Wireless Video Transmission with Over-the-Air Packet Mixing

In this paper, we propose a system for wireless video transmission with a wireless physical layer (PHY) that supports cooperative forwarding of interfered/superimposed packets. Our system model considers multiple and independent unicast transmissions between network nodes while a number of them serve as relays of the interfered/superimposed signals. For this new PHY the average transmission rate that each node can achieve is estimated first. Next, we formulate a utility optimization framework for the video transmission problem and we show that it can be simplified due to the features of the new PHY. Simulation results reveal the system operating regions for which superimposing wireless packets is a better choice than a typical cooperative PHY.Comment: 2012 Packet Video Worksho

Multi-Source Cooperative Communication with Opportunistic Interference Cancelling Relays

In this paper we present a multi-user cooperative protocol for wireless networks. Two sources transmit simultaneously their information blocks and relays employ opportunistically successive interference cancellation (SIC) in an effort to decode them. An adaptive decode/amplify-and-forward scheme is applied at the relays to the decoded blocks or their sufficient statistic if decoding fails. The main feature of the protocol is that SIC is exploited in a network since more opportunities arise for each block to be decoded as the number of used relays NRU is increased. This feature leads to benefits in terms of diversity and multiplexing gains that are proven with the help of an analytical outage model and a diversity-multiplexing tradeoff (DMT) analysis. The performance improvements are achieved without any network synchronization and coordination. In the final part of this work the closed-form outage probability model is used by a novel approach for offline pre-selection of the NRU relays, that have the best SIC performance, from a larger number of NR nodes. The analytical results are corroborated with extensive simulations, while the protocol is compared with orthogonal and multi-user protocols reported in the literature.Comment: in IEEE Transactions on Communications, 201

Design methodology for 360-degree immersive video applications

360-degree immersive video applications for Head Mounted Display (HMD) devices offer great potential in providing engaging forms of experiential media solutions. Design challenges emerge though by this new kind of immersive media due to the 2D form of resources used for their construction, the lack of depth, the limited interaction, and the need to address the sense of presence. In addition, the use of Virtual Reality (VR) is related to cybersickness effects imposing further implications in moderate motion design tasks. This research project provides a systematic methodological approach in addressing those challenges and implications in 360-degree immersive video applications design. By studying and analysing methods and techniques efficiently used in the area of VR and Games design, a rigorous methodological design process is proposed. This process is introduced by the specification of the iVID (Immersive Video Interaction Design) framework. The efficiency of the iVID framework and the design methods and techniques it proposes is evaluated through two phases of user studies. Two different 360-degree immersive video prototypes have been created to serve the studies purposes. The analysis of the purposes of the studies ed to the definition of a set of design guidelines to be followed along with the iVID framework for designing 360-degree video-based experiences that are engaging and immersive

PRISMA: PRoximal Iterative SMoothing Algorithm

Motivated by learning problems including max-norm regularized matrix completion and clustering, robust PCA and sparse inverse covariance selection, we propose a novel optimization algorithm for minimizing a convex objective which decomposes into three parts: a smooth part, a simple non-smooth Lipschitz part, and a simple non-smooth non-Lipschitz part. We use a time variant smoothing strategy that allows us to obtain a guarantee that does not depend on knowing in advance the total number of iterations nor a bound on the domain

Analysis and evaluation of SafeDroid v2.0, a framework for detecting malicious Android applications

Android smartphones have become a vital component of the daily routine of millions of people, running a plethora of applications available in the official and alternative marketplaces. Although there are many security mechanisms to scan and filter malicious applications, malware is still able to reach the devices of many end-users. In this paper, we introduce the SafeDroid v2.0 framework, that is a flexible, robust, and versatile open-source solution for statically analysing Android applications, based on machine learning techniques. The main goal of our work, besides the automated production of fully sufficient prediction and classification models in terms of maximum accuracy scores and minimum negative errors, is to offer an out-of-the-box framework that can be employed by the Android security researchers to efficiently experiment to find effective solutions: the SafeDroid v2.0 framework makes it possible to test many different combinations of machine learning classifiers, with a high degree of freedom and flexibility in the choice of features to consider, such as dataset balance and dataset selection. The framework also provides a server, for generating experiment reports, and an Android application, for the verification of the produced models in real-life scenarios. An extensive campaign of experiments is also presented to show how it is possible to efficiently find competitive solutions: the results of our experiments confirm that SafeDroid v2.0 can reach very good performances, even with highly unbalanced dataset inputs and always with a very limited overhead

Integrating Preclinical and Clinical Models of Negative Urgency

Overwhelming evidence suggests that negative urgency is robustly associated with rash, ill-advised behavior, and this trait may hamper attempts to treat patients with substance use disorder. Research applying negative urgency to clinical treatment settings has been limited, in part, due to the absence of an objective, behavioral, and translational model of negative urgency. We suggest that development of such a model will allow for determination of prime neurological and physiological treatment targets, the testing of treatment effectiveness in the preclinical and the clinical laboratory, and, ultimately, improvement in negative-urgency-related treatment response and effectiveness. In the current paper, we review the literature on measurement of negative urgency and discuss limitations of current attempts to assess this trait in human models. Then, we review the limited research on animal models of negative urgency and make suggestions for some promising models that could lead to a translational measurement model. Finally, we discuss the importance of applying objective, behavioral, and translational models of negative urgency, especially those that are easily administered in both animals and humans, to treatment development and testing and make suggestions on necessary future work in this field. Given that negative urgency is a transdiagnostic risk factor that impedes treatment success, the impact of this work could be large in reducing client suffering and societal costs