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Mobile, AL, United States

The University of Mobile is an American four-year, private, Baptist-affiliated university in Prichard, Alabama. The master's-level university has an enrollment of 1,577. Wikipedia.

Cheng Y.-H.,University of Mobile
Lecture Notes in Engineering and Computer Science | Year: 2015

Particle swarm optimizations (PSOs) have been applied to many fields. In PSOs, the inertia weight is an important parameter for performing global search and local search in solution search. In this paper, we use a sine-based chaotic map to chaotically adapt inertia weight of PSO based on different shift deviations (sd) to perform five multimodal benchmark functions with many local optima. The experimental results show that the method using sd 0.3-0.5 can get better results for multimodal benchmark functions with many local optima. Furthermore, the method is superior to PSO with inertia weight. This study is useful to help us to set appropriate shift deviation for multimodal benchmark functions with many local optima when using sine-based chaotic map to adapt inertia weight of PSO. Source

Cheng Y.-H.,University of Mobile
IEEE/ACM Transactions on Computational Biology and Bioinformatics | Year: 2016

Many single nucleotide polymorphisms (SNPs) for complex genetic diseases are genotyped by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) in small-scale basic research studies. It is an essential work to design feasible PCR-RFLP primer pair and find out available restriction enzymes to recognize the target SNP for PCR experiments. However, many SNPs are incapable of performing PCR-RFLP makes SNP genotyping become unpractical. A genetic algorithm (GA) had been proposed for designing mutagenic primer and get available restriction enzymes, but it gives an unrefined solution in mutagenic primers. In order to improve the mutagenic primer design, we propose TLBOMPD (TLBO-based Mutagenic Primer Design) a novel computational intelligence-based method that uses the notion of "teaching and learning" to search for more feasible mutagenic primers and provide the latest available restriction enzymes. The original Wallace's formula for the calculation of melting temperature is maintained, and more accurate calculation formulas of GC-based melting temperature and thermodynamic melting temperature are introduced into the proposed method. Mutagenic matrix is also reserved to increase the efficiency of judging a hypothetical mutagenic primer if involve available restriction enzymes for