Fixture Design for Parasitic Capacitances of Mosfets for Emi Applications

Due to the fast-switching nature of modern power converters, up to hundreds of MHz of common-mode noise can easily be generated. The characterization of switching components, e.g., Si MOSFETs, is essential for noise reduction. However, limited by the bandwidth of instruments, the voltage-dependent capacitances of high voltage MOSFETs are typically characterized at approximately 1 MHz, which is insufficient for EMI applications. In this paper, the measurement method and the test fixtures are presented. The measurement bandwidth is pushed to 30 MHz and higher, and frequency-dependent capacitances of a MOSFET are observed through measurements

HetCAN: A Heterogeneous Graph Cascade Attention Network with Dual-Level Awareness

Heterogeneous graph neural networks(HGNNs) have recently shown impressive capability in modeling heterogeneous graphs that are ubiquitous in real-world applications. Most existing methods for heterogeneous graphs mainly learn node embeddings by stacking multiple convolutional or attentional layers, which can be considered as capturing the high-order information from node-level aspect. However, different types of nodes in heterogeneous graphs have diverse features, it is also necessary to capture interactions among node features, namely the high-order information from feature-level aspect. In addition, most methods first align node features by mapping them into one same low-dimensional space, while they may lose some type information of nodes in this way. To address these problems, in this paper, we propose a novel Heterogeneous graph Cascade Attention Network (HetCAN) composed of multiple cascade blocks. Each cascade block includes two components, the type-aware encoder and the dimension-aware encoder. Specifically, the type-aware encoder compensates for the loss of node type information and aims to make full use of graph heterogeneity. The dimension-aware encoder is able to learn the feature-level high-order information by capturing the interactions among node features. With the assistance of these components, HetCAN can comprehensively encode information of node features, graph heterogeneity and graph structure in node embeddings. Extensive experiments demonstrate the superiority of HetCAN over advanced competitors and also exhibit its efficiency and robustness.Comment: Accepted by ECML-PKDD 202

Enhancing Graph Neural Networks with Structure-Based Prompt

Graph Neural Networks (GNNs) are powerful in learning semantics of graph data. Recently, a new paradigm "pre-train, prompt" has shown promising results in adapting GNNs to various tasks with less supervised data. The success of such paradigm can be attributed to the more consistent objectives of pre-training and task-oriented prompt tuning, where the pre-trained knowledge can be effectively transferred to downstream tasks. However, an overlooked issue of existing studies is that the structure information of graph is usually exploited during pre-training for learning node representations, while neglected in the prompt tuning stage for learning task-specific parameters. To bridge this gap, we propose a novel structure-based prompting method for GNNs, namely SAP, which consistently exploits structure information in both pre-training and prompt tuning stages. In particular, SAP 1) employs a dual-view contrastive learning to align the latent semantic spaces of node attributes and graph structure, and 2) incorporates structure information in prompted graph to elicit more pre-trained knowledge in prompt tuning. We conduct extensive experiments on node classification and graph classification tasks to show the effectiveness of SAP. Moreover, we show that SAP can lead to better performance in more challenging few-shot scenarios on both homophilous and heterophilous graphs

A cooperative-based model for smart-sensing tasks in fog computing

OAPA Fog Computing is currently receiving a great deal of focused attention. Fog Computing can be viewed as an extension of cloud computing that services the edges of networks. A cooperative relationship among applications to collect data in a city is a fundamental research topic in Fog Computing (FC). When considering the Green Cloud (GC), people or vehicles with smart-sensor devices can be viewed as users in FC and can forward sensing data to the data center (DC). In a traditional sensing process, rewards are paid according to the distances between the users and the platform, which can be seen as the existing solution. Because users with smart-sensing devices tend to participate in tasks with high rewards, the number of users in suburban regions is smaller, and data collection is sparse and cannot satisfy the demands of the tasks. However, there are many users in urban regions, which makes data collection costly and of low quality. In this paper, a cooperative-based model for smartphone tasks, named a Cooperative-based Model for Smart-Sensing Tasks (CMST), is proposed to promote the quality of data collection in FC networks. In the CMST scheme, we develop an allocation method focused on improving the rewards in suburban regions. The rewards to each user with a smart sensor are distributed according to the region density. Moreover, for each task there is a cooperative relationship among the users; they cooperate with one another to reach the volume of data that the platform requires. Extensive experiments show that our scheme improves the overall data-coverage factor by 14.997% to 31.46%, and the platform cost can be reduced by 35.882

