* indicates that this line can be assigned as a paper's topic.
| 1: | Audio and Acoustic Signal Processing | ||
| 1.1: | Modeling and Analysis of Acoustic Environments | ||
| 1.1.1*: | Acoustic system modeling | ||
| 1.1.2*: | Acoustic system and room response measurement, modeling and simulation | ||
| 1.1.3*: | Room geometry inference and reflector localization | ||
| 1.1.4*: | Reverberation time and direct-to-reverberant ratio estimation | ||
| 1.2: | Detection and Classification of Acoustic Scenes and Events | ||
| 1.2.1*: | Acoustic scene classification and detection | ||
| 1.2.2*: | Acoustic event detection and classification | ||
| 1.2.3*: | Environmental audio analysis | ||
| 1.2.4*: | Bioacoustics and medical acoustics | ||
| 1.3: | Auditory Modeling and Hearing Instruments | ||
| 1.3.1*: | Human audition and psychoacoustics, binaural hearing | ||
| 1.3.2*: | Signal processing in hearing aids and cochlear implants | ||
| 1.3.3*: | Computational auditory scene analysis | ||
| 1.3.4*: | Perceptual and psychophysical models of audio algorithms and systems | ||
| 1.4: | Acoustic Sensor Array Processing | ||
| 1.4.1*: | Source localization and tracking, SLAM, and array calibration | ||
| 1.4.2*: | Microphone array design and beamforming methods | ||
| 1.4.3*: | Multi-microphone speech enhancement | ||
| 1.4.4*: | Distributed and ad-hoc microphone arrays (acoustic sensor networks) | ||
| 1.4.5*: | Deep-learning based multi-microphone acoustic signal processing | ||
| 1.5: | Active Noise Control, Echo Reduction and Feedback Reduction | ||
| 1.5.1*: | Active noise cancellation and suppression | ||
| 1.5.2*: | Acoustic echo cancellation, double-talk detection | ||
| 1.5.3*: | Acoustic feedback cancellation and suppression | ||
| 1.6: | System Identification and Reverberation Reduction | ||
| 1.6.1*: | SIMO and MIMO acoustic system identification | ||
| 1.6.2*: | Single-microphone dereverberation | ||
| 1.6.3*: | Multi-microphone dereverberation and channel equalization | ||
| 1.6.4*: | Deep-learning based dereverberation (inc. generative or adversarial methods) | ||
| 1.7: | Audio and Speech Source Separation | ||
| 1.7.1*: | Single-microphone separation | ||
| 1.7.2*: | Multi-microphone separation | ||
| 1.7.3*: | NMF-based separation | ||
| 1.7.4*: | Deep-learning-based separation (inc. generative or adversarial methods) | ||
| 1.8: | Signal Enhancement and Restoration | ||
| 1.8.1*: | Single microphone noise estimation and speech enhancement | ||
| 1.8.2*: | Deep-learning based signal enhancement and restoration (inc. generative or adversarial methods) | ||
| 1.8.3*: | Bandwidth expansion | ||
| 1.8.4*: | Audio denoising, equalization and clipping restoration | ||
| 1.9*: | Quality and Intelligibility Measures | ||
| 1.10: | Spatial Audio Recording and Reproduction | ||
| 1.10.1*: | Analysis and synthesis of sound fields | ||
| 1.10.2*: | Spatial sound reproduction (inc. wavefield synthesis, ambisonics, multipoint synthesis, binaural synthesis) | ||
| 1.10.3*: | Loudspeaker array processing, crosstalk cancellation, loudspeaker equalization and room compensation | ||
| 1.10.4*: | Measurement and modeling of head-related transfer functions | ||
| 1.10.5*: | Artificial reverberation algorithms | ||
| 1.11: | Audio for Multimedia and Audio Processing Systems | ||
| 1.11.1*: | Audio and speech modeling, coding and transmission | ||
| 1.11.2*: | Joint audio-video processing | ||
| 1.11.3*: | Virtual/augmented reality | ||
| 1.11.4*: | User interfaces for audio editing/processing/analysis/synthesis | ||
| 1.11.5*: | Deep-learning based (inc. generative or adversarial) methods for audio coding/processing and multimedia | ||
| 1.12: | Music Signal Analysis, Processing and Synthesis | ||
| 1.12.1*: | Models and representation for music signals | ||
| 1.12.2*: | Pitch and multi-pitch estimation | ||
| 1.12.3*: | Source separation of music and vocal signals | ||
| 1.12.4*: | Melody, note, chord, key, and rhythm estimation and detection | ||
| 1.12.5*: | Modeling of analog audio systems and audio effects | ||
| 1.12.6*: | Deep-learning based music signal analysis, processing and synthesis | ||
| 1.13*: | Music Information Retrieval and Music Language Processing | ||
| 1.13.1: | Transcription, annotation and structure analysis | ||
| 1.13.2*: | Content-based music analysis, classification and processing | ||
| 1.13.3*: | Symbolic music processing, symbolic music corpora and grammar-based models | ||
| 1.13.4*: | Music composition, improvisation and score following | ||
| 1.13.5*: | Deep-learning based music information retrieval | ||
| 1.13.6*: | Fingerprinting and data mining | ||
| 1.14: | Audio security | ||
| 1.14.1*: | Audio security and audio privacy | ||
| 1.14.2*: | Audio analysis for forensics | ||
| 1.14.3*: | Audio watermarking and data hiding in audio streams | ||
| 1.14.4*: | Acoustic event detection for forensics | ||
| 2: | Bio Imaging and Signal Processing | ||
| 2.1*: | Medical imaging | ||
| 2.1.1*: | Image formation | ||
| 2.1.2*: | Reconstruction and restoration | ||
| 2.1.3*: | Computed tomography (CT, PET or SPECT) | ||
| 2.1.4*: | Biomedical Imaging | ||
| 2.1.5*: | Magnetic resonance imaging | ||
| 2.1.6*: | Ultrasound imaging | ||
| 2.2: | Medical image analysis | ||
| 2.2.1*: | Segmentation | ||
| 2.2.2*: | Registration | ||
| 2.2.3*: | Feature extraction and classification | ||
| 2.3: | Bioimaging and microscopy | ||
| 2.3.1*: | Cellular and molecular imaging | ||
| 2.3.2*: | Deconvolution and inverse problems | ||
| 2.3.3*: | Segmentation and analysis | ||
| 2.3.4*: | Tracking and motion analysis | ||
| 2.4: | Biomedical signal processing | ||
| 2.4.1*: | Physiological signals (ECG, EEG, ...) | ||
