03596oam 2200577 450 99646562130331620210512232359.03-540-85565-310.1007/978-3-540-85565-1(CKB)1000000000490738(SSID)ssj0000355546(PQKBManifestationID)11277613(PQKBTitleCode)TC0000355546(PQKBWorkID)10319783(PQKB)11373853(DE-He213)978-3-540-85565-1(MiAaPQ)EBC3063496(MiAaPQ)EBC6408043(PPN)129062529(EXLCZ)99100000000049073820210512d2008 uy 0engurnn|008mamaatxtccrKnowledge-based intelligent information and engineering systems 12th International Conference, KES 2008, Zagreb, Croatia, September 3-5, 2008, Proceedings, Part II. /Ignac Lovrek, Robert J. Howlett, Lakhmi C. Jain (editors)1st ed. 2008.Berlin :Springer,[2008]©20081 online resource (XXXVIII, 1043 p.) Lecture Notes in Artificial Intelligence ;5178Bibliographic Level Mode of Issuance: Monograph3-540-85564-5 Includes bibliographical references and index.The three volume set LNAI 5177, LNAI 5178, and LNAI 5179, constitutes the refereed proceedings of the 12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008, held in Zagreb, Croatia, in September 2008. The 316 revised papers presented were carefully reviewed and selected. The papers present a wealth of original research results from the field of intelligent information processing in the broadest sense; topics covered in the second volume are artificial intelligence driven engineering design optimization; biomedical informatics: intelligent information management from nanomedicine to public health; communicative intelligence; computational intelligence for image processing and pattern recognition; computational intelligence in human cancer research; computational intelligence techniques for Web personalization; computational intelligent techniques for bioprocess modelling, monitoring and control; intelligent computing for Grid; intelligent security techniques; intelligent utilization of soft computing techniques; reasoning-based intelligent systems: relevant reasoning for discovery and prediction; spatio-temporal database concept support for organizing virtual earth; advanced knowledge-based systems; chance discovery; innovation-oriented knowledge management platform; knowledge-based creativity support systems; knowledge-based interface systems; knowledge-based multi-criteria decision support; and knowledge-based systems for e-business.Lecture notes in computer science.Lecture notes in artificial intelligence ;5178.Electronic apparatus and appliancesAutomatic controlCongressesArtificial intelligenceCongressesExpert systems (Computer science)CongressesElectronic apparatus and appliancesAutomatic controlArtificial intelligenceExpert systems (Computer science)006.3MiAaPQMiAaPQUtOrBLWBOOK996465621303316Knowledge-Based Intelligent Information and Engineering Systems771988UNISA07172nam 2200637 450 991013234530332120230807211227.01-118-70636-61-118-70638-2(CKB)3710000000222764(EBL)1767916(MiAaPQ)EBC1767916(DLC) 2014018076(Au-PeEL)EBL1767916(CaPaEBR)ebr10910132(CaONFJC)MIL637379(OCoLC)888747544(EXLCZ)99371000000022276420140828h20152015 uy 0engur|n|---|||||rdacontentrdamediardacarrierAddressing techniques of liquid crystal displays /Temkar N. RuckmongathanChichester, England :Wiley,2015.©20151 online resource (357 p.)Wiley Series in Display TechnologyDescription based upon print version of record.1-118-70639-0 1-119-94045-1 Includes bibliographical references and index.Addressing Techniques of Liquid Crystal Displays; Contents; Series Editors Foreword; Acknowledgements; 1 Introduction; 2 Liquid Crystal Displays; 2.1 Matrix Displays; 2.2 Display Fonts and Formats; 2.3 Liquid Crystals; 2.4 Physical Properties of Liquid Crystals; 2.5 Basics of Electro-optic Effects with Liquid Crystals; 2.6 Twisted Nematic Effect; 2.7 Super Twisted Nematic (STN)-LCD; 2.8 STN-LCD with a 270° Twist (STN-270); 2.9 STN-LCD with a 180° Twist (STN-180); 2.10 In-plane Switching; 2.11 