Optimizing the Coverage via the UAVs With Lower Costs for Information-Centric Internet of Things

The recent developments in the areas of the Internet of Things (IoTs) have provided a rapid growth in the epoch of the novel information-centric collections (IC-IoTs). In the IC-IoTs, expanding the ranges of information collections and reducing the costs are important issues for the information required platform. In the previous scenarios, the information is collected in a random manner, which leads to lower coverages and higher costs. Thus, a "optimizing the coverage via the unmanned aerial vehicles (UAVs) with lower costs for information-centric Internet of Things" (OCLC-IoTs) approach is established, which targets to improve the coverage ratio and to reduce the costs of the information-required platform via the cooperation of the information collectors and the UAVs. First, to improve the coverage ratio, an intensive strategy is proposed to inspire the information collectors to bid for the tasks published by the platform and an improved rolling horizon strategy (IRHS) strategy is designed to plan the flying routes of the UAVs to reach more coverage ranges. Then, to reduce the costs factor, the IRHS strategy is designed to shorten the flying routes of the UAVs under the prerequisite of guaranteeing coverage ratio to achieve fewers costs. Finally, a comprehensive theoretical analysis and experiments are provided, which indicates the advancements of the OCLC-IoTs scheme. Compared with the previous studies, the OCLC-IoTs scheme can improve the coverage ratio of information by 21.42% approximately and can reduce the cost ratio by 13.335% to 34.32%

SIoTFog: Byzantine-resilient IoT fog networking

The current boom in the Internet of Things (IoT) is changing daily life in many ways, from wearable devices to connected vehicles and smart cities. We used to regard fog computing as an extension of cloud computing, but it is now becoming an ideal solution to transmit and process large-scale geo-distributed big data. We propose a Byzantine fault-tolerant networking method and two resource allocation strategies for IoT fog computing. We aim to build a secure fog network, called “SIoTFog,” to tolerate the Byzantine faults and improve the efficiency of transmitting and processing IoT big data. We consider two cases, with a single Byzantine fault and with multiple faults, to compare the performances when facing different degrees of risk. We choose latency, number of forwarding hops in the transmission, and device use rates as the metrics. The simulation results show that our methods help achieve an efficient and reliable fog network

Tannase application in secondary enzymatic processing of inferior Tieguanyin oolong tea

Background: Inferior Tieguanyin oolong tea leaves were treated with tannase. The content and bioactivity of catechins in extracts from the treated tea leaves were investigated to assess the improvement in the quality of inferior Tieguanyin oolong tea. Results: Analysis showed that after treatment, the esterified catechin content decreased by 23.5%, whereas non-galloylated catechin and gallic acid contents increased by 15.3% and 182%, respectively. The extracts from tannase-treated tea leaves showed reduced ability to bind to BSA and decreased tea cream levels. The extracts also exhibited increased antioxidant ability to scavenge OH and DPPH radicals, increased ferric reducing power, and decreased inhibitory effects on pancreatic \u3b1-amylase and lipase activities. Conclusions: These results suggested that tannase treatment could improve the quality of inferior Tieguanyin oolong tea leaves

A Trust-Based Model for Security Cooperating in Vehicular Cloud Computing

VCC is a computing paradigm which consists of vehicles cooperating with each other to realize a lot of practical applications, such as delivering packages. Security cooperation is a fundamental research topic in Vehicular Cloud Computing (VCC). Because of the existence of malicious vehicles, the security cooperation has become a challenging issue in VCC. In this paper, a trust-based model for security cooperating, named DBTEC, is proposed to promote vehicles’ security cooperation in VCC. DBTEC combines the indirect trust estimation in Public board and the direct trust estimation in Private board to compute the trust value of vehicles when choosing cooperative partners; a trustworthy cooperation path generating scheme is proposed to ensure the safety of cooperation and increase the cooperation completion rates in VCC. Extensive experiments show that our scheme improves the overall cooperation completion rates by 6~7%