| 2.4.2*: | Detection and estimation | ||
| 2.4.3*: | Feature extraction and classification | ||
| 2.4.4*: | Multi-channel processing | ||
| 2.5: | Bioinformatics | ||
| 2.5.1*: | Genomics and proteomics | ||
| 2.5.2*: | Computational biology and biological networks | ||
| 3: | Design and Implementation of Signal Processing Systems | ||
| 3.1*: | Algorithm and architecture co-optimization | ||
| 3.2*: | Compilers and tools for DSP implementation | ||
| 3.3*: | DSP algorithm implementation in hardware and software | ||
| 3.4*: | Low-power signal processing techniques and architectures | ||
| 3.5*: | Programmable and reconfigurable DSP architectures | ||
| 3.6*: | System-on-chip architectures for signal processing | ||
| 3.7*: | Hardware security | ||
| 3.8: | Design and Implementation of emerging signal processing systems | ||
| 3.8.1*: | Machine learning | ||
| 3.8.2*: | Neuromorphic computing | ||
| 3.8.3*: | Internet of things | ||
| 3.8.4*: | Big data | ||
| 3.8.5*: | Signal Processing for storage | ||
| 3.9: | Signal processing for emerging applications | ||
| 3.9.1*: | Autonomous vehicles | ||
| 3.9.2*: | Financial technology (FinTech) | ||
| 3.9.3*: | Virtual Reality/Augmented Reality | ||
| 3.9.4*: | Drones | ||
| 4: | Image, Video, and Multidimensional Signal Processing | ||
| 4.1: | Image/Video Coding | ||
| 4.1.1*: | Still Image Coding | ||
| 4.1.2*: | Video Coding | ||
| 4.1.3*: | Stereoscopic and 3-D Coding | ||
| 4.1.4*: | Distributed Source Coding | ||
| 4.1.5*: | Image/Video Transmission | ||
| 4.2: | Image/Video Processing | ||
| 4.2.1*: | Image Filtering | ||
| 4.2.2*: | Restoration | ||
| 4.2.3*: | Enhancement | ||
| 4.2.4*: | Image Segmentation | ||
| 4.2.5*: | Video Segmentation and Tracking | ||
| 4.2.6*: | Morphological Processing | ||
| 4.2.7*: | Stereoscopic and 3-D Processing | ||
| 4.2.8*: | Image Feature Extraction | ||
| 4.2.9*: | Image Analysis | ||
| 4.2.10*: | Video Feature Extraction | ||
| 4.2.11*: | Video Analysis | ||
| 4.2.12*: | Modeling | ||
| 4.2.13*: | Biometrics | ||
| 4.2.14*: | Interpolation and Super-resolution | ||
| 4.2.15*: | Motion Detection and Estimation | ||
| 4.3: | Image Formation | ||
| 4.3.1*: | Remote Sensing Imaging | ||
| 4.3.2*: | Geophysical and Seismic Imaging | ||
| 4.3.3*: | Optical Imaging | ||
| 4.3.4*: | Synthetic-Natural Hybrid Image Systems | ||
| 4.4: | Image Scanning, Display, and Printing | ||
| 4.4.1*: | Scanning and Sampling | ||
| 4.4.2*: | Quantization and Halftoning | ||
| 4.4.3*: | Color Reproduction | ||
| 4.4.4*: | Image Representation and Rendering | ||
| 4.4.5*: | Display and Printing Systems | ||
| 4.4.6*: | Image Quality Assessment | ||
| 4.5: | Image/Video Storage, Retrieval | ||
| 4.5.1*: | Image and Video Databases | ||
| 4.5.2*: | Image Indexing and Retrieval | ||
| 4.5.3*: | Video Indexing, Retrieval and Editing | ||
| 5: | Information Forensics and Security | ||
| 5.1: | Watermarking and Steganography | ||
| 5.1.1*: | Content transforms | ||
| 5.1.2*: | Theoretical watermarking models | ||
| 5.1.3*: | Watermark embedding/detection algorithms | ||
| 5.1.4*: | Perceptual modeling and watermark shaping | ||
| 5.1.5*: | Watermark resynchronization techniques | ||
| 5.1.6*: | Watermark benchmarking and security analysis | ||
| 5.1.7*: | Steganography | ||
| 5.1.8*: | Steganalysis | ||
| 5.2: | Passive Forensic Analysis | ||
| 5.2.1*: | Sensor and channel forensics | ||
| 5.2.2*: | Signal processing forensics | ||
| 5.2.3*: | Behavior and social interactions forensics | ||
| 5.2.4*: | Communications forensics | ||
| 5.2.5*: | Anti-forensics and countermeasures | ||
| 5.2.6*: | Fusion and cross-references | ||
| 5.2.7*: | Benchmarking | ||
| 5.2.8*: | Forensic protocols | ||
| 5.3: | Biometrics | ||
| 5.3.1*: | Biometric sensor design | ||
| 5.3.2*: | Feature extraction algorithms | ||
| 5.3.3*: | Feature matching and fusion techniques | ||
| 5.3.4*: | Biometric security and privacy | ||
| 5.3.5*: | Databases and performance evaluation | ||
| 5.4: | Multimedia Content Hash | ||
| 5.4.1*: | Global content hash functions | ||
| 5.4.2*: | Local features detectors | ||
| 5.4.3*: | Local features descriptors | ||
| 5.4.4*: | Nearest neighbor search algorithms | ||
| 5.4.5*: | Benchmarking and reference datasets | ||
| 5.4.6*: | Security analysis | ||
| 5.5: | Communications and Physical Layer Security | ||
| 5.5.1*: | Jamming and anti-jamming | ||
| 5.5.2*: | Covert or stealthy communication | ||
| 5.5.3*: | Secret key extraction from channels | ||
| 5.5.4*: | Security and trust in communications | ||
| 5.5.5*: | Threats and attacks analysis | ||
| 5.6: | Information Theoretic Security | ||
| 5.6.1*: | Security over channels: wire-tap, broadcast, multiple access, etc | ||
| 5.6.2*: | Data security and cryptography | ||
| 5.6.3*: | Secret key generation, sharing, distribution | ||
| 5.6.4*: | Privacy and trust | ||
| 5.6.5*: | Shannon theory | ||
| 5.7: | Signal Processing and Cryptography | ||
| 5.7.1*: | Multimedia encryption | ||
| 5.7.2*: | Signal processing in the encrypted domain | ||
| 5.7.3*: | Traitor tracing codes | ||
| 5.7.4*: | Visual secret sharing | ||
| 5.7.5*: | Side channel attacks | ||
| 5.8*: | Video- and Signal-based Surveillance | ||
| 5.8.1: | Sensor-centric processing | ||
| 5.8.2*: | Processing, detection, tracking & recognition | ||
| 5.8.3*: | Visualization and interaction concepts for surveillance systems | ||
| 5.8.4*: | Analytics, situation awareness and decision making | ||
| 5.8.5*: | Security and privacy | ||
| 5.9: | Security-related applications | ||
| 5.9.1*: | Surveillance and intelligence gathering | ||
| 5.9.2*: | Access control systems | ||
| 5.9.3*: | Content protection, identification and monitoring | ||
