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Temperature-depth data storage tags (DSTs) continuously store individual measurements of water depth and water temperature of the habitat used by free-ranging fish. To analyse the data, the DSTs need to be returned. Usually they are returned by fishers whereas returns of fish tagged with DSTs that died due to natural causes are rare. During an international tagging project, one DST was returned by an ornithologist which found the data logger in a pellet in a cormorant colony. Although the cod itself was not available since it was already digested, the DST provided 90 days of data prior to the predation event. The cod mostly remained in waters shallower than 40 m and conducted daily in- and offshore movements covering the whole water column, leading to daily temperature differences of up to 8.8°C. In May, during one trip where the cod used very shallow waters (1.5m water depth) in the morning (09:00 h), it was taken by a cormorant. Digestion, indicated by temperatures of 39.3°C to 41.2°C, took 31 hours. This returned DST emphasizes that large-scale tagging projects should not only raise awareness in the fisheries sector but also among ornithologists when avian predators target the tagged fish species.

This work aims at making Apollo, a popular autonomous driving framework, safer and more secure by designing a multi-domain architecture, where its components are split between a feature-rich domain running Linux and a critical domain running a real-time operating system (RTOS). The two domains are isolated by a hypervisor. We implemented a prototype where the control component has been ported from Linux to the Erika automotive-grade RTOS, and we discuss a number of challenges that have been faced in moving the component to Erika. The proposed solution has been experimentally evaluated by measuring the latencies involving processing paths passing through the control component.

Safety can be listed as one of the most important aspects of every working environment, from the low risky to the most dangerous one. Construction sites can be clearly identified among the riskiest working fields, mainly because several complex and fast maneuvers are executed in a very crowded, dynamic and uncertain scenario. Specifically referring to construction machines, typical operations such as digging, earthmoving, heavy weights loading/unloading, etc. become incredibly risky if the machine stability is not guaranteed by the operator. To this purpose, this paper proposes a practical implementation of a dynamic fall prediction system for excavators based on the concept of the Zero Moment Point (ZMP). A commercially available excavator has been dynamically modelled and equipped with a set of sensors to reconstruct the state of the machine. When the proposed system is running, an alarm rings if a critical condition is reached, thus allowing the operator to avoid risky and potentially harmful situations.

In this paper, using Reddit, we investigated on the efficacy of using an AI-based mood analysis methodology to understand how the prescription of different prenatal diagnostic tests (invasive vs non-invasive), and their corresponding outcomes, may have an impact onto the mood of parents-to-be. We found an interesting causal relationship between tests and mood that deserves a deeper comprehension by means of healthcare professionals. We have also demonstrated that combining AI-based dialogue analysis methodologies with advanced statistics may bring to a smart model, suitable for transforming a large amount of empirical data into correct medical interpretations.

The value of cooperation in pursuit-evasion games is investigated. The considered setting is that of three pursuers chasing one evader in a planar environment. The optimal evader trajectory for a well-known non-cooperative pursuer strategy is characterized. This result is instrumental to derive upper and lower bounds to the game length, in the case in which the pursuers cooperate in the chasing strategy. It is shown that the cooperation cannot reduce the capture time by more than one half with respect to the non-cooperative case, and that such bound is tight.

Deep Learning and Artificial Intelligence are slowly revolutionizing many fields of applications, having the potential to replace humans in a variety of tasks and jobs. Nevertheless, creativity has always been considered something inherently human: recent research shows that, however, this may not always be the case. From this standpoint, this paper focuses on music and on the recent advancements in deep learning applied to the generation of musical content. We argue that, while those models are able to produce results that could actually be considered music, the role of the human musician still remains preponderant in the production of a musical piece. We here reflect on such limitations, directing our efforts to imagining new tools and instruments that may allow to experience new forms of interaction while supporting novel processes of creativity and music production.

Network densification in future fifth generation wireless networks is considered a key technology to fulfill the challenging requirements in terms of requested capacity. In addition, the ultra dense network (UDN) can be a useful mean to increase the security in the wireless link, where a huge amount of sensitive and confidential data will be transmitted. In particular, the dense network deployment offers new opportunities for achieving security at physical layer because wireless channels are more random and the inter-cell interference can be beneficial. In this context, where each user equipment is under the coverage of several cells, the user association policy can be suitably designed to increase the physical-layer security. This paper investigates a new metric for the security-based user association in UDNs. In particular, since the position of the eavesdropper is typically not known, a measure of the secure area is introduced, and then a new association policy based on this metric is proposed and its performance is compared with that of the classical best quality-channel association. Numerical results show that this approach significantly outperforms the traditional one.

In this paper, the problem of optimal charging of plug-in electric vehicles in a parking lot is addressed. The parking time is assumed to be uncertain and characterized by a statistical distribution with fixed first and second order moments. The energy price during the day is assumed to be known on a day ahead basis. In this context, the problem of maximizing the profit of a charging station is formulated in a receding horizon framework, whose solution provides the optimal charging policy. To accommodate stochastic uncertainty affecting the parking time, a distributionally robust joint chance constraint approach is adopted when formulating the overall optimization problem. The optimal solution guarantees a customer satisfaction criterion expressed as a probabilistic confidence level. Simulation results on a case study show effectiveness and computational feasibility of the proposed approach.

