This paper proposes a combined network structure between convolutional neural (CNN) and long-short term memory (LSTM) quantifier for WiFi fingerprinting indoor localization. In contrast to conventional methods that utilize only spatial data with classification models, our CNN-LSTM extracts both space time features of the received channel state information (CSI) from single router. Furthermore, proposed builds quantification model rather than limited as in most literature work, which enables...

Intelligent IoT provides services for machine learning applications by collecting real-time data. The correctness of the data has a crucial impact on training accuracy models. owner hosts incremental collected in real time to cloud and uses blockchain as public query, usage, certification platform. Malicious attackers can launch attacks model parameters tampering withdata, such poisoning. Blockchain-based integrity verification (auditing) schemes effectively monitor illegal behavior...

With the expansive aging of global population, service robot with living assistance applied in indoor scenes will serve as a crucial role field elderly care and health future. Service robots need to detect multiple targets when completing auxiliary tasks. However, are usually complex there many types interference factors, leading great challenges detection. To overcome this technical difficulty, novel improved Mask RCNN method for detection is proposed paper. The model utilizes network...

Classification and segmentation are crucial in medical image analysis as they enable accurate diagnosis disease monitoring. However, current methods often prioritize the mutual learning features shared model parameters, while neglecting reliability of performances. In this paper, we propose a novel Uncertainty-informed Mutual Learning (UML) framework for reliable interpretable analysis. Our UML introduces to joint classification tasks, leveraging with uncertainty improve performance. To...

Few-shot learning aims provide precise predictions for unseen data through from only one or few labelled samples of each class. However, it often suffers the overfitting problem because insufficient training data. In this paper, we propose a novel metric-based few-shot method, multi-branch network (MBN), with new augmentation module to improve generalization ability model. Specifically, generate different types noise contaminated multiple branches in simulate real-world scenarios when noisy...

This paper proposes passive WiFi indoor localization. Instead of using signals received by mobile devices as fingerprints, we use routers to locate the carrier. Consequently, software installation on device is not required. To resolve data insufficiency problem, flow control such request send (RTS) and clear (CTS) are utilized. In our model, signal strength indicator (RSSI) channel state information (CSI) used fingerprints for several algorithms, including deterministic, probabilistic neural...

Traditional approaches to improving users' quality of experience (QoE) focus on minimizing the latency server side. Through an analysis 15 million users, however, we find that for short-form video apps, user depends both response and recommendation accuracy. This observation brings a dilemma service providers since accuracy requires adopting complex strategies demand heavy computation, which substantially increases latency.

Few-shot image classification aims to provide accurate predictions for novelty by learning from a limited number of samples. Classical few-shot methods usually use data augmentation and self-supervision compensate the lack training sample, introduce migration meta-learning pre-train model or accelerate optimization, which improves performance model. However, with small amount labeled sample data, these cannot meet requirements model's ability characterize features, resulting in that is...

PDF HTML阅读 XML下载 导出引用 引用提醒 字节码虚拟机的构造和验证 DOI: 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: Supported by the National Natural Science Foundation of China under Grant Nos.90818019, 90816006 (国家自然科学基金); High-Tech Research and Development Plan Nos.2008AA01Z102, 2009AA011902 (国家高技术研究发展计划(863)) Construction Certification a Bytecode Virtual Machine Author: Affiliation: Fund Project: 摘要 | 图/表 访问统计 参考文献 相似文献 引证文献 资源附件 文章评论 摘要:提出一种虚拟机构造和验证方案.给出字节码程序运行环境BVM(bytecode virtual...

Deep reinforcement learning (DRL) has exhibited considerable promise in the training of control agents for mapless robot navigation. However, DRL-trained are limited to specific dimensions used during training, hindering their applicability when robot's dimension changes task-specific requirements. To overcome this limitation, we propose a dimension-variable navigation method based on DRL. Our approach involves meta agent simulation and subsequently transferring skill dimension-varied using...

The problem of multi-object tracking is a fundamental computer vision research focus, widely used in public safety, transport, autonomous vehicles, robotics, and other regions involving artificial intelligence. Because the complexity natural scenes, object occlusion semi-occlusion usually occur tasks. These can easily lead to ID switching, loss, detect errors, misaligned limitation boxes. conditions have significant impact on precision tracking. In this paper, we design new tracker for above...

In order to improve the ability of normal mode blind equalizer deal with inter-code crosstalk, a equalization algorithm based on brainstorming is proposed. The starts problem unsatisfactory initialization equalizer, and uses quickly accurately find advantages global solution, reducing initial mean square error system;on this basis, from perspective convergence speed stability weighing instrument, adaptive variable step function used effect equalizer. Experimental results show that new can...