Low-Power Analog Circuits for Sub-Band Speech Processing

The need for efficient electronics has been increasing by the day, as have the constraints on power and size of the devices. Also the increase in use of mobile and wearable electronics has been leading to innovative methods to conserve power and increase functionality. The traditional approach of signal processing heavily relies on the Digital Signal Processing (DSP) hardware to perform most of the tasks, which has lead to power-hungry circuits. Use of analog front-end devices could prove to be efficient, since most of the real-world data is analog and since the DSP could be spared for more application-specific tasks within the system, thereby resulting in more efficient mixed-signal systems.;The focus in this work is to develop an analog front-end for speech-processing applications with inspiration from biology, and trying to mimic human auditory perception techniques. The circuits are designed in 600nm, 350nm and 180nm CMOS processes and are biased in the sub-threshold region to consume low-power. Also, various modules of the system are connected using multiplexing circuits to allow post-fabrication reconfigurability to suit various applications. These circuits are biased using a network of floating-gate transistors which allow reconfigurability and increased bias accuracy. This thesis mainly describes two modules of the analog front-end used for speech processing: derivative circuit and voltage-mode subtractor circuit, which are used for processing spectrally decomposed signals. These circuits could be used for applications like audio analysis or event detection

Edge-Vertex Dominating Set in Unit Disk Graphs

Given an undirected graph $G=(V,E)$, a vertex $v\in V$ is edge-vertex (ev) dominated by an edge $e\in E$ if $v$ is either incident to $e$ or incident to an adjacent edge of $e$. A set $S^{ev}\subseteq E$ is an edge-vertex dominating set (referred to as ev-dominating set) of $G$ if every vertex of $G$ is ev-dominated by at least one edge of $S^{ev}$. The minimum cardinality of an ev-dominating set is the ev-domination number. The edge-vertex dominating set problem is to find a minimum ev-domination number. In this paper we prove that the ev-dominating set problem is {\tt NP-hard} on unit disk graphs. We also prove that this problem admits a polynomial-time approximation scheme on unit disk graphs. Finally, we give a simple 5-factor linear-time approximation algorithm

Distributed Floor Control Protocols for Supporting Priorities in Collaborative Applications

Collaborative applications are becoming popular with availability of broadband wide area networks. There are several collaborative applications such as computer controlled priority right auctioning, multiplayer online video games, collaborative editing and collaborative simulations. Important issues in distributed floor controls are to provide exclusive access communication channel for single user at a time and to maintain causal ordering of messages. This paper presents an implementation of CSMA MAC protocol to solve floor control problem. In many situations it is necessary to have priorities in the group collaboration to give higher access to users who deserve, but the distributed floor control protocols implemented till now do not provide priority based group collaboration. In this paper, I propose to implement priority based distributed floor control protocols using Aloha, DQDB and CSMA MAC protocols on overlay networks. Finally, I compare the efficiencies of these MAC protocols with and without priority using network simulator-2.Computer Science Departmen

Automaton Distillation: Neuro-Symbolic Transfer Learning for Deep Reinforcement Learning

Reinforcement learning (RL) is a powerful tool for finding optimal policies in sequential decision processes. However, deep RL methods suffer from two weaknesses: collecting the amount of agent experience required for practical RL problems is prohibitively expensive, and the learned policies exhibit poor generalization on tasks outside of the training distribution. To mitigate these issues, we introduce automaton distillation, a form of neuro-symbolic transfer learning in which Q-value estimates from a teacher are distilled into a low-dimensional representation in the form of an automaton. We then propose two methods for generating Q-value estimates: static transfer, which reasons over an abstract Markov Decision Process constructed based on prior knowledge, and dynamic transfer, where symbolic information is extracted from a teacher Deep Q-Network (DQN). The resulting Q-value estimates from either method are used to bootstrap learning in the target environment via a modified DQN loss function. We list several failure modes of existing automaton-based transfer methods and demonstrate that both static and dynamic automaton distillation decrease the time required to find optimal policies for various decision tasks

Analytics Based on Artificial Neural Network: A Case Study Based on Iowa Corn Yield Forecasting