Providing actionable information to soldiers in a timely manner remains one of the primary goals of information management systems. Unfortunately, the challenging tactical networking environment composed of wireless links that are unreliable, prone to disconnection, present high latencies, and constrained in available bandwidth make information management for the tactical domain a difficult problem. The problem is exacerbated by the enterprise-style systems that are often used in operations centers that need to exchange information with the tactical edge. These enterprise systems typically rely on Service-Oriented Architectures and enterprise message / service buses to rapidly integrate capabilities and services for information management. However, the architectures and protocols utilized here do not transition very well into the tactical domain. This paper describes new capabilities that have been developed to help bridge the impedance mismatch between enterprise and tactical domains and support the information management needs of the warfighter in the tactical domain.

Disaster recovery operations are extremely challenging and place significant demands on multiple resources, including local and international emergency response personnel, non-governmental organizations, and the military. In the immediate aftermath of a disaster, one of the most pressing requirements is for situational awareness (SA) so that resources, including personnel and supplies, may be prioritized to have the most impact and help those in the most need. As the recovery operations continue, the SA needs to be continuously updated based on changing conditions in the affected areas. There are many sources of information to provide SA, including reporting by the victims of the disaster as well as observations made by responding personnel. In this context, SA can be significantly enhanced via information obtained from Internet of Things (IoT) devices, especially in a smart city environment. This paper explores the potential to exploit Smart City IoT capabilities to help with disaster recovery operations.

In this paper, a new family of pursuit strategies is presented for a fundamental pursuit-evasion game: the lion and man problem. The classic formulation due to David Gale is considered: the players move alternatively in a discrete-time setting and are constrained to remain in the non-negative quadrant of the plane. The proposed solution relies on the stepwise optimization of a suitable functional, whose generality provides an appealing degree of freedom for customization within specific pursuit problems and bounded environments. Numerical simulations show the benefits of the new approach with respect to existing pursuit strategies.

Efficient network communications are essential to enable network-centric warfare. However, tactical edge networks present an extremely challenging and heterogeneous networking environment due to heterogeneous architectures, dynamic topologies, unwanted adversarial behavior, interference, and other wireless channel conditions. In order to support end-user applications and guarantee system performance and interoperability in such constrained environments, it is critical to adapt the volume and type of traffic generated by applications to the continuously varying network conditions. To this end, we extended the Agile Computing Middleware (ACM) with capabilities specifically designed to provide network state detection and adaptation in constrained networks. In this paper, we present SENSEI (for Smart Estimation of Network StatE Information), a set of components of the ACM that provides effective strategies for the dissemination of network state information. SENSEI implements a content- and context-aware clustering-based algorithm for the distribution of network state information that can significantly reduce the overhead associated with network state information sharing.

This paper deals with the problem of minimizing the electricity bill of smart buildings equipped with centralized heating systems and thermal and electrical storage devices. Building participation in a Demand-Response program in the form of price-volume signals is also considered. The proposed solution is based on a Model Predictive Control approach to operate the heating system and the storage devices in an optimal fashion, under thermal comfort and technological constraints.

This paper deals with the problem of minimizing the electricity bill of smart buildings equipped with centralized heating systems and thermal and electrical storage devices. Building participation in a Demand-Response program in the form of price-volume signals is also considered. The proposed solution is based on a Model Predictive Control approach to operate the heating system and the storage devices in an optimal fashion, under thermal comfort and technological constraints.

Tactical Edge Networks are one of the most challenging communication environments, where node mobility, constrained devices, and the wide use of wireless ad hoc channels for communications cause frequent link disruption and network partitioning. Additionally, applications running in Tactical Edge Networks often follow one-to-many and many-to-many communication models that require the nodes' cooperation to enable data delivery. Many studies proposed the use of peer-to-peer systems in mobile ad hoc networks to remove any dependency on centralized nodes and to cope with link disruptions and network partitioning phenomena that derive from the extreme dynamicity of Tactical Edge Networks. In this paper, we compare the performance of file sharing in mobile ad hoc networks using an implementation of the BitTorrent peer-to-peer protocol and DisService, a middleware for information dissemination specifically designed for Tactical Edge Networks. DisService supports applications with routing and peer-to-peer content delivery in mobile ad hoc networks. We ran several experiments in an emulated environment to evaluate the performance of each solution when sharing large files. The obtained results show that TEN-specific solutions such as DisService can outperform traditional peer-to-peer solutions like BitTorrent.

The paper deals with the problem of early detecting cracks in composite materials for avionic applications. In particular, the authors present a THz imaging system that exploits compressive sampling (CS) to detect submillimeter cracks with a reduced measurement burden. Traditional methods for THz imaging usually involve raster scan of the issue of interest by means of highly collimated radiations and the corresponding image is achieved by measuring the received THz power in different positions (pixels) of the desired image. As it can be expected, the higher the required resolution, the longer the measurement time. On the contrary, two different approaches for THz imaging (namely, continuous wave and time domain spectroscopy) combined with a proper CS solution are used to assure as good results as those granted by traditional raster scan; a proper set of masks (each of which characterized by a specific random pattern) are defined to the purpose. A number of tests conducted on simulated data highlighted the promising performance of the proposed method thus suggesting its implementation in an actual measurement setup.