Joint Doctoral Programme in Interactive and Cognitive Environments

The Joint Doctoral Programme in Interactive and Cognitive Environments (JD ICE) is a PhD programme organised by the Centre for Intelligent Sensing at Queen Mary University of London and the Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture at University of Genoa.

JD ICE provides PhD training in the fields of multimedia signal processing, data fusion, computer vision, cognitive dynamic systems and machine learning, and offers advanced research projects designed for ambitious students willing to become word-leading researchers. PhD students will benefit from a joint-supervision model and will spend part of their PhD programme in London, UK, and part in Genoa, Italy.

People & projects

Development of an m-learning system based on advanced Human-Computer Interaction

The goal of the PhD project is to design and develop an e-learning application for mobile devices. The system will be able to sense the environment/user (in particular emotions), assess the performance, adapt to the user and provide feedback and coaching. This requires studying and developing artificial intelligence algorithms such as SVM, k-NN, NNs, etc. Human-Computer Interaction will involve 3D augmented reality and advanced video gaming (in particular serious games, for instructional purposes) features. PhD Student Supervisors Nikesh Bajaj Nikesh did his Masters in Communication and Information Systems (Signal Processing stream) from Aligarh Muslim University, India. With five years of teaching experience, he worked in Signal Processing, Image and Audio Processing and Machine Leaning. His current research interests focus on Brain Signal Processing with Machine Learning for Serious Games. Alessandro De Gloria Jesús R. Carrión

Personal data, thinking inside the box

The research is in the general area of Personal Data & Human-Data Interaction. There is a critical need to provide technologies that enable alternative practices, so that individuals can participate in the collection, management and consumption of their personal data. In this studentship, we will investigate the feasibility of having a Databox, a personal networked device (and associated apps/services) that collates and mediates access to personal data, allowing us to recover control of our online lives. We hope the Databox is a first step to re-balancing power between us, the data subjects, and the corporations that collect and use our data. The Databox can act as a hub for individuals' mobility and physical activity data, social media data, third party tracking, and ambient data from the environment. PhD Student Supervisors Yousef Amar Yousef has a background in Electronic Engineering and Systems Engineering. He gained experience in computer vision, machine learning and the real-time processing of large amounts of data through building drone sensor systems. His current research interests focus on personal data, networks and privacy. Gareth Tyson Lucio Marcenaro

Eco-friendly naturalistic vehicular sensing and driving behavior profiling

This project aims to analyse naturalistic vehicular driving behaviour from data extracted from mobile sensors, fused with in-car sensor data accessed using an On-Board Diagnostics-II (OBD-II) system and a Bluetooth/Wi-Fi/wired adapter. This will be used to profile and evaluate the driving behaviour in real time, taking into account eco-driving concerns and an awareness of the physical environment context. An additional focus is in applying the concept of serious games to help incentivise more eco driving through sharing driving metrics and comparing oneself with friends and other peers. PhD Student Supervisors Rana Massoud Rana received her BSC in Computer Sciences in 2013 and MS of Computer Sciences in Software Eng in 2015 from Lebanese University-Faculty of Sciences, Lebanon. She has experience on VOIP, Windows Phone and the .Net Framework having worked for more than one year at Telepaty Holding, Tripoli, Lebanon. Her current research interests are naturalistic vehicular data analysis, data science, On-Board Diagnostics protocol, automotive sensing data, eco-driving, driving behavior profiling, and serious games. Francesco Bellotti Stefan Poslad

Cognitive radio for PHY-layer security against jamming attacks

This PhD project focuses on algorithms and methods based on compressed-sensing theory as enabler for cognitive radio terminals for opportunistic spectrum usage. Since the proposed research addresses the problem of "non-stationary spectrum", time-frequency analysis in a compressed-sensing framework is investigated through the S-transform in order to perform signal processing in both the domains. The subsequent objective is to exploit recent advances in Machine Learning such as Generative Adversarial Networks to detect potential malicious signals and predict the strategy used by the jammer to disrupt legitimate communications. The applications of the proposed research are the "nSHIELD project" based on a hardware platform with software-defined devices to optimise external attack robustness and "TV White Space (TVWS)" composed of unused channels in the digital terrestrial TV band which can be occupied by secondary users. PhD Student Supervisors Andrea Toma Andrea obtained his Masters in Communication Engineering in 2014 from University of Salento (Italy). His main research focused on the localisation problem in Wireless Sensor Networks where he gained experience at Austrian Institute of Technology (Vienna) and University of Salento. He is currently a PhD candidate and involved in the JD ICE programme. Carlo Regazzoni Frank Gao

