University of Bridgeport

About: University of Bridgeport is a education organization based out in Bridgeport, Connecticut, United States. It is known for research contribution in the topics: Wireless sensor network & Key distribution in wireless sensor networks. The organization has 1008 authors who have published 1639 publications receiving 22740 citations.

Bid ask spread in a competitive market with institutions and order size

Abstract: Large orders, particularly from institutions, are quite common these days and hence there is interest to know if institutional trading has any bearing on the price effect associated with large trades. Recent empirical studies contradict earlier evidence of negative price effect on selling large blocks and find no price effect associated with large trades. Existing theoretical framework suggests a monotonic and increasing adverse price effect for large trades, where the motivation for a large trade is private information. We model a trading system where pure information, information-liquidity, and pure liquidity traders trade small and large sizes. The pure information traders strategically choose an order size. Institutions trade only large sizes because of their low execution costs for large trades; they are information-liquidity traders whose ability to use an information signal to determine their trades is subject to a binding liquidity constraint. We show that in such a market a separating equilibrium where trade size is informative does not exist and hence there is no price effect for large trades. Trade size may be revealing only if there is a buy sell asymmetry (large buy size is not equal to large sell size) or the corresponding price effect is asymmetric (price effect due to a large buy is not equal to that of a large sell). Further for a pooling equilibrium to exist, where trade size is not informative, the width of the market denoted by the ratio of order size (large size/small size) needs to be small, while the shallowness (inverse depth) of the market denoted by the ratio between pure information and institutional trades and the information signal needs to be stronger (higher). Our results on bid and ask prices and spread confirm recent empirical evidence on price effect of large and institutional trades found in the literature.

The Automated General Manager: Can an Algorithmic System for Drafts, Trades, and Free Agency Outperform Human Front Offices?

Abstract: An automated system using machine learning methods, applied to a broad historical database, while avoiding survivorship bias, and for a variety of performance metrics, is developed and tested again...

System Dynamics Modeling for Smartphone-Based Healthcare Tools: Case Study on ECG Monitoring

Abstract: With the rapid growth of technology and the advances in smart healthcare, patients nowadays struggle with finding suitable smart healthcare interfaces that offer the most effective and manageable personalized care. System dynamics (SD) investigates the behavior of various factors of a system and thus, can offer plausible solutions in this regard. This article presents the research ideas and conceptualization of a framework to determine the factors that influence the effectiveness of a personal healthcare response monitoring system from a systems engineering perspective. A SD model for smartphone-based healthcare monitoring is introduced. Specifically, smartphone-based heart monitoring using electrocardiogram (ECG) is studied in this article from a SD point of view. The model includes factors such as patient wellbeing and care, cost, convenience, user friendliness, in addition to other embedded ECG system design and performance metrics (e.g., accuracy, real-time response, etc.). The model has been rigorously tested and the simulation results reflect the dynamics of the model and the effectiveness of smartphone-based ECG monitoring in various scenarios. The proposed framework has the potential to facilitate visualizing the effectiveness of smartphone-based healthcare monitoring systems for users.

A Shared Secret Key Initiated by EPR Authentication and Qubit Transmission Channels

Abstract: Quantum key distribution (QKD) is an innovative solution in the cryptography world to prevent the information leakage that can sometimes be deliberate. Several QKD protocols were recently presented for building a secure shared key, of which the BB84 protocol is one of those interesting protocols. The authentication between the communicating parties is one of the issues that cause a huge argument. Furthermore, the current well-known QKD protocols are not yet ready to realize the personality of either the sender or the receiver, although the QKD protocol is already protected by the rules of physics and quantum mechanics to detect any interruption. This paper introduces a new QKD protocol that utilizes two quantum channels to provide authenticated communications for legitimate parties. Moreover, the proposed QKD protocol uses two type of physical behaviors, entanglement and superposition states. The entangled states are utilized to confirm the authentication between the end users, while the superposition states carry the secret key that will be shared between the users.

Abnormal image detection using texton method in wireless capsule endoscopy videos

Abstract: One of the main goals of Wireless Capsule Endoscopy (WCE) is to detect the mucosal abnormalities such as blood, ulcer, polyp, and so on in the gastrointestinal tract. Only less than 5% of total 55,000 frames of a WCE video typically have abnormalities, so it is critical to develop a technique to automatically discriminate abnormal findings from normal ones. We introduce “Texton” method which has been successfully used for image texture classification in non-medical domains. A histogram of Textons (exemplar responses occurring after convolving an image with a set of filters called “Filter bank”) called a “Texton Histogram” is used to represent an abnormal or a normal region. Then, a classifier (i.e., SVM or K-NN, and etc.) is trained using the Texton Histograms to distinguish images with abnormal regions from ones without them. Experimental results on our current data set show that the proposed method achieves promising performances.