This work is an application of computing techniques to solve real world problems. Agriculture has been playing a crucial role in the growth of world economy and has been associated with production of basic food demands. The World�s food demand is increasing day by day with the population but the cultivable land which is a limited resource is not increasing in proportion with the food demand. In order to maximize the production and improvise the quality of crop, various firms have come up with variety of fertilizes, genetic seeds, modern equipment, etc. While the technological advancement is still going on, there is another area of interest where most of the technological firms are now focusing, which is predicting the approximate crop yield in the different geographical locations.In order to address the above problem, we proposed a model to understand the effects of various crop yield influencing parameters such as field location, soil properties, availability water in the ground, ground slope and climate conditions such as rainfall and temperature. We proposed this ANN model to predict corn yield of IOWA region with these influencing parameters which will help the various agricultural firms to give the recommendations to their growers to maximize the crop yield. According to our knowledge, this is the first implementation of ANN model to predict corn yield of IOWA region with these set of data features. In this work, the ANN model produced more consistent yield prediction and it resulted in R2 of 0.70 and RMSE of around 1750.Computer Scienc

GSU Event Portal

This application system for Hotel Booking is an intranet application, which provides various hotels that are available for the users in the city. The application helps users to select their favorite dishes among the continental dishes that are available. Users also have the choice of choosing their flavors, ingredients, and can add many other choices to their recipe. This application provides an option through which users who have diabetes and blood pressure can place their order according to their choice. After placing the orders, users can add their item to the cart along with the previous orders they made and they can pay through credit card online system. After completing the payment process, application directs the users to the map to locate their address and their order is delivered to their doorstep. Besides the online order facility, the application has an alluring facility, through which users can book for dining and they can also book a banquet or can arrange a hall for parties. Through the responses given by the users the applications provide list of hotels or restaurants that have the required facilities

A HIGH PERFORMANCE RADIX10 MULTIPLICATION ARCHITECTURE BASED ON REDUNDANT BCD CODES

The decimal multiplication is one of the most important decimal arithmetic operations which have a growing demand in the area of commercial, financial, and scientific computing. It has been revived in recent years due to the large amount of data in commercial applications. In this paper, we propose a parallel decimal multiplication algorithm with three components, which are a partial product generation, a partial product reduction, and a final digit-set conversion. First, a redundant number system is applied to recode not only the multiplier, but also multiples of the multiplicand in signed-digit (SD) numbers. Furthermore, we present a multi operand SD addition algorithm to reduce the partial product array. We consider the problem of multi operand parallel decimal addition with an approach that uses binary arithmetic, suggested by the adoption of binary-coded decimal (BCD) numbers. This involves corrections in order to obtain the BCD result or a binary-to-decimal (BD) conversion. The BD conversion moreover allows an easy alignment of the sums of adjacent columns. We treat the design of BCD digit adders using fast carry-free adders and the conversion problem through a known parallel scheme using elementary conversion cells. Spread sheets have been developed for adding several BCD digits and for simulating the BD conversion as a design tool. In this project Xilinx-ISE tool is used for simulation, logical verification, and further synthesizing

A survey on computational intelligence approaches for predictive modeling in prostate cancer

Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex forconventional statistical techniques to process quickly and eciently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes evolutionary algorithms (also known asmetaheuristic optimisation, nature inspired optimisation algorithms), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these,as well as Bayesian based approaches, and Markov models. Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimising the performance of prostate cancer predictive models, and the suitability of these approaches are discussed

Blood Cardioplegia Induction, Perfusion Storage and Graft Dysfunction in Cardiac Xenotransplantation

BackgroundPerioperative cardiac xenograft dysfunction (PCXD) describes a rapidly developing loss of cardiac function after xenotransplantation. PCXD occurs despite genetic modifications to increase compatibility of the heart. We report on the incidence of PCXD using static preservation in ice slush following crystalloid or blood-based cardioplegia versus continuous cold perfusion with XVIVO© heart solution (XHS) based cardioplegia.MethodsBaboons were weight matched to genetically engineered swine heart donors. Cardioplegia volume was 30 cc/kg by donor weight, with del Nido cardioplegia and the addition of 25% by volume of donor whole blood. Continuous perfusion was performed using an XVIVO © Perfusion system with XHS to which baboon RBCs were added.ResultsPCXD was observed in 5/8 that were preserved with crystalloid cardioplegia followed by traditional cold, static storage on ice. By comparison, when blood cardioplegia was used followed by cold, static storage, PCXD occurred in 1/3 hearts and only in 1/5 hearts that were induced with XHS blood cardioplegia followed by continuous perfusion. Survival averaged 17 hours in those with traditional preservation and storage, followed by 11.47 days and 15.03 days using blood cardioplegia and XHS+continuous preservation, respectively. Traditional preservation resulted in more inotropic support and higher average peak serum lactate 14.3±1.7 mmol/L compared to blood cardioplegia 3.6±3.0 mmol/L and continuous perfusion 3.5±1.5 mmol/L.ConclusionBlood cardioplegia induction, alone or followed by XHS perfusion storage, reduced the incidence of PCXD and improved graft function and survival, relative to traditional crystalloid cardioplegia-slush storage alone