Ego things: networks of self-aware intelligent objects

This PhD project focuses on probabilistic signal-processing architectures and techniques to represent and capture generality, self-awareness, adaptability, flexibility and reconfigurability of an Internet of Things system through a learning experience in order to make the “Things” adapting their behaviors in the non-stationary environments where they operate. Specific focus will be given to the communication and cooperation issues between the entities (ego things) in the network. An in-depth analysis of the Cognitive Dynamic System framework associated with the 'Ego-Things' will be performed to understand how Dynamic Bayesian models and machine learning techniques can be employed. PhD Student Supervisors Divya Thekke Kanapram Divya did her BSC in Electronics and Communication Engineering at University College of Engineering, Trivandrum, India. After a period in Industry, she obtained her MS in Multimedia Signal Processing and Telecommunication Networks in 2014 from University of Genoa, Italy. Her research interests include machine learning and Internet of Things for interactive and cognitive environments, and networks of 'Ego things'. Mario Marchese Eliane L. Bodanese

Incremental learning techniques for proactive anti-jamming in Cognitive and Software Radio

This PhD project develops the understanding of jamming and anti-jamming attacks in Cognitive Radio Networks. The study involves the investigation of jamming and anti-jamming techniques in the dynamic spectrum of cognitive radio with the objective of detecting and tracking jammer present in a cognitive radio spectrum along with legitimate users, using Dynamic Bayesian Networks. The dynamic spectrum modeling is developed using Bayesian filtering, Switching models, Particle filter, Kalman filter and Machine learning techniques as a tool, can switch a user transmission in the case of jammer attacks, and attempts to minimize jamming attacks in cognitive radio. PhD Student Supervisors Muhammad Farrukh Shahid Muhammad did his BSC in Telecommunication Eng. (2010) and MEng in Communication Systems and Networks (2013) at Mehran Uni. of Eng. and Technology, Pakistan. He worked as a lecturer in the Dep. of Electrical Eng. at COMSATS Inst. of Inf. Technology (2013-2015) and in the College of Eng. at the Karachi Inst. of Economics and Technology (2015-2017), Pakistan. His research interests focus on dynamic spectrum modeling for cognitive radio, machine learning techniques for cognitive environment and adaptive modulation techniques for wireless communication. Carlo Regazzoni Akram Alomainy

Cognitive environments for telerobotics

Remotely controlled robots heavily rely on human supervisory control approaches. The quality of performed telerobotic tasks depends on the human-operator's experience, cognitive state and availability of required feedback information to support the decisions. Colour, depth cameras and range sensors are commonly used to create the visual and spatial representation of the remote environment for the human-operator. Additionally, in some applications force and tactile sensing data is required to provide the human-operator with haptic feedback from the robot's end-effector. We propose to integrate visual, spatial and haptic feedback with the help of interactive virtual reality environment to improve the efficiency of remote robot control. However, a direct merging of the sensory modalities may increase cognitive loads of the human-operator and therefore affecting the performance. We propose an intelligent feedback adaptation system which will automatically adjust the representation of the feedback information to provide better situation awareness (better field of view for visual feedback, relevant tactile information, and vision-based spatial maps). Such system will need to learn from previous experience (recorded sensing and actuation data from the robot and the operator) to form an internal dynamic task-model which will also be updated on-fly based on the actual task-specific operation parameters. The developed system will be composed of a mobile robotic manipulation system equipped with RGBD-cameras, a VR interface and an application for merging visual, spatial and haptic feedback from the robot and human body movement tracker to control the robotic system and algorithmic component implementing cognitive adaptation of the VR-interface with respect to actual user and task state. This research will use methods from robotics, computer vision, haptic and visual rendering for VR and ergonomics. PhD Student Supervisors Bukeikhan Omarali Bukeikhan received his BSc in Aerospace Engineering from TU Delft, Netherlands and MSc in Mechanical Engineering from Nazarbayev University, Kazakhstan. He has worked for several years at the Nazarbayev University as a lab/research assistant at the Robotics department as well as an instructor for an elective CAD/CAM course. His research interests lies in robot teleoperation, predictive control and virtual environments. Ildar Farkhatdinov Maurizio Valle