Ferroelectric LCD (FLCD); 2.12 Summary; 3 Review of Addressing Techniques; 3.1 Addressing Techniques3.2 Matrix Addressing3.3 Nonlinear Characteristics; 3.4 Cross-Talk in a Matrix LCD; 3.5 Driving Matrix Displays; 3.6 Bi-phase Addressing; 3.7 Line-by-Line Addressing (LLA); 3.8 Half-Select Technique; 3.9 Two-Third-Select Technique (TTST); 3.10 Selection Ratio (SR) and the Maximum Selection Ratio; 3.11 Limitations of Matrix Addressing; 3.12 Principle of Restricted Pattern Addressing; 3.13 Pulse Coincidence Technique (PCT); 3.14 Pseudo Random Technique (PRT); 3.15 Restricted Pattern Addressing Technique (RPAT); 3.16 Addressing Technique for Dial Type Displays; 3.17 Frame Frequency3.18 Large Area Display3.19 Dielectric Relaxation; 3.20 Supply Voltage of Drivers; 3.21 Nonuniformity Due to Resistance Mismatches; 3.22 Need for Multiline Addressing Techniques; 4 Binary Addressing; 4.1 Principle; 4.2 Binary Addressing Technique (BAT); 4.3 Analysis of the BAT; 4.4 Practical Aspects of the BAT; 4.5 Drivers for Driving the LCD with the BAT; 5 Orthogonal Functions and Matrix Addressing; 5.1 Orthogonal Functions; 5.2 Multiplexing; 5.3 Matrix Addressing; 5.4 Line-by-Line Addressing; 5.5 Multiline Addressing; 5.6 Discussion; 6 Active Addressing; 6.1 Principle6.2 Active Addressing Technique (AAT)6.3 Summary; 7 Hybrid Addressing; 7.1 Principle; 7.2 Hybrid Addressing Technique (HAT); 7.3 Analysis of the HAT; 7.4 Drivers of the Hybrid Addressing Technique; 7.5 Discussion; 8 Improved Hybrid Addressing; 8.1 Principle; 8.2 Improved Hybrid Addressing Technique (IHAT); 8.3 Analysis of IHAT; 8.4 Discussion; 9 Improved Hybrid Addressing Special Case 3; 9.1 Principle; 9.2 Analysis; 9.3 Summary; 10 Improved Hybrid Addressing Special Case 4; 10.1 Principle; 10.2 Analysis; 10.3 Summary; 11 Sequency Addressing; 11.1 Principle; 11.2 Technique; 11.3 Discussion12 Restricted Pattern Addressing12.1 Principle; 12.2 Technique; 12.3 Analysis; 12.4 Summary; 13 Review of Methods to Display Greyscales; 13.1 Greyscales in Liquid Crystal Displays; 13.2 Basics of Greyscale; 13.3 Frame Modulation; 13.4 Pulse Width Modulation; 13.5 Row Pulse Height Modulation; 13.6 Data Pulse Height Modulation; 13.7 Summary; 14 Amplitude Modulation; 14.1 Principle; 14.2 Amplitude Modulation - Split Time Interval; 14.3 Amplitude Modulation in Multiline Addressing; 14.4 Pulse Height Modulation; 14.5 Discussion; 15 Successive Approximation; 15.1 Principle; 15.2 Technique15.3 Analysis"Unique reference source that can be used from the beginning to end of a design project to aid choosing an appropriate LCD addressing technique for a given applicationThis book will be aimed at design engineers who are likely to embed LCD drivers and controllers in many systems including systems on chip. Such designers face the challenge of making the right choice of an addressing technique that will serve them with best performance at minimal cost and complexity. Readers will be able to learn about various methods available for driving matrix LCDs and the comparisons at the end of each chapter will aid readers to make an informed design choice.The book will address the various driving techniques related to LCDs. Due to the non-linear response of the liquid crystal to external voltages, different driving methods such as passive and active matrix driving can be utilized. The associated theoretical basis of these driving techniques is introduced, and this theoretical analysis is supplemented by information on the implementation of drivers and controllers to link the theory to practice. Written by an experienced research scientist with over 30 years in R&D in this field. Acts as an exhaustive review and comparison of techniques developed for passive-matrix addressing of twisted nematic and super-twisted nematic (STN) LCDs. Discusses the trend towards "High Definition" displays and that a hybrid approach to drive matrix LCDs (combination of active and passive matrix addressing) will be the future of LCD addressing. Contains the author's recent work on Bit-Slice Addressing that is useful for fast responding LCDs, as well as a chapter on driving ferroelectric LCDs Provides an objective comparison that will enable designers to make an informed choice of an addressing technique for a specific application. Includes examples of the practical applications of addressing techniques. Organised in a way that each chapter can be read independently; with the basic knowledge and historical background gained from the introductory chapters, adequate for understanding the techniques that are presented in the remaining chapters making it a self-contained reference. "--Provided by publisher."This book will be aimed at design engineers who are likely to embed LCD drivers and controllers in many systems including systems on chip"--Provided by publisher.Wiley Series in Display TechnologyLiquid crystal displaysAutomatic controlDevice drivers (Computer programs)Liquid crystal displaysAutomatic control.Device drivers (Computer programs)004.7/7TEC008000bisacshRuckmongathan Temkar N.969521MiAaPQMiAaPQMiAaPQBOOK9910132345303321Addressing techniques of liquid crystal displays2203200UNINA07315nam 22006495 450 99666846520331620250723130322.0981-9500-36-210.1007/978-981-95-0036-9(CKB)39766994900041(DE-He213)978-981-95-0036-9(MiAaPQ)EBC32269965(Au-PeEL)EBL32269965(EXLCZ)993976699490004120250723d2025 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierAdvanced Intelligent Computing Technology and Applications 21st International Conference, ICIC 2025, Ningbo, China, July 26–29, 2025, Proceedings, Part XXVIII /edited by De-Shuang Huang, Wei Chen, Yijie Pan, Haiming Chen1st ed. 2025.Singapore :Springer Nature Singapore :Imprint: Springer,2025.1 online resource (XXI, 511 p. 181 illus., 169 illus. in color.) Lecture Notes in Bioinformatics,2366-6331 ;15869981-9500-35-4 -- Machine Learning. -- Identifying spatial domains by fusing spatial transcriptomics and histological images through contrastive learning. -- A Medical Image Segmentation Network Based on Adaptive Feature Attention and Multi-scale Feature Extraction. -- A Preliminary Exploration of Children Autism Spectrum Disorder Detection Based on Environmental Variables. -- A Novel Approach for Drug-Drug Interaction Prediction: Utilizing Enhanced Graph Convolutional Networks and 3D Chemical Structures. -- BMC-Net: A Framework for IDH Genotyping of Gliomas Based on Bi directional Mamba Sequences. -- Integrating Radiomics and Deep Learning for Enhanced Three-Dimensional Meningioma Grading. -- SeqAlignXGBoost: Sequence Alignment and Feature Selection for m1A Modification Site Identification. -- Leveraging Large Language Models for Early Diagnosis of Inherited Metabolic Diseases Evaluation and Optimization. -- HAP-MT: Alternating Perturbation Strategies Across Data and Feature Levels in semi-supervised medical image segmentation. -- MTSN: A Multi-granularity Temporal Sleep Network for Sleep Apnea Detection. -- Fre-CrossFormer: Utilizing Frequency Domain Cross Attention for Accurate Noninvasive Blood Pressure Measurement. -- A Latent Diffusion Model for Molecular Optimization. -- BAGP: A Biomedical Entity-Relation Joint Extraction Model Integrating Adversarial Training with Biaffine Attention. -- A Contrastive Learning Framework for Alzheimer's Disease Classification (CLFAD). -- Intelligent Computing in Computer Vision. -- ABANet: Adaptive Boundary Aggregation Network for Medical Image Segmentation. -- SC3L-Net: Semi-supervised Retinal Layer Segmentation via Cross-task Consistency and Contrastive Learning. -- Interactive Calibration Learning and Atrous Pyramid Spatial-Channel Attention for