ISSPIT 2023 Paper Review Categories
* indicates that this line can be assigned as a paper's topic.
| BIO: | Biomedical Signal Processing | |||
| BIO.1*: | BIO-BCI - Brain/human-computer interfaces | |||
| BIO.2*: | BIO-PHY - Physiological signal processing (ECG, EEG, EMG) | |||
| BIO.3*: | BIO-BMI - Biological and medical imaging | |||
| SMDSP: | Sampling, Multirate Signal Processing and Digital Signal Processing | |||
| SMDSP.1*: | SMDSP-ASAL - Adaptive sensing algorithms | |||
| SMDSP.2*: | SMDSP-BANK - Filter bank design and theory | |||
| SMDSP.3*: | SMDSP-CPSL - Compressive and nonuniform sampling | |||
| SMDSP.4*: | SMDSP-MULT - Multirate processing and multiresolution methods | |||
| SMDSP.5*: | SMDSP-RECO - Algorithms for signal filtering, restoration, enhancement, and reconstruction | |||
| SMDSP.6*: | SMDSP-SAMP - Sampling, extrapolation, and interpolation | |||
| SMDSP.7*: | SMDSP-SAP - Sparsity-aware processing | |||
| SMDSP.8*: | SMDSP-TFSR - Time-frequency analysis and signal representation | |||
| MDS: | Multidimensional Signal Processing | |||
| MDS.1*: | MDS-ALGO - Algorithms and multidimensional transforms | |||
| MDS.2*: | MDS-APPL - Applications of multidimensional signal processing | |||
| MDS.3*: | MDS-FILT - Filtering | |||
| MDS.4*: | MDS-SMOD - Signal and system modeling and identification | |||
| MLR: | Machine Learning | |||
| MLR.1*: | MLR-APPL - Applications of machine learning | |||
| MLR.2*: | MLR-DEEP - Deep learning techniques | |||
| MLR.3*: | MLR-DFED - Distributed/federated learning | |||
| MLR.4*: | MLR-LEAR - Learning theory and algorithms | |||
| MLR.5*: | MLR-PRCL - Pattern recognition and classification | |||
| MLR.6*: | MLR-REI - Reinforcement learning | |||
| MLR.7*: | MLR-SLER - Sequential learning; sequential decision methods | |||
| MLR.8*: | MLR-SSUP - Self-supervised and semi-supervised learning | |||
| OTH: | Other Areas and Applications | |||
| OTH.1*: | OTH-BGDT - Big data | |||
| OTH.2*: | OTH-CPSY - Cyber-physical systems | |||
| OTH.3*: | OTH-EDU - Education | |||
| OTH.4*: | OTH-EMRG - Emerging topics | |||
| OTH.5*: | OTH-GSSP - Green and sustainable signal processing | |||
| OTH.6*: | OTH-IOT - Internet of things | |||
| OTH.7*: | OTH-IRO - Intelligent Robotics | |||
| OTH.8*: | OTH-AUS - Autonomous systems | |||
| OTH.9*: | OTH-QUAN - Quantum signal processing | |||
| RAS: | Radar and Sonar Signal Processing | |||
| RAS.1*: | RAS-DTCL - Target detection, classification | |||
| RAS.2*: | RAS-IMFR - Radar image formation and reconstruction | |||
| RAS.3*: | RAS-LCLZ - Source localization | |||
| RAS.4*: | RAS-SARI - Synthetic aperture radar/sonar and imaging | |||
| RAS.5*: | RAS-SORD - Sonar and radar signal processing | |||
| RAS.6*: | RAS-TRCK - Target tracking | |||
| SPC: | Signal Processing for Communications | |||
| SPC.1*: | SPC-CHAN - Channel characterization, modelling, estimation and equalization | |||
| SPC.2*: | SPC-CMPR - Signal representation, coding and compression | |||
| SPC.3*: | SPC-CROP - Cross-layer optimization | |||
| SPC.4*: | SPC-EAC - Energy aware communications | |||
| SPC.5*: | SPC-ML - Machine learning for communications | |||
| SPC.6*: | SPC-MIMO - Multiple-input multiple-output communication systems | |||
