In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

The PASCAL Visual Object Classes Challenge

It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >

On the capacity of a cellular CDMA system

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper, we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

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Deep Learning in Mobile and Wireless Networking: A Survey

Over the last few years, the convincing forward steps in the development of Internet of Things (IoT)-enabling solutions are spurring the advent of novel and fascinating applications. Among others, mainly radio frequency identification (RFID), wireless sensor network (WSN), and smart mobile technologies are leading this evolutionary trend. In the wake of this tendency, this paper proposes a novel, IoT-aware, smart architecture for automatic monitoring and tracking of patients, personnel, and biomedical devices within hospitals and nursing institutes. Staying true to the IoT vision, we propose a smart hospital system (SHS), which relies on different, yet complementary, technologies, specifically RFID, WSN, and smart mobile, interoperating with each other through a Constrained Application Protocol (CoAP)/IPv6 over low-power wireless personal area network (6LoWPAN)/representational state transfer (REST) network infrastructure. The SHS is able to collect, in real time, both environmental conditions and patients’ physiological parameters via an ultra-low-power hybrid sensing network (HSN) composed of 6LoWPAN nodes integrating UHF RFID functionalities. Sensed data are delivered to a control center where an advanced monitoring application (MA) makes them easily accessible by both local and remote users via a REST web service. The simple proof of concept implemented to validate the proposed SHS has highlighted a number of key capabilities and aspects of novelty, which represent a significant step forward compared to the actual state of the art.

An IoT-Aware Architecture for Smart Healthcare Systems

Binary local features represent an effective alternative to real-valued descriptors, leading to comparable results for many visual analysis tasks while being characterized by significantly lower computational complexity and memory requirements. When dealing with large collections, a more compact representation based on global features is often preferred, which can be obtained from local features by means of, e.g., the bag-of-visual word model. Several applications, including, for example, visual sensor networks and mobile augmented reality, require visual features to be transmitted over a bandwidth-limited network, thus calling for coding techniques that aim at reducing the required bit budget while attaining a target level of efficiency. In this paper, we investigate a coding scheme tailored to both local and global binary features, which aims at exploiting both spatial and temporal redundancy by means of intra- and inter-frame coding. In this respect, the proposed coding scheme can conveniently be adopted to support the analyze-then-compress (ATC) paradigm. That is, visual features are extracted from the acquired content, encoded at remote nodes, and finally transmitted to a central controller that performs the visual analysis. This is in contrast with the traditional approach, in which visual content is acquired at a node, compressed and then sent to a central unit for further processing, according to the compress-then-analyze (CTA) paradigm. In this paper, we experimentally compare the ATC and the CTA by means of rate-efficiency curves in the context of two different visual analysis tasks: 1) homography estimation and 2) content-based retrieval. Our results show that the novel ATC paradigm based on the proposed coding primitives can be competitive with the CTA, especially in bandwidth limited scenarios.

Coding Local and Global Binary Visual Features Extracted From Video Sequences

Recent technological advances in integrated circuits, wireless communications, and physiological sensing have boosted the development of miniaturized, lightweight, ultra-low power, intelligent monitoring devices. A number of these devices can be integrated to form a wireless body area network (WBAN), a wireless sensor network whose devices, called motes, are positioned on the body of a person to monitor biometric parameters and vital signs. To this extent, the latest advances in mircoelectro mechanical systems (MEMS) technology have opened up the opportunity to realize miniaturized accelerometers, a sensing element that measures acceleration. The aim of the presented work is to showcase an integrated system to monitor execution of fitness and rehabilitation exercises and to provide a feedback to the user in order to correct errors and avoid hurts. The focus of our work is to analyze a simple fitness exercise like biceps curl by using a network of wireless sensors equipped with two-axes accelerometers. The sensed data from the acceleromenters is delivered to a PC which runs a graphical interface and a movement analyzer which returns in real-time the "goodness" of the performed exercise. Moreover, the analyzer also generates a more accurate analysis of the exercise which is returned to the user upon completion of the movement.

The Virtual Trainer: Supervising Movements Through a Wearable Wireless Sensor Network

Wireless Sensor Networks (WSNs) have enjoyed dramatic developments over the last decade. The availability of CMOS cameras and microphones enlarged the scope of WSNs paving the way to the development of Wireless Multimedia Sensor Networks (WMSN). Among the envisaged WMSN applications, Real-time Multimedia Monitoring constitutes one of the most promising. However, the resource requirements of these applications place difficult challenges in terms of network lifetime and scalability. This paper starts by identifying the main characteristics and requirements of Real-time Multimedia Monitoring applications and then highlights key research directions that may help to overcome those challenges.

Real-time multimedia monitoring in large-scale wireless multimedia sensor networks: Research challenges

A robust hash, or content-based fingerprint, is a succinct representation of the perceptually most relevant parts of a multimedia object. A key requirement of fingerprinting is that elements with perceptually similar content should map to the same fingerprint, even if their bitlevel representations are different. In this work we propose BAMBOO (Binary descriptor based on AsymMetric pairwise BOOsting), a binary local descriptor that exploits a combination of content-based fingerprinting techniques and computationally efficient filters (box filters, Haar-like features, etc.) applied to image patches. In particular, we define a possibly large set of filters and iteratively select the most discriminative ones resorting to an asymmetric pair-wise boosting technique. The output values of the filtering process are quantized to one bit, leading to a very compact binary descriptor. Results show that such descriptor leads to compelling results, significantly outperforming binary descriptors having comparable complexity (e.g., BRISK), and approaching the discriminative power of state-of-the-art descriptors which are significantly more complex (e.g., SIFT and BinBoost).

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Bamboo: A fast descriptor based on AsymMetric pairwise BOOsting

The Internet of Things (IoT) paradigm is expected to play a key role in the realization of Smart Cities characterized by heterogeneous service and stakeholders. The IoT ecosystem is further characterized by multiple available communication technologies and solutions with distinctive features in terms of network architectures, performance, cost, flexibility, and availability. We provide here an overview of the main IoT-based communication technologies which can enable smart services for Smart Cities, further commenting on the main advantages, disadvantages, and open challenges involved in applying each technology to the Smart City ecosystem.

https://re.public.polimi.it/bitstream/11311/1080258/1/main.pdf

Matteo Cesana

Papers

Coding Local and Global Binary Visual Features Extracted From Video Sequences

The Virtual Trainer: Supervising Movements Through a Wearable Wireless Sensor Network

Real-time multimedia monitoring in large-scale wireless multimedia sensor networks: Research challenges

Bamboo: A fast descriptor based on AsymMetric pairwise BOOsting

IoT Communication Technologies for Smart Cities