A5100678479- Computational Drug Discovery Methods
- Energy Efficient Wireless Sensor Networks
- Mobile Ad Hoc Networks
- Machine Learning in Bioinformatics
- Protein Structure and Dynamics
- Adaptive Dynamic Programming Control
- Adaptive Control of Nonlinear Systems
- Radiomics and Machine Learning in Medical Imaging
- Age of Information Optimization
- Opportunistic and Delay-Tolerant Networks
- Distributed Sensor Networks and Detection Algorithms
- Control Systems and Identification
- Advanced MIMO Systems Optimization
- Cooperative Communication and Network Coding
- Cloud Computing and Resource Management
- Water Quality Monitoring Technologies
- Indoor and Outdoor Localization Technologies
- Image Retrieval and Classification Techniques
- Vehicle License Plate Recognition
- Underwater Vehicles and Communication Systems
- IoT and Edge/Fog Computing
- Energy Harvesting in Wireless Networks
- Cell Image Analysis Techniques
- Autonomous Vehicle Technology and Safety
- Video Surveillance and Tracking Methods
Tongji University
2010-2024
Anhui University of Technology
2024
Shenzhen University
2021
Ministry of Education of the People's Republic of China
2011-2012
Shanghai Liangyou (China)
2010
With the recent introduction of Spot Instances in Amazon Elastic Compute Cloud (EC2), users can bid for resources and, thus, control balance reliability versus monetary costs. Mechanisms and tools that deal with cost-reliability tradeoffs under this scheme are great value seeking to reduce their costs while maintaining high reliability. In paper, we propose a set bidding strategies several service-level agreement (SLA) constraints. particular, aim minimize cost volatility resource...
We study the general scaling laws of capacity for random wireless networks under generalized physical model. The generality this work is embodied in three dimensions denoted by (λ ∈ [1, n], n <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> (1, n]). It means that: (1) network a node density λ rather than only either dense (RDN, = n) or extended (REN, 1) as literature. (2) focus on...
The task of drug-target interaction (DTI) prediction plays important roles in drug development. experimental methods DTIs are time-consuming, expensive and challenging. To solve these problems, machine learning-based introduced, which restricted by effective feature extraction negative sampling. In this work, features with electrotopological state (E-state) fingerprints for drugs amphiphilic pseudo amino acid composition (APAAC) target proteins tested. E-state extracted based on both...
In this work, for a wireless sensor network (WSN) of n randomly placed sensors with node density \lambda \in [1,n], we study the tradeoffs between aggregation throughput and gathering efficiency. The efficiency refers to ratio number whose data have been gathered total sensors. Specifically, design two efficient schemes, called single-hop-length (SHL) scheme multiple-hop-length (MHL) scheme. By novelly integrating these theoretically prove that our protocol achieves optimal tradeoffs, derive...
System identification is very important for the controller design of unmanned helicopters, and it has significant impacts on quality flight missions. With recent advances reinforcement learning, we propose a generalized orthonormal basis function (GOBF)-based system scheme helicopters. Using GOBF, traditional parameter estimation problem in not only becomes better numerical conditioned but also can be affected by prior knowledge. The proposed novel GOBF-based enable users to make good use...
Backgroud: The prediction of drug–target interactions (DTIs) is great significance in drug development. It time-consuming and expensive traditional experimental methods. Machine learning can reduce the cost limited by characteristics imbalanced datasets problems essential feature selection. Methods: method based on Ensemble model Multiple Feature Pairs (Ensemble-MFP) introduced. Firstly, three negative sets are generated according to Euclidean distance pairs. Then, samples validation...
To ensure sustainable operations of wireless sensor networks, environmental energy harvesting has been well recognized as one promising solution for long-term applications. Unlike in battery-powered we are targeting a duty-cycle adjustment to optimize the network performance, e.g., delay minimization, with full harvested utilization. In this paper, introduce set schemes that will minimize cross traffic (CTD) energy-harvesting networks. We first present an offline by assuming link reliability...
Non-contrast CT (NCCT) imaging may reduce image contrast and anatomical visibility, potentially increasing diagnostic uncertainty. In contrast, contrast-enhanced (CECT) facilitates the observation of regions interest (ROI). Leading generative models, especially conditional diffusion model, demonstrate remarkable capabilities in medical modality transformation. Typical models commonly generate images with guidance segmentation labels for modal Limited access to authentic its low cardinality...
Localization problem in wireless sensor networks (WSNs) has been widely studied recently. However, most previous work simply assume that all the nodes stay awake during localization phase. This assumption clearly overlooks common scenario are usually duty-cycled order to save energy. In this paper we propose a kind of novel DV (distance vector)-based algorithm which performs pretty good network. get accuracy, DV-based positioning algorithms need keep critical minimum average neighborhood...