05738nam 2200661 450 991083090050332120240219155401.01-282-34567-297866123456780-470-06107-30-470-06106-510.1002/9780470061077(CKB)1000000000356735(EBL)291035(SSID)ssj0000228776(PQKBManifestationID)11197452(PQKBTitleCode)TC0000228776(PQKBWorkID)10167588(PQKB)10522901(MiAaPQ)EBC291035(CaBNVSL)mat08039581(IDAMS)0b00006485f0d502(IEEE)8039581(OCoLC)86245336(CaSebORM)9780470057384(PPN)250547163(EXLCZ)99100000000035673520171024d2008 uy engur|n|---|||||txtccrProgramming mobile devices an introduction for practitioners /Tommi Mikkonen1st editionChichester, England ;John Wiley,c2007.[Piscataqay, New Jersey] :IEEE Xplore,[2007]1 online resource (246 p.)Description based upon print version of record.0-470-05738-6 Includes bibliographical references (p. [215]-218) and index.Foreword by Jan Bosch -- Foreword by Antero Taivalsaari -- Preface -- Acknowledgments -- 1 Introduction -- 1.1 Motivation -- 1.2 Commonly Used Hardware and Software -- 1.3 Development Process -- 1.4 Chapter Overview -- 1.5 Summary -- 1.6 Exercises -- 2 Memory Management -- 2.1 Overview -- 2.2 Strategies for Allocating Variables to Memory -- 2.3 Design Patterns for Limited Memory -- 2.4 Memory Management in Mobile Java -- 2.5 Symbian OS Memory Management -- 2.6 Summary -- 2.7 Exercises -- 3 Applications -- 3.1 What Constitutes an Application? -- 3.2 Workflow for Application Development -- 3.3 Techniques for Composing Applications -- 3.4 Application Models in Mobile Java -- 3.5 Symbian OS Application Infrastructure -- 3.6 Summary -- 3.7 Exercises -- 4 Dynamic Linking -- 4.1 Overview -- 4.2 Implementation Techniques -- 4.3 Implementing Plugins -- 4.4 Managing Memory Consumption Related to Dynamically Linked Libraries -- 4.5 Rules of Thumb for Using Dynamically Loaded Libraries -- 4.6 Mobile Java and Dynamic Linking -- 4.7 Symbian OS Dynamic Libraries -- 4.8 Summary -- 4.9 Exercises -- 5 Concurrency -- 5.1 Motivation -- 5.2 Infrastructure for Concurrent Programming -- 5.3 Faking Concurrency -- 5.4 MIDP Java and Concurrency -- 5.5 Symbian OS and Concurrency -- 5.6 Summary -- 5.7 Exercises -- 6 Managing Resources -- 6.1 Resource-Related Concerns in Mobile Devices -- 6.2 Common Concerns -- 6.3 MIDP Java -- 6.4 Symbian OS -- 6.5 Summary -- 6.6 Exercises -- 7 Networking -- 7.1 Introduction -- 7.2 Design Patterns for Networking Environment -- 7.3 Problems with Networking Facilities and Implementations -- 7.4 MIDP Java and Web Services -- 7.5 Symbian OS and Bluetooth Facilities -- 7.6 Summary -- 7.7 Exercises -- 8 Security -- 8.1 Overview -- 8.2 Secure Coding and Design -- 8.3 Infrastructure for Enabling Secured Execution -- 8.4 Security Features in MIDP Java -- 8.5 Symbian OS Security Features -- 8.6 Summary -- 8.7 Exercises -- References -- Index.With forewords by Jan Bosch, Nokia and Antero Taivalsaari, Sun Microsystems. Learn how to programme the mobile devices of the future! The importance of mobile systems programming has emerged over the recent years as a new domain in software development. The design of software that runs in a mobile device requires that developers combine the rules applicable in embedded environment; memory-awareness, limited performance, security, and limited resources with features that are needed in workstation environment; modifiability, run-time extensions, and rapid application development. Programming Mobile Devices is a comprehensive, practical introduction to programming mobile systems. The book is a platform independent approach to programming mobile devices: it does not focus on specific technologies, and devices, instead it evaluates the component areas and issues that are common to all mobile software platforms. This text will enable the designer to programme mobile devices by mastering both hardware-aware and application-level software, as well as the main principles that guide their design. Programming Mobile Devices: . Provides a complete and authoritative overview of programming mobile systems.. Discusses the major issues surrounding mobile systems programming; such as understanding of embedded systems and workstation programming.. Covers memory management, the concepts of applications, dynamically linked libraries, concurrency, handling local resources, networking and mobile devices as well as security features.. Uses generic examples from JavaTM and Symbian OS to illustrate the principles of mobile device programming. Programming Mobile Devices is essential reading for graduate and advanced undergraduate students, academic and industrial researchers in the field as well as software developers, and programmers.Mobile computingWireless communication systemsMobile computing.Wireless communication systems.004.165621.3845621.38456Mikkonen Tommi1636034CaBNVSLCaBNVSLCaBNVSLBOOK9910830900503321Programming mobile devices3977123UNINA06779nam 22006855 450 991050638830332120251113181208.03-030-88004-410.1007/978-3-030-88004-0(CKB)4950000000283637(MiAaPQ)EBC6789382(Au-PeEL)EBL6789382(OCoLC)1280416072(PPN)258296046(DE-He213)978-3-030-88004-0(EXLCZ)99495000000028363720211007d2021 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierPattern Recognition and Computer Vision 4th Chinese Conference, PRCV 2021, Beijing, China, October 29 – November 1, 2021, Proceedings, Part I /edited by Huimin Ma, Liang Wang, Changshui Zhang, Fei Wu, Tieniu Tan, Yaonan Wang, Jianhuang Lai, Yao Zhao1st ed. 2021.Cham :Springer International Publishing :Imprint: Springer,2021.1 online resource (634 pages)Image Processing, Computer Vision, Pattern Recognition, and Graphics,3004-9954 ;130193-030-88003-6 Object Detection, Tracking and Recognition -- High-performance Discriminative Tracking with Target-aware Feature Embeddings.