Learning self-awareness models for physical layer security in Cognitive Radios

This PhD project focuses on learning dynamic environmental models in Cognitive Radios. Different machine learning algorithms and techniques will be exploited to analyse the dynamic behaviour of user and jammer within a cognitive radio network, and to optimise the cognitive radio performance against jammer attacks. The user and jammer activities will be characterised by their statistical properties through Bayesian models, and the current state of the observed entities will be estimated and their actions in the near future will be predicted through probabilistic graphical models. The project will demonstrate how the interaction between user and jammer in cognitive radios can be modelled by coupling multiple Dynamic Bayesian networks in a way that users can successfully avoid the jammer. PhD Student Supervisors Ali Krayani Ali received his Bachelor’s and Master’s degree in Telecommunication Engineering from Politecnico di Torino (Italy) in 2014 and from University of Florence (Italy) in 2017, respectively. He worked as a Software Engineer for several companies and he is currently a JD ICE PhD candidate. His current research interests include Cognitive Radios, self-awareness, Dynamic Bayesian Networks and Machine Learning. Carlo Regazzoni Atm Alam

Audio-visual assisted human-robot interaction

The research focuses on developing a model with multimodal perception skills to meaningfully detect and integrate information from their multiple sensory channels. The majority of existing works in signal processing and computer vision address these problems by utilising audio signals alone, or visual information only. However, due to the complexity of real scenes, the use of video and audio as separate cues do not always provide optimum and robust solutions, since each modality necessarily has flaws or ambiguities. The project will also address the issues that emerge when dealing with the implementation of deep learning on electronic embedded systems. In this regard, the main goal is to find a trade-off among different aspects: the generalisation ability of the predictors based on deep learning architectures, the computational complexity of the deep learning architectures and the need to limit power consumption. PhD Student Supervisors Ashish Alex Ashish received his bachelor's degree in electronics and communication from Vellore Institute of Technology (India) in 2017 and received his master's in computers systems and networks from University of Greenwich (London) in 2018. He then worked as a software developer intern at Hostmaker (London). His research interests include audio-visual signal processing, computer vision, machine learning and deep neural networks. Lin Wang Paolo Gastaldo

Active exploration of unknown objects using haptic information

This PhD project focuses on using humanoid robotic hands and tactile sensors to perform exploratory manoeuvres in order to extract information about completely unknown objects. The end-goal of the operation is to be able to recognise the object’s shape, as well as some of its properties, in order to easily interact with it, for example in manipulation and gripping actions. A probabilistic process is designed to intelligently explore the object with the aim of saving time while performing the operations, and of reducing software and movement costs. PhD Student Supervisors Augusto Bonzini Augusto studied at "Universidad Tecnologica Nacional" (National Technology University) in Buenos Aires, Argentina, where he received a 6-year Engineering degree with a specialisation in Electronics. He also worked as software developer for 2 years in the ExxonMobil IT Corporation. His interests include Robotics, Mathematics, Optimisation and Machine Learning. Lorenzo Jamone Lucia Seminara

Empathica – machine learning model for detecting human emotions from behavioural patterns

Emotions play a central role in the day to day choices and decisions that people make. Automated emotion detection has therefore, become a widely used tool for a diversity of goals. However, there are scenarios when data for a person's facial image, speech in audio or transcribed forms may not be available and the data at hand is only for individual behavioural traits. We hypothesise that, emotional states can be inferred from behavioural patterns and to do it consistently with acceptable accuracy for a diverse population demographic, a computational model of human emotions for behavioural patterns is required, which to the best of our knowledge, does not exist at this time. This research will focus on addressing this gap by using machine learning techniques to create the said model. We will develop a framework based on conventional deep learning architectures for translating behavioural patterns into emotional states. This will be useful for inferring pre-determined factors of decision-making such as emotional and cognitive bias as well as intention, for instance, in automated user acceptance testing and detection of suicidal tendencies. We believe that the outcome of this research would enable us to usher a new dimension of interaction with computer systems, the 'Human-centric' computer interaction, where the system will be capable of knowing exact emotional state of its user and cater the user experience accordingly. PhD Student Supervisors Fahad Ahmed Fahad completed his Bachelors of Science in Computer Science & Software Engineering in 2014 and his Masters of Science in Computer Science (Summa Cum Laude) in 2016 from the American Int. University-Bangladesh (AIUB). He worked as a tertiary education teacher for 4 years and then as Assistant Professor at AIUB, prior to commencing his PhD. His current research interests spans across Affective Computing, Machine Learning, Computational Modelling and Data Analytics. Jesús R. Carrión Riccardo Berta