| 5.9.4*: | Content authentication and tamper detection | ||
| 5.9.5*: | Cloud and distributed computing systems | ||
| 5.9.6*: | Smart grid and power/energy systems | ||
| 5.9.7*: | Social media and network systems | ||
| 5.9.8*: | Privacy enhancing technologies | ||
| 5.9.9*: | Crime scene analysis | ||
| 5.9.10*: | Signal processing for secure documents | ||
| 6: | Industry DSP Technology | ||
| 6.1: | Emerging Signal Processing Applications | ||
| 6.1.1*: | Industrial IoT Applications | ||
| 6.1.2*: | Display, Cameras and 3D Technology | ||
| 6.1.3*: | Applications of Social Signal Processing | ||
| 6.1.4*: | Green Technologies for Signal Processing | ||
| 6.1.5*: | DSP for Smart Devices | ||
| 6.1.6*: | Cloud/Fog/Edge Computing and Services | ||
| 6.1.7*: | Signal Processing for Mobile and Embedded Application | ||
| 6.1.8*: | Business Intelligence and Big Data Applications | ||
| 6.1.9*: | Digital and Software RF Processing | ||
| 6.1.10*: | Terahertz Technology and Signal Processing | ||
| 6.1.11*: | Blockchain Applications | ||
| 6.2: | DSP Tools and Rapid Prototyping | ||
| 6.2.1*: | DSP Simulation Tools | ||
| 6.2.2*: | Rapid Prototyping and Languages | ||
| 6.2.3*: | DSP Libraries | ||
| 6.2.4*: | Operating Systems | ||
| 6.2.5*: | Programming Models and Languages | ||
| 6.3: | Communication Technologies | ||
| 6.3.1*: | Cellular and Satellite Telephony | ||
| 6.3.2*: | Data Communications and Networking | ||
| 6.3.3*: | Software-Defined Radios | ||
| 6.3.4*: | Vocoders | ||
| 6.3.5*: | Smart Home and Smart City | ||
| 6.4: | Speech Processing Applications | ||
| 6.4.1*: | Speaker Recognition | ||
| 6.4.2*: | Speech Compression | ||
| 6.4.3*: | Speech Enhancement | ||
| 6.4.4*: | Speech Recognition | ||
| 6.4.5*: | Speech Synthesis | ||
| 6.5: | Multimedia and DTV Technologies | ||
| 6.5.1*: | DSP Implementations of Music, Speech, and Audio | ||
| 6.5.2*: | Image and Video Applications | ||
| 6.5.3*: | Standards and Format Conversions | ||
| 6.5.4*: | Internet and Teleconferencing | ||
| 6.6*: | Adaptive Interference Cancellation | ||
| 6.6.1*: | Smart Antennas | ||
| 6.6.2*: | Active Sound Reduction | ||
| 6.6.3*: | Acoustic and Electrical Noise and Echo Cancellation | ||
| 6.6.4*: | Hands-Free Telephony | ||
| 6.6.5*: | Smart and AI Speakers | ||
| 6.6.6*: | SP for Wearable Devices | ||
| 6.7: | Automotive Applications | ||
| 6.7.1*: | Intelligent Dashboards, Vehicles, and Highways (IVHS) | ||
| 6.7.2*: | Engine Management | ||
| 6.7.3*: | Route Planning and Tracking | ||
| 6.7.4*: | New Consumer Applications | ||
| 6.7.5*: | Power Systems and Motor Controls | ||
| 6.8: | Defense and Security Applications | ||
| 6.8.1*: | Optical Correlation | ||
| 6.8.2*: | Decluttering Target Identification and Tracking | ||
| 6.8.3*: | DSP-Based Cryptography, Stenography, and Watermarking | ||
| 6.8.4*: | Radar and Sonar | ||
| 6.9: | DSP Chips and Architectures | ||
| 6.9.1*: | Mixed Signal Processing | ||
| 6.9.2*: | Special-Purpose and FPGA DSPs | ||
| 6.9.3*: | Host-Based Signal Processing | ||
| 6.9.4*: | Multiprocessor Architectures | ||
| 6.9.5*: | Converge of Algorithm, Computing and Memory Access | ||
| 6.9.6*: | SP for Data Memory and Storage Applications | ||
| 6.10: | Other ITT Topics | ||
| 6.10.1*: | Biometrics | ||
| 6.10.2*: | Biomedical | ||
| 6.10.3*: | Wearable Technology | ||
| 6.10.4*: | SP Related Standard Proposals | ||
| 6.10.5*: | Quantum Computing and Communication | ||
| 6.10.6*: | Other Sub-Topics (Industry and Emerging Applications) | ||
| 7: | Machine Learning for Signal Processing | ||
| 7.1*: | Learning Theory and Modeling | ||
| 7.2*: | Bayesian Learning and Modeling | ||
| 7.3*: | Sequential learning; sequential decision methods | ||
| 7.4*: | Information-theoretic learning | ||
| 7.5*: | Neural networks and deep learning | ||
| 7.6*: | Graphical and kernel models | ||
| 7.7*: | Bounds on performance | ||
| 7.8*: | Source separation and independent component analysis | ||
| 7.9*: | Signal detection, Pattern Recognition and Classification | ||
| 7.10*: | Bioinformatics Applications | ||
| 7.11*: | Biomedical Applications and Neural Engineering | ||
| 7.12*: | Intelligent Multimedia and Web Processing | ||
| 7.13*: | Communications Applications | ||
| 7.14*: | Speech and Audio Processing Applications | ||
| 7.15*: | Image and Video Processing Applications | ||
| 7.16*: | Tensor and Structured Matrix Methods | ||
| 7.17*: | Machine learning for big data | ||
| 7.18*: | Large scale learning | ||
| 7.19*: | Dictionary learning, subspace and manifold learning | ||
| 7.20*: | Semi-supervised and unsupervised learning | ||
| 7.21*: | Active and reinforcement learning | ||
| 7.22*: | Learning from multimodal data | ||
| 7.23*: | Resource efficient machine learning | ||
| 7.24*: | Other Applications | ||
| 8: | Multimedia Signal Processing | ||
| 8.1: | Multimodal signal processing | ||
| 8.1.1*: | Joint processing/presentation of audio-visual and multimodal information | ||
| 8.1.2*: | Fusion/fission of sensor information or multimodal data | ||
| 8.1.3*: | Integration of media, art, and multimedia technology | ||
| 8.1.4*: | Analysis and feature extraction of multimodal data | ||
| 8.2: | Virtual reality and 3D imaging | ||
| 8.2.1*: | 2D and 3D graphics/geometry coding and animation | ||
| 8.2.2*: | 3D audio and video processing | ||
| 8.2.3*: | Point cloud processing | ||
| 8.2.4*: | Virtual reality and mixed-reality in networked environments | ||
| 8.3: | Big data and learning-based media processing | ||
| 8.3.1*: | Big data and cloud media processing | ||
| 8.3.2*: | Traditional learning-based media processing | ||
| 8.3.3*: | Deep learning-based media processing | ||
| 8.3.4*: | Multimedia data mining | ||
