Adjustable dynamic range for paper reduction schemes in large-scale MIMO-OFDM systems

In a multi-input-multi-output (MIMO) communication system there is a necessity to limit the power that the output antenna amplifiers can deliver. Their signal is a combination of many independent channels, so the demanded amplitude can peak to many times the average value. The orthogonal frequency division multiplexing (OFDM) system causes high peak signals to occur because many subcarrier components are added by an inverse discrete Fourier transformation process at the base station. This causes out-of-band spectral regrowth. If simple clipping of the input signal is used, there will be in-band distortions in the transmitted signals and the bit error rate will increase substantially. This work presents a novel technique that reduces the peak-to-average power ratio (PAPR). It is a combination of two main stages, a variable clipping level and an Adaptive Optimizer that takes advantage of the channel state information sent from all users in the cell. Simulation results show that the proposed method achieves a better overall system performance than that of conventional peak reduction systems in terms of the symbol error rate. As a result, the linear output of the power amplifiers can be minimized with a great saving in cost

Report on the Acoustic Network Arctic Deployment, March 1994

This report describes the March 1994 Arctic deployment undertaken by the Acoustic Telemetry Group of WHOI. The deployment was a part of the 1994 Sea Ice Mechanics Initiative (SIMI) project and was based at the west SIMI camp, approximately 150 nautical miles north-east of Prudhoe Bay, Alaska. The goal of the deployment was to install a network of six high-performance acoustic modems, developed at WHOI, and to obtain a data set demonstrating the communications and acoustic monitoring capabilties of the network. The six modems in the network were deployed over an area of 22 square km and communicated via radio Ethernet with a computer at the SIMI camp. Each model had a global positioning system, an acoustic source and an 8 element receiving array. The network was operated in a round-robin broadcast mode (i.e., each modem in turn transmitted a packet of data while the others received). The transmissions were 5000 bits-per-second QPSK with a 15kHz carrier. An extensive data set including raw acoustic data source localization information, and modem position was collected during the deployment. An additional function of the acoustic network was to communicate with, and track, the Odyssey, an autonomous underwater vehicle operated by the MIT group at the SIMI camp. To this end, the Odyssey was equipped with a Datasonics modem configured for periodic QPSK transmission to the network. A data set was obtained from which both the up-link communication and localization capabilties of the network can be determined.Funding was provided by the Office of Naval Research under Contract No. N00014-93-1-0988 and the Advanced Research Projects Agency under Contract Nos. MDA972-91-j-1004 and MDA972-93-1-0019

Feasible, Robust and Reliable Automation and Control for Autonomous Systems

The Special Issue book focuses on highlighting current research and developments in the automation and control field for autonomous systems as well as showcasing state-of-the-art control strategy approaches for autonomous platforms. The book is co-edited by distinguished international control system experts currently based in Sweden, the United States of America, and the United Kingdom, with contributions from reputable researchers from China, Austria, France, the United States of America, Poland, and Hungary, among many others. The editors believe the ten articles published within this Special Issue will be highly appealing to control-systems-related researchers in applications typified in the fields of ground, aerial, maritime vehicles, and robotics as well as industrial audiences

Load/Price Forecasting and Management of Demand Response for Smart Grids: Methodologies and Challenges

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Compressive Sensing for Target Detection and Tracking within Wireless Visual Sensor Networks-based Surveillance applications

