School of Electrical Engineering and Computer Science
http://hdl.handle.net/1957/7302
2015-05-22T21:11:16ZA Prediction Algorithm for Coexistence Problem in Multiple-WBAN Environment
http://hdl.handle.net/1957/55836
A Prediction Algorithm for Coexistence Problem in Multiple-WBAN Environment
Jin, Zilong; Han, Yoonjeong; Cho, Jinsung; Lee, Ben
The coexistence problem occurs when a single wireless body area network (WBAN) is located within a multiple-WBAN
environment. This causes WBANs to suffer from severe channel interference that degrades the communication performance of each
WBAN. Since a WBAN handles vital signs that affect human life, the detection or prediction of coexistence condition is needed to
guarantee reliable communication for each sensor node of a WBAN. Therefore, this paper presents a learning-based algorithm to
efficiently predict the coexistence condition in a multiple-WBAN environment. The proposed algorithm jointly applies PRR and
SINR, which are commonly used in wireless communication as a way to measure the quality of wireless connections. Our extensive
simulation study using Castalia 3.2 simulator based on the OMNet++ platform shows that the proposed algorithm provides more
reliable and accurate prediction than existing methods for detecting the coexistence problem in a multiple-WBAN environment.
This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by the Hindawi Publishing Corporation. The published article can be found at: http://www.hindawi.com/journals/ijdsn/.
2015-01-01T00:00:00ZAn empirical comparison of mutant selection approaches
http://hdl.handle.net/1957/55691
An empirical comparison of mutant selection approaches
Oregon State University. School of Electrical Engineering and Computer Science; Gopinath, Rahul; Alipour, Amin; Ahmed, Iftekhar; Jensen, Carlos (Computer scientist); Groce, Alex
Mutation analysis is a well-known method for measuring the quality of test suites. However, it is computationally intensive compared to other measures, which makes it hard to use in practice. Choosing a smaller subset of mutations to run is a simple approach that can alleviate this problem. Mutation operator selection has been heavily researched. Recently, researchers have found that sampling mutants can achieve accuracy and mutant reduction similar to operator selection. However, the empirical support for these conclusions has been limited, due to the small number of subject programs investigated. The best sampling technique is also an open problem. Our research compares a large number of sampling and operator selection criteria based on their ability to predict the full mutation score as well as the consistency of mutation reduction ratios achieved. Our results can be used to choose an appropriate mutation reduction technique by the reduction and level of fidelity to full mutation results required. We find that all sampling approaches perform better than operator selection methods, when considering ability to predict the full mutation score as well as the consistency of mutation reduction ratios achieved.
2015-04-27T00:00:00ZInvestigation of ultra-thin In-Ga-Zn-O thin-film transistors
http://hdl.handle.net/1957/55642
Investigation of ultra-thin In-Ga-Zn-O thin-film transistors
Chiang, Tsung-Han
The objective of the work reported herein is to explore the impact of decreasing channel thickness on radio-frequency (RF) sputtered amorphous indium-gallium-zinc oxide (a-IGZO) thin-ﬁlm transistors (TFTs) electrical performance through the evaluation of drain current versus gate voltage (I[subscript D] − V[subscript G]) transfer curves. For a ﬁxed set of process parameters, it is found that the turn-on voltage, V[subscript ON] (off drain current, I[superscript OFF][subscript D]) increases (decreases) with decreasing a-IGZO channel thickness (h) for h < 11 nm. The V[subscript ON] − h trend is attributed to a large density (3.5 × 10¹² cm⁻²) of backside surface acceptor-like traps and an enhanced density (3 × 10¹⁸ cm⁻³) of donor-like trap states within the upper ∼11 nm from the backside surface. The precipitous decrease observed in I[superscript OFF][subscript D] − h when h < 11 nm is ascribed to the backside surface acceptor-like traps and the closer physical proximity of the backside surface when the channel layer is ultra-thin. By altering the process parameters of gas ratio of Ar/O₂ from 9/1 to 10/0 and reducing the anneal temperature from 400 to 150°C, a h ≈ 5 nm a-IGZO TFT is demonstrated with V[subscript ON] ≈ 0 V, field-effect mobility of µFE = 9 cm⁻²V⁻¹s⁻¹, subthreshold slope of S = 90 mV/dec, and drain current on–to-off ratio of I[superscript ON/OFF][subscript D] = 2.0×10⁵.
Graduation date: 2015
2015-03-12T00:00:00ZScheduling Conservation Designs for Maximum Flexibility via Network Cascade Optimization
http://hdl.handle.net/1957/55622
Scheduling Conservation Designs for Maximum Flexibility via Network Cascade Optimization
Xue, Shan; Fern, Alan; Sheldon, Daniel
One approach to conserving endangered species is to purchase and protect a set of land parcels in a way that maximizes the expected future population spread. Unfortunately, an ideal set of parcels may have a cost that is beyond the immediate budget constraints and must thus be purchased incrementally. This raises the challenge of deciding how to schedule the parcel purchases in a way that maximizes the flexibility of budget usage while keeping population spread loss in control. In this paper, we introduce a formulation of this scheduling problem that does not rely on knowing the future budgets of an organization. In particular, we consider scheduling purchases in a way that achieves a population spread no less than desired but delays purchases as long as possible. Such schedules offer conservation planners maximum flexibility and use available budgets in the most efficient way. We develop the problem formally as a stochastic optimization problem over a network cascade model describing a commonly used model of population spread. Our solution approach is based on reducing the stochastic problem to a novel variant of the directed Steiner tree problem, which we call the set-weighted directed Steiner graph problem. We show that this problem is computationally hard, motivating the development of a primal-dual algorithm for the problem that computes both a feasible solution and a bound on the quality of an optimal solution. We evaluate the approach on both real and synthetic conservation data with a standard population spread model. The algorithm is shown to produce near optimal results and is much more scalable than more generic off-the-shelf optimizers. Finally, we evaluate a variant of the algorithm to explore the trade-offs between budget savings and population growth.
This is the publisher’s final pdf. The published article is copyrighted by the AI Access Foundation and can be found at: http://www.jair.org/.
2015-01-01T00:00:00Z