| 8.4: | Graph signal processing for multimedia | ||
| 8.4.1*: | Graph-based audio, image and video processing | ||
| 8.4.2*: | Prediction and learning in graphs for multimedia | ||
| 8.4.3*: | Applications for graph signal processing | ||
| 8.5: | Multimedia communications and networking | ||
| 8.5.1*: | Wireless and mobile multimedia communication | ||
| 8.5.2*: | Media streaming, media content distribution, and storage | ||
| 8.5.3*: | Quality of service provisioning | ||
| 8.5.4*: | Cross-layer design for multimedia communication | ||
| 8.5.5*: | Overlay, peer-to-peer, and peer-assisted networking for multimedia | ||
| 8.5.6*: | Home networking for multimedia | ||
| 8.5.7*: | Location-aware multimedia computing | ||
| 8.5.8*: | Multimedia sensor and ad hoc networks | ||
| 8.5.9*: | Media compression and related standardization activities | ||
| 8.5.10*: | Distributed source and source-channel coding | ||
| 8.5.11*: | Social network and media sharing | ||
| 8.6: | Multimedia human-machine interface and interaction | ||
| 8.6.1*: | Human perception modelling | ||
| 8.6.2*: | Modeling of multimodal perception | ||
| 8.6.3*: | Human-human and human-computer dialog | ||
| 8.6.4*: | Multimodal interfaces | ||
| 8.6.5*: | Brain-computer interfaces | ||
| 8.7: | Quality Assessment | ||
| 8.7.1*: | Subjective visual quality assessment | ||
| 8.7.2*: | Objective visual quality assessment | ||
| 8.7.3*: | Subjective auditory quality assessment | ||
| 8.7.4*: | Objective auditory quality assessment | ||
| 8.7.5*: | Evaluation of user experience, cross-modal assessment | ||
| 8.7.6*: | Standardization activities | ||
| 8.8: | Multimedia databases and digital libraries | ||
| 8.8.1*: | Visual indexing, analysis and representation | ||
| 8.8.2*: | Audio indexing, analysis and representation | ||
| 8.8.3*: | Content-based and context-based information retrieval | ||
| 8.8.4*: | Knowledge and semantics in media annotation and retrieval | ||
| 8.8.5*: | Fingerprinting and duplicate detection | ||
| 8.9: | Multimedia computing systems and applications | ||
| 8.9.1*: | Multimedia system design | ||
| 8.9.2*: | Distributed multimedia systems | ||
| 8.9.3*: | Entertainment and gaming | ||
| 8.9.4*: | e-Health and telemedicine | ||
| 8.9.5*: | IP video/web conferencing | ||
| 8.9.6*: | e-learning | ||
| 8.10: | Hardware and software for multimedia systems | ||
| 8.10.1*: | Multimedia hardware design | ||
| 8.10.2*: | Real-time multimedia systems | ||
| 8.10.3*: | Implementations on graphics processing units (GPUs) | ||
| 8.10.4*: | Implementations on general-purpose processors, multimedia processors, DSPs, multi-core processors | ||
| 8.10.5*: | Implementations in portable/wearable systems | ||
| 8.10.6*: | Power-aware systems for multimedia | ||
| 8.11: | Haptic technology and interaction | ||
| 8.11.1*: | Processing and rendering of haptic signals | ||
| 8.11.2*: | Compression and transmission of haptic signals | ||
| 8.11.3*: | Audio-visual-haptic environments | ||
| 8.11.4*: | Multimedia applications using haptics | ||
| 8.12*: | Perceptual and bio-inspired multimedia systems and signal processing | ||
| 8.12.1*: | Perceptual bio-inspired signal processing for multimedia | ||
| 8.12.2*: | Multimodal signal fusion in humans and animals | ||
| 8.12.3*: | Joint perceptual, bio-inspired and conventional multimedia signal processing | ||
| 8.13: | Other multimedia applications | ||
| 8.13.1*: | Multimedia authoring and composition | ||
| 8.13.3*: | Multimedia applications using Crowdsourcing | ||
| 8.13.4*: | Multimedia signal processing for robotics and automation | ||
| 8.13.5*: | Social, mobile, and Internet of things media Processing | ||
| 9: | Sensor Array and Multichannel Signal Processing | ||
| 9.1: | Sensor Array Processing | ||
| 9.1.1*: | Beamforming | ||
| 9.1.2*: | Physics-based sensor array processing | ||
| 9.1.3*: | Inverse methods | ||
| 9.1.4*: | Array calibration methods | ||
| 9.1.5*: | Synthetic aperture methods | ||
| 9.1.6*: | Signal detection and parameter estimation | ||
| 9.1.7*: | Direction-of-arrival estimation | ||
| 9.1.8*: | Source/target localization, classification, and tracking | ||
| 9.1.9*: | Source separation and channel identification | ||
| 9.2: | Adaptive Array Signal Processing | ||
| 9.2.1*: | Adaptive beamforming | ||
| 9.2.2*: | Space-time adaptive processing | ||
| 9.2.3*: | MIMO radar and waveform diversity | ||
| 9.2.4*: | Computational advances in array processing | ||
| 9.3: | Multi-channel Signal Processing | ||
| 9.3.1*: | Channel modeling and equalization | ||
| 9.3.2*: | Multi-channel transceiver design | ||
| 9.3.3*: | Sparsity structures in multichannel signal processing | ||
| 9.3.4*: | Multi-channel processing with non-wave based sensors | ||
| 9.3.5*: | Tensor-based signal processing for multi-sensor systems | ||
| 9.4: | Multi-antenna and Multi-channel Signal Processing for Communications | ||
| 9.4.1*: | MIMO systems and algorithms | ||
| 9.4.2*: | Space-time coding and decoding algorithms | ||
| 9.4.3*: | MIMO space-time code design and analysis | ||
| 9.4.4*: | Multi-user MIMO networks | ||
| 9.4.5*: | Array processing for wireless communications | ||
| 9.4.6*: | Multi-antenna/multi-channel processing for cognitive radios | ||
| 9.4.7*: | Massive MIMO array processing | ||
| 9.5: | Sensor and Relay Networks | ||
| 9.5.1*: | Sensor and relay network signal processing | ||
| 9.5.2*: | Network beamforming and coding | ||
| 9.5.3*: | Distributed processing and optimization, cooperative algorithms | ||
| 9.5.4*: | Data fusion and decision fusion from multiple sensor types | ||
| 9.5.5*: | Multi-Sensor processing for smart grid and energy systems | ||
| 9.5.6*: | Network agent activity monitoring | ||