Satellite networks in the 5G ecosystem

This Ph.D. project addresses the need for the integration of satellite networks into the 5G ecosystems. This integration will support many 5G use cases which are intended to satisfy the growing need for communication between people or machines anywhere and at any time with good Quality of Service (QOS). For this purpose, an open-source System-level simulator based on the Network Simulator 3 (NS-3) will be developed. The developed system-level simulator will allow simulating 5G Satellite and Terrestrial Integrated Networks and will serve as the tool for testing and optimizing different integrated 5G Network scenarios and architectures. PhD Student Supervisors Nour Badini Nour got her Master's degree in Computer and Communication Engineering from the Lebanese University-Faculty of Engineering in 2019, with a thesis concerning a novel design of a Multi-Band Antenna for 5G Applications. She is currently a JD ICE Ph.D. student at both UNIGE-Italy and QMUL-London. Her current research interests include Satellite networks, 5G-and-beyond communication systems, Cognitive Radio, and Machine learning. Mario Marchese Mona Jaber

Affordance detection techniques for resource-constrained devices

Affordance detection consists in predicting the possibility of a specific action on an object. While this problem is generally defined for fully autonomous robotic platforms, the focus is on affordance detection for a semi-autonomous scenario, with a human in the loop. In this scenario, a human first moves their robotic prosthesis (e.g. lower arm and hand) towards an object and then the prosthesis selects the part of the object to grasp. The main challenges are the indirectly controlled camera position, which influences the quality of the view, and the limited computational resources available. The key foundation concept of the research activity is the trade-off among different constraints such as power consumption, latency, memory footprint and accuracy. PhD Student Supervisors Tommaso Apicella Tommaso obtained his Master's degree in Electronic Engineering in 2019 from University of Genoa, Italy. He is pursuing a PhD in Interactive and Cognitive Environments, a collaboration between the Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture (DITEN) at the University of Genoa and Centre for Intelligent Sensing at the Queen Mary University of London. His main research areas include: Machine Learning and Pattern Recognition for Embedded Devices. Paolo Gastaldo Andrea Cavallaro

Supporting machine learning edge to cloud

The goal of this PhD project is to design and implement an environment for efficient development of machine learning applications on resource-constrained edge devices. The environment should support training on PC/Cloud, and inference performance in a distributed continuum from edge to cloud, considering issues such as latency, bandwidth, energy consumption, and privacy. The deployment should be dynamically configurable to account for dynamic variations in the overall environment (e.g., power and compute availability at the edge, network traffic, cost) and to meet dynamic requirements in terms of accuracy, latency, etc. The framework should be as platform-independent as possible and extensible so that new algorithms can be seamlessly integrated. PhD Student Supervisors Fouad Sakr Fouad received his Bachelor and Master degrees in Computer and Telecommunication Engineering from the Lebanese International University in 2017 and 2019, respectively. His current research interests include artificial intelligence, embedded machine and deep learning, edge computing, and model optimisation for the efficient deployment in constrained devices. Francesco Bellotti Joseph Doyle

Spectrum sensing strategies based on explainable machine learning methods for cognitive radio applications in V2X

This PhD studies how dynamic Bayesian models, machine learning techniques, and deep learning, i.e. graph neural networks, reinforcement learning and active inference, can be used with featured explainabilty to enhance trust and legal compliance for user in a Cognitive Dynamic System framework of a V2X scenario. The autonomous vehicles are self-aware and have inherent interruption/intrusion mitigation and radio resource allocation mechanisms to accommodate the highly dynamic and interactive (city) environment. PhD Student Supervisors Nobel John William Nobel received his B.E. in Electronic and Communications Engineering from Anna University, India, in 2008 and his M.Sc. in Telecommunications from University of Valencia, Spain, in 2012. He has been an engineer and a project manager for telecommunication and energy consulting companies, and a Faculty member at a leading Indian Business school. His research interests include Machine Learning, Cognitive Radios, Internt of Things, Dynamic Bayesian Networks and Graph Neural Networks. Carlo Regazzoni Zhijin Qin

Project confirmed and to be assigned

PhD Student Supervisors

Publications

Journal papers

- F. Sakr, R. Berta, J. Doyle, A. De Gloria, F. Bellotti; Self-learning pipeline for low-energy resource-constrained devices, Energies, vol. 14, no. 20, p. 6636, October 2021. [PDF]

- N. Bajaj, J. R. Carrión, F. Bellotti, R. Berta, A. De Gloria; Analysis of factors affecting the auditory attention of non-native speakers in e-learning environments, Electronic Journal of e-Learning, vol. 19, no. 3, Jul 2021. [PDF]

