Nearest Neighbour Decoding and Pilot-Aided Channel Estimation in Stationary Gaussian Flat-Fading Channels

We study the information rates of non-coherent, stationary, Gaussian, multiple-input multiple-output (MIMO) flat-fading channels that are achievable with nearest neighbour decoding and pilot-aided channel estimation. In particular, we analyse the behaviour of these achievable rates in the limit as the signal-to-noise ratio (SNR) tends to infinity. We demonstrate that nearest neighbour decoding and pilot-aided channel estimation achieves the capacity pre-log - which is defined as the limiting ratio of the capacity to the logarithm of SNR as the SNR tends to infinity - of non-coherent multiple-input single-output (MISO) flat-fading channels, and it achieves the best so far known lower bound on the capacity pre-log of non-coherent MIMO flat-fading channels.Comment: 5 pages, 1 figure. To be presented at the IEEE International Symposium on Information Theory (ISIT), St. Petersburg, Russia, 2011. Replaced with version that will appear in the proceeding

Digital Forensics for Skulls Classification in Physical Anthropology Collection Management

The size, shape, and physical characteristics of the human skull are distinct when considering individual humans. In physical anthropology, the accurate management of skull collections is crucial for storing and maintaining collections in a cost-effective manner. For example, labeling skulls inaccurately or attaching printed labels to skulls can affect the authenticity of collections. Given the multiple issues associated with the manual identification of skulls, we propose an automatic human skull classification approach that uses a support vector machine and different feature extraction methods such as gray-level co-occurrence matrix features, Gabor features, fractal features, discrete wavelet transforms, and combinations of features. Each underlying facial bone exhibits unique characteristics essential to the face's physical structure that could be exploited for identification. Therefore, we developed an automatic recognition method to classify human skulls for consistent identification compared with traditional classification approaches. Using our proposed approach, we were able to achieve an accuracy of 92.3–99.5% in the classification of human skulls with mandibles and an accuracy of 91.4–99.9% in the classification of human skills without mandibles. Our study represents a step forward in the construction of an effective automatic human skull identification system with a classification process that achieves satisfactory performance for a limited dataset of skull images

An Assessment on the Hidden Ecological Factors of the Incidence of Malaria

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).Confounding effects of climatic factors temporally contribute to the prevalence of malaria. In this study, we explore a new framework for assessment and identification of hidden ecological factors to the incidence of malaria. A statistical technique, partial least squares path modeling and exploratory factor analysis, is employed to identify hidden ecological factors. Three hidden factors are identified: Factor I is related to minimum temperature and relative humidity, Factor II is related to maximum temperature and solar radiation and Factor III is related to precipitation and wind speed, respectively. Factor I is identified as the most influential hidden ecological factor of malaria incidence in the study area, as evaluated by communality and Dillon-Goldstein’s indices

On the Impact of Transposition Errors in Diffusion-Based Channels

In this work, we consider diffusion-based molecular communication with and without drift between two static nano-machines. We employ type-based information encoding, releasing a single molecule per information bit. At the receiver, we consider an asynchronous detection algorithm which exploits the arrival order of the molecules. In such systems, transposition errors fundamentally undermine reliability and capacity. Thus, in this work we study the impact of transpositions on the system performance. Towards this, we present an analytical expression for the exact bit error probability (BEP) caused by transpositions and derive computationally tractable approximations of the BEP for diffusion-based channels with and without drift. Based on these results, we analyze the BEP when background is not negligible and derive the optimal bit interval that minimizes the BEP. Simulation results confirm the theoretical results and show the error and goodput performance for different parameters such as block size or noise generation rate.Comment: This paper has been submitted to IEEE Transactions on Communication

A 3D-collaborative wireless network: towards resilient communication for rescuing flood victims

Every year, floods result in huge damage and devastation both to lives and properties all over the world. Much of this devastation and its prolonged effects result from a lack of collaboration among the rescue agents as a consequence of the lack of reliable and resilient communication platform in the disrupted and damaged environments. In order to counteract this issue, this paper aims to propose a three-dimensional (3D)- collaborative wireless network utilizing air, water and ground based communication infrastructures to support rescue missions in flood-affected areas. Through simulated Search and Rescue(SAR) activities, the effectiveness of the proposed network model is validated and its superiority over the traditional SAR is demonstrated, particularly in the harsh flood environments. The model of the 3D-Collaborative wireless network is expected to significantly assist the rescuing teams in accomplishing their task more effectively in the corresponding disaster areas

Collab-SAR:A Collaborative Avalanche Search-and-Rescue Missions Exploiting Hostile Alpine Networks

