@conference {18546,
	title = {Moving Convolutional Neural Networks to Embedded Systems: The Alexnet and VGG-16 Case},
	booktitle = {Proceedings of the 17th ACM/IEEE International Conference on Information Processing in Sensor Networks},
	year = {2018},
	publisher = {IEEE Press},
	organization = {IEEE Press},
	address = {Piscataway, NJ, USA},
	keywords = {approximate computing, convolutional neural networks, deep learning, embedded systems},
	isbn = {978-1-5386-5298-5},
	doi = {10.1109/IPSN.2018.00049},
	url = {https://doi.org/10.1109/IPSN.2018.00049},
	author = {Alippi, Cesare and Disabato, Simone and Roveri, Manuel}
}
@conference {18539,
	title = {Detecting changes at the sensor level in cyber-physical systems: Methodology and technological implementation},
	booktitle = {2017 International Joint Conference on Neural Networks (IJCNN)},
	year = {2017},
	month = {May},
	keywords = {actuators, adaptation mechanisms, adaptive systems, Change detection, change-point method, Computational modeling, cyber-physical systems, datastreams, fault detection and diagnosis, fault tolerant computing, ICI-based change detection test, Intelligence for Embedded and Cyber-physical Systems, Mann-Whitney change-point method, Mathematical model, model-free change detection test, Predictive models, Random variables, self-adaptive CPS, self-adaptive cyber-physical systems, self-configuration, self-healing skills, self-management, sensor acquisitions, sensor level, sensors, signal detection, Smart Sensor Networks, ST STM32 Nucleo platform, time-variant environments, Training},
	doi = {10.1109/IJCNN.2017.7966066},
	author = {Alippi, Cesare and D{\textquoteright}Alto, Viviana and Falchetto, Mirko and Pau, Danilo and Roveri, Manuel}
}
@conference {18541,
	title = {Learning in Nonstationary Environments: A Hybrid Approach},
	booktitle = {Artificial Intelligence and Soft Computing},
	year = {2017},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Solutions present in the literature to learn in nonstationary environments can be grouped into two main families: passive and active. Passive solutions rely on a continuous adaptation of the envisaged learning system, while the active ones trigger the adaptation only when needed. Passive and active solutions are somehow complementary and one should be preferred than the other depending on the nonstationarity rate and the tolerable computational complexity. The aim of this paper is to introduce a novel hybrid approach that jointly uses an adaptation mechanism (as in passive solutions) and a change detection triggering the need to retrain the learning system (as in active solutions).},
	isbn = {978-3-319-59060-8},
	author = {Alippi, Cesare and Qi, Wen and Roveri, Manuel},
	editor = {Rutkowski, Leszek and Korytkowski, Marcin and Scherer, Rafa{\l} and Tadeusiewicz, Ryszard and Zadeh, Lotfi A. and Zurada, Jacek M.}
}
@conference {18537,
	title = {A lightweight and energy-efficient Internet-of-birds tracking system},
	booktitle = {2017 IEEE International Conference on Pervasive Computing and Communications (PerCom)},
	year = {2017},
	month = {March},
	keywords = {animal movement tracking, application server, Birds, cellular radio, cloud computing, energy assessment, energy conservation, energy consumption, energy efficient Internet-of-birds tracking system, GSM, GSM-based tracking device energy consumption reduction, Internet of Things, Internet-of-things vision, joint localization-transmission phase, Northern Italy, quality of service, Radar tracking, Receivers, Satellites, telecommunication power management, Tracking, Transmitters},
	doi = {10.1109/PERCOM.2017.7917862},
	author = {Alippi, Cesare and Ambrosini, Roberto and Longoni, Violetta and Cogliati, Dario and Roveri, Manuel}
}
@article {18531,
	title = {Model-Free Fault Detection and Isolation in Large-Scale Cyber-Physical Systems},
	journal = {IEEE Transactions on Emerging Topics in Computational Intelligence},
	volume = {1},
	year = {2017},
	month = {Feb},
	pages = {61-71},
	keywords = {Change detection algorithms, clustering methods, Computational modeling, cyber-physical systems, Fault detection, Hidden Markov models, monitoring, Sensor phenomena and characterization, Sensor systems},
	doi = {10.1109/TETCI.2016.2641452},
	author = {Alippi, Cesare and Ntalampiras, Stavros and Roveri, Manuel}
}
@article {18534,
	title = {The (Not) Far-Away Path to Smart Cyber-Physical Systems: An Information-Centric Framework},
	journal = {Computer},
	volume = {50},
	year = {2017},
	month = {April},
	pages = {38-47},
	keywords = {adaptive systems, autonomic computing, communication technologies, CPSs, cyber-physical systems, cybersecurity, embedded systems, Energy management, fault detection and diagnosis, Fault diagnosis, Green computing, homogeneous integrated framework, information-centric framework, intelligent functionalities, Intelligent sensors, Intelligent systems, Internet of Things, IoT, learning (artificial intelligence), low-cost sensors, security, smart cyber-physical systems, smart technology},
	issn = {0018-9162},
	doi = {10.1109/MC.2017.111},
	author = {Alippi, Cesare and Roveri, Manuel}
}
@conference {18447,
	title = {Change Detection in Multivariate Datastreams: Likelihood and  Detectability Loss},
	booktitle = {25th International Joint Conference on Artificial Intelligence (IJCAI-16)},
	year = {2016},
	month = {07/2016},
	address = { New York, USA},
