hero image
Professor Robert Piechocki - University of Bristol. Bristol, , GB

Professor Robert Piechocki Professor Robert Piechocki

Professor of Wireless Systems | University of Bristol

Bristol, UNITED KINGDOM

Exploring how connected technologies contribute to effective road management systems for driverless vehicles

Areas of Expertise (7)

Patient Monitoring

Signal Processing

Traffic Management Systems

Driverless cars

Autonomous Vehicles

Wireless Systems

Remote Healthcare

Biography

Professor Robert Piechocki is based in the Department of Electronic Engineering where his research concerns the use of connected technologies in relation to driverless vehicles. He is also investigating remote healthcare at-home monitoring of patients through the use of sensors to track movement and to inform remote healthcare management.

His early career involved signal processing and 3G wireless systems. He now works on the creation of automated systems that allow for more autonomous approaches to life through, for example, digital health and through remote traffic planning and coordination. Professor Piechoki looks at connectivity and the reliability of communications systems, working with car manufacturers, road planners and others from government and industry to support his research. He has published more than 170 papers in international journals and holds 13 patents.

Media

Publications:

Documents:

Photos:

Videos:

FLOURISH - The Benefits of Connectivity SCL South West Group event - Driverless cars: robotics, connectivity and cooperation University of Bristol previews 5G for the UK public

Audio/Podcasts:

Social

Education (2)

University of Bristol: Ph.D., Wireless Communications 2002

Wroclaw University of Science and Technology: M.Sc., Electronics 1997

Media Appearances (5)

For self-driving cars, winter is coming

ZDNet  online

2020-03-05

Robert Piechocki, professor of wireless systems at the University of Bristol, told ZDNet: "It's a bit of a reality check, after a lot of upbeat announcements recently. We thought connected cars deployment would be imminent, but it's much slower than initially thought. We are entering a sort of connected cars winter."

view more

The hunt for security flaws in self-driving cars steps up a gear

Autonomous Tech News  online

2020-01-31

“Ministers understand that this is an important topic,” Robert Piechocki, professor of wireless systems at the University of Bristol, told ZDNet. “But in short, we need more money. New ideas are being introduced all the time, and there might be additional vulnerabilities that we don’t know of.”

view more

Self-driving cars: The hunt for security flaws steps up a gear

ZDNet  online

2020-01-29

"Ministers understand that this is an important topic," Robert Piechocki, professor of wireless systems at the University of Bristol, told ZDNet. "But in short, we need more money. New ideas are being introduced all the time, and there might be additional vulnerabilities that we don't know of."

view more

FLOURISH project reports on connectivity research

Highways Magazine  online

2019-12-03

Robert Piechocki, Professor of Wireless Systems at the University of Bristol, explained, “Digitally connected vehicles have the potential to revolutionise the cities we live in and the way we travel. Our research into the optimum conditions for the robust, effective and resilient transfer of data is the cornerstone of a new customer journey experience.

view more

University of Bristol lead project using WiFi for medical radar

TechSpark  online

2018-05-14

“A great deal of scientific and engineering ingenuity around the world goes into the creation of bespoke sensing systems. There is certainly a place for such systems,” said Dr Robert Piechocki, principle investigator and Reader in Wireless Connectivity in the Department of Electrical and Electronic Engineering at the University of Bristol.

view more

Articles (5)

Vesta: A digital health analytics platform for a smart home in a box

Future Generation Computer Systems

2020 This paper presents Vesta, a digital health platform composed of a smart home in a box for data collection and a machine learning based analytic system for deriving health indicators using activity recognition, sleep analysis and indoor localization. This system has been deployed in the homes of 40 patients undergoing a heart valve intervention in the United Kingdom (UK) as part of the EurValve project, measuring patients health and well-being before and after their operation.

view more

Passive WiFi Radar for Human Sensing Using a Stand-Alone Access Point

IEEE Transactions on Geoscience and Remote Sensing

2020 Human sensing using WiFi signal transmissions is attracting significant attention for future applications in e-healthcare, security, and the Internet of Things (IoT). The majority of WiFi sensing systems are based around processing of channel state information (CSI) data which originates from commodity WiFi access points (APs) that have been primed to transmit high data-rate signals with high repetition frequencies.

view more

DRIVE: A Digital Network Oracle for Cooperative Intelligent Transportation Systems

arXiv

2020 In a world where Artificial Intelligence revolutionizes inference, prediction and decision-making tasks, Digital Twins emerge as game-changing tools. A case in point is the development and optimization of Cooperative Intelligent Transportation Systems (C-ITSs): a confluence of cyber-physical digital infrastructure and (semi) automated mobility.

view more

TSCH Networks for Health IoT: Design, Evaluation, and Trials in the Wild

ACM Transactions on Internet of Things

2020 The emerging Internet of Things has the potential to solve major societal challenges associated with healthcare provision. Low-power wireless protocols for residential Health Internet of Things applications are characterized by high reliability requirements, the need for energy-efficient operation, and the need to operate robustly in diverse environments in the presence of external interference.

view more

From bits of data to bits of knowledge—an on-board classification framework for wearable sensing systems

Sensors

2020 Wearable systems constitute a promising solution to the emerging challenges of healthcare provision, feeding machine learning frameworks with necessary data. In practice, however, raw data collection is expensive in terms of energy, and therefore imposes a significant maintenance burden to the user, which in turn results in poor user experience, as well as significant data loss due to improper battery maintenance.

view more