Machine Learning for Indoor Localization and Navigation - Tapa dura

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Saideep Tiku is a Walter Scott Jr. College of Engineering Ph.D. candidate in the Department of Electrical and Computer Engineering Department at Colorado State University, Fort Collins, Colorado, USA. He is a Research Assistant at the Embedded, High Performance, and Intelligent Computing (EPIC) Laboratory and his interests include indoor localization, and energy efficiency for fault tolerant embedded systems. His work in the domain of machine learning-based indoor localization has been published and recognized globally in conferences and journals including ACM GLSVLSI 2018, ACM TECS 2019, ACM/IEEE DAC 2019, ACM TCPS 2021, IEEE DATE 2021. He is the recipient of two best paper/poster awards and currently holds 10 (1 awarded, 9 filed) patents in the domain of machine learning-based indoor localization and other fields of applications for machine learning on embedded systems. Saideep Tiku received his B.E. degree in Electronics and Electrical Communication from Panjab University, India in2013. During his time at CSU, he has worked on embedded projects with companies such as Fiat-Chrysler Automobiles, Mentor Graphics (now Siemens), and Micron Technology. He is the mentor for the undergraduate senior design program at CSU for teams in the domain of indoor localization which was also awarded funding from Keysight technologies. He has served as the INTO program tutor for CSU and the Teaching Assistant for the coursework Hardware/Software Design of Embedded Systems. Saideep Tiku has reviewed 13 publications for reputable conferences and journals and also served as the student volunteer for ACM/IEEE ESWEEK 2021. He is a Student Member of the IEEE.

 

Sudeep Pasricha is a Walter Scott Jr. College of Engineering Professor in the Department of Electrical and Computer Engineering, the Department of Computer Science, and the Department of Systems Engineering at Colorado State University. He is Director of the Embedded, High Performance, and Intelligent Computing(EPIC) Laboratory and the Chair of Computer Engineering. Prof. Pasricha received the B.E. degree in Electronics and Communication Engineering from Delhi Institute of Technology, India, in 2000, and his Ph.D. in Computer Science from the University of California, Irvine in 2008. He joined Colorado State University (CSU) in 2008. Prior to joining CSU, he spent several years working in STMicroelectronics and Conexant Inc. Prof. Pasricha’s research broadly focuses on software algorithms, hardware architectures, and hardware-software co-design for energy-efficient, fault-tolerant, real-time, and secure computing. These efforts target multi-scale computing platforms, including embedded and Internet of Things (IoT) systems, cyber-physical systems, mobile devices, and datacenters. He has received funding for his research from various sponsors such as the NSF, SRC, AFOSR, ORNL, DoD, Fiat-Chrysler, and NASA. He has co-authored five books, contributed to several book chapters, and published morethan 250 research articles in peer-reviewed conferences, journals, and books.

Prof. Pasricha has received 16 Best Paper Awards and Nominations at various IEEE and ACM conferences, including at DAC, ASPDAC, NOCS, GLSVLSI, SLIP, AICCSA, and ISQED. Other notable awards include: the 2022 ACM Distinguished Speaker selection, 2019 George T. Abell Outstanding Research Faculty Award, the 2016-2018 University Distinguished Monfort Professorship, 2016-2019 Walter Scott Jr. College of Engineering Rockwell-Anderson Professorship, 2018 IEEE-CS/TCVLSI mid-career research

Achievement Award, the 2015 IEEE/TCSC Award for Excellence for a mid-career researcher, the 2014 George T. Abell Outstanding Mid-career Faculty Award, and the 2013 AFOSR Young Investigator Award.

Prof. Pasricha is currently the Vice Chair and Conference Chair of ACM SIGDA and a Senior Associate Editor for the ACM Journal of Emerging Technologies in Computing (JETC). He is currently or has been an Associate Editorfor the ACM Transactions on Embedded Computing Systems (TECS), IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), IEEE Consumer Electronics (CM), and IEEE Design & Test of Computers (D&T). He also serves as the Chair of the steering committee of IEEE Transactions on Sustainable Computing (TSUSC). He is currently or has been an Organizing Committee Member of several IEEE/ACM conferences such as DAC, ESWEEK, ICCAD, GLSVLSI, NOCS, RTCSA, etc. He has served as the General Chair for various IEEE/ACM conferences such as NOCS, HCW, IGSC, iSES, ICESS, etc.; and as Program Chair for CODES+ISSS, NOCS, IGSC, iNIS, VLSID, HCW, DAC PhD Forum, ICCAD Cadathlon, etc. He is also in the Technical Program Committee of several IEEE/ACM conferences such as DAC, DATE, ICCAD, ICCD, NOCS, etc. He holds an affiliate faculty member position at the Center for Embedded and Cyber-Physical Systems at UC Irvine. He has also received multiple awards for professional service,including the 2019 ACM SIGDA Distinguished Service Award, the 2015 ACM SIGDA Service Award, and the 2012 ACM SIGDA Technical Leadership Award.

De la contraportada

While GPS is the de-facto solution for outdoor positioning with a clear sky view, there is no prevailing technology for GPS-deprived areas, including dense city centers, urban canyons, buildings and other covered structures, and subterranean facilities such as underground mines, where GPS signals are severely attenuated or totally blocked. As an alternative to GPS for the outdoors, indoor localization using machine learning is an emerging embedded and Internet of Things (IoT) application domain that is poised to reinvent the way we navigate in various indoor environments. This book discusses advances in the applications of machine learning that enable the localization and navigation of humans, robots, and vehicles in GPS-deficient environments. The book explores key challenges in the domain, such as mobile device resource limitations, device heterogeneity, environmental uncertainties, wireless signal variations, and security vulnerabilities. Countering these challenges can improve the accuracy, reliability, predictability, and energy-efficiency of indoor localization and navigation. The book identifies several

novel energy-efficient, real-time, and robust indoor localization techniques that utilize emerging deep machine learning and statistical techniques to address the challenges for indoor localization and navigation. In particular, the book: 1) provides comprehensive coverage of the application of machine learning to the domain of indoor localization and navigation; 2) presents techniques to adapt and optimize machine learning models for fast, energy-efficient, and robust indoor localization and navigation; and 3) covers design and deployment of indoor localization and navigation frameworks on mobile, IoT, and embedded devices in real-world conditions.

• Provides comprehensive coverage of the application of machine learning to the domain of indoor localization;
• Presents techniques to adapt and optimize machine learning models for fast, energy-efficient indoor localization;
• Covers design and deployment of indoor localization frameworks on mobile, IoT, and embedded devices in real conditions.