A new beamforming microphones array with acoustic insulation baffle

To eliminate the influence of the interference acoustic source behind the array on the source identification performance, some modifications on the traditional single-layer beamforming microphone array are made, a new array with acoustic insulation baffle in the microphone plane is given out to suppress the influence of interference acoustic source. Simulation and verification results show that, with the presence of the interference acoustic source in the rear of traditional planar array, the acoustic source position and amplitude deviations are larger and it indicates that the interference acoustic source seriously affects the identification performance of target acoustic source. Compared to the performance of traditional planar array, the new microphone array with acoustic insulation baffle can effectively eliminate the influence of interference acoustic source behind the array and improve the accuracy of the location and amplitude of the target acoustic source. The experiment verifies the correctness of the simulation conclusions and the applicability of the modified microphone array in the cabin of automotive and aircraft and other narrow space

Research and Application on Spark Clustering Algorithm in Campus Big Data Analysis

Big data analysis has penetrated into all fields of society and has brought about profound changes. However, there is relatively little research on big data supporting student management regarding college and university’s big data. Taking the student card information as the research sample, using spark big data mining technology and K-Means clustering algorithm, taking scholarship evaluation as an example, the big data is analyzed. Data includes analysis of students’ daily behavior from multiple dimensions, and it can prevent the unreasonable scholarship evaluation caused by unfair factors such as plagiarism, votes of teachers and students, etc. At the same time, students’ absenteeism, physical health and psychological status in advance can be predicted, which makes student management work more active, accurate and effective

THz ISAC: A Physical-Layer Perspective of Terahertz Integrated Sensing and Communication

The Terahertz (0.1-10 THz) band holds enormous potential for supporting unprecedented data rates and millimeter-level accurate sensing thanks to its ultra-broad bandwidth. Terahertz integrated sensing and communication (ISAC) is viewed as a game-changing technology to realize connected intelligence in 6G and beyond systems. In this article, challenges from THz channel and transceiver perspectives, as well as difficulties of ISAC are elaborated. Motivated by these challenges, THz ISAC channels are studied in terms of channel types, measurement and models. Moreover, four key signal processing techniques to unleash the full potential of THz ISAC are investigated, namely, waveform design, receiver processing, narrowbeam management, and localization. Quantitative studies demonstrate the benefits and performance of the state-of-the-art signal processing methods. Finally, open problems and potential solutions are discussed

Millimeter Wave MIMO Channel Tracking Systems

We consider channel/subspace tracking systems for temporally correlated millimeter wave (e.g., E-band) multiple-input multiple-output (MIMO) channels. Our focus is given to the tracking algorithm in the non-line-of-sight (NLoS) environment, where the transmitter and the receiver are equipped with hybrid analog/digital precoder and combiner, respectively. In the absence of straightforward time-correlated channel model in the millimeter wave MIMO literature, we present a temporal MIMO channel evolution model for NLoS millimeter wave scenarios. Considering that conventional MIMO channel tracking algorithms in microwave bands are not directly applicable, we propose a new channel tracking technique based on sequentially updating the precoder and combiner. Numerical results demonstrate the superior channel tracking ability of the proposed technique over independent sounding approach in the presented channel model and the spatial channel model (SCM) adopted in 3GPP specification.Comment: 6 pages, 3 figures, conferenc

Research on Intelligent Organization and Application of Multi-source Heterogeneous Knowledge Resources for Energy Internet

ABSTRACTTo improve the informationization and intelligence of the energy Internet industry and enhance the capability of knowledge services, it is necessary to organize the energy Internet body of knowledge from existing knowledge resources of the State Grid, which have the characteristics of large scale, multiple sources, and heterogeneity. At the same time, the business fields of State Grid cover a wide range. There are many sub-fields under each business field, and the relationship between fields is diverse and complex. The key to establishing the energy Internet body of knowledge is how to fuse the heterogeneous knowledge resources from multiple sources, extract the knowledge contents from them, and organize the different relationships. This paper considers transforming the original knowledge resources of State Grid into a unified and well-organized knowledge system described in OWL language to meet the requirements of heterogeneous resource integration, multi-source resource organization, and knowledge service provision. For the State Grid knowledge resources mainly in XML format, this paper proposes a Knowledge Automatic Fusion and Organization idea and method based on XSD Directed Graph. According to the method, the XML corresponding XSD documents are transformed into a directed graph in the first stage during which the graph neural network detects hidden knowledge inside the structure to add semantic information to the graph.In the second stage, for other structured knowledge resources (e.g., databases, spreadsheets), the knowledge contents and the relationships are analyzed manually to establish the mappings from structured resources to graph structures, using which the original knowledge resources are transformed into graph structures, and merged with the directed graphs obtained in the first stage to achieve the fusion of heterogeneous knowledge resources. And expert knowledge is introduced for heterogeneous knowledge fusion to further extend the directed graph. And in the third stage, the expanded directed graph is converted to the body of knowledge in the form of OWL. This paper takes the knowledge resources in the field of human resources of the State Grid as an example, to establish the ontology of the human resources training field in a unified manner, initially demonstrating the effectiveness of the proposed method

