On the benefits of Cross Layer Feedback in Multi-hop Wireless Networks

Wireless networks operate under harsh and time-varying channel conditions. In wireless networks the time varying channel conditions lead to variable SINR and high BER. The wireless channel is distinct from and more unpredictable than the far more reliable wireline channel. {\em Cross layer feedback} is a mechanism where layers provide {\em selective} information to other layers to boost the performance of wireless networks. {\em Cross layer feedback} can lead to a tremendous increase in the performance of the TCP/IP stack in wireless networks, and an increase in the user's satisfaction level. However, it is possible that naive feedbacks (or optimizations) can work non-coherently; therefore, these can negatively effect the performance of the TCP/IP stack. In this paper, we holistically analyze each layer of the TCP/IP stack, and propose possible Cross layer feedbacks which work coherently. The proposed Cross layer feedbacks can greatly enhance the performance of the TCP/IP stack in wireless networks

Thermal analysis of heat exchangers for earth satellites

Call number: LD2668 .R4 1962 R19

Alpha MAML: Adaptive Model-Agnostic Meta-Learning

Model-agnostic meta-learning (MAML) is a meta-learning technique to train a model on a multitude of learning tasks in a way that primes the model for few-shot learning of new tasks. The MAML algorithm performs well on few-shot learning problems in classification, regression, and fine-tuning of policy gradients in reinforcement learning, but comes with the need for costly hyperparameter tuning for training stability. We address this shortcoming by introducing an extension to MAML, called Alpha MAML, to incorporate an online hyperparameter adaptation scheme that eliminates the need to tune meta-learning and learning rates. Our results with the Omniglot database demonstrate a substantial reduction in the need to tune MAML training hyperparameters and improvement to training stability with less sensitivity to hyperparameter choice.Comment: 6th ICML Workshop on Automated Machine Learning (2019

Development and Implementation of an Artificially Intelligent Search Algorithm for Sensor Fault Detection Using Neural Networks

This work is aimed towards the development of an artificially intelligent search algorithm used in conjunction with an Auto Associative Neural Network (AANN) to help locate and reconstruct faulty sensor inputs in control systems. The AANN can be trained to detect when sensors go faulty but the problem of locating the faulty sensor still remains. The search algorithm aids the AANN to help locate the faulty sensors and reconstruct their actual values. The algorithm uses domain specific heuristics based on the inherent behavior of the AANN to achieve its task. Common sensor errors such as drift, shift and random errors and the algorithms response to them have been studied. The issue of noise has also been investigated. These areas cover the first part of this work. The second part focuses on the development of a web interface that implements and displays the working of the algorithm. The interface allows any client on the World Wide Web to connect to the engineering software called MATLAB. The client can then simulate a drift, shift or random error using the graphical user interface and observe the response of the algorithm

Einstein and Jordan frame correspondence in quantum cosmology: Expansion-collapse duality

The conformal correspondence between FLRW universes in the Einstein and Jordan frames allows for an expansion-collapse duality -- an always expanding Einstein frame universe can have a dual Jordan frame description that is contracting forever. The scenario eventually runs into an apparent paradox. When a collapsing universe approaches singularity, the classical description of the spacetime becomes inadequate. The contracting Jordan frame universe is expected to develop quantum characteristics when its scale factor becomes sufficiently small. However, at the same time, the corresponding Einstein frame universe is expected to behave classically, due to the arbitrarily large size it has grown to. In this case, the conformal map appears to be providing a duality between a quantum effect-dominated universe and a universe behaving classically. We investigate the status of the conformal map at the quantum level in such a scenario, focusing on addressing this paradox. The Einstein and Jordan frame universes are quantized individually using the Wheeler-DeWitt prescription. We show that the classical conformal map holds true at the quantum level when compared through the expectation values of the scale factor operators in the two frames. The relative quantum fluctuation in the scale factor is found to be conformally invariant, and it increases in both the past and future directions according to the internal clock. Expectedly, the quantum fluctuations in the collapsing Jordan frame keep on increasing as it shrinks towards singularity. More surprisingly, the quantum fluctuations in the expanding Einstein frame keep on increasing as well, even as its classical scale factor becomes larger. Despite having drastically different cosmological evolutions, the rise in quantum characteristics in a collapsing frame implies the same in its expanding counterpart, thereby resolving the apparent paradox.Comment: 21 pages, 4 figure