Abstract:Chiplet-based multi-chip integration designs provide a flexible and scalable solution that surpasses traditional system on chip monolithic integration. However, inter-Chiplet communication has become a significant bottleneck affecting overall system performance. The network on interposer plays a pivotal role in multi-chip systems, directly influencing both performance and development costs. This paper reviews the communication topologies of Chiplet-based network on interposer structures and delves into the design and implementation methods of current inter-Chiplet communication architectures. It comprehensively covers the communication process from protocol, interface, to application layers, classifying interconnect topologies based on structural configurations, and providing in-depth analyses and cross-comparisons for each category. Additionally, this paper explores the future directions of inter-Chiplet communication technologies, emphasizing technical challenges and potential solutions, and highlights the importance of workload-oriented, reusable interposer layers and topology design. This review aims to provide researchers with a comprehensive overview of the current state of network on interposer technology while simultaneously forecasting its development trends in future multi-chip integrated systems, offering systematic insights to advance frontier research in semiconductor technologies.

Abstract:Unlike the popular MapReduce computing framework, LOGO is a new distributed computing framework using a LOcal-GlObal computing paradigm. Under the LOGO framework, big data distributed computing is completed in two steps. The LO operation runs a serial algorithm in a number of slave nodes or virtual machines to process independently the random sample data blocks, generating local results. The GO operation uploads all local results to the master node and integrate them to obtain the approximate result of the big data set. The LOGO computing framework eliminates data communication between nodes during iterations of the algorithm, greatly improving computing efficiency, reducing memory requirements, and enhancing data scalability. This article proposes a new distributed machine learning algorithm library under the LOGO computing framework. A new distributed computing is divided into two parts: the serial algorithm executed by the LO operation and the ensemble algorithm executed in the GO operation. The LO operation can directly execute existing serial machine learning algorithms without the need to rewrite them according to MapReduce. The GO operation executes ensemble algorithms of different kinds depending on the ensemble tasks. In this article, the principle of LOGO distributed computing is introduced first, followed by the algorithm library structure, the method for packaging existing serial algorithms and the ensemble strategy. Finally, implementation in Spark, App development, and the results of performance tests for various algorithms are demonstrated.

Abstract:In the era of big data, the storage of massive amounts of data has become a challenging problem. DNA storage technology, as a cutting-edge solution to this challenge, particularly focuses on the development and challenges of information editing technology. Initially, DNA storage primarily served “cold” data, but the latest advancements in the technology have driven its development towards supporting data updates and management for more advanced applications. This paper proposes an incremental management method for secure DNA storage, designing a hybrid encryption mechanism that supports multi-party editing and a DNA incremental storage model. While ensuring security, this model achieves secure and efficient information editing and management under existing technological constraints through a partitioned storage scheme and efficient indexing encoding. This approach meets the modern data management requirements for flexibility and cost-effectiveness, providing new perspectives and strategies for addressing core issues in DNA data management.

Abstract:Existing indoor three-dimensional (3D) object detection is able to detect a limited number of object categories, thus limiting the application on intelligent robotics. Open-vocabulary object detection is able to detect all objects of interest in a given scene without defining object categories, thus solving the shortcomings of indoor 3D object detection. At the same time, the large language model with prior knowledge can significantly improve the performance of visual tasks. However, existing researches on open-vocabulary indoor 3D object detection only focuses on object information and ignores contextual information. The input data for indoor 3D object detection is mainly point cloud, which suffers from sparsity and noise problems. Relying only on the object point cloud can negatively affect the 3D detection results. Contextual information contains scene information, which can complement the object information to promote the recognition on object category. For this reason, this paper proposes an open-vocabulary 3D object detection algorithm based on contextual information assistance. The algorithm integrates contextual information and object information through a large language model, and then performs chain-of-thought reasoning. The proposed algorithm is validated on SUN RGB-D and ScanNetV2 datasets, and the experimental results show the effectiveness of the proposed algorithm.

