Bandwith allocation and scheduling in photonic networks

This thesis describes a framework for bandwidth allocation and scheduling in the Agile All-Photonic Network (AAPN). This framework is also applicable to any single-hop communication network with significant signalling delay (such as satellite-TDMA systems). Slot-by-slot scheduling approaches do not provide adequate performance for wide-area networks, so we focus on frame-based scheduling. We propose three novel fixed-length frame scheduling algorithms (Minimum Cost Search, Fair Matching and Minimum Rejection) and a feedback control system for stabilization.MCS is a greedy algorithm, which allocates time-slots sequentially using a cost function. This function is defined such that the time-slots with higher blocking probability are assigned first. MCS does not guarantee 100% throughput, thought it has a low blocking percentage. Our optimum scheduling approach is based on modifying the demand matrix such that the network resources are fully utilized, while the requests are optimally served. The Fair Matching Algorithm (FMA) uses the weighted max-min fairness criterion to achieve a fair share of resources amongst the connections in the network. When rejection is inevitable, FMA selects rejections such that the maximum percentage rejection experienced in the network is minimized. In another approach we formulate the rejection task as an optimization problem and propose the Minimum Rejection Algorithm (MRA), which minimizes total rejection. The minimum rejection problem is a special case of maximum flow problem. Due to the complexity of the algorithms that solve the max-flow problem we propose a heuristic algorithm with lower complexity.Scheduling in wide-area networks must be based on predictions of traffic demand and the resultant errors can lead to instability and unfairness. We design a feedback control system based on Smith's principle, which removes the destabilizing delays from the feedback loop by using a "loop cancelation" technique. The feedback control system we propose reduces the effect of prediction errors, increasing the speed of the response to sudden changes in traffic arrival rates and improving the fairness in the network through equalization of queue-lengths

Improved Estimation of Sir in Mobile Cdma Systems by Integration of Artificial Neural Network and Time Series Technique

Abstract: This study presents an integrated Artificial Neural Network (ANN) and time series framework to estimate and predict Signal to Interference Ratio (SIR) in Direct Sequence Code Division Multiple Access (DS/CDMA) systems. It is difficult to model uncertain behavior of SIR with only conventional ANN or time series and the integrated algorithm could be an ideal substitute for such cases. Artificial Neural Network (ANN) approach based on supervised multi layer perceptron (MLP) network are used in the proposed algorithm. All type of ANN-MLP are examined in present study. At last, Coefficient of Determination (R ) is used for selecting preferred model from different 2 constructed MLP-ANN. One of unique feature of the proposed algorithm is utilization of Autocorrelation Function (ACF) to define input variables whereas conventional methods which use trial and error method. This is the first study that integrates ANN and time series for improved estimation of SIR in mobile CDMA systems

FAIR MATCHING ALGORITHM: FIXED-LENGTH FRAME SCHEDULING IN ALL-PHOTONIC NETWORKS

Internal switches in all-photonic networks do not perform data conversion into the electronic domain, thereby eliminating a potential capacity bottleneck, but they introduce network scheduling challenges. In this paper we focus on scheduling fixed-length frames in all-photonic star-topology networks. We describe the Fair Matching (FMA) and Equal Share (ESA) algorithms, novel scheduling procedures that result in maxmin fair allocation of extra demand and achieve zero rejection for admissible demands. We analyze through simulation the delay and throughput performance

Investigating the effect of G-Bond and Z-PRIME Plus on the bond strength between prefabricated zirconia posts and the canal wall (in in vitro conditions)

Purpose: The aim of this study is to analyze the effect of G-Bond and Z-PRIME Plus on the bond strength between prefabricated zirconia posts and the canal wall. Material and Method: The study was carried out on 21 premolar teeth with similar conditions. The collected samples were cut at the CEJ. After root canal treatment of the roots, the post space was prepared with a length of 10mm. The samples were randomly allocated into two groups of 10. G-bond was used in one group and Z-PRIME plus in the other to prepare the posts’ surface. After cementation and mounting the samples in polyester, the post was cut from the apical area into three equal sections. The bond strength of the samples was tested using the push out on a universal testing machine. The acquired data was analyzed using the T-Test. Results: The average for the control group was 14.3N, the G-bond group had an average of 27.6±11.8N and the Z-PRIME plus group’s average was 27.4±13.4N. There is no statistically significant relationship between the two groups (P<0.9). Both methods of surface treatment increased bond strength. The bond strength in different sections such as coronal, middle and apical for each group is different. Conclusion: There is no statistically significant relationship between the G-bond and Z-prime plus groups and both products increase the bond strength of prefabricated zirconia posts

MINIMUM REJECTION SCHEDULING IN ALL-PHOTONIC NETWORKS

Internal switches in all-photonic networks do not perform data conversion into the electronic domain, thereby eliminating a potential capacity bottleneck, but the inability to perform efficient optical buffering introduces network scheduling challenges. In this paper we focus on the problem of scheduling fixed-length frames in allphotonic star-topology networks with the goal of minimizing rejected demand. We formulate the task as an optimization problem and characterize its complexity. We describe the Minimum Rejection Algorithm (MRA), which minimizes total rejection, and demonstrate that the Fair Matching Algorithm (FMA) minimizes the maximum percentage rejection of any connection. We analyze through OPNET simulation the rejection and delay performance. 1

Neuroanatomy of transgender persons in a Non-Western population and improving reliability in clinical neuroimaging

Although the neuroanatomy of transgender persons is slowly being charted, findings are presently discrepant. Moreover, the major body of work has focused on Western populations. One important factor is the issue of power and low signal-to-noise (SNR) ratio in neuroimaging studies of rare study populations including endocrine or neurological patient groups. The present study focused on the structural neuroanatomy of a Non-Western (Iranian) sample of 40 transgender men (TM), 40 transgender women (TW), 30 cisgender men (CM), and 30 cisgender women (CW), while assessing whether the reliability of findings across structural anatomical measures including gray matter volume (GMV), cortical surface area (CSA), and cortical thickness (CTh) could be increased by using two back-to-back within-session structural MRI scans. Overall, findings in transgender persons were more consistent with sex assigned at birth in GMV and CSA, while no group differences emerged for CTh. Repeated measures analysis also indicated that having a second scan increased SNR in all regions of interest, most notably bilateral frontal poles, pre- and postcentral gyri and putamina. The results suggest that a simple time and cost-effective measure to improve SNR in rare clinical populations with low prevalence rates is a second anatomical scan when structural MRI is of interest