| 9.5.7*: | Wireless acoustic sensor networks | ||
| 9.5.8*: | Graph signal processing for sensor networks | ||
| 9.6: | Applications of Sensor Array and Multi-channel Signal Processing | ||
| 9.6.1*: | Radar array processing | ||
| 9.6.2*: | Sonar array processing | ||
| 9.6.3*: | Microphone array processing | ||
| 9.6.4*: | Hyperspectral processing and unmixing | ||
| 9.6.5*: | Integrated multi-model sensing | ||
| 9.6.6*: | Super-resolution sensing and reconstruction | ||
| 9.6.7*: | Multi-channel imaging | ||
| 9.6.8*: | Multi-channel biological and medical modeling and processing | ||
| 9.6.9*: | Sensor array applications of compressive sensing | ||
| 9.6.10*: | Machine learning and fusion techniques | ||
| 9.6.11*: | Other applications of SAM signal processing | ||
| 10: | Signal Processing for Communications and Networking | ||
| 10.1: | Signal Transmission and Reception | ||
| 10.1.1*: | Signal detection, estimation, separation and equalization | ||
| 10.1.2*: | Channel modeling and estimation, training schemes | ||
| 10.1.3*: | Capacity and performance analysis/optimization | ||
| 10.1.4*: | Acquisition, synchronization and tracking | ||
| 10.1.5*: | Signal representation, modulation, coding and compression | ||
| 10.1.6*: | Joint source-channel coding and quantization | ||
| 10.1.7*: | Demodulation and decoding | ||
| 10.1.8*: | Compensation of hardware impairments | ||
| 10.1.9*: | Sparse signal processing for communications | ||
| 10.1.10*: | Ultra-reliable communications | ||
| 10.1.11*: | Low latency communications | ||
| 10.1.12*: | Millimeter wave (mmWave) and extremely high-frequency band | ||
| 10.2: | Communication Systems and Applications | ||
| 10.2.1*: | Multi-carrier, OFDM, and DMT communication | ||
| 10.2.2*: | CDMA and spread spectrum communication | ||
| 10.2.3*: | Ultra-wideband communication | ||
| 10.2.4*: | Telephone networks, DSL and powerline communication | ||
| 10.2.5*: | Applications involving signal processing for communication | ||
| 10.2.6*: | Underwater communication systems | ||
| 10.2.7*: | Free-space optical communication | ||
| 10.2.8*: | Physical layer security | ||
| 10.2.9*: | Energy harvesting in communication systems | ||
| 10.2.10*: | Machine-type communications and Internet of Things (IoT) | ||
| 10.2.11*: | Machine learning for communications and networking | ||
| 10.3: | MIMO and Multi-User MIMO Communications and Signal Processing | ||
| 10.3.1*: | MIMO precoder/decoder design, receiver algorithms | ||
| 10.3.2*: | MIMO channel estimation and equalization | ||
| 10.3.3*: | MIMO capacity and performance analysis | ||
| 10.3.4*: | Space-time coding | ||
| 10.3.5*: | MIMO multi-user and multi-access schemes | ||
| 10.3.6*: | Massive MIMO | ||
| 10.3.7*: | Random access and massive connectivity | ||
| 10.4: | Communication Networks | ||
| 10.4.1*: | Cooperative and cooordinated multi-cell techniques | ||
| 10.4.2*: | Interference management techniques | ||
| 10.4.3*: | Power and resource allocation | ||
| 10.4.4*: | Energy management | ||
| 10.4.5*: | Relaying and cooperative networks | ||
| 10.4.6*: | Spectrum sensing | ||
| 10.4.7*: | Cognitive radio and dynamic spectrum access | ||
| 10.4.8*: | Heterogeneous networks | ||
| 10.4.9*: | Ad-hoc networks | ||
| 10.4.10*: | Network coding | ||
| 10.4.11*: | Scheduling and queuing protocols | ||
| 10.4.12*: | Optimization of communication networks | ||
| 10.5: | Communication and sensing aspects of other networks | ||
| 10.5.1*: | Distributed estimation and consensus | ||
| 10.5.2*: | Collaborative signal processing | ||
| 10.5.3*: | Distributed channel and source coding, | ||
| 10.5.4*: | Collaborative signal processing for smart grid | ||
| 10.5.5*: | Computation, communication, and control for smart grid | ||
| 10.5.6*: | Communication/networking issues in social networks | ||
| 10.5.7*: | Computation, communication, and control for biological networks | ||
| 10.5.8*: | Network science | ||
| 11: | Signal Processing Theory and Methods | ||
| 11.1: | Sampling and Reconstruction | ||
| 11.1.1*: | Sampling theory and methods | ||
| 11.1.2*: | Quantization | ||
| 11.1.3*: | Compressed and nonuniform sampling | ||
| 11.1.4*: | Signal reconstruction, restoration, and enhancement | ||
| 11.2: | Signal and System Modeling and Estimation | ||
| 11.2.1*: | System and signal modeling: Theory, performance analysis | ||
| 11.2.2*: | System identification and approximation | ||
| 11.2.3*: | Non-stationary signals and time-varying systems | ||
| 11.2.4*: | Time-frequency and time-scale analysis | ||
| 11.3: | Statistical Signal Processing | ||
| 11.3.1*: | Detection theory and methods | ||
| 11.3.2*: | Estimation theory and methods | ||
| 11.3.3*: | Classification and pattern recognition | ||
| 11.3.4*: | Performance analysis and bounds | ||
| 11.3.5*: | Robust methods | ||
| 11.3.6*: | Signal separation methods | ||
| 11.3.7*: | Bayesian signal processing | ||
| 11.3.8*: | Tracking algorithms | ||
| 11.4: | Adaptive Signal Processing | ||
| 11.4.1*: | Adaptive filter analysis and design | ||
| 11.4.2*: | Adaptive processing on networks | ||
| 11.4.3*: | Applications of adaptive filters | ||
| 11.5: | Nonlinear Systems and Signal Processing | ||
| 11.5.1*: | Nonlinear systems and signal processing | ||
| 11.5.2*: | Polynomial and kernel methods for Signal Processing | ||
| 11.6: | Digital and Multirate Signal Processing | ||
| 11.6.1*: | Multiresolution analysis, filter banks, and wavelets | ||
| 11.6.2*: | Fast algorithms and transforms | ||
| 11.7: | Signal Processing Over Graphs | ||
| 11.7.1*: | Statistical approaches (models, etc.) | ||
| 11.7.2*: | Deterministic approaches (graph filtering, graph transforms, etc.) | ||