Abstract:Hand pose estimation based on RGB images holds wide application prospects in dynamic gesture recognition and human-computer interaction. However, existing methods face challenges such as high hand self-similarity and densely distributed keypoints, making it difficult to achieve high-precision predictions with low computational costs, thereby limiting their performance in complex scenarios. To address these challenges, this paper proposes a 2D hand pose estimation model named FAR-HandNet, based on the YOLOv8 network. The model ingeniously integrates a focused linear attention module, a keypoint alignment strategy, and a regression residual fitting module, effectively enhancing feature capture capabilities for small target regions (e.g., hands) while mitigating the adverse effects of self-similarity on the localization accuracy of hand keypoints. Additionally, the regression residual fitting module leverages a flow-based generative model to fit the residual distribution of keypoints, significantly improving regression precision. Experiments were conducted on the Carnegie Mellon University panorama dataset (CMU) and the FreiHAND dataset. Results demonstrate that FAR-HandNet exhibits remarkable advantages in parameter size and computational efficiency. Compared to existing methods, it achieves superior performance in the percentage of correct keypoints under varying thresholds. Furthermore, the model achieves an inference time of only 32 ms. Ablation studies further validate the effectiveness of each module, conclusively verifying the efficacy and superiority of FAR-HandNet in hand pose estimation tasks.

Abstract:With the rapid development of deep learning technology, autism screening based on neural signals such as electroencephalography (EEG) is gradually emerging as a novel diagnostic approach. However, due to the complexity of EEG data acquisition, especially for children, insufficient data often poses a challenge. Data augmentation methods are commonly used to address the scarcity of real-world data, with generative adversarial networks (GANs) being a frequently applied technique. However, due to the limited scale and diversity of data, current augmentation methods have not yet to achieve optimal classification performance. This study introduces an improved conditional diffusion model to enhance both raw EEG signals and their corresponding functional connectivity temporal graphs. Experimental results demonstrate that this method significantly improves autism classification performance, achieving maximum classification accuracies of 84.38% and 79.01% for resting-state and task-state data, respectively. These findings validate the effectiveness of data augmentation based on the conditional diffusion model in enhancing autism screening outcomes.

Abstract:Cancer is a genetically related disease with multiple subtypes, each exhibiting significant differences in genetics, phenotype, and treatment response. Accurate classification of cancer subtypes is critical for personalized treatment, as it helps improve therapeutic outcomes. However, cancer subtype classification methods based on patient gene expression data often struggle to effectively distinguish rare subtypes in the presence of imbalanced samples. To address this issue, a cancer subtype classification method called MFP-VAE (meta-learning few-shot prototype learning VAE) is proposed, focusing on handling datasets with imbalanced samples. This method improves the sampling strategy to ensure balanced consideration of different subtypes in meta-learning tasks. The model employs a variational autoencoder for feature extraction and classifies samples by calculating the distance between the samples and their corresponding cancer subtype prototypes. Experimental results show that MFP-VAE outperforms existing methods on two public cancer datasets, significantly improving classification performance, especially under imbalanced sample conditions. Furthermore, survival analysis reveals that the distinguished cancer subtypes exhibit significant differences in clinical characteristics.

Abstract:Cerebral blood flow velocity (CBFV) reconstruction plays a crucial role in evaluating cerebrovascular function, particularly in the early diagnosis of cerebrovascular diseases, optimizing treatment plans, and preventing strokes. Existing CBFV reconstruction methods face challenges in accuracy and efficiency when processing multivariate time-series signals, particularly in the context of data scarcity and complex signal processing. This study proposes a multivariate time-series model based on a Transformer encoder, which achieves high-precision CBFV reconstruction using arterial blood pressure and CO₂ time-series signals. The model design is based on a long short-term memory module, which effectively compensates for the limitations of the global attention mechanisms in processing local information and enhances local feature learning. Additionally, a hybrid loss function is employed to optimize local waveform errors, improving reconstruction accuracy. Furthermore, to address the issue of data scarcity in the target domain, this study introduces a transfer learning strategy based on the correlation between arterial blood pressure and electrocardiogram signals, alleviating the impact of limited data on model performance. Experimental results demonstrate that the proposed model outperforms traditional regression and deep learning models in the CBFV reconstruction task, with a Pearson correlation coefficient of 0.51870, a dynamic time warping distance of 17.879, and mutual information of 0.34375, while completing the reconstruction of 200 data points in 0.04 s. The study validates the effectiveness of this method in precision medicine and provides innovative solutions for clinical diagnosis, disease prevention, and personalized treatment, with broad application prospects, particularly in medical signal processing, intelligent healthcare, and health monitoring.

