Abrasion Resistance of Field-Assisted Ion-Exchanged Glass

The fracture strength of HF-etched samples was measured as a function of field-assisted K-Na ion exchange depth, as well as the severity of abrasion. As expected, the more severe the abrasion, the greater the depth required to make the strength insensitive to abrasion. An abrasion-independent weakening mechanism that increased with exchange depth was also found to be operative.     

GENs: generative encoding networks

Machine Learning - Mapping data from and/or onto a known family of distributions has become an important topic in machine learning and data analysis. Deep generative models (e.g., generative...     

Optimum image thresholding via class uncertainty and regionhomogeneity

Thresholding is a popular image segmentation method that converts a gray-level image into a binary image. The selection of optimum thresholds has remained a challenge over decades. Besides being a segmentation tool on its own, often it is also a step in many advanced image segmentation techniques in spaces other than the image space. We introduce a thresholding method that accounts for both intensity-based class uncertainty-a histogram-based property-and region homogeneity-an image morphology-based property. A scale-based formulation is used for region homogeneity computation. At any threshold, intensity-based class uncertainty is computed by fitting a Gaussian to the intensity distribution of each of the two regions segmented at that threshold. The theory of the optimum thresholding method is based on the postulate that objects manifest themselves with fuzzy boundaries in any digital image acquired by an imaging device. The main idea here is to select that threshold at which pixels with high class uncertainty accumulate mostly around object boundaries. To achieve this, a threshold energy criterion is formulated using class-uncertainty and region homogeneity such that, at any image location, a high energy is created when both class uncertainty and region homogeneity are high or both are low. Finally, the method selects that threshold which corresponds to the minimum overall energy. The method has been compared to a maximum segmented image information method. Superiority of the proposed method was observed both qualitatively on clinical medical images as well as quantitatively on 250 realistic phantom images generated by adding different degrees of blurring, noise, and background variation to real objects segmented from clinical images     

Analytical study of nuclear-coupled density-wave instability in a natural circulation pressure tube type boiling water reactor

An analytical model has been developed to study the nuclear-coupled density-wave instability in the Indian advanced heavy water reactor (AHWR) which is a natural circulation pressure tube type boiling water reactor. The model considers a point kinetics model for the neutron dynamics and a lumped parameter model for the fuel thermal dynamics along with the conservation equations of mass, momentum and energy and equation of state for the coolant. In addition, to study the effect of neutron interactions between different parts of the core, the model considers a coupled multipoint kinetics equation in place of simple point kinetics equation. Linear stability theory was applied to reveal the instability of in-phase and out-of-phase modes in the boiling channels of the AHWR. The results indicate that the stability behavior of the reactor is greatly influenced by the void reactivity coefficient, fuel time constant, radial power distribution and channel inlet orificing. The delayed neutrons were found to have a strong influence on the Type I and Type II instabilities observed at low and high channel powers, respectively. Also, it was found that the coupled multipoint kinetics model and the modal point kinetics model predict the same threshold power for out-of-phase instability if the coupling coefficient in the former model is half the eigen value separation between the fundamental and the first harmonic mode in the latter model. Decay ratio maps were predicted considering various operating parameters of the reactor, which are useful for its design.     

Multilayer Hierarchical Model for Mobility Management in IPv6: A Mathematical Exploration

The mobility solution provided by Mobile IPv6 (MIPv6) imposes too much signaling load to the network and enforces large handoff latency to end user. Hierarchical MIPv6 (HMIPv6) on the other hand, is designed by organizing MIPv6 in layered architecture and performs better than MIPv6 in terms of handoff latency and signaling load. Observation shows that, there is still possibility to shrink the handoff latency and the signaling load by further extending HMIPv6 into multiple layers. To explore this possibility of enhanced performance through layered architecture, this paper aimed at mathematical exploration of an N-layered MIPv6 network architecture in order to figure out the optimal levels of hierarchy for mobility management. A widespread analysis is carried out on various parameters such as location update frequency and cost, handoff latency and packet delivery cost. Influence of queuing delay on handoff latency is examined by modeling M/M/1/K queue in the architecture and user mobility is modeled using Markov chain. Analytical investigation reveals that three levels of hierarchy in MIPv6 architecture provide an optimal solution for mobility management.     