| 11.7.3*: | Graph representations and analysis | ||
| 11.7.4*: | Adaptation and learning over graphs | ||
| 11.8: | Sparsity-aware processing | ||
| 11.8.1*: | Sparse/low-dimensional signal recovery, parameter estimation and regression | ||
| 11.8.2*: | Structured matrix factorization, low-rank models, matrix completion | ||
| 11.8.3*: | Dictionary learning; subspace and manifold learning | ||
| 11.9: | Optimization Tools | ||
| 11.9.1*: | Convex optimization and relaxation | ||
| 11.9.2*: | Non-convex methods | ||
| 11.9.3*: | Sparse and distributed optimization | ||
| 11.10*: | Signal Processing on Networks | ||
| 11.10.1: | Distributed processing and optimization | ||
| 11.10.2*: | Social networks, social learning models | ||
| 11.10.3*: | Estimation, detection and learning on networks | ||
| 11.11*: | Quantum signal processing | ||
| 12: | Speech Processing | ||
| 12.1: | Speech Production | ||
| 12.1.1*: | Models of speech production | ||
| 12.1.2*: | Models of disordered speech | ||
| 12.1.3*: | Singing and properties of the musical voice | ||
| 12.2: | Speech Perception and Psychoacoustics | ||
| 12.2.1*: | Models of Speech Perception | ||
| 12.2.2*: | Hearing and Psychoacoustics | ||
| 12.2.3*: | Models of multimodal speech processing | ||
| 12.2.4*: | Hearing assistive technology for speech | ||
| 12.3: | Speech Analysis | ||
| 12.3.1*: | Spectral and other time-frequency analysis techniques | ||
| 12.3.2*: | Pitch/fundamental frequency analysis | ||
| 12.3.3*: | Timing/duration/speaking rate analysis | ||
| 12.3.4*: | Acoustic-phonetic features (e.g., formants etc.) | ||
| 12.3.5*: | Extraction of non-linguistic information (e.g., gender, emotion, etc.) | ||
| 12.3.6*: | Speaker physiological traits and voice quality | ||
| 12.3.7*: | Voice, speech, and language disorders | ||
| 12.3.8*: | Emotions and other non-verbal communication cues | ||
| 12.3.9*: | Sentiment analysis, opinion mining, and social signal processing | ||
| 12.4: | Speech Synthesis and Generation, including TTS | ||
| 12.4.1*: | Machine learning for speech synthesis | ||
| 12.4.2*: | Signal processing for speech synthesis | ||
| 12.4.3*: | Text processing for speech synthesis | ||
| 12.4.4*: | Systems, tools, and resources for speech synthesis | ||
| 12.4.5*: | Quality assessment/evaluation metrics in speech synthesis | ||
| 12.4.6*: | Prosody, emotion, and expression in speech synthesis | ||
| 12.4.7*: | Multilingual and cross-lingual speech synthesis | ||
| 12.4.8*: | Multi-modal speech synthesis | ||
| 12.4.9*: | Articulatory synthesis | ||
| 12.4.10*: | Voice transformation | ||
| 12.5: | Speech Coding | ||
| 12.5.1*: | Narrow-band and wide-band Speech Coding | ||
| 12.5.2*: | Theory and techniques for signal coding (e.g., waveform, transform) | ||
| 12.5.3*: | Unified speech and audio coding | ||
| 12.5.4*: | Speech transmission system | ||
| 12.5.5*: | Quality assessment/evaluation metrics (e.g., PESQ) in coding | ||
| 12.6: | Speech Enhancement | ||
| 12.6.1*: | Control and reduction of channel noise (e.g., reverb, room response) | ||
| 12.6.2*: | Perceptual enhancement of non-noisy speech | ||
| 12.6.3*: | Speech enhancement for humans with hearing impairments | ||
| 12.6.4*: | Non-acoustic microphones for enhancement | ||
| 12.6.5*: | Bandwidth expansion | ||
| 12.6.6*: | Speech denoising, extraction, and separation | ||
| 12.6.7*: | Multi channel enhancement (e.g., beamforming) | ||
| 12.6.8*: | Model for enhancement (e.g., matrix and tensor factorization) | ||
| 12.6.9*: | (Deep) learning based enhancement | ||
| 12.7: | Acoustic Modeling for Automatic Speech Recognition | ||
| 12.7.1*: | Feature Extraction | ||
| 12.7.2*: | Feature representation learning | ||
| 12.7.3*: | Novel network architecture | ||
| 12.7.4*: | Pronunciation modeling at the acoustic level | ||
| 12.7.5*: | State clustering and novel state definitions | ||
| 12.7.6*: | Prosody and other speech characteristics | ||
| 12.7.7*: | Dialect, accent, and idiolect at the acoustic level | ||
| 12.7.8*: | Sequence discriminative training | ||
| 12.7.9*: | Optimization methods | ||
| 12.7.10*: | Articulatory and physiological modeling | ||
| 12.7.11*: | Feature Transformation and Normalization | ||
| 12.8: | Robust Speech Recognition | ||
| 12.8.1*: | Features specifically for robust ASR (noise, channel, etc.) | ||
| 12.8.2*: | Data generation for matched/multi conditions | ||
| 12.8.3*: | Speech recognition with microphone arrays | ||
| 12.8.4*: | Model/backend based robust ASR | ||
| 12.8.5*: | Integration of speech enhancement and recognition | ||
| 12.8.6*: | Confidence measures and rejection | ||
| 12.8.7*: | Speech Activity/End-point/Barge-in detection | ||
| 12.9: | Speech Adaptation/Normalization | ||
| 12.9.1*: | Speaker adaptation and normalization (e.g., VTLN) | ||
| 12.9.2*: | Speaker adapted training methods | ||
| 12.9.3*: | Environmental/Channel adaptation | ||
| 12.9.4*: | Register, dialect, idiolect adaptation | ||
| 12.10: | General Topics in Speech Recognition | ||
| 12.10.1*: | Distributed Speech Recognition - Client/Server methods | ||
| 12.10.2*: | Computational efficiency for GPU/FPGA/other hardware usage | ||
| 12.10.3*: | Alternative Statistical/Machine Learning Methods (e.g., no HMMs, DNNs) | ||
| 12.10.4*: | Word spotting | ||
| 12.10.5*: | Metadata (e.g., emotion, speaker, accent) extraction from acoustics | ||
| 12.10.6*: | New algorithms, computational strategies, data- structures for ASR | ||
| 12.10.7*: | Multi-modal (such as audio-visual) speech recognition | ||
| 12.10.8*: | Corpora, annotation, and other resources | ||
| 12.10.9*: | Algorithm approximation methods in ASR | ||
| 12.10.10*: | Structured classification approaches | ||