	abstract = {We address the problem of detecting changes in multivariate datastreams, and we investigate the intrinsic difficulty that change-detection methods have to face when the data-dimension scales. In particular, we consider the general approach that detects changes by comparing the distribution of the log-likelihood of the datastream over different time windows. Despite the fact that this approach constitutes the frame for several change-detection methods, its effectiveness when the dimension of data scales has never been investigated, which is indeed the goal of our paper. We show that the magnitude of the change can be naturally measured by the symmetric Kullback-Leibler divergence between the pre- and post-change distributions, and that the detectability of a change of a given magnitude worsens when the data-dimension increases. This structural problem, which we refer to as detectability loss, is due to the linear relationship existing between the variance of the log-likelihood and the data dimension, and reveals to be harmful even at low data-dimensions (say, 10). We analytically derive the detectability loss on Gaussian-distributed datastreams, and empirically demonstrate that this problem holds also on real-world datasets.},
	author = {Alippi, Cesare and Boracchi, Giacomo and Carrera, Diego and Roveri, Manuel}
}
@article {18511,
	title = {A Cloud to the Ground: The New Frontier of Intelligent and Autonomous Networks of Things},
	journal = {IEEE Communication Magazine},
	volume = {54},
	issue = {12},
	year = {2016},
	month = {11/2016},
	pages = {14 - 20},
	chapter = {14},
	abstract = {The Internet-of-Things (IoT) paradigm is supporting -and will support- an ever-increasing number of services and applications impacting on almost every aspect of our everyday life. The current trend is forecasting IoT to connect tens of billion objects by 2020 yielding a very-high volume of data to be acquired, transmitted and processed. IoT typically relies on Cloud Computing to process, analyze and store the data acquired by IoT entities. Unfortunately, the need to transmit all data from the information producing objects to the Cloud for a subsequent processing/analysis phase would require a large bandwidth and increase the latency in the {\textquotedblleft}decision-making process{\textquotedblright} whenever decisions/reactions must be promptly taken by the IoT units.
The Fog Computing (FC) paradigm aims at addressing these problems by extending Cloud Computing towards the edge of the network. In this direction, this paper introduces a novel FC-IoT paradigm designed
to move computing, storage and applications/services close to IoT objects so as to reduce communication bandwidth and energy consumption as well as {\textquotedblleft}decision-making{\textquotedblright} latency. The proposed IoT-based solution has been designed to have intelligent and autonomous IoT objects that are integrated with a FC and Fog Networking approach. The distinguishing features of the intelligent FC-IoT platform are low-latency, self-adaptation, low energy consumption and spectrum efficiency.
},
	doi = { 10.1109/MCOM.2016.1600541CM},
	author = {Alippi, Cesare and Fantacci, Romano and Marabissi, Dania and Roveri, Manuel}
}
@conference {18513,
	title = {An improved Hilbert-Huang Transform for non-linear and time-variant signals},
	booktitle = {26th Italian Workshop on Neural Networks (WIRN 2016)},
	year = {2016},
	month = {05/2016},
	pages = {1-8},
	address = {Vietri sul Mare, Salerno, Italy},
	abstract = {Learning in non-stationary/evolving environments requires methods able to process and deal with non-stationary streams. In this paper we propose a novel algorithm providing a time-frequency decomposition
of time-variant signals. Outcoming signals can be used to identify anomalous events/patterns or extract features associated with the time variance of the signal, precious information for any consequent learning
action. The paper extends the Hilbert-Huang Transform notoriously used to deal with time-variant signals by introducing (i) a new Empirical Mode Decomposition that identies the number of frequency modes of
the signal and (ii) an extension of the Hilbert Transform that eliminates negative frequency-values in the time-frequency spectrum. The effectiveness of the proposed Transform has been tested on both synthetic and real time-variant signals acquired by a real-world intelligent system for landslide monitoring.
},
	author = {Alippi, Cesare and Wen, Qi and Roveri, Manuel}
}
@conference {18450,
	title = {Online Model-free Sensor Fault Identification and Dictionary Learning in Cyber-Physical Systems},
	booktitle = {IEEE-INNS  International Joint Conference on Neural Networks (IJCNN16)},
	year = {2016},
	month = {07/2016},
	address = {Vancouver, Canada},
	abstract = {This paper presents a model-free method for the online identification of sensor faults and learning of their fault dictionary. The method, designed having in mind Cyber-Physical Systems (CPSs), takes advantage of functional relationships among the datastreams acquired by CPS sensing units. Existing model-free change detection mechanisms are proposed to detect faults and identify the fault type thanks to a fault dictionary
which is built over time. The main features of the proposed algorithm are its ability to operate without requiring any a priori information about the system under inspection or the nature of the possibly occurring faults. As such, the method follows the model-free approach, characterized by the fact the fault dictionary
is constructed online once faults are detected. Whenever available, humans can be considered in the loop to label a fault or a fault class in the dictionary as well as introduce fault instances generated thanks to a priori information. Experimental results on both synthetic and real datasets corroborate the effectiveness of the proposed fault diagnosis system.
},
	author = {Alippi, Cesare and Ntalampiras, Stavros and Roveri, Manuel}
}
