FMEC 2022

Prof. Eiman Kanjo, Nottingham Trent University, UK
Abstract: Sensing on The Edge: Smartening up Sensors
Between the sensors gathering data and the cloud computing services processing data, there is an array of new technologies emerging. These technologies allow for complex processing and storage, closer to where data is collected — right at the edge. While Cloud Computing has received tremendous attention from both academia and industry for connecting many devices to the internet, the ever-growing number of IoT and mobile devices has created the demand for lower network latency and processing capability closer to the users. Edge computing provides an opportunity for wearable devices to access more resources without violating the constraints on weight, size, and sensing capabilities. It involves placing computing resources closer to where data originates (i.e. heart rate monitor, pumps, step counter, or other sensors) — or at the “edge.” Furthermore, edge computing provides many required on-device processing capabilities, which can then help in protecting users’ private data as raw personal data (such as images and videos) don’t need to be shared remotely. These computing resources may be located in the devices themselves or in hyper-local, small-scale data centres. This talk will look at the potential of edge computing for multimodal sensing while protecting users’ privacy and will showcase several examples from recent work at the Smart Sensing Lab in this area.

Biography:
Prof. Eiman Kanjo is a Professor of Pervasive Sensing at the Computer Science department, and the head of the Smart Sensing lab. She conducts research in Mobile Sensing, wearable, and Edge Computing She is a recent winner of the Top 50 Women in Engineering by the Women in Engineering Society. She is the NTU-Turing Network Development Award Lead funded by Alan Turing Institute. She is the academic lead of Smart Nottingham and Smart Campus initiatives.Smart Sensing team (www.SmartSensingLab.com) under the leadership of Prof. Kanjo has won the 2020/2021 Vice-Chancellor’s Outstanding Researcher Team Award. She is the academic lead of Smart Nottingham and Smart Campus initiatives. Eiman conducts research in Mobile Sensing, and she has written some of the earliest papers on the subject ((NoiseSpy, 2010), (GeoMobSens, 2008), and (MobSens, 2009)). Eiman is also an expert in developing digital technologies for well-being. She has been PI or Co-I on grants worth over £2.4m, funders including DCMS (5G Connected Forest, £10m project in collaboration with Nottingham County Council and many industrial organizations , wwwTagWithMe.com), InnovateUK, DSTL, ERDF, MoD, Lottery Fund. She works closely with Mental Health charities, local authorities and industry. Previously Prof. Kanjo worked for more than three years as a Research Fellow at the Computer Laboratory, University of Cambridge. Prof. Kanjo carried out research work at the MRL (Mixed Reality Lab), Computer Science Department, University of Nottingham.

Prof. Jaime Lloret, Universitat Politecnica de Valencia, Spain.
Abstract: Edge Computing in Precision agriculture
The computing capability of small embedded systems has greatly increased in the last few years. There are many small devices available in the market with several wireless interfaces, such as Bluetooth, WiFi, Zigbee and LoRa, and high computing capacity. Moreover, the power consumption of these devices has been reduced very much in the last decade. Researchers and industry developers are taking profit from these advances to include complex algorithms and even artificial intelligence in edge nodes in order to process the gathered data locally and run complex communication protocols. In this keynote speech we will show how edge computing systems can improve precision agriculture. Moreover, we will show how edge computing has been applied in several Spanish and European projects .

Biography:
Prof. Jaime Lloret (jlloret@dcom.upv.es) received his Ph.D. in telecommunication engineering (Dr. Ing.) in 2006. He is Full Professor at the Polytechnic University of Valencia. He is the Chair of the Integrated Management Coastal Research Institute (IGIC) since January 2017. He was the founder of the “Communications and Networks” research group of the IGIC and he is the head (and founder) of the "Active and collaborative techniques and use of technologic resources in the education (EITACURTE)" Innovation Group. He is the director of the University Diploma “Redes y Comunicaciones de Ordenadores” and he has been the director of the University Master "Digital Post Production" for the term 2012-2016. He was Vice-chair for the Europe/Africa Region of Cognitive Networks Technical Committee (IEEE Communications Society) for the term 2010-2012 and Vice-chair of the Internet Technical Committee (IEEE Communications Society and Internet society) for the term 2011-2013. He has been Internet Technical Committee chair (IEEE Communications Society and Internet society) for the term 2013-2015. He has authored 14 books and has more than 650 research papers published in national and international conferences, international journals (more than 375 with Clarivate Analytics JCR). He has been the co-editor of 54 conference proceedings and guest editor of several international books and journals. Since 2016 till today he is the Spanish researcher with highest h-index in the TELECOMMUNICATIONS journal list according to Clarivate Analytics Ranking. Moreover, he is included in the world’s top 2% scientists according to the Stanford University List since 2020. He has been general chair (or co-chair) of 75 International workshops and conferences. He is IEEE Senior Member, ACM Senior, IARIA Fellow and EAI Fellow.

Prof. Tatiana Kalganova, University of Brunel, UK
Abstract: Faster and more accurate machine learning techniques with less data
With the latest development of deep learning techniques and ability to process the data in acceptable timeframe, the need to consider the aspects of environmentally-friendly machine learning techniques has arose. In addition, the latest development of IoT technologies led to the trend where the data are collected and actively used in various machine learning techniques. The lecture will explorer how to reduce the computational requirements of machine learning techniques during training, how to identify the completeness of dataset and ensure that only “useful” data have been used to enhance the training models? How can we design environmentally-friendly machine learning that requires minimum CO2 and respectively computational resources?

Biography:
Prof. Tatiana Kalganova BSc (Hons), PhD, is a Professor of Intelligent Systems at Brunel. She has over 25 years of experience in design and implementation of applied Intelligent Systems. She led the Brunel team to success in collaboration with Supply Dynamics and Blue roofs, becoming a Champions of 2019 AFWERX Microelectronics Supply Chain Provenance Challenge. Her novel ensemble-based deep learning approaches are ranked No 1 between 2020-2022 in terms of the number of trainable parameters at paperswithcode for MNIST dataset and lately ranked No 2 overall. Her work on hybridisation of LSTM and capsules is ranked No 1 for change point and outlier detection for SKAB dataset. Her research into Ant Colony Optimization (ACO) and graph mathematics have been deployed into Caterpillar’s current GEMSTONE supply chain optimization process. This invention led to multiple internal and external international awards, including the 2016 Caterpillar Chairman’s Award for Process/Business Innovation, the 2016 Global Excellence in Analytics Award by the International Institute of Analytics, 2017 Finalist for the INFORMS Innovation in Analytics prize and 2020 Winner of Research Impact Awards. Inventor of 6 patented technologies and author of over 120 peer-refereed papers in highly respect journals and in conference proceedings, Prof. Kalganova’s work has attracted in research funding from a combination of industrial, national and international research entities. She serves on the editorial board for several journals, including Genetic Programming and Evolvable Machines and member of EPSRC Full College.

FMEC 2022

FMEC 2022

Women Collection

New Season

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