- R. Massoud, R. Berta, S. Poslad, A. De Gloria, F. Bellotti; IoT sensing for reality-enhanced serious games, a fuel-efficient drive use case, Sensors, vol. 21, no. 10, pp. 3559, May 2021. [PDF]

- D. T. Kanapram, M. Marchese, E. L. Bodanese, D. M. Gomez, L. Marcenaro, Carlo Regazzoni; Dynamic Bayesian collective awareness models for a network of ego-things, IEEE Internet of Things Journal, vol. 8, no. 5, pp. 3224-3241, March 2021. [PDF]

- D. T. Kanapram, F. Patrone, P. Marin-Plaza, M. Marchese, E. L. Bodanese, L. Marcenaro, D. Martín-Gómez, C. Regazzoni; Collective awareness for abnormality detection in connected autonomous vehicles, IEEE Internet of Things Journal, vol. 7, no. 5, pp. 3774-3789, May 2020. [PDF]

- A. Toma, A. Krayani, M. Farrukh, H. Qi, L. Marcenaro, Y. Gao, C.S. Regazzoni; AI-based abnormality detection at the PHY-layer of cognitive radio by learning generative models, IEEE Transactions on Cognitive Communications and Networking, vol. 6, no. 1, pp. 21-34, March 2020. [Link]

- N. Bajaj, J.R. Carrión, F. Bellotti, R. Berta, A. De Gloria; Automatic and tunable algorithm for EEG artifact removal using wavelet decomposition with applications in predictive modeling during auditory tasks, Biomedical Signal Processing and Control, vol. 55, 101624, January 2020. [Link]

- A. Toma, T. Nawaz, Y. Gao, L. Marcenaro, C. S. Regazzoni; Interference mitigation in wideband radios using spectrum correlation and neural network, IET Communications, vol. 13, no. 10, pp. 1336-1347, June 2019. [PDF]

- R. Massoud, S. Poslad, F. Bellotti, K. Mehran, R. Berta, A. De Gloria; A fuzzy logic module to estimate a driver’s fuel consumption for reality-enhanced serious games, International Journal of Serious Games, vol. 5, no. 4, pp. 45–62, 2018. [PDF]

Conference papers

- A. Krayani, N. J. William, A. S. Alam, L. Marcenaro, Z. Qin, A. Nallanathan, C. S. Regazzoni; Generalized filtering with transport planning for joint modulation conversion and classification in AI-enabled radios, IEEE International Conference on Communications, to appear, Seoul, South Korea, 16-20 May 2022. [Link]

- T. Apicella, A. Cavallaro, R. Berta, P. Gastaldo, F. Bellotti, E. Ragusa; An affordance detection pipeline for resource-constrained devices, IEEE International Conference on Electronics, Circuits, and Systems, Dubai, United Arab Emirates, 28 November-1 December 2021. [Link]

- B. Omarali, K. Althoefer, F. Mastrogiovanni, M. Valle, I. Farkhatdinov; Workspace scaling and rate mode control for virtual reality based robot teleoperation, IEEE International Conference on Systems, Man, and Cybernetics, Melbourne, Australia, 17-20 October 2021. [Link]

- A. Alex, L. Wang, P. Gastaldo, A. Cavallaro; Mixup augmentation of generalizable speech separation, IEEE International Workshop on Multimedia Signal Processing, Tampere, Finland, 6-8 October 2021. [PDF]

- F. Sakr, F. Bellotti, R. Berta, A. De Gloria, J. Doyle; Memory-efficient CMSIS-NN with replacement strategy, International Conference on Future Internet of Things and Cloud, Rome, Italy, 23-25 August 2021. [Link]

- A. Krayani, A. S. Alam, M. Calipari, L. Marcenaro, A. Nallanathan, C. S. Regazzoni; Automatic modulation classification in cognitive-IoT radios using generalized dynamic Bayesian networks, IEEE World Forum on Internet of Things, New Orleans, LA, USA, 14 June-31 July 2021. [Link]

- A. Krayani, M. Baydoun, L. Marcenaro, A. S. Alam, C. Regazzoni; Self-learning Bayesian generative models for jammer detection in cognitive-UAV-radios, IEEE Global Communications Conference, 20383498, Taipei, Taiwan, 7-11 December 2020. [Link]