Semi-supervised Medical Image Segmentation. -- MVCA-UNet: A Multi-scale Visual Convolutional Attention Architecture for Skin Lesion Segmentation. -- MSFM-UNet: Multi-Scan and Frequency Domain Mamba UNet for Medical Image Segmentation. -- APG-UNet: A Lightweight and Efficient Network for Medical Image Segmentation. -- DAMF-UNet: The Dual Attention Multi-Scale Information Fusion Network for Medical Image Segmentation. -- Multi-rater Medical Image Segmentation via a Mixture-of-experts Training. -- BIRF-SDG: Band Importance Aware Random Frequency Filter Based Single-source Domain Generalization for Retinal Vessel Segmentation. -- Genap: Generalizing Across the Augmentation Gap in Medical Image Segmentation Using Single-Source Domain. -- BEA-UNet: Boundary-enhanced Dual Attention UNet for Medical Image Segmentation. -- FreqSAM2-UNet: Adapter Fine-tuning Frequency-Aware Network of SAM2 for Universal Medical Segmentation. -- LDMWSeg: Latent Diffusion Models for Weakly Supervised Medical Image Segmentation. -- FSISNet: Exploring Mamba and Transformer for Polyp Segmentation. -- Mamba Based Feature Extraction and Adaptive Multilevel Feature Fusion for 3D Tumor Segmentation from Multi-modal Medical Image. -- Diakd: A Source-Free Domain Adaptation Method for Medical Image Segmentation Based on Domain-Aware Indicator and Adaptive Knowledge Distillation. -- KD-MedSAM: Lightweight Knowledge Distillation of Segment Anything Model for Multi-modality Medical Image Segmentation. -- Uncertainty-guided Feature Learning Network for Accurate Medical Image Segmentation. -- Transformer-Based Multi-label Protein Subcellular Localization Prediction. -- Gaze-and-Machine Dual-driven Attention Fusion Network for Medical Image Classification. -- Enhanced FCM for Medical Image Segmentation Using Superpixel and Convolutional Autoencoder. -- ARB-ABD: Robust Medical Image Segmentation with Adversarial and Boundary Enhancement. -- Attentional feature fusion for pulmonary X-ray image classification. -- Co-Training with Soft-Hard Pseudo-Labels for Semi-Supervised Liver Tumor Segmentation. -- Multimodal Integration Based on Weak Alignment for Rectal Tumor Grading. -- A Unified Framework for Few-Shot Medical Image Classification via Multi Agent Description Generation and Refined Contrastive Learning. -- WCG-Net: A Multi-Frequency Perception Network for Medical Image Segmentation. -- TransEdge: Leveraging Transformer and EfficientKAN with Edge Sensitivity for Advanced Medical Image Segmentation.The 20-volume set LNCS 15842-15861, together with the 4-volume set LNAI 15862-15865 and the 4-volume set LNBI 15866-15869, constitutes the refereed proceedings of the 21st International Conference on Intelligent Computing, ICIC 2025, held in Ningbo, China, during July 26-29, 2025. The 1206 papers presented in these proceedings books were carefully reviewed and selected from 4032 submissions. They deal with emerging and challenging topics in artificial intelligence, machine learning, pattern recognition, bioinformatics, and computational biology. .Lecture Notes in Bioinformatics,2366-6331 ;15869Computational intelligenceComputer networksMachine learningApplication softwareComputational IntelligenceComputer Communication NetworksMachine LearningComputer and Information Systems ApplicationsComputational intelligence.Computer networks.Machine learning.Application software.Computational Intelligence.Computer Communication Networks.Machine Learning.Computer and Information Systems Applications.006.3Huang De-Shuangedthttp://id.loc.gov/vocabulary/relators/edtChen Weiedthttp://id.loc.gov/vocabulary/relators/edtPan Yijieedthttp://id.loc.gov/vocabulary/relators/edtChen Haimingedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK996668465203316Advanced Intelligent Computing Technology and Applications4410353UNISA