CATECAS: a content-aware transmission efficiency based channel allocation scheme for cognitive radio users with improved QoE

Cognitive radio (CR) is a promising technology for the upcoming 5G communication which addresses opportunistic channel usage for enhanced spectrum utilization. However, Quality of Service (QoS) provisioning is a major challenge for CR Network due to the service interruption and packet error caused by random primary activities. In addition to this, periodic spectrum sensing for primary user protection reduces the effective throughput of the secondary users (SUs). However, to ensure QoS of SUs especially for video application, throughput enhancement is necessary which can be achieved by efficient spectrum sensing and channel allocation policy. As the QoS requirements are different for different secondary applications, we propose a novel content aware channel allocation scheme that enhances the Quality of Experience (QoE) of SUs. At first, the proposed scheme analyzes the QoS requirements of different SUs and prioritizes them. Consequently, the optimum sensing duration is determined to maximize the transmission efficiency and throughput of SUs. Finally, a novel content aware transmission efficiency-based channel assignment scheme (CATECAS) is proposed for SUs, considering the estimated channel quality and QoS requirements concurrently. Extensive performance analysis of CATESCAS on real-time video and file download applications confirms significant QoE improvement for SUs especially for rapid movement type of video application, which is considered as the most critical among different secondary applications.

     

Traffic grooming in WDM optical network with grooming resources at Max Connectivity nodes

In this paper, we propose Max Connectivity grooming in WDM mesh networks under static lightpath connection requests. The grooming and wavelength conversion resources are placed at the nodes having maximum connections. We propose a heuristic genetic algorithm (GA) model to solve grooming, routing and wavelength assignment. The GA algorithm has been used to optimize the cost of grooming and wavelength conversion resources. The blocking probability has been investigated under different lightpath connections. The performance of Max Connectivity grooming has been compared with other grooming policies. Our results indicate the improvement of resource utilization with minimum blocking probability.     

Use of ridge regression for improved estimation of kinetic constants from PET data

The estimation of parameters in radio-tracer models from positron emission tomography (PET) data by nonlinear least squares (NLS) often leads to results with unacceptable mean square error (ME) characteristics. The introduction of constraints on parameters has the potential to address this problem. We examine a ridge-regression technique that augments the standard NLS criterion by the addition of a term which penalizes estimates which deviate from physiologically reasonable values. A variation on a plug-in methodology of Hoerl et al. [7] is examined for data-dependent selection of the degree of reliance to place on the penalizing term. A simulation study is carried out to evaluate the performance of this approach in the context of estimation of kinetic constants in the three-compartment model used to analyze data from PET studies with fluoro-deoxyglucose (FDG). Results show that over a range of realistic noise levels, the ridge-regression procedure can be expected to reduce the root ME of parameter estimates by 60%. This result is not found to be substantially dependent on the precise formulation of the penalty function used. Thus, the use of ridge regression for estimation of kinetic parameters in PET studies is considered to be a promising tool.     

Addressing space‐constraint driven selfishness in smart opportunistic environment

A smart opportunistic environment is a physical space, which allows the smart physical objects to communicate in the presence of disruption in connectivity. Because, the objects in such an environment are buffer constrained, some of the objects will not participate in data forwarding, when there is scarcity of storage (buffer) space. In this paper, we focus on such selfish behavior of objects triggered by space constraints in a smart opportunistic environment. We propose a novel data forwarding algorithm, selfishness and buffer‐aware routing (SBR), in which a node is chosen as a relay, based on its capability, which is a function of its available buffer space and past encounter history (delivery predictability) with the destination. SBR can efficiently utilize the limited buffer space in a node with a buffer management scheme, WSD. It can also detect space constraint driven selfish behavior of nodes and resolve it using a reputation‐based technique, MSD. We have conducted simulation using both synthetic and real‐world traces for evaluating our proposed SBR algorithm. For analyzing the performance of the algorithm in real‐time, a smart vehicular test‐bed is developed. Simulation results and test‐bed implementation show that our algorithm performs better in terms of higher delivery ratio, lower overhead ratio, and lower delivery delay, compared with existing opportunistic data forwarding algorithms.