Urban travel demand estimation using genetic algorithm

Constant increase in urban road traffic is forcing traffic authorities to confront unavoidable congestion problems these days. This creates new challenges for city planners to generate traffic routes with better designs and improve the existing roads. In order to analyze and improve traffic routes, there is a need to understand movement patterns of major vehicular traffic inside the city. Origin Destination(OD) estimation is one such method for understanding the movement patterns. The objective is to find an optimal OD matrix for city traffic, which is a subtle process for classic algorithms. The solution to this problem is Genetic Algorithm (GA) approach which is a search heuristics to find optimal solution from a set of random solutions.

A Genetic Algorithm approach has been developed to find an optimal OD matrix, showing the actual travel patterns of significant number of vehicles inside the city. Once the best OD matrix is obtained, it yields a big opportunity for traffic planners to analyze and improve traffic scenario. Genetic Algorithm can play a major role in solving complex problems. In this paper, one such problem of estimating OD matrix of a city is discussed and implemented. In conjunction to genetic algorithm, Mean Absolute Percentage Error(MAPE) has been used as a fitness criteria for finding the optimal OD matrix.

A Large-Scale SUMO-Based Emulation Platform

A hardware-in-the-loop simulation platform for emulating large-scale intelligent transportation systems is presented. The platform embeds a real vehicle into SUMO, a microscopic road traffic simulation package. Emulations, consisting of the real vehicle, and potentially thousands of simulated vehicles, are run in real time.

The platform provides an opportunity for real drivers to gain a feel of being in a large-scale connected vehicle scenario. Various applications of the platform are presented.

Performance Evaluation of a VANET Simulation System Using NS-3 and SUMO

In this paper, we investigate the performance of HWMP, OLSR and DD protocols in a VANET crossroad scenario. The mobility patterns of vehicles are generated by means of SUMO (Simulation of Urban Mobility) and as communication protocol simulator is used NS3 (Network Simulator 3). For the simulations, we used IEEE 802.11p standard, Two Ray Ground Propagation Loss Model and sent multiple CBR flows over UDP between 20 source-destination pairs.

We use Packet Delivery Ratio (PDR), throughput and delay as evaluation metrics. We compared the performance of three protocols and the simulation results shows that the DD protocol has better PDR and throughput compared with HWMP and OLSR protocol. However, the DD protocol has a long delay because is a delay tolerant protocol.

An Improved Empirical Model for Retrieving Bottom Reflectance in Optically Shallow Water

Satellite remote sensing has become an essential observing system to obtain comprehensive information on the status of coastal habitats. However, a significant challenge in remote sensing of optically shallow water is to correct the effects of the water column. This challenge becomes particularly difficult due to the spatial and temporal variability of water optical properties. In order to model the light distribution for optically shallow water and retrieve the bottom reflectance, a parameterized model was proposed by introducing an important adjusted factor g. The synthetic data sets generated by HYDROLIGHT were utilized to train a neural network (NN) and then to derive the adjustable parameter values. The parameter g was found to vary with water depth, water optical properties, and bottom reflectance. Specifically, it revealed two obvious patterns among the different benthic habitat types.

In coral reef, seagrass, and macrophyte habitats, g exhibited a remarkable peak at about 550 nm. The peak has a value of about 2.47-2.49. In white sand or hardpan habitats, g spectra are relatively flat. The semi-empirical model was applied to calculate the bottom reflectance from the new weighting factor, the downward diffuse attenuation coefficient, and the irradiance reflectance just below the sea surface collected in Sanya Bay in 2008 and 2009. Good agreement between the predicted and measured values demonstrated that the weighting factor g is an effective tool to modify the model for interpreting and predicting bottom reflectance without the need for any localized input (R2 > 0.79).

RF performance of InGaAs-based T-gate junctionless field-effect transistors which applicable for high frequency network systems

The T-gate InGaAs-based JLFET’s which has high frequency RF characteristics have been demonstrated by TCAD tool. To achieve advanced performance of RF characteristics, the T-gate structure is applied, also. By T-gate structure we decrease gate resistance (RG) and achieve a higher maximum oscillation frequency (fmax) compare with planar-type structure.