| SPC.7*: | SPC-OPTWC - Optical wireless communications | |||
| SPC.8*: | SPC-PHYS - Physical layer security | |||
| SSP: | Statistical Signal Processing | |||
| SSP.1*: | SSP-BSP - Bayesian signal processing | |||
| SSP.2*: | SSP-CLAS - Classification methods | |||
| SSP.3*: | SSP-HIER - Hierarchical models & tree structured signal processing | |||
| SSP.4*: | SSP-HOSM - Higher-order statistical methods | |||
| SSP.5*: | SSP-NPAR - Non-parametric methods | |||
| SSP.6*: | SSP-CNSP - Cyclostationary and Nonstationary signal processing | |||
| SSP.7*: | SSP-REST - Signal restoration | |||
| SSP.8*: | SSP-SSAN - Statistical signal analysis | |||
| HLT: | Human Language Technology | |||
| HLT.1*: | HLT-LANG - Language modelling | |||
| HLT.2*: | HLT-MTSW - Machine translation for spoken and written language | |||
| HLT.3*: | HLT-UNDE - Spoken language understanding and computational semantics | |||
| HLT.4*: | HLT-SDTM - Spoken document retrieval and text mining | |||
| HLT.5*: | HLT-LACL - Language acquisition and learning | |||
| HLT.6*: | HLT-MLMD - Machine learning methods | |||
| SPE: | Speech Processing | |||
| SPE.1*: | SPE-SPER - Speech perception and psychoacoustics | |||
| SPE.2*: | SPE-ANLS - Speech analysis | |||
| SPE.3*: | SPE-SYNT - Speech synthesis and generation | |||
| SPE.4*: | SPE-CODI - Speech coding | |||
| SPE.5*: | SPE-ENHA - Speech enhancement and separation | |||
| SPE.6*: | SPE-ROBU - Robust speech recognition | |||
| SPE.7*: | SPE-SPKR - Speaker recognition and characterization | |||
| AUD: | Audio and Acoustic Signal Processing | |||
| AUD.1*: | AUD-CLAS - Detection and classification of acoustic scenes and events | |||
| AUD.2*: | AUD-ASAP - Acoustic sensor array processing | |||
| AUD.3*: | AUD-SIRR - System identification and reverberation reduction | |||
| AUD.4*: | AUD-SEP - Audio and speech source separation | |||
| AUD.5*: | AUD-SEN - Signal enhancement and restoration | |||
| AUD.6*: | AUD-QIM - Quality and intelligibility measures | |||
| AUD.7*: | AUD-SARR - Spatial audio recording and reproduction | |||
| AUD.8*: | AUD-AMCT - Audio and speech modeling, coding and transmission | |||
| AUD.9*: | AUD-MSP - Music signal analysis, processing and synthesis | |||
| AUD.10*: | AUD-MIR - Music information retrieval and music language processing | |||
| AUD.11*: | AUD-SEC - Audio security | |||
| CNS: | Communications, Networking, and Sensing | |||
| CNS.1*: | CNS-DMS - Distributed monitoring and sensing | |||
| CNS.2*: | CNS-SPDCN - Signal processing for distributed communications and networking | |||
| CNS.3*: | CNS-NSPRA - Optimal network signal processing and resource allocation | |||
| CNS.4*: | CNS-CCMCT - Cooperative and coordinated multi-cell techniques | |||
| CNS.5*: | CNS-DSCD - Distributed source and channel decoding | |||
| CNS.6*: | CNS-LLC - Low latency communications | |||
| CNS.7*: | CNS-PHYL - Physical layer issues | |||
| SMR: | Image & Video Sensing, Modeling, and Representation | |||
| SMR.1*: | SMR-SEN - Image & video sensing and acquisition | |||
| SMR.2*: | SMR-SMD - Statistical-model based methods | |||
| SMR.3*: | SMR-STM - Structural-model based methods | |||
| SMR.4*: | SMR-REP - Image & video representation | |||
