Optimal Investment Under Transaction Costs: A Threshold Rebalanced Portfolio Approach

We study optimal investment in a financial market having a finite number of assets from a signal processing perspective. We investigate how an investor should distribute capital over these assets and when he should reallocate the distribution of the funds over these assets to maximize the cumulative wealth over any investment period. In particular, we introduce a portfolio selection algorithm that maximizes the expected cumulative wealth in i.i.d. two-asset discrete-time markets where the market levies proportional transaction costs in buying and selling stocks. We achieve this using "threshold rebalanced portfolios", where trading occurs only if the portfolio breaches certain thresholds. Under the assumption that the relative price sequences have log-normal distribution from the Black-Scholes model, we evaluate the expected wealth under proportional transaction costs and find the threshold rebalanced portfolio that achieves the maximal expected cumulative wealth over any investment period. Our derivations can be readily extended to markets having more than two stocks, where these extensions are pointed out in the paper. As predicted from our derivations, we significantly improve the achieved wealth over portfolio selection algorithms from the literature on historical data sets.Comment: Submitted to IEEE Transactions on Signal Processin

Examination of Existent Propagation Models Over Large Inhomogeneous Terrain Profiles Using Fast Integral Equation Solution

Cataloged from PDF version of article.The accuracyof most widelyused empirical models are investigated using the spectrallyaccelerated forward-backward (FBSA) method as a benchmark solution. First, FBSA results are obtained for propagation over large scale terrain profiles and compared with measurements to assess the accuracyof FBSA. Then, accuracyof some International Telecommunication Union (ITU) and Federal Communications Commission (FCC) propagation models are investigated. It has been observed that, for rural areas, the prediction of the most recent ITU recommended propagation model (Rec. 1546) deviates much more than older models do

Computerized Hittite Cuneiform Sign Recognition and Knowledge-Based System Application Examples

The Hittites had lived in Anatolia more than 4000 years ago. The Hittite language is one of the oldest and may be the only one still readable and grammar rules are known member of Indo-European language family. The Hittites had a cuneiform script of their own written on soft clay pads or tablets. Tablets made durable and permanent by baking them after writing with some tools. That is why they could endure for thousands of years buried in the ground. The study of Hittite language has been made manually on the Hittite cuneiform tablets. Unfortunately, field scientists have read and translated only a relatively small number of unearthed tablets. Many more tablets are still waiting under and over ground in Anatolia for reading and translation into various languages. To read and translate the cuneiform signs, using computeraided techniques would be a significant contribution not only to Anatolian and Turkish but also to human history. In this paper, recognition of Hittite cuneiform signs by using computer based image-processing techniques is reported. Additionally, uses of data-mining applications are also included in the paper. Most importantly, the authors also demonstrated feasibility of an expert system on the Hittite cuneiform script

A Model for Hydrogen Thermal Conductivity and Viscosity Including the Critical Point

In order to conduct a thermal analysis of heat transfer to liquid hydrogen near the critical point, an accurate understanding of the thermal transport properties is required. A review of the available literature on hydrogen transport properties identified a lack of useful equations to predict the thermal conductivity and viscosity of liquid hydrogen. The tables published by the National Bureau of Standards were used to perform a series of curve fits to generate the needed correlation equations. These equations give the thermal conductivity and viscosity of hydrogen below 100 K. They agree with the published NBS tables, with less than a 1.5 percent error for temperatures below 100 K and pressures from the triple point to 1000 KPa. These equations also capture the divergence in the thermal conductivity at the critical poin

Characteristic Basis Function Method for Solving Electromagnetic Scattering Problems over Rough Terrain Profiles

Cataloged from PDF version of article.A computationally efficient algorithm, which combines the characteristic basis function method (CBFM), the physical optics (PO) approach (when applicable) with the forward backward method (FBM), is applied for the investigation of electromagnetic scattering from—and propagation over—large-scale rough terrain problems. The algorithm utilizes high-level basis functions defined on macro-domains (blocks), called the characteristic basis functions (CBFs) that are constructed by aggregating low-level basis functions (i.e., conventional sub-domain basis functions). The FBM as well as the PO approach (when applicable) are used to construct the aforementioned CBFs. The conventional CBFM is slightly modified to handle large-terrain problems, and is further embellished by accelerating it, as well as reducing its storage requirements, via the use of an extrapolation procedure. Numerical results for the total fields, as well as for the path loss are presented and compared with either measured or previously published reference solutions to assess the efficiency and accuracy of the algorithm

A Deterministic Analysis of an Online Convex Mixture of Expert Algorithms

Cataloged from PDF version of article.We analyze an online learning algorithm that adaptively combines outputs of two constituent algorithms (or the experts) running in parallel to model an unknown desired signal. This online learning algorithm is shown to achieve (and in some cases outperform) the mean-square error (MSE) performance of the best constituent algorithm in the mixture in the steady-state. However, the MSE analysis of this algorithm in the literature uses approximations and relies on statistical models on the underlying signals and systems. Hence, such an analysis may not be useful or valid for signals generated by various real life systems that show high degrees of nonstationarity, limit cycles and, in many cases, that are even chaotic. In this paper, we produce results in an individual sequence manner. In particular, we relate the time-accumulated squared estimation error of this online algorithm at any time over any interval to the time-accumulated squared estimation error of the optimal convex mixture of the constituent algorithms directly tuned to the underlying signal in a deterministic sense without any statistical assumptions. In this sense, our analysis provides the transient, steady-state and tracking behavior of this algorithm in a strong sense without any approximations in the derivations or statistical assumptions on the underlying signals such that our results are guaranteed to hold. We illustrate the introduced results through examples. © 2012 IEEE

Web-based Integrated Development Environment for Event-Driven Applications

Event-driven programming is a popular methodology for the development of resource-constrained embedded systems. While it is a natural abstraction for applications that interface with the physical world, the disadvantage is that the control flow of a program is hidden in the maze of event handlers and call-back functions. TinyOS is a representative event-driven operating system, designed for wireless sensor networks, featuring a component-based architecture that promotes code reuse. In this paper, we present a web-based model-driven graphical design environment for TinyOS that visualizes the component hierarchy of an application, and captures its eventbased scheduling mechanism. In contrast with existing visual environments, our representation explicitly captures the control flow of the application through events and commands, which makes it easier to understand the program logic than studying the source code. The design environment supports two-way code generation: mapping the visual representation to TinyOS source code, as well as building visual models from existing sources

Systematic Design of edical Capsule Robots

Medical capsule robots that navigate inside the body as diagnostic and interventional tools are an emerging and challenging research area within medical CPSs. These robots must provide locomotion, sensing, actuation, and communication within severe size, power, and computational constraints. This paper presents the first effort for an open architecture, platform design, software infrastructure, and a supporting modular design environment for medical capsule robots to further this research area