ISAC-Enabled Beam Alignment for Terahertz Networks: Scheme Design and Coverage Analysis

As a key pillar technology for the future 6G networks, terahertz (THz) communication can provide high-capacity transmissions, but suffers from severe propagation loss and line-of-sight (LoS) blockage that limits the network coverage. Narrow beams are required to compensate for the loss, but they in turn bring in beam misalignment challenge that degrades the THz network performance. The high sensing accuracy of THz signals enables integrated sensing and communication (ISAC) technology to assist the LoS blockage and user mobility-induced beam misalignment, enhancing THz network coverage. In line with the 5G beam management, we propose a joint synchronization signal block (SSB) and reference signal (RS)-based sensing (JSRS) scheme to predict the need for beam switches, and thus prevent beam misalignment. We further design an optimal sensing signal pattern that minimizes beam misalignment with fixed sensing resources, which reveals design insights into the time-to-frequency allocation. We derive expressions for the coverage probability and spatial throughput, which provide instructions on the ISAC-THz network deployment and further enable evaluations for the sensing benefit in THz networks. Numerical results show that the JSRS scheme is effective and highly compatible with the 5G air interface. Averaged in tested urban use cases, JSRS achieves near-ideal performance and reduces around 80% of beam misalignment, and enhances the coverage probability by about 75%, compared to the network with 5G-required positioning ability

Estimation of surface shortwave radiation components under all sky conditions: Modeling and sensitivity analysis

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The differential diagnosis value of radiomics-based machine learning in Parkinson’s disease: a systematic review and meta-analysis

BackgroundIn recent years, radiomics has been increasingly utilized for the differential diagnosis of Parkinson’s disease (PD). However, the application of radiomics in PD diagnosis still lacks sufficient evidence-based support. To address this gap, we carried out a systematic review and meta-analysis to evaluate the diagnostic value of radiomics-based machine learning (ML) for PD.MethodsWe systematically searched Embase, Cochrane, PubMed, and Web of Science databases as of November 14, 2022. The radiomics quality assessment scale (RQS) was used to evaluate the quality of the included studies. The outcome measures were the c-index, which reflects the overall accuracy of the model, as well as sensitivity and specificity. During this meta-analysis, we discussed the differential diagnostic value of radiomics-based ML for Parkinson’s disease and various atypical parkinsonism syndromes (APS).ResultsTwenty-eight articles with a total of 6,057 participants were included. The mean RQS score for all included articles was 10.64, with a relative score of 29.56%. The pooled c-index, sensitivity, and specificity of radiomics for predicting PD were 0.862 (95% CI: 0.833–0.891), 0.91 (95% CI: 0.86–0.94), and 0.93 (95% CI: 0.87–0.96) in the training set, and 0.871 (95% CI: 0.853–0.890), 0.86 (95% CI: 0.81–0.89), and 0.87 (95% CI: 0.83–0.91) in the validation set, respectively. Additionally, the pooled c-index, sensitivity, and specificity of radiomics for differentiating PD from APS were 0.866 (95% CI: 0.843–0.889), 0.86 (95% CI: 0.84–0.88), and 0.80 (95% CI: 0.75–0.84) in the training set, and 0.879 (95% CI: 0.854–0.903), 0.87 (95% CI: 0.85–0.89), and 0.82 (95% CI: 0.77–0.86) in the validation set, respectively.ConclusionRadiomics-based ML can serve as a potential tool for PD diagnosis. Moreover, it has an excellent performance in distinguishing Parkinson’s disease from APS. The support vector machine (SVM) model exhibits excellent robustness when the number of samples is relatively abundant. However, due to the diverse implementation process of radiomics, it is expected that more large-scale, multi-class image data can be included to develop radiomics intelligent tools with broader applicability, promoting the application and development of radiomics in the diagnosis and prediction of Parkinson’s disease and related fields.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=383197, identifier ID: CRD42022383197

Hydrocarbon Detection Based on Phase Decomposition in Chaoshan Depression, Northern South China Sea

Located in the northern South China Sea, Chaoshan Depression is mainly a residual Mesozoic depression, with a construction of Meso-Cenozoic strata over 7000m thick and good hydrocarbon accumulation conditions. Amplitude attribute of -90°phase component derived by phase decomposition is employed to detect Hydrocarbon in the zone of interest (ZOI) in Chaoshan Depression. And it is found that there are evident amplitude anomalies occurring around ZOI. Phase decomposition is applied to forward modeling results of the ZOI, and high amplitudes occur on the -90°phase component more or less when ZOI is charged with hydrocarbon, which shows that the amplitude abnormality in ZOI is probably caused by oil and gas accumulation