| 12.11: | Multilingual Recognition and Identification | ||
| 12.11.1*: | Language ID and dialect identification | ||
| 12.11.2*: | Language, dialect, and accent identification | ||
| 12.11.3*: | Multilingual speech processing and recognition | ||
| 12.12: | Lexical Modeling and Access | ||
| 12.12.1*: | Pronunciation modeling at the lexical level | ||
| 12.12.2*: | Dialect, accent, and idiolect at the lexical level | ||
| 12.12.3*: | Multilingual aspects (e.g., unit selection) | ||
| 12.12.4*: | Automatic lexicon learning | ||
| 12.13: | Large Vocabulary Continuous Recognition/Search | ||
| 12.13.1*: | Decoding algorithms and implementation | ||
| 12.13.2*: | Lattices | ||
| 12.13.3*: | Multi-pass strategies | ||
| 12.13.4*: | Miscellaneous Topics | ||
| 12.13.5*: | End-to-end speech recognition | ||
| 12.14: | Speaker Recognition and Characterization | ||
| 12.14.1*: | Features and characteristics for speaker recognition | ||
| 12.14.2*: | Robustness to variable and degraded channels | ||
| 12.14.3*: | Speaker verification and identification | ||
| 12.14.4*: | Segmentation and clustering | ||
| 12.14.5*: | Speaker characterization and adaptation | ||
| 12.14.6*: | Corpora, annotation, evaluation, tools, and other resources | ||
| 12.14.7*: | Speaker diarization (time) and speaker localization (space) | ||
| 12.15: | Resource constrained speech recognition | ||
| 12.15.1*: | Low-power and reduced computation speech recognition | ||
| 12.15.2*: | ASR techniques for highly portable/mobile devices and embedded systems | ||
| 13: | Human Language Technology | ||
| 13.1: | Spoken Language Understanding | ||
| 13.1.1*: | Machine learning for understanding | ||
| 13.1.2*: | Semantic classification | ||
| 13.1.3*: | Entity extraction from speech | ||
| 13.1.4*: | Spoken document summarization and topic modeling | ||
| 13.1.5*: | Question/answering from speech | ||
| 13.1.6*: | Paralinguistic (emotion, age, gender, rate, etc.) and nonlinguistic information | ||
| 13.1.7*: | Detecting linguistic/discourse structure (e.g., disfluencies, sentence/topic boundaries, speech acts) | ||
| 13.2: | Human Spoken Language Acquisition, Development and Learning | ||
| 13.2.1*: | Language acquisition, development, and learning models | ||
| 13.2.3*: | Attributes and modeling techniques for assessment of language fluency | ||
| 13.2.3*: | Computer assisted pronunciation training, computer assisted language learning | ||
| 13.3: | Spoken and Multimodal Dialog Systems and Applications | ||
| 13.3.1*: | Spoken and multimodal dialog systems, applications, and architectures | ||
| 13.3.2*: | Machine learning and neural methods for dialog modeling | ||
| 13.3.3*: | Response Generation | ||
| 13.3.4*: | Evaluation metrics and standards | ||
| 13.3.5*: | Speech/voice-based human-computer interfaces (HCI) | ||
| 13.3.6*: | Applications | ||
| 13.4: | Machine Translation of Speech | ||
| 13.4.1*: | Speech processing for machine translation | ||
| 13.4.2*: | Para-linguistic and non-linguistic processing for machine translation | ||
| 13.4.3*: | Corpora, annotation, tools, and other resources | ||
| 13.4.4*: | Integration of speech and linguistic processing | ||
| 13.4.5*: | Machine translation for named entities | ||
| 13.4.6*: | Machine translation for code-switching speech | ||
| 13.4.7*: | Machine translation for multi-lingual dialogue | ||
| 13.4.8*: | Incremental processing for machine translation | ||
| 13.4.9*: | Evaluation metrics (e.g., BLEU) | ||
| 13.4.10*: | Systems and applications for MTS | ||
| 13.4.11*: | End-to-end machine translation of speech | ||
| 13.5: | Language Modeling, for Speech and HLT | ||
| 13.5.1*: | N-grams, their generalizations and smoothing methods. | ||
| 13.5.2*: | Language Model Adaptation | ||
| 13.5.3*: | Dialect, accent, and idiolect at the language level | ||
| 13.5.4*: | Discriminative LM Training Methods | ||
| 13.5.5*: | Representation learning approaches to LM, e.g., deep learning | ||
| 13.5.6*: | Other approaches to LMs | ||
| 13.6: | Speech Retrieval | ||
| 13.6.1*: | Spoken keyword search (search term is text) | ||
| 13.6.2*: | Spoken audio search or query-by-example spoken-term detection (search query is audio) | ||
| 13.6.3*: | Search and retrieval of spoken documents | ||
| 13.7: | Data mining and language resources | ||
| 13.7.1*: | Speech data mining | ||
| 13.7.2*: | General corpora, annotation, tools and other resources | ||
| 14: | Signal Processing for Big Data | ||
| 14.1: | Computational models and representations for big data | ||
| 14.1.1*: | Compressive sampling for big data | ||
| 14.1.2*: | Tensor factorization models for multi-way data | ||
| 14.1.3*: | Randomized linear algebra for big data | ||
| 14.1.4*: | Scalable (fast) | ||
| 14.1.5*: | Spectral decompositions for representation of big data | ||
| 14.1.6*: | Graph signal processing theory | ||
| 14.1.7*: | Transforms for graph signals | ||
| 14.1.8*: | Graph simplification and multi-resolution methods | ||
| 14.2: | Big data acquisition, storage, retrieval, interpretation | ||
| 14.2.1*: | Protocols for networked storage, indexing and retrieval | ||
| 14.2.2*: | Signal processing hardware and architectures for massive datasets | ||
| 14.2.3*: | Data resiliency to node failure | ||
| 14.2.4*: | Lossless data compression for massive datasets | ||
| 14.2.5*: | Lossy data compression for massive datasets | ||
| 14.2.6*: | Sketching, streaming, and real time data retrieval for time varying (spatio-temporal) data | ||
| 14.3: | Learning and inference with big data | ||
| 14.3.1*: | High dimensional spatio-temporal models | ||
| 14.3.2*: | Theoretical limits of high dimensional statistical inference | ||
| 14.3.3*: | Methods of anomaly/change detection with time varying big data | ||
