All tutorials are in the afternoon of Jan. 11; starting at 2:00PM, 3:45PM and 5:30PM, each tutorial should be 1.5 hours maximum.
STMicroelectronics
The tutorial will cover different aspects of wheeled robot navigation strategies. Starting from the simple task of obstacle avoidance, we will explore more complex solutions, i.e., point-to-point navigation, follow-me, etc. Building blocks, such as odometry and SLAM (Simultaneous Localization And Mapping) will be analyzed too. Different kinds of sensors will be considered, such as: Lidar, ToF (Time of Flight), IMU, image and depth cameras. We will start from simple state of the art algorithms to underline more complex ones using machine learning, sensor data fusion and deep learning. Are robot navigation and SLAM completely solved problems?
Giuseppe Spampinato received the Degree in Computer Science from the University of Catania (Italy) in 1996. From 1999 he is working in SRA (System Research Application) Catania Lab of STMicroelectronics as a system engineer, and he also serves as technical staff member. He is author of various papers and patents in different fields: image and video signal processing, computer vision, automotive and robotics. He is presenter and reviewer at different international conferences, obtaining also best paper awards. He is an IEEE senior member, and he is a contributor and voting member of IEEE Ethics of Autonomous and Intelligent Systems Standardization. He is an active member of IEEE "Audio/Video Systems and Signal Processing (AVS)" Technical Stream Committee, where he also serves as reviewer.
Kyungpook National University, Korea
Speech emotion recognition (SER) is a crucial domain in affective computing, focused on detecting and classifying emotional states from speech signals, which vary dynamically over time. These signals contain complex relationships between features at different time scales, which can effectively indicate a speaker’s emotional state. Despite advancements in SER, the field is hindered by the scarcity of labeled data, a significant challenge given the data-intensive nature of deep learning models. This scarcity leads to small, imbalanced datasets and limits model generalization. Current strategies to address these issues include feature selection, data augmentation, domain adaptation, and data fusion. However, these methods often result in high computational costs, especially when deployed on low-resource devices, and suffer from problems such as non-affective sample generation, domain mismatches, data sparsity, and loss of emotionally rich information. This study explores approaches for both acoustic and bimodal SER that optimize the use of limited datasets through concurrent feature learning. By leveraging the inherent spatial, temporal, and semantic relationships among features, and including grammatical tendencies in the case of bimodal SER, we propose a framework for locally and globally learning these representations concurrently. This enhances the effectiveness and robustness of SER systems while maintaining low computational demands. We introduce attention-based multi-learning approaches that utilize emotionally rich features for both acoustic and bimodal SER, demonstrating commendable performance. Comparative analyses with similar existing approaches are also presented, highlighting the advantages of our proposed methods.
Dong Seog Han (Senior Member, IEEE) received his B.S. degree in Electronic Engineering from Kyungpook National University (KNU), Daegu, South Korea, in 1987, and the M.S. and Ph.D. degrees in Electrical Engineering from Korea Advanced Institute of Science and Technology, Daejeon, South Korea, in 1989 and 1993, respectively. From 1987 to 1996, he was with Samsung Electronics Company Ltd., where he developed the transmission systems for HDTV receivers. Since 1996, he has been a professor at the School of Electronics Engineering, KNU, as a professor. In 2004, he was a Courtesy Associate Professor in the Department of Electrical and Computer Engineering at the University of Florida. He was the Director of the Center of Digital TV and Broadcasting at the Institute for Information Technology Advancement from 2006 to 2008. He has been the Director of the Center for ICT and Automotive Convergence, KNU, since 2011. His research interests include intelligent signal processing and autonomous vehicles.
SRI International
This hands-on tutorial will focus on a central challenge in deploying AI models to edge or consumer devices. Domain adaptation involves taking an off-the-shelf model and adapting it to make the model precise in a specific domain context (e.g., a specific user). Domain adaptation is challenging on consumer edge devices and when the target domain is different from the domains covered in offline pre-training. Hyperdimensional Computing is an edge friendly method that is well-suited for this task. The tutorial will delve into Hyperdimensional Computing as a way to perform domain adaptation at the edge. The applications will range from few shot learning, to multi-modal processing for visual grounding, to retrieval augmented generation (RAG). A basic understanding of machine learning terminology is useful but not required.
Aswin Raghavan, PhD, is a Senior Computer Scientist at SRI International's Center for Vision Technologies. Aswin received his PhD in Computer Science from Oregon State University in 2017. Since joining SRI, he has contributed to the field of Edge Intelligence and created Deep Learning solutions that can operate at the edge with low Size, Weight, and Power (SWaP), spanning topics such as quantization learning, data thinning, Hyperdimensional Computing, adversarial robustness, and few-shot domain adaptation at the edge. He has also made significant contributions in reinforcement learning, multi-agent reinforcement learning, and lifelong learning. Currently, he leads fundamental research projects as a Principal Investigator (PI) and leads multi-institution teams.
