Detection and removal of functional redundancy in multi-level logic circuits

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references (leaves ).Whenever digital designs are created, they may contain many logic redundancies. Minimization tools are then used to remove these redundancies. The minimized circuit should be smaller, faster, and cheaper while still behaving like the original circuit. This research will focus on finding non-traditional methods for minimizing multi-level logic circuits

Pensions and the Labor Market

Employers typically view their investment in pension plans as a means of providing retirement income for their workers. Economists, on the other hand, view pension programs as a way to increase workplace productivity. Dorsey, Cornwell and Macpherson explore the theoretical and empirical basis for this perspective and, in the process, offer a complete and up-to-date discussion on the productivity theory of pensions.https://research.upjohn.org/up_press/1067/thumbnail.jp

PET and P300 Relationships in Early Alzheimer\u27s Disease

The P300 (P3) wave of the auditory brain event-related potential was investigated in patients with probable Alzheimer\u27s disease to determine whether P300 latency discriminated these patients from controls and whether prolonged P300 latency correlated with rates of brain glucose metabolism as measured by Positron Emission Tomography. P300 latency was prolonged by more than 1.5 standard deviations from age expectancy in 14 of 18 patients, but none of 17 controls. In these subjects P300 latency was shown to be inversely correlated with relative metabolic rates of parietal and, to a lesser extent, temporal and frontal association areas, but not with subcortical areas

Pensions and Productivity

Employers typically view their investment in pension plans as a means of providing retirement income for their workers. Economists, on the other hand, view pension programs as a way to increase workplace productivity. Dorsey, Cornwell and Macpherson explore the theoretical and empirical basis for this perspective and, in the process, offer a complete and up-to-date discussion on the productivity theory of pensions.https://research.upjohn.org/up_press/1067/thumbnail.jp

Detection and removal of functional redundancy in multi-level logic circuits

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references (leaves ).Whenever digital designs are created, they may contain many logic redundancies. Minimization tools are then used to remove these redundancies. The minimized circuit should be smaller, faster, and cheaper while still behaving like the original circuit. This research will focus on finding non-traditional methods for minimizing multi-level logic circuits

Estimating the expected latency to failure due to manufacturing defects

Manufacturers of digital circuits test their products to find defective parts so they are not sold to customers. Despite extensive testing, some of their products that are defective pass the testing process. To combat this problem, manufacturers have developed a metric called defective part level. This metric measures the percentage of parts that passed the testing that are actually defective. While this is useful for the manufacturer, the customer would like to know how long it will take for a manufacturing defect to affect circuit operation. In order for a defect to be detected during circuit operation, it must be excited and observed at the same time. This research shows the correlation between defect detection during automatic test pattern generation (ATPG) testing and normal operation for both combinational and sequential circuits. This information is then used to formulate a mathematical model to predict the expected latency to failure due to manufacturing defects

Cross-Modal Health State Estimation

Individuals create and consume more diverse data about themselves today than any time in history. Sources of this data include wearable devices, images, social media, geospatial information and more. A tremendous opportunity rests within cross-modal data analysis that leverages existing domain knowledge methods to understand and guide human health. Especially in chronic diseases, current medical practice uses a combination of sparse hospital based biological metrics (blood tests, expensive imaging, etc.) to understand the evolving health status of an individual. Future health systems must integrate data created at the individual level to better understand health status perpetually, especially in a cybernetic framework. In this work we fuse multiple user created and open source data streams along with established biomedical domain knowledge to give two types of quantitative state estimates of cardiovascular health. First, we use wearable devices to calculate cardiorespiratory fitness (CRF), a known quantitative leading predictor of heart disease which is not routinely collected in clinical settings. Second, we estimate inherent genetic traits, living environmental risks, circadian rhythm, and biological metrics from a diverse dataset. Our experimental results on 24 subjects demonstrate how multi-modal data can provide personalized health insight. Understanding the dynamic nature of health status will pave the way for better health based recommendation engines, better clinical decision making and positive lifestyle changes.Comment: Accepted to ACM Multimedia 2018 Conference - Brave New Ideas, Seoul, Korea, ACM ISBN 978-1-4503-5665-7/18/1

Analysis of Slotted ALOHA with Multipacket Messages in Clustered Surveillance Networks

This work presents an analysis of a cluster of finite population of low cost sensor nodes operating in a p-persistent S-Aloha framework with multipacket messages. Using this analytical framework, we consider the issue of partitioning the nodes and available frequencies into groups so as to maximize the system throughput. Assigning the nodes and frequencies into “groups” is important because the size of the group impacts the tradeoff between the benefits of frequency diversity and the cost of collision on the shared medium imposed by the nodes in a group. We study this tradeoff through analytical and numerical results and show how the correct choice of group sizes can vary depending on various factors like the ratio of nodes to frequencies and the overall system load

Robust deployment and control of sensors in wireless monitoring networks

Advances in Micro Electro-Mechanical Systems (MEMS) technology, including MEMS sensors, have allowed the deployment of small, inexpensive, energy-efficient sensors with wireless networking capabilities. The continuing development of these technologies has given rise to increased interest in the concept of wireless sensor networks (WSNs). A WSN is composed of a large number (hundreds, even thousands) of sensor nodes, each consisting of sensing, data processing, and communication components. The sensors are deployed onto a region of interest and form a network to directly sense and report on physical phenomena. The goal of a monitoring wireless sensor network is to gather sensor data from a specified region and relay this information to a designated base station (BSt). In this study, we focus on deploying and replenishing wireless sensor nodes onto an area such that a given mission lifetime is met subject to constraints on cost, connectivity, and coverage of the area of interest. The major contributions of this work are (1) a technique for differential deployment (meaning that nodes are deployed with different densities depending on their distance from the base station); the resulting clustered architecture extends lifetime beyond network lifetime experienced with a uniform deployment and other existing differential techniques; (2) a characterization of the energy consumption in a clustered network and the energy remaining after network failure, this characterization includes the overhead costs associated with creating hierarchies and retrieving data from all sensors ; (3) a characterization of the effects and costs associated with hop counts in the network; (4) a strategy for replenishing nodes consisting of determining the optimal order size and the allocation over the deployment region. The impact of replenishment is also integrated into the network control model using intervention analysis. The result is a set of algorithms that provide differential deployment densities for nodes (clusterhead and non-clusterhead) that maximize network lifetime and minimize wasted energy. If a single deployment is not feasible, the optimal replenishment strategy that minimizes deployment costs and penalties is calculated.Ph.D., Electrical Engineering -- Drexel University, 201