In studying the performance of coded communications over memoryless channels (with or without fading), the results are given as upper bounds on the average bit error probability (BEP). In principle, there are three different approaches to arriving at these bounds, all of which employ obtaining the so-called pairwise error probability , or the probability of choosing one symbol sequence over another for a given pair of possible transmitted symbol sequences, followed by a weighted summation over all pairwise events. In this chapter, we will focus on the results obtained from the third approach since these provide the tightest upper bounds on the true performance. The first emphasis will be placed on evaluating the pairwise error probability with and without CSI, following which we shall discuss how the results of these evaluations can be used via the transfer bound approach to evaluate average BEP of coded modulation transmitted over the fading channel.

Coded Communication over Fading Channels

This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.

/pdf/online-short-term-solar-power-forecasting-1ljgpbshkb.pdf

Online Short-term Solar Power Forecasting

Visible light communication: Applications, architecture, standardization and research challenges

Modern society faces serious problems with transportation systems, including but not limited to traffic congestion, safety, and pollution. Information communication technologies have gained increasing attention and importance in modern transportation systems. Automotive manufacturers are developing in-vehicle sensors and their applications in different areas including safety, traffic management, and infotainment. Government institutions are implementing roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. By seamlessly integrating vehicles and sensing devices, their sensing and communication capabilities can be leveraged to achieve smart and intelligent transportation systems. We discuss how sensor technology can be integrated with the transportation infrastructure to achieve a sustainable Intelligent Transportation System (ITS) and how safety, traffic control and infotainment applications can benefit from multiple sensors deployed in different elements of an ITS. Finally, we discuss some of the challenges that need to be addressed to enable a fully operational and cooperative ITS environment.

Sensor Technologies for Intelligent Transportation Systems.

Considering the increase of urban population and traffic congestion, smart parking is always a strategic issue to work on, not only in the research field, but also from economic interests. Thanks to information and communication technology evolution, drivers can more efficiently find satisfying parking spaces with smart parking services. The existing and ongoing works on smart parking are complicated and transdisciplinary. While deploying a smart parking system, cities, as well as urban engineers, need to spend a very long time to survey and inspect all the possibilities. Moreover, many varied works involve multiple disciplines, which are closely linked and inseparable. To give a clear overview, we introduce a smart parking ecosystem and propose a comprehensive and thoughtful classification by identifying their functionalities and problematic focuses. We go through the literature over the period of 2000–2016 on parking solutions as they were applied to smart parking development and evolution, and propose three macro-themes: information collection, system deployment, and service dissemination. In each macro-theme, we explain and synthesize the main methodologies used in the existing works and summarize their common goals and visions to solve current parking difficulties. Finally, we give our engineering insights and show some challenges and open issues. Our survey gives an exhaustive study and a prospect in a multidisciplinary approach. Besides, the main findings of the current state-of-the-art throw out recommendations for future research on smart cities and the Internet architecture.

/pdf/a-survey-of-smart-parking-solutions-29hqwxlnoj.pdf

A Survey of Smart Parking Solutions

http://www.scielo.org.mx/pdf/jart/v11n5/v11n5a11.pdf

A Survey of Intelligent Car Parking System

A fast learning neuro-evolutionary technique that evolves Artificial Neural Networks using Cartesian Genetic Programming (CGPANN) is used to detect the presence of breast cancer. Features from breast mass are extracted using fine needle aspiration (FNA) and are applied to the CGPANN for diagnosis of breast cancer. FNA data is obtained from the Wisconsin Diagnostic Breast Cancer website and is used for training and testing the network. The developed system produces fast and accurate results when compared to contemporary work done in the field. The error of the model comes out to be as low as 1% for Type-I (classifying benign sample falsely as malignant) and 0.5% for Type-II (classifying malignant sample falsely as benign).

Breast cancer detection using cartesian genetic programming evolved artificial neural networks

One of the major problems, caused by traffic congestion, owes its existence to the unwanted delay experienced by the priority vehicles. The evaluation of two scheduling algorithms as adaptive traffic control algorithms has been proposed here to reduce this unwanted delay. One of these algorithms is the earliest deadline first (EDF) algorithm, whereas the other is the fixed priority (FP) algorithm. The performance of both algorithms as adaptive traffic light control algorithms is evaluated for isolated traffic intersections. A comparative study is performed here, where the performance of these algorithms is compared against a fixed static traffic light controller. Moreover, their performance is also compared against each other. Conclusive results from the simulation of the algorithms reveal that the number of stops, average delay, and mean trip time of the priority vehicles is significantly reduced by the implementation of these algorithms. Furthermore, it has been shown that the overall performance of EDF is much better than FP in terms of improvement of different performance measures for congestion reduction of priority vehicles.

/pdf/earliest-deadline-based-scheduling-to-reduce-urban-traffic-2e5dt7krpi.pdf

Earliest-deadline-based scheduling to reduce urban traffic congestion

Traffic congestion is not only a cause of nuisance for general commuters but also a factor that has a measurable impact on the economy if not handled proactively. When congestion increases, the waiting time for commuters increases which results in wasted fuel and wasted time. Wasted fuel adds to the import bill of a country and lost time results in loss of productivity. Traffic can be regulated at various points in order to reduce congestion and eliminate bottleneck areas. In this paper, we propose the use of conventional scheduling to regulate traffic at intersections in order to reduce congestion. We propose minimum destination distance first (MDDF) and minimum average destination distance first (MADDF) algorithms and compare them with some of the relevant existing scheduling algorithms. The proposed algorithms can not only be easily implemented on low cost hardware but also show better performance and outperform the existing algorithms that are considered, based on simulation results. Simulation results show that the MDDF and MADDF algorithms reduce the traffic congestion at intersections by up to 80% in some cases compared to static traffic lights.

Sahibzada Ali Mahmud

Papers

A Survey of Intelligent Car Parking System

Breast cancer detection using cartesian genetic programming evolved artificial neural networks

Earliest-deadline-based scheduling to reduce urban traffic congestion

Shortest Processing Time Scheduling to Reduce Traffic Congestion in Dense Urban Areas

Foreign Currency Exchange Rates Prediction Using CGP and Recurrent Neural Network