| SMR.5*: | SMR-HPM - Perception and quality models for images & video | |||
| TEC: | Image & Video Processing Techniques | |||
| TEC.1*: | TEC-FIL - Linear and nonlinear filtering of images & video | |||
| TEC.2*: | TEC-MRS - Multiresolution processing of images & video | |||
| TEC.3*: | TEC-RST - Restoration and enhancement | |||
| TEC.4*: | TEC-ISR - Interpolation, super-resolution, and mosaicing | |||
| TEC.5*: | TEC-FOR - Formation and reconstruction | |||
| TEC.6*: | TEC-BIP - Biomedical and biological image processing | |||
| TEC.7*: | TEC-MLI - Machine learning for image processing | |||
| COM: | Image & Video Communications | |||
| COM.1*: | COM-IVPN - Image and video processing over networks | |||
| COM.2*: | COM-MST - Multimedia streaming and transport | |||
| COM.3*: | COM-SMCN - Social media computing and networking | |||
| COM.4*: | COM-COD - Lossy and lossless coding of images & video | |||
| COM.5*: | COM-MMC - Image & video multimedia communications | |||
| ELI: | Electronic Imaging | |||
| ELI.1*: | ELI-SDP - Image scanning and capture | |||
| ELI.2*: | ELI-COL - Color and multispectral imaging | |||
| ELI.3*: | ELI-DOC - Scanned document analysis, processing, and coding | |||
| ARS: | Image & Video Analysis, Synthesis, and Retrieval | |||
| ARS.1*: | ARS-IVA - Image & video analysis | |||
| ARS.2*: | ARS-IIU - Image & video interpretation and understanding | |||
| ARS.3*: | ARS-SRE - Image & video storage and retrieval | |||
| ARS.4*: | ARS-SRV - Image & video synthesis, rendering, and visualization | |||
| 3D: | Image & Three-Dimensional Image and Video Processing | |||
| 3D.1*: | 3D-LFI - Light-field Image Processing and Compression | |||
| 3D.2*: | 3D-VID - 3D Image and Video Analysis and Compression | |||
| 3D.3*: | 3D-STE - Stereoscopic and multiview processing and display | |||
| 3D.4*: | 3D-PCP - Point cloud processing | |||
| 3D.5*: | 3D-AVR - Image and video processing augmented and virtual reality | |||
| CIF: | Computational Image Formation | |||
| CIF.1*: | CIF-SBR - Sparsity-based reconstruction | |||
| CIF.2*: | CIF-SBI - Statistically-based inversion | |||
| CIF.3*: | CIF-MIF - Multi-image & sensor fusion | |||
| CIF.4*: | CIF-OBI - Optimization-based inversion methods | |||
| CIF.5*: | CIF-MLI - Machine learning based computational image formation | |||
| CIF.6*: | CIF-IRR - Image reconstruction and restoration | |||
| IFS: | Information Forensics and Security | |||
| IFS.1*: | IFS-ADP - Anonymization and data privacy | |||
| IFS.2*: | IFS-BIO - Biometrics | |||
| IFS.3*: | IFS-MMF - Multimedia forensics | |||
| IFS.4*: | IFS-MMH - Multimedia content hash | |||
| IFS.5*: | IFS-SUR - Surveillance | |||
| IFS.6*: | IFS-WAT - Watermarking and data hiding | |||
| HCM: | Human Centric Multimedia | |||
| HCM.1*: | HCM-MHMI - MHMI multimodal human-machine interfaces and interaction | |||
| HCM.2*: | HCM-MPIM - MPIM multimodal perception, integration, and multisensory fusion | |||
| HCM.3*: | QAUE - Subjective and objective quality assessment, and user experience | |||
| ME: | Multimedia Environments | |||
| ME.1*: | ME-AVEW - Audio-visual-haptic environments and workspaces | |||
| ME.2*: | ME-MTAC - Multimodal telepresence and collaboration | |||
| ME.3*: | ME-VAAR - Virtual and augmented reality | |||