University of Strathclyde
Semantic communication was first discussed by Claude E Shannon and Warren Weaver in 1949, when they classified communications as a problem with three levels: physical, semantic, and effectiveness. The physical problem concerns itself with accurate transmission of the data content of a message and led to the birth of information theory, while the semantic problem deals with ensuring the meaning (or semantic) of a message is delivered, and the effectiveness problem is whether the intended action by the message was achieved. Although physical communications evolved at an exponential pace from the early days of information theory, creating the foundation on which the current gaming, entertainment, and media ecosystems are built, semantic communication was not explored further, mainly due to the lack of appropriate tools for its implementation. However, with recent advancements in deep learning and computer performance, developing semantic communication as a useful paradigm to improve the capacity and reliability of communication systems has become a possibility. We explore how the concepts behind semantic communications can be used to complement conventional multimedia communication systems, with special focus on image compression and transmission and video compression and transmission. The early results show promise in achieving better quality reconstructions of images and video for a given bandwidth compared to state-of-the-art image and video compression techniques, but also have several key challenges to be overcome to be commercially adapted. We present the background, current state, and future roadmap for using semantic communications for multimedia applications.
Anil received his BSc. (Hons.) degree in Electronics and Telecommunications Engineering from the University of Moratuwa, Sri Lanka in 1995 and his MSc degree (with Distinction) in Telecommunications from the Asian Institute of Technology (AIT), Thailand in 1997. He completed his PhD in Video Coding and Communications from the Department of Electrical and Electronic Engineering, University of Bristol, UK, in 2001. He is currently a Professor at the Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK and is leading the multimedia communications research group. His research interests include video processing/coding and communications, Semantic Communications, Artificial Intelligence and Machine Learning, resource optimization, Quality of Experience, intelligent video encoding for wireless systems, and video communication in 5G/6G. He has published over 450 peer reviewed articles in international journals and conference proceedings on these domains and graduated over 120 PhD students. Anil led the video communications research group at the University Surrey between 2007 and 2021. Due to his contributions towards video communications, his work has been recognized by international research community though the IEEE International Shall award and NAB 2020 award.
Consumer Reports
The Internet of Things has brought great benefits to the consumer, and connected products are becoming prevalent throughout the home. The proliferation of IoT products raises issues regarding the collection and use of consumer data, as well as digital privacy and security of the devices. This tutorial will discuss consumer attitudes about digital privacy, examine concepts related to privacy and security of Internet of Things (IoT) products, and explore consumer-focused guidelines for improving these products.
As the Senior Director of Testing and Research at Consumer Reports, Maria Rerecich leads teams who evaluate and rate the performance of consumer products, as well as teams performing survey research, statistics and data science, and consumer experience and usability research. Maria is involved in CR’s initiatives to tackle privacy, security, and data concerns, focusing in particular on the testing of internet of things (IoT) devices. She led a pilot test of several mobile applications, which resulted in an app developer making immediate improvements to protect consumers’ data and privacy. Prior to joining Consumer Reports, she worked for 29 years for Standard Microsystems Corporation in the semiconductor industry and was responsible for integrated circuit design, validation, and product engineering of silicon chips used in PCs. Maria was on the National Institute of Standards and Technology IoT Advisory Board from 2022-2024. She holds bachelor’s and master’s degrees in electrical engineering from Massachusetts Institute of Technology.
IEEE Fellow in CTSoc
The speaker will present frameworks and systematic approaches to plan an R&D project portfolio that aligns with the overall business strategy of your organization. Because R&D funds and engineers are limited resources, ensuring you are making well founded resource allocations to the right projects will contribute to the success of your organizations.
Stu Lipoff is an IEEE Life Fellow and past president of IEEE CTSoc, past VP of publications, and VP of Industry and Standards Activities for CTSoc. He is currently serving as The Historian for CTSoc. He has also been active in the organizing and conduct of The IEEE International Conference on Consumer Electronics (ICCE). In his day job, he is president of IP Action Partners Inc which provides contract engineering, technology assessment, and consulting services to stakeholders in the Telecom, Info Tech, Media, and Electronics (TIME) industries. Among his major contributions has been leading the effort that developed the first DOCSIS cable modem specifications and developing adopted recommendations for hybrid fiber optics cable TV networks. In work performed under contract to MSTV and NAB, his analysis supported recommendations adopted by the FCC for a time-table for the rollout of digital TV. In projects performed over nearly 50 years he has worked in nearly every industry from consumer to military and with a wide range of technologies. His expertise includes RF and communications systems. Previous employment was with Motorola, Bell & Howell, and Arthur D Little, Inc. Mr. Lipoff has earned BA, BSEE, BS Eng Physics, MSEE, and MBA degrees. He is a licensed professional engineer in Massachusetts and Nevada as well as holding a Certificate in Data Processing (CDP).
CES 2025 : January 10 Consumer Technology Industrial Summit February 3-5 Full Conference
CTSoc AdministratorCharlotte Kobert charlotte.kobert@ieee.org
