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Abstract-This paper presents an automated real-time atrial fibrillation (AF) detection approach that relies on the observation of two characteristic irregularities of AF episodes in the electrocardiogram (ECG) signal. The results generated after the analysis of these irregularities are subsequently analyzed in real-time using a new fuzzy classifier. We have optimized this novel AF classification framework to require very limited processing, memory storage and energy resources, which makes it able to operate in real-time on a wearable wireless sensor platform. Moreover, our experimental results indicate that the proposed on-line approach shows a similar accuracy to stateof-the-art off-line AF detectors, achieving up to 96% sensitivity and 93% specificity. Finally, we present a detailed energy study of each component of the target wearable wireless sensor platform, while executing the automated AF detection approach in a real operating scenario, in order to evaluate the lifetime of the overall system. This study indicates that the lifetime of the platform is increased by using the proposed method to detect AF in real-time and diagnose the patient with respect to a streaming application that sends the raw signal to a central coordinator (e.g., smartphone or laptop) for its ulterior processing.

Abstract-Wireless body sensor networks (WBSNs) are a rising technology that allows constant and unobtrusive monitoring of the vital signals of a patient. The configuration of a WBSN node proves to be critical in order to maximize its lifetime, while meeting the predefined performance during signal sensing, preprocessing, and wireless transmission to the base station. In this work, we propose a model-based optimization framework for WBSN nodes, which is centered on a detailed analytical characterization of the most energy-demanding components of this application domain. We also propose a multi-objective exploration algorithm to evaluate the node configurations and the corresponding performance tradeoffs. A case study is discussed to validate the proposed framework, proving that our model captures the behavior of real WBSNs and efficiently leads to the determination of the Pareto-optimal configurations.

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Wireless sensor networks (WNSs) are gradually evolving from a promising technology to a well-established reality in a large set of different domains. In order to fulfill the requirements of the specific scenario, a WSN must provide the right tradeoff between performance and lifetime, which is heavily determined by the network design. However, although the complexity of WSNs is increasing, the design space exploration is often carried out manually without the support of a general analytical methodology. In this paper, we advocate a model-based approach as an efficient and scalable way to explore the energy-performance tradeoffs during the design. In particular, we show that it is possible to define systemlevel models to describe wide classes of WSNs, providing a quick and accurate network evaluation. As a proof of concept, we propose a general model that describes the main characteristics of a class of WSNs for human health monitoring, and we apply it to a real case study. The results show that the energy-performance estimation error of the model never exceeds 1.74% compared to real data, while the evaluation time is reduced by up to 6 orders of magnitude with respect to an accurate network simulation.

Partial dynamic self-reconfiguration can be obtained, in Xilinx's Virtex families of FPGAs, through the Internal Configuration Access Port (ICAP). Reconfiguration time is thus bounded to the ICAP rate. Different techniques have been proposed to speedup the reconfiguration process and one of the most promising one uses a memory to store the bitstream inside the IP-Core that controls the ICAP port. The size of this memory can be chosen during the implementation phase in order to find a trade off between resource's requirement and reconfiguration throughput. Moreover, a good level of customization can be achieved by choosing both the bus interface used by the ICAP controller and the implementation type, Slices or BRAMs, of its internal memory. This paper describes a framework used to create the most suitable controller according to the reconfiguration scenario where it will be used. To set all the parameters used to create the controller, a set of metrics, used to describe the reconfiguration scenario, has been defined. These metrics are used in the proposed flow to find the setting of the ICAP controller that best suits the scenario in which it will operate.

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The complexity of Wireless Sensor Networks (WSNs) has been constantly increasing over the last decade, and the necessity of efficient CAD tools has been growing accordingly. In fact, the size of the design space of a WSN has become large, and an exploration conducted by using semi-random algorithms (such as the popular genetic or simulated annealing algorithms) requires an unacceptable amount of time to converge due to the high number of parameters involved. To address this issue, in this paper we introduce a knowledgebased design space exploration algorithm for the WSN domain, which is based on a discrete-space Markov decision process (MDP). In order to enhance the performance of the proposed algorithm and to increase its scalability, we tailor the classical MDP approach to the specific aspects that characterize the WSN domain. We exploit domain-specific knowledge to choose the best node-level configuration in WSNs using slotted star topology in order to reduce the exploration time. The proposed approach has been tested on IEEE 802.15.4 star networks with various configurations of the number of nodes and their packet rates. Experimental results show that the proposed algorithm reduces the number of simulations required to converge, with respect to state-of-the-art algorithms (e.g., NSGA-II, PMA and MOSA), from 60 to 87%.