However, the increase of parasitic gate capacitance degrades current gain cut-off frequency (fT) and this trade-off between parasitic components and optimal device structure will be discussed.

Nonmechanical Laser Beam Steering Based on Polymer Polarization Gratings: Design Optimization and Demonstration

We present a wide-angle, nonmechanical laser beam steerer based on polymer polarization gratings with an optimal design approach for maximizing field-of-regard ($F!O!R$ ). The steering design offers exponential scaling of the number of steering angles, called suprabinary steering. The design approach can be easily adapted for any 1-D or 2-D (e.g., symmetric or asymmetric FOR) beam steering. We simulate a system using a finite difference and ray tracing tools and fabricate coarse beam steerer with 65$^{circ}$ $F!O!R$ with $sim$ 8$^{circ}$ resolution at 1550 nm.

We demonstrate high optical throughput (84$%$ –87$%$) that can be substantially improved by optimizing substrates and electrode materials. This beam steerer can achieve very low sidelobes and supports comparatively large beam diameters paired with a very thin assembly and low beam walk-off. We also demonstrate using a certain type of LC variable retarder that the total switching time from any steering angle to another can be 1.7 ms or better.

Dual band microstrip patch antenna for MIMO system

This work targets design of microstrip patch antenna that will resonate at two different band of frequencies which can be used as elements of array for MIMO system. The proposed dual band antenna operates at ISM band (2.37Ghz-2.48Ghz) and Wimax band (3.46 GHz-3.56 GHz).Bandwidth of operation offered by the given microstrip antenna in ISM band is around 110 Mhz while in Wimax band is 110 Mhz.

The designed dual band antenna was optimized using simulation tool CAD-FEKO_v6.2 which works on Method of Moments. Antenna was manufactured on FR4 substrate having εr= 4.4.The results obtained from simulating antenna with the help of simulation software matches with results of manufactured antenna obtained from Antritsu vector network analyzer.

Tropospheric scintillation estimation using 10 years meteorological data

This paper presents estimation of tropospheric scintillation based on ITU-R model from previous 10 years meteorological data. Scintillation As(p) is critical in designing of microwave links for achieving optimal performance. There are many phenomenons that cause degradation to signal during transmission through the earth’s atmosphere one of them is scintillation. Scintillation estimation is based on measurements of surface temperature T and relative humidity RH.

Input parameter for ITU-R scintillation estimation model is the monthly average of wet part of refractivity Nwet. MATLAB software tool is employed to show the results for tropospheric scintillation in various seasons for time percentage p and frequencies ranging from 4GHz to 20GHz.

Production strategies for maximizing recovery from a strong bottom water drive reservoir

This paper examines the effect of production rates on the ultimate gas recovery from a high permeable strong bottom water drive reservoir. A real gas field simulation model was history matched applying manual history matching techniques .Well behavior and recovery were observed for different prediction scenarios. It was found that cumulative recovery with additional wells would be higher during the initial years but ultimate recovery will be lowest because of a faster rate of water table movement.

Maximum ultimate recovery can be achieved by continuing the production with timely and sequential workover of high water cut wells to plug back the lower perforations. High permeability and rapid movement of water table have made the reservoir highly rate sensitive and lower production rate may result in higher ultimate recovery.

Empirical rapid and accurate prediction model for data mining tasks in cloud computing environments

With the arrival of big data and cloud computing as a computing concept, it is becoming ever more critical to efficiently choose the most optimum machine on which to execute a program, for example in the healthcare environment. This process of choice is also complicated by the fact that numerous machines are available as virtual machines. Hence, predicting the most optimum choice of machine based on a target application is a challenge. Prediction techniques consume large amount of computing resources when operating with multi-dimensional data that can cause long delays compounded by cross validation process in evaluating and choosing the most optimum prediction model.

We propose a model of prediction techniques to predict and classify some of the health datasets to retrieve useful knowledge to illustrate how a data miner can choose a suitable machine especially in cloud environment with good accuracy in a timely manner. Our results show that the execution time has an inverse relation with the use of resources of a machine and the accuracy of prediction could be different from one machine to another using the same predicting technique and dataset.