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.

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.

Energy savings based upon use of smart WiFi thermostats ranging from 10 to 15% have been documented, as new features such as geofencing have been added. Here, a new benefit of smart WiFi thermostats is identified and investigated; namely, as a tool to improve the estimation accuracy of residential energy consumption and, as a result, estimation of energy savings from energy system upgrades, when only monthly energy consumption is metered. This is made possible from the higher sampling frequency of smart WiFi thermostats. In this study, collected smart WiFi data are combined with outdoor temperature data and known residential geometrical and energy characteristics. Most importantly, unique power spectra are developed for over 100 individual residences from the measured thermostat indoor temperature in each and used as a predictor in the training of a singular machine learning models to predict consumption in any residence. The best model yielded a percentage mean absolute error (MAE) for monthly gas consumption ±8.6%. Applied to two residences to which attic insulation was added, the resolvable energy savings percentage is shown to be approximately 5% for any residence, representing an improvement in the ASHRAE recommended approach for estimating savings from whole-building energy consumption that is deemed incapable at best of resolving savings less than 10% of total consumption. The approach posited thus offers value to utility-wide energy savings measurement and verification.

Predicting cooling load is essential for many applications such as diagnosing the health of existing chillers, providing better control functionality, and minimizing peak loads. In this study, short-term chiller and total building demand are acquired for five different commercial buildings in the Midwest USA. Four different machine learning models are then used to predict the chiller demand using the total building demand, outdoor weather data, and day/time information. Two data collection scenarios are considered. The first relies upon use of multiple weeks of data collection that includes very warm periods and season transitional periods where the outdoor temperature ranged from very warm to cool conditions in order to envelope all cooling season weather conditions. The second scenario employs use of contiguous data for a several weeks during only the warmest period of the year. The results show that using two or more separate time periods to envelope most of the weather data yields a much more accurate model in comparison to use of data for only one time period. These research findings have importance to energy service companies which often do short term audits (measurements) in order to estimate potential savings from chiller system upgrades (controls or otherwise).

Design parameters are presented for Tajoura reactor core utilizing the new fuel assemblies with low enriched uranium (LEU, using IRT-4M fuel assemblies) in the steady state safety operational parameters and Loss of Flow transient mathematical models (NATIRT - computer program. The calculated results of the model are presented in the cases of forced convection steady state, transient during emergency tank filling and natural convection after emergency tank filling modes at different reactor core thermal power level. The results of NATIRT for all cases of flow were in good agreement with the PARET and PLTEMP computer programs.