Every year, Alpine experiences a considerable number of avalanches causing danger to visitor and saviors, where most of the existing techniques to mitigate the number of fatalities in such hostile environments are based on a non-collaborative approach and is time- and effort-inefficient. A recently completed European project on Smart collaboration between Humans and ground-aErial Robots for imProving rescuing activities in Alpine environments (SHERPA) has proposed a novel collaborative approach to improve the rescuing activities. To be an integral part of the SHERPA framework, this paper considers deployment of an air-ground collaborative wireless network (AGCWN) to support search and rescue (SAR) missions in hostile alpine environments. We propose a network infrastructure for such challenging environments by considering the available network components, hostility of the environments, scenarios, and requirements. The proposed infrastructure also considers two degrees of quality of service, in terms of high throughput and long coverage range, to enable timely delivery of videos and images of the long patrolled area, which is the key in any searching and rescuing mission. We also incorporate a probabilistic search technique, which is suitable for collaborative search assuming AGCWN infrastructure for sharing information. The effectiveness of the proposed infrastructure and collaborative search technique, referred to as Collab-SAR, is demonstrated via a series of computer simulations. The results confirm the effectiveness of the proposal

L-CAQ: Joint link-oriented channel-availability and channel-quality based channel selection for mobile cognitive radio networks

Channel availability probability (CAP) and channel quality (CQ) are two key metrics that can be used to efficiently design a channel selection strategy in cognitive radio networks. For static scenarios, i.e., where all the users are immobile, the CAP metric depends only on the primary users' activity whereas the CQ metric remains relatively constant. In contrast, for mobile scenarios, the values of both metrics fluctuate not only with time (time-variant) but also over different links between users (link-variant) due to the dynamic variation of primary- and secondary-users' relative positions. As an attempt to address this dynamic fluctuation, this paper proposes L-CAQ: a link-oriented channel-availability and channel-quality based channel selection strategy that aims to maximize the link throughput. The L-CAQ scheme considers accurate estimation of the aforementioned two channel selection metrics, which are governed by the mobility-induced non-stationary network topology, and endeavors to select a channel that jointly maximizes the CAP and CQ. The benefits of the proposed scheme are demonstrated through numerical simulation for mobile cognitive radio networks

A cyber-enabled mission-critical system for post-flood response:Exploiting TV white space as network backhaul links

A crucial problem in post-flood recovery actions is the ability to rapidly establish communication and collaboration among rescuers to conduct timely and effective search and rescue (SAR) mission given disrupted telecommunication infrastructure to support the service. Aimed at providing such proximity service (ProSe) for mission-critical data exchange in the post-flood environment, the majority of existing solutions rely heavily upon ad-hoc networking approaches, which suffer from restricted communication range and the limited scope of interaction. As an effort to broaden the ProSe coverage and expand integrated global-local information exchange in the post-flood SAR activities, this paper proposes a novel network architecture in the form of a cyber-enabled mission-critical system (CEMCS) for acquiring and communicating post-flood emergency data by exploiting TV white space spectrum as network backhaul links. The primary method of developing the proposed system builds upon a layered architecture of wireless local, regional and wide-area communications, and incorporates collaborative network components among these layers. The desirable functionalities of CEMCS are showcased through formulation and the development of an efficient global search strategy exploiting a wide range of collaboration among network agents. The simulation results demonstrate the capability of CEMCS to provide ProSe in the post-flood scenarios as reflected by reliable network performance (e.g., packet delivery ratio nearing 80%-90%) and the optimality of efficient search algorithm

Detection of JavaScript Injection Eavesdropping on WebRTC communications

WebRTC is a Google-developed project that allows users to communicate directly. It is an open-source tool supported by all major browsers. Since it does not require additional installation steps and provides ultra-low latency streaming, smart city and social network applications such as WhatsApp, Facebook Messenger, and Snapchat use it as the underlying technology on the client-side both on desktop browsers and mobile apps. While the open-source tool is deemed to be secure and despite years of research and security testing, there are still vulnerabilities in the real-time communication application programming interface (API). We show in this paper how eavesdropping can be enabled by exploiting weaknesses and loopholes found in official WebRTC specifications. We demonstrate through real-world implementation how an eavesdropper can intercept WebRTC video calls by installing a malicious code onto the WebRTC webserver. Furthermore, we identify and discuss several, easy to perform, ways to detect wiretapping. Our evaluation shows that several indicators within webrtc-internals API traces can be used to detect anomalous activities, without the need for network monitoring tools