- F. Bellotti, R. Berta, A. De Gloria, J. Doyle, F. Sakr; Exploring unsupervised learning on STM32 F4 microcontroller, International Conference on Applications in Electronics Pervading Industry, Environment and Society, pp 39-46, 19-20 November 2020. [Link]

- B. Omarali, B. Denoun, K. Althoefer, L. Jamone, M. Valle, I. Farkhatdinov; Virtual reality based telerobotics framework with depth cameras, IEEE International Conference on Robot and Human Interactive, 20056394, Naples, Italy, 31 August-4 September 2020. [PDF]

- A. Toma, A. Krayani, L. Marcenaro, Y. Gao, C. S. Regazzoni; Deep learning for spectrum anomaly detection in cognitive mmWave radios, IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, 20035057, London, UK, 31 August-3 September 2020. [PDF]

- A. Krayani, M. Baydoun, L. Marcenaro, Y. Gao, C. S Regazzoni; Smart jammer detection for self-aware cognitive UAV radios, IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, 20035052, London, UK, 31 August-3 September 2020. [Link]

- R. Massoud, F. Bellotti, S. Poslad, R. Berta, and A. De Gloria; Towards a reality-enhanced serious game to promote eco-driving in the wild, GALA Conference, pp. 245-255, Athens, Greece, 27-29 November 2019. [Link]

- A. Krayani, M. Farrukh, M. Baydoun, L. Marcenaro, Y. Gao, C. S. Regazzoni, Jammer detection in M-QAM-OFDM by learning a dynamic Bayesian model for the cognitive radio, European Signal Processing Conference, 19172298, A Coruna, Spain, 2-6 September 2019. [PDF]

- Y. Amar, G. Tyson, G. Antichi, L. Marcenaro; Towards cheap scalable browser multiplayer, IEEE Conference on Games (CoG), 19013869, London, UK, 20-23 August 2019. [PDF]

- R. Massoud, F. Bellotti, S. Poslad, R. Berta, A. De Gloria; Eco-driving profiling and behavioral shifts using IoT vehicular sensors combined with serious games, 19027326, IEEE Conference on Games, London, UK, 20-23 August 2019. [PDf]

- B. Omarali, F. Palermo, M. Valle, S. Poslad, K. Althoefer, I. Farkhatdinov; Position and velocity control for telemanipulation with interoperability protocol, Annual Conference Towards Autonomous Robotic Systems, pp. 316-324, London, UK, 3-5 July 2019. [PDF]

- D. T. Kanapram, D. Campo, M. Baydoun, L. Marcenaro, E. L. Bodanese, C. Regazzoni, M. Marchese; Dynamic Bayesian approach for decision-making in ego-things, IEEE World Forum on Internet of Things, 18833678, Limerick, Ireland, 15-18 April 2019. [PDF]

- M. Farrukh, A. Krayani, M. Baydoun, L. Marcenaro, Y. Gao, C. Regazzoni; Learning a switching Bayesian model for jammer detection in the cognitive-radio-based internet of things, IEEE World Forum on Internet of Things, 18833674, Limerick, Ireland, 15-18 April 2019. [PDF]

- R. Massoud, F. Bellotti, R. Berta, A. De Gloria, S. Poslad; Exploring fuzzy logic and random forest for car drivers’ fuel consumption estimation in IoT-enabled serious games, IEEE International Symposium on Autonomous Decentralized Systems, 19889597, Utrecht, The Netherlands, 8-10 April 2019. [DOC]

- N. Bajaj, F. Bellotti, R. Berta, J. R. Carriòn, A. De Gloria; Auditory attention, implications for serious game design, International Conference on Games and Learning Alliance, pp 201-209, Palermo, Italy, 5-7 December 2018. [Link]

- A. Toma, T. Nawaz, L. Marcenaro, C. Regazzoni, Y. Gao; Exploiting ST-based representation for high sampling rate dynamic signals, International Conference on Wireless Intelligent and Distributed Environment for Communication, pp. 203-217, New Delhi, India, 16-18 February 2018. [Link]

Book chapters

- A. Toma, C. Regazzoni, L. Marcenaro, Y. Gao; Learning dynamic jamming models in cognitive radios, Cognitive Radio Applications and Practices, Handbook of Cognitive Radio, Springer, 2018. doi:10.1007/978-981-10-1389-8_64-1. [Link]

Datasets

- N. Bajaj, J. R. Carrión, F. Bellotti, PhyAAt: Physiology of Auditory Attention to speech dataset, arXiv preprint arXiv:2005.11577, https://phyaat.github.io