Wireless Visual Sensor Networks (WVSNs) have gained significant importance in the last few years and have emerged in several distinctive applications. The main aim of this research is to investigate the use of adaptive Compressive Sensing (CS) for efficient target detection and tracking in WVSN-based surveillance applications. CS is expected to overcome the WVSN resource constraints such as memory limitation, communication bandwidth and battery constraints. In addition, adaptive CS dynamically chooses variable compression rates according to different data sets to represent captured images in an efficient way hence saving energy and memory space. In this work, a literature review on compressive sensing, target detection and tracking for WVSN is carried out to investigate existing techniques. Only single view target tracking is considered to keep minimum number of visual sensor nodes in a wake-up state to optimize the use of nodes and save battery life which is limited in WVSNs. To reduce the size of captured images an adaptive block CS technique is proposed and implemented to compress the high volume data images before being transmitted through the wireless channel. The proposed technique divides the image to blocks and adaptively chooses the compression rate for relative blocks containing the target according to the sparsity nature of images. At the receiver side, the compressed image is then reconstructed and target detection and tracking are performed to investigate the effect of CS on the tracking performance. Least mean square adaptive filter is used to predicts target’s next location, an iterative quantized clipped LMS technique is proposed and compared with other variants of LMS and results have shown that it achieved lower error rates than other variants of lMS. The tracking is performed in both indoor and outdoor environments for single/multi targets. Results have shown that with adaptive block compressive sensing (CS) up to 31% measurements of data are required to be transmitted for less sparse images and 15% for more sparse, while preserving the 33dB image quality and the required detection and tracking performance. Adaptive CS resulted in 82% energy saving as compared to transmitting the required image with no C

Data-driven approach for highlighting priority areas for protection in marine areas beyond national jurisdiction

One of the aims of the United Nations (UN) negotiations on the conservation and sustainable use of marine biodiversity in areas beyond national jurisdiction (ABNJ) is to develop a legal process for the establishment of area-based management tools, including marine protected areas, in ABNJ. Here we use a conservation planning algorithm to integrate 55 global data layers on ABNJ species diversity, habitat heterogeneity, benthic features, productivity, and fishing as a means for highlighting priority regions in ABNJ to be considered for spatial protection. We also include information on forecasted species distributions under climate change. We found that parameterizing the planning algorithm to protect at least 30% of these key ABNJ conservation features, while avoiding areas of high fishing effort, yielded a solution that highlights 52,545,634 km2 (23.7%) of ABNJ as high priority regions for protection. Instructing the planning model to avoid ABNJ areas with high fishing effort resulted in relatively minor shifts in the planning solution, when compared to a separate model that did not consider fishing effort. Integrating information on climate change had a similarly minor influence on the planning solution, suggesting that climate-informed ABNJ protected areas may be able to protect biodiversity now and in the future. This globally standardized, data-driven process for identifying priority ABNJ regions for protection serves as a valuable complement to other expert-driven processes underway to highlight ecologically or biologically significant ABNJ regions. Both the outputs and methods exhibited in this analysis can additively inform UN decision-making concerning establishment of ABNJ protected areas

Data-driven approach for highlighting priority areas for protection in marine areas beyond national jurisdiction

One of the aims of the United Nations (UN) negotiations on the conservation and sustainable use of marine biodiversity in areas beyond national jurisdiction (ABNJ) is to develop a legal process for the establishment of area-based management tools, including marine protected areas, in ABNJ. Here we use a conservation planning algorithm to integrate 55 global data layers on ABNJ species diversity, habitat heterogeneity, benthic features, productivity, and fishing as a means for highlighting priority regions in ABNJ to be considered for spatial protection. We also include information on forecasted species distributions under climate change. We found that parameterizing the planning algorithm to protect at least 30% of these key ABNJ conservation features, while avoiding areas of high fishing effort, yielded a solution that highlights 52,545,634 km2 (23.7%) of ABNJ as high priority regions for protection. Instructing the planning model to avoid ABNJ areas with high fishing effort resulted in relatively minor shifts in the planning solution, when compared to a separate model that did not consider fishing effort. Integrating information on climate change had a similarly minor influence on the planning solution, suggesting that climate-informed ABNJ protected areas may be able to protect biodiversity now and in the future. This globally standardized, data-driven process for identifying priority ABNJ regions for protection serves as a valuable complement to other expert-driven processes underway to highlight ecologically or biologically significant ABNJ regions. Both the outputs and methods exhibited in this analysis can additively inform UN decision-making concerning establishment of ABNJ protected areas