modality-fusion methods, with benchmark studies on AV-FFIA. Our experimental results indicate that the multi-modal approach substantially outperforms the single-modality based approach, especially in noisy environments. While multimodal approaches provide a performance gain for FFIA, it inherently increase the computational cost. To overcome this issue, we further present a novel unified model, termed as U-FFIA. U-FFIA is a single model capable of processing audio, visual, or audio-visual modalities, by leveraging modality dropout during training and knowledge distillation from single-modality pre-trained models. We demonstrate that U-FFIA can achieve performance better than or on par with the state-of-the-art modality-specific FFIA models, with significantly lower computational overhead. Our proposed U-FFIA approach enables a more robust and efficient method for FFIA, with the potential to contribute to improved management practices and sustainability in aquaculture

Regulation of 130kDa smooth muscle myosin light chain kinase expression by an intronic CArG element

The mylk1 gene encodes a 220-kDa nonmuscle myosin light chain kinase (MLCK), a 130-kDa smooth muscle MLCK (smMLCK), as well as the non-catalytic product telokin. Together, these proteins play critical roles in regulating smooth muscle contractility. Changes in their expression are associated with many pathological conditions; thus, it is important to understand the mechanisms regulating expression of mylk1 gene transcripts. Previously, we reported a highly conserved CArG box, which binds serum response factor, in intron 15 of mylk1. Because this CArG element is near the promoter that drives transcription of the 130-kDa smMLCK, we examined its role in regulating expression of this transcript. Results show that deletion of the intronic CArG region from a β-galactosidase reporter gene abolished transgene expression in mice in vivo. Deletion of the CArG region from the endogenous mylk1 gene, specifically in smooth muscle cells, decreased expression of the 130-kDa smMLCK by 40% without affecting expression of the 220-kDa MLCK or telokin. This reduction in 130-kDa smMLCK expression resulted in decreased phosphorylation of myosin light chains, attenuated smooth muscle contractility, and a 24% decrease in small intestine length that was associated with a significant reduction of Ki67-positive smooth muscle cells. Overall, these data show that the CArG element in intron 15 of the mylk1 gene is necessary for maximal expression of the 130-kDa smMLCK and that the 130-kDa smMLCK isoform is specifically required to regulate smooth muscle contractility and small intestine smooth muscle cell proliferation

WavJourney: Compositional Audio Creation with Large Language Models

Large Language Models (LLMs) have shown great promise in integrating diverse expert models to tackle intricate language and vision tasks. Despite their significance in advancing the field of Artificial Intelligence Generated Content (AIGC), their potential in intelligent audio content creation remains unexplored. In this work, we tackle the problem of creating audio content with storylines encompassing speech, music, and sound effects, guided by text instructions. We present WavJourney, a system that leverages LLMs to connect various audio models for audio content generation. Given a text description of an auditory scene, WavJourney first prompts LLMs to generate a structured script dedicated to audio storytelling. The audio script incorporates diverse audio elements, organized based on their spatio-temporal relationships. As a conceptual representation of audio, the audio script provides an interactive and interpretable rationale for human engagement. Afterward, the audio script is fed into a script compiler, converting it into a computer program. Each line of the program calls a task-specific audio generation model or computational operation function (e.g., concatenate, mix). The computer program is then executed to obtain an explainable solution for audio generation. We demonstrate the practicality of WavJourney across diverse real-world scenarios, including science fiction, education, and radio play. The explainable and interactive design of WavJourney fosters human-machine co-creation in multi-round dialogues, enhancing creative control and adaptability in audio production. WavJourney audiolizes the human imagination, opening up new avenues for creativity in multimedia content creation.Comment: Project Page: https://audio-agi.github.io/WavJourney_demopage

A Myb Transcription Factor of Phytophthora sojae, Regulated by MAP Kinase PsSAK1, Is Required for Zoospore Development

PsSAK1, a mitogen-activated protein (MAP) kinase from Phytophthora sojae, plays an important role in host infection and zoospore viability. However, the downstream mechanism of PsSAK1 remains unclear. In this study, the 3'-tag digital gene expression (DGE) profiling method was applied to sequence the global transcriptional sequence of PsSAK1-silenced mutants during the cysts stage and 1.5 h after inoculation onto susceptible soybean leaf tissues. Compared with the gene expression levels of the recipient P. sojae strain, several candidates of Myb family were differentially expressed (up or down) in response to the loss of PsSAK1, including of a R2R3-type Myb transcription factor, PsMYB1. qRT-PCR indicated that the transcriptional level of PsMYB1 decreased due to PsSAK1 silencing. The transcriptional level of PsMYB1 increased during sporulating hyphae, in germinated cysts, and early infection. Silencing of PsMYB1 results in three phenotypes: a) no cleavage of the cytoplasm into uninucleate zoospores or release of normal zoospores, b) direct germination of sporangia, and c) afunction in zoospore-mediated plant infection. Our data indicate that the PsMYB1 transcription factor functions downstream of MAP kinase PsSAK1 and is required for zoospore development of P. sojae

Space advanced technology demonstration satellite

The Space Advanced Technology demonstration satellite (SATech-01), a mission for low-cost space science and new technology experiments, organized by Chinese Academy of Sciences (CAS), was successfully launched into a Sun-synchronous orbit at an altitude of similar to 500 km on July 27, 2022, from the Jiuquan Satellite Launch Centre. Serving as an experimental platform for space science exploration and the demonstration of advanced common technologies in orbit, SATech-01 is equipped with 16 experimental payloads, including the solar upper transition region imager (SUTRI), the lobster eye imager for astronomy (LEIA), the high energy burst searcher (HEBS), and a High Precision Magnetic Field Measurement System based on a CPT Magnetometer (CPT). It also incorporates an imager with freeform optics, an integrated thermal imaging sensor, and a multi-functional integrated imager, etc. This paper provides an overview of SATech-01, including a technical description of the satellite and its scientific payloads, along with their on-orbit performance

Weakly labelled audio tagging via convolutional networks with spatial and channel-wise attention

Multiple instance learning (MIL) with convolutional neural networks (CNNs) has been proposed recently for weakly labelled audio tagging. However, features from the various CNN filtering channels and spatial regions are often treated equally, which may limit its performance in event prediction. In this paper, we propose a novel attention mechanism, namely, spatial and channel-wise attention (SCA). For spatial attention, we divide it into global and local submodules with the former to capture the event-related spatial regions and the latter to estimate the onset and offset of the events. Considering the variations in CNN channels, channel-wise attention is also exploited to recognize different sound scenes. The proposed SCA can be employed into any CNNs seamlessly with affordable overheads and is end-to-end trainable fashion. Extensive experiments on weakly labelled dataset Audioset show that the proposed SCA with CNNs achieves a state-of-the-art mean average precision (mAP) of 0.390

Equilibrium Shapes of Ag, Ni, and Ir Nanoparticles under CO Conditions

Metal nanoparticles are widely used in catalysis by virtue of their excellent physicochemical properties, which are closely related to their morphology. In this work, we predict the reshaping of Ag, Ni, and Ir metal nanoparticles under a CO atmosphere using the recently proposed multiscale structure reconstruction model. In the low-pressure environment, temperature has little effect on the structures of Ag nanoparticles. However, the structures of Ag nanoparticles will change significantly in high- and low-temperature environments. Ni and Ir nanoparticles are greatly affected by the environment due to their stronger interactions with CO. This study demonstrates the structural changes of Ag, Ni, and Ir nanoparticles under different pressures and temperatures, providing theoretical guidance for in situ experiments and the rational design of nanocatalysts