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ABSTRACT :

Transport modes are very important to Libyan’s Gharian travellers for their

activities and daily trips. However, the increase of total number of private car and

private transport namely (taxi and micro buses) on the road increases and causes

many road problems such as traffic congestion, accidents and noise pollution. The

aim of this research is to study the two important factors to shifting to public

transportation. Analyses about relation between factors as travel time and cost

have been made to answer research questions. A questionnaire survey in Gharian

city was carried out to study the potential reduction of private cars on road through

optimal use of suitable public transportation system (PT) in order to reduce traffic

congestion problems such as travel time. Logistic regression model has been used

to analyse the factors that influence users to shift their trips to public transportation

alternatives.

Key words: travel time, travel cost, private car, public transport (PT), traffic

congestion and Logistic regression.

In recent years, wireless communication systems have a high demand for high data throughput, high quality and spectrum efficiency. Massive MIMO is one of the new ideas in 5G. The idea is to expand the conventional MIMO system by installing large number of antennas in the base station to make better use of spatial diversity in order to provide higher throughput while using the same spectrum. However, in order to separate users and data streams and eliminate interference, an effective channel estimation technique with minimum overhead is crucial. In this paper, we propose a channel estimation technique to estimate the channel state information based on Kalman Filter. The received predetermined data is used by Kalman filter to estimate the channel, which is then used to detect the following data symbols in the same frame. The effectiveness of the proposed scheme is compared to the well-known Least square (LS) and Minimum Mean square Error (MMSE) schemes. The performance of the proposed scheme is assessed using computer simulation, and the obtained results for the proposed channel estimation demonstrate improved bit error rate performance in time-varying Rayleigh fading channels.

 Cognitive radio (CR) is a form of wireless communication in which a transceiver can intelligently detect which communication channels are in use. Upon the result of the detection, the system instantly moves into vacant channels while avoiding occupied ones. To improve the capacity of CR systems, the unused sub-channels by primary user (PU) could be aggregated. However, when secondary user (SU) system aggregates more channels, more collisions may occur. This collision occurs due to the PU backto reuse its channels during SU transmission on them. Moreover, there are other reasons for collisions as misdetection which happens when wrongly detected absence of PU may occur as well.

In this paper a Proposed Channel Aggregation Algorithm is introduced in which the CR data rate is maximized by aggregating multiple channels for the SU while satisfying the minimum collision requirements. Moreover, channel capacity is maximized under collision constraints. Matlab simulation is conducted to implement the proposed algorithm and the rustles showed that, very low collision probability was observed as compared to previous work. The capacity of CR was also improved as well.

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MANETs is a type of Mobile Ad Hoc Networks and it contains a set of nodes that do not need central management to communicate with each other because each node can act as a sender, receiver and router at the same time. All these nodes can perform self-configuration. The ad hoc stationary or mobile networks face many challenges such as limited bandwidth, congestion and data transfer speed. Therefore, it is extremely important to develop an intelligent routing protocol to encounter these challenges. An evaluation study is also important to perform the backoff contention window effectively in order to know the obstacles and avoid them. This paper concentrates on the performance of backoff contention window using the main routing protocols such as DSR, DSDV and AODV for MANETs networks in crowded areas with random movement. The important parameters which include network throughput …

Cooling accounts for 12-38% of total energy consumption in schools in the US, depending on the region. In this study, stacking learning is utilized to predict chiller running capacity for four school buildings (regression) and to predict the chiller status for four another schools (classification) using a collection of interval chiller data and building demand. Singular and multiple measurement periods within one or more seasons are considered. A generalized methodology for modeling building energy systems is posited that informs selection of features, data balancing to attain the best model possible, ensemble-based stacked learning in order to prevent over-fitting, and final model development based upon the results from the stacked learning. The results show that ensemble-based stacked learning improves the model performance substantially; providing the most accurate results for both regression and classification. for both classification and regression. For, classification, the balanced accuracy is 99.79% while Kappa is 99.39%. For regression, the R-squared value, the mean absolute error (MAE) error, and the root mean squared error (RMSE) are 1.78 kW, 2.77 kW, and 0.983 respectively.

Traditional building energy audits are both expensive, in the range of USD $1.29/m 2-$5.37/m 2, and inconsistent in their prediction of potential energy savings. Automation to reduce costs of evaluating the energy effectiveness of buildings is strongly needed. A key element of such automation is a means to estimate the building envelope energy effectiveness. We present a method that addresses this need by using infrared thermography to characterize building wall envelope effectiveness. To date, thermal imaging approaches for estimating wall R-Values, based upon thermal-physical models of walls, require additional manual measurements and analysis which prohibit low-cost, large-scale implementation. To overcome this implementation challenge, a machine learning approach is used to predict wall R-Values for a set of residences with known thermal resistance by utilizing the measured wall imaging temperature, prior weather conditions, historical energy consumption data, and available building geometrical data. The developed model is shown to predict wall R-Values with a maximum test-set root mean squared error of 7% using as few as nine training houses. This result has significant implications for low-cost large-scale envelope energy effectiveness characterization.

Voice in IP networks is transmitted as packets over IP (VoIP), the voice signals are converted to IP packets after beingdigitized and compressed for transmission. However, some packets can be missed in their way to the receiving side, due to network congestion. The loss of these packets degrades the speech quality in the listener side at VoIP system transmission. Since voice is transmitted is real time, the receiver cannot request a retransmission for any lost packets. Voice and data multiplexing in VoIP network always face problems when huge TCP traffic is transmitted resulting the voice packet to be stuck in the network during congestion. Therefore, VoIP packets will be delayed. Since delay and loss are the main parameters that affect the quality of a voice signal in a VoIP network.

This paper presents a design and a simulation study of voice and data integration in a VoIP network and analyses which scenario will suite the best performance for voice packets in traffic congestion to have a high voice quality rating when using a single data TCP source and a multiple TCP sources when multiplexed with a UDP voice source. This is accomplished by using NS-2 network simulator (version 2), and M/D/1 queue type with various queuing systems such as First in First out (FIFO), Fair queue (FQ) and Deficit Round Robin (DRR), which represent the technique mechanism to serve voice and data packets in a queueing system. Then loss and delay are measured for each scenario to determine the quality of voice.