-3D Multi-Object Detection and Tracking with Sparse Stationary LiDAR -- CRNet: Centroid Radiation Network for Temporal Action Localization -- Weakly Supervised Temporal Action Localization with Segment-Level Labels -- Locality-constrained collaborative representation with multi-resolution dictionary for face recognition -- Fast and Fusion: Real-time Pedestrian Detector Boosted by Body-head Fusion -- STA-GCN: Spatio-Temporal AU Graph Convolution Network for Facial Micro-Expression Recognition -- Attentive Contrast Learning Network for Fine-grained Classification -- Relation-Based Knowledge Distillation for Anomaly Detection -- High Power-efficient and Performance-density FPGA Accelerator for CNN-based Object Detection -- Relation-Guided Actor Attention for Group Activity Recognition -- MVAD-Net: Learning View-Aware and Domain-Invariant Representation for Baggage Re-Identification -- Joint Attention Mechanism for Unsupervised Video Object Segmentation.-Foreground Feature Selection and Alignment for Adaptive Object Detection -- Exploring Category-shared and Category-specific Features for Fine-Grained Image Classification.-Deep Mixture of Adversarial Autoencoders Clustering Network -- SA-InterNet: Scale-aware Interaction Network for Joint Crowd Counting and Localization -- Conditioners for Adaptive Regression Tracking -- Attention Template Update Model for Siamese Tracker -- Insight on Attention Modules for Skeleton-Based Action Recognition -- AO-AutoTrack: Anti-Occlusion Real-Time UAV Tracking Based on Spatio-temporal Context -- Two-stage Recognition Algorithm for Untrimmed Converter Steelmaking Flame Video -- Scale-aware Multi-branch Decoder for Salient Object Detection -- Dense End Face Detection Network for Counting Bundled Steel Bars Based on Densely End Face Detection Network for Counting Bundled Steel Bars Based on YoloV5 -- POT: A Dataset of Panoramic Object Tracking -- DP-YOLOv5:Computer Vision-Based Risk Behavior Detection in Power Grids.-Distillation-based Multi-Exit Fully Convolutional Network for Visual Tracking.-Handwriting Trajectory Reconstruction using Spatial-Temporal Encoder-Decoder Network -- Scene Semantic Guidance for Object Detection -- Training Person Re-Identification Networks with Transferred Images -- ACFIM: Adaptively Cyclic Feature Information- interaction model for Object Detection -- Research of robust video object tracking algorithm based on Jetson Nano embedded platform -- Classification-IoU Joint Label Assignment For End-to-End Object Detection -- Joint Learning Appearance and Motion Models for Visual Tracking -- ReFlowNet: Revisiting Coarse-to-fine Learning of Optical Flow -- Local Mutual Metric Network for Few-Shot Image Classification -- SimplePose V2: Greedy Offset-Guided Keypoint Grouping for Human Pose Estimation -- Control Variates for Similarity Search -- Pyramid Self-Attention for Semantic Segmentation.-Re-identify Deformable Targets for Visual Tracking -- End-to-End Detection and Recognition of Arithmetic Expressions -- FD-Net: A Fully Dilated Convolutional Network for Historical Document Image Binarization -- Appearance-Motion Fusion Network for Video Anomaly Detection -- Can DNN Detectors Compete against Human Vision in Object Detection Task? -- Group Re-Identification Based on single feature attention learning network(SFALN) -- Contrastive Cycle Consistency Learning for Unsupervised Visual Tracking -- Group-Aware Disentangle Learning for Head Pose Estimation -- Facilitating 3D Object Tracking in Point Clouds with Image Semantics and Geometry -- Multi-Criteria Confidence Evaluation for Robust Visual Tracking.The 4-volume set LNCS 13019, 13020, 13021 and 13022 constitutes the refereed proceedings of the 4th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2021, held in Beijing, China, in October-November 2021. The 201 full papers presented were carefully reviewed and selected from 513 submissions. The papers have been organized in the following topical sections: Object Detection, Tracking and Recognition; Computer Vision, Theories and Applications, Multimedia Processing and Analysis; Low-level Vision and Image Processing; Biomedical Image Processing and Analysis; Machine Learning, Neural Network and Deep Learning, and New Advances in Visual Perception and Understanding.Image Processing, Computer Vision, Pattern Recognition, and Graphics,3004-9954 ;13019Computer visionComputer engineeringComputer networksComputer systemsMachine learningComputer VisionComputer Engineering and NetworksComputer Communication NetworksComputer System ImplementationMachine LearningComputer vision.Computer engineering.Computer networks.Computer systems.Machine learning.Computer Vision.Computer Engineering and Networks.Computer Communication Networks.Computer System Implementation.Machine Learning.621.367Ma HuiminMiAaPQMiAaPQMiAaPQBOOK9910506388303321Pattern recognition and computer vision1972598UNINA