Abstract:This article introduces a ocean buoy data acquisition system based on modular design concept. The proposed system is divided into 3 modules according to the functions of ocean buoy data, i.e. the meteorological safety, hydrology biochemistry and communication. The device can realize continuous acquisition and processing of multiple devices of buoy, as well as real-time two-way communication and other functions. According to the characteristics of different modules, multiplexers and serial port expansion chips are used to realize the interface expansion of the system, which improves the carrying capacity of the system from the hardware. The communication module uses direct memory access technology to realize the forwarding, retransmission and remote control of dual-channel real-time data, realizes the reliable and safe operation of the buoy system at sea, and also improves its human-computer interaction function. The system has been tested in the laboratory and on-site operation experiments at sea to verify its stability, reliability and measurement accuracy.

Abstract:Electromagnetic pulse sound source (boomer) is a commonly used explosion sound source in marine seismic exploration, and the deep-sea application of such explosion sound source needs to solve cavitation suppression problem. In this paper, a deep sea boomer source based on pressure compensation balance is proposed. A boomer transducer with a maximum working pressure of 20.0 MPa is developed and tested in a high-pressure anechoic tank. Through the analysis of the hydrophone outputs under different energy and pressure levels, it can be seen that an air sac with the initial pressure of 0.5 MPa can effectively balance the internal and external pressure of the transducer, solve the problem of cavitation suppression, and realize the excitation of broadband pulse sound waves. The repeatability of the acoustic wave is very good, and the minimum correlation coefficient is to 0.986. With the increase of working pressure from 0.5 MPa to 20.0 MPa, the main change in acoustic characteristics is the amplitude attenuation (204.6 dB to 194.2 dB) and width compression (182 μs to 88 μs), and the main frequency (2.3 kHz as the center) slightly shifted to high frequency. Compared with the hydrophone output in the process of pressure rising and downing in the high-pressure anechoic tank, it can be seen that the repeatability of the acoustic wave is better. The higher the pressure, the better the waveform consistency, indicating that the boomer transducer based on pressure compensation balance has a more stable performance under high pressure environment.

Abstract:The high-pressure anechoic water tank is an important experimental testing platform for the development of deep-sea transducers, sensors, and other acoustic instruments and equipment. In this paper, background noise and acoustic field fluctuations at different frequencies were measured for the homemade 20 MPa high-pressure anechoic tank. The echo interference level under fixed measurement position and distance conditions was calculated, and the echo interference curve was drawn. The time-frequency characteristics of signals under typical low-frequency and high-frequency conditions were analysed. The measurement results of background noise show that although the background noise inside the tank is relatively high and has characteristic peaks in the frequency range of 10–12 kHz, it allows for measurement experiments with sufficient signal-to-noise ratio conditions. Meanwhile, the time-domain waveform results of sound field fluctuations measured in different frequencies show that the signal amplitude rapidly decays after a transmission width of 2 ms, and the higher the frequency, the faster the attenuation, indicating that the sound absorption cone inside the tank has a good sound absorption effect. The calculation results of echo interference level show that most frequency points above 10 kHz do not exceed ±1 dB. The designed fixed measurement position meets the requirements of free field testing, especially the echo interference of frequency points such as 20 kHz, 28 kHz, and 34 kHz does not exceed ±0.5 dB, which meets the requirements of precision measurement.