| 14.3.4*: | Random matrix models and non-commutative information theory for big data | ||
| 14.3.5*: | Statistical modeling of heterogeneous data types | ||
| 14.3.6*: | Learning correlation networks for big data | ||
| 14.3.7*: | Learning Bayes networks for big data | ||
| 14.3.8*: | Deep learning for big data | ||
| 14.3.9*: | Non-parametric learning for big data | ||
| 14.3.10*: | Crowdsourcing/human computation for big data processing | ||
| 14.3.11*: | Stream Mining and Decision Making from Big Data | ||
| 14.3.12*: | Signal processing approaches to discovery/creativity/machine science | ||
| 14.4: | SP Methods for Big Data Analytics | ||
| 14.4.1*: | Visualization and summarization of big data | ||
| 14.4.2*: | Social media, recommendation systems and collaborative filtering | ||
| 14.4.3*: | Defense, intelligence and security | ||
| 14.4.4*: | Biology and medicine | ||
| 14.4.5*: | Astronomy and other physical sciences | ||
| 14.4.6*: | Urban informatics | ||
| 14.4.7*: | Big Data in Social sciences | ||
| 14.4.8*: | Business analytics, forensics and finance | ||
| 14.4.9*: | E-Teaching | ||
| 14.5: | Distributed signal and information processing for big data on networks | ||
| 14.5.1*: | Distributed processing over heterogeneous networks | ||
| 14.5.2*: | Distributed processing over time-varying networks | ||
| 14.5.3*: | Distributed algorithms for processing big data on networks | ||
| 14.5.4*: | Distributed detection and estimation | ||
| 14.5.5*: | Distributed control, optimization and learning | ||
| 14.5.6*: | Distributed storage, indexing and retrieval | ||
| 15: | Signal Processing for Internet of Things | ||
| 15.1: | IOT Communication and networking | ||
| 15.1.1*: | Signal processing issues in IoT with 5G and beyond | ||
| 15.1.2*: | mm-wave based IoT systems | ||
| 15.1.3*: | Wired and wireless M2M Communication and networking | ||
| 15.1.4*: | Energy-efficient communication | ||
| 15.1.5*: | Spectrum efficiency and management for IoT | ||
| 15.2: | IOT Information Processing | ||
| 15.2.1*: | Low-power, Distributed data processing on sensors | ||
| 15.2.2*: | Intelligent signal and information processing | ||
| 15.2.3*: | Data mining | ||
| 15.2.4*: | Information fusion | ||
| 15.2.5*: | Processing algorithms and theories for IoT | ||
| 15.2.6*: | Cross-layer processing for IoT | ||
| 15.3: | IOT Security | ||
| 15.3.1*: | Cyber security for IOT | ||
| 15.3.2*: | Reliability for IOT | ||
| 15.3.3*: | Privacy perseverance and trust for IOT | ||
| 15.4: | IOT Systems | ||
| 15.4.1*: | Human-cyber-physical interaction | ||
| 15.4.2*: | Sensing, networking and computing with smartphones and wearable devices | ||
| 15.4.3*: | Computing and processing platforms for IoT Implementations | ||
| 15.4.4*: | Programing and Developing Tools to enable IoT | ||
| 15.4.5*: | Quality of Experiences and Quality of Services in IoT applications | ||
| 15.4.6*: | Prototypes, test-beds and field trials on smart services and applications, e.g., Smart cities, intelligent transportation, building automation, assisted living, e-health, etc | ||
| 16: | Signal Processing (SP) Education | ||
| 16.1*: | Novel laboratory, computer-based, and distance teaching methods | ||
| 16.2*: | New SP pedagogy, including MOOCs and flipped classes | ||
| 16.3*: | New technologies in SP education | ||
| 16.4*: | SP across the engineering curriculum | ||
| 16.5*: | SP curriculum issues (early/late, simulation/real-time, theory/practice) | ||
| 16.6*: | Industry and SP education: Linking academic knowledge with industrial needs | ||
| 16.7*: | Educational practices on signal acquisition, conditioning, and other analog issues | ||
| 16.8*: | Teaching embedded systems and other hardware for processing signals | ||
| 16.9*: | SP outreach programs | ||
| 16.10*: | Education strategies to encourage participation of women and underrepresented students in SP careers | ||
| 16.11*: | SP tips and tricks | ||
| 17: | Computational Imaging | ||
| 17.1: | IMT Computational Imaging Methods and Models | ||
| 17.1.1*: | IMT-CIS Coded Image Sensing | ||
| 17.1.2*: | IMT-CST Compressed Sensing | ||
| 17.1.3*: | IMT-SIM Statistical Image Models | ||
| 17.1.4*: | IMT-SLM Sparse and Low Rank Models | ||
| 17.1.5*: | IMT-GIM Graphical Image Models | ||
| 17.1.6*: | IMT-LBM Learning-Based Models | ||
| 17.1.7*: | IMT-PIM Perceptual Image Models | ||
| 17.2: | CIF Computational Image Formation | ||
| 17.2.1*: | CIF-SBR Sparsity-Based Reconstruction | ||
| 17.2.2*: | CIF-SBI Statistically-Based Inversion | ||
| 17.2.3*: | CIF-MIF Multi-Image & Sensor Fusion | ||
| 17.2.4*: | CIF-OBI Optimization-based Inversion Methods | ||
| 17.2.5*: | CIF-MLI Machine Learning based Computational Image Formation | ||
| 17.3: | CIS Computational Imaging Systems | ||
| 17.3.1*: | CIS-CPH Computational Photography | ||
| 17.3.2*: | CIS-MIS Mobile Imaging | ||
| 17.3.3*: | CIS-PIS Pervasive Imaging | ||
| 17.3.4*: | CIS-HCC Human Centric Computing | ||
| 17.3.5*: | CIS-CMI Computational Microscopy | ||
| 17.3.6*: | CIS-SSI Spectral Sensing | ||
| 17.3.7*: | CIS-TIM Tomographic Imaging | ||
| 17.3.8*: | CIS-MRI Magnetic Resonance Imaging | ||
| 17.3.9*: | CIS-AIM Acoustic Imaging | ||
| 17.3.10*: | CIS-RIM Radar Imaging | ||
| 17.3.11*: | CIS-NCI Novel Computational Imaging Systems | ||
| 17.3.12*: | CIS-NLC Non-Linear Computational Imaging Systems | ||
| 17.4: | HSS Computational Imaging Hardware and Software | ||
| 17.4.1*: | HSS-HPC High-performance embedded computing systems | ||
| 17.4.2*: | HSS-BDC Big Data Computational Imaging | ||
| 17.4.3*: | HSS-HDD Integrated Hardware/Digital Design | ||
| 17.4.4*: | HSS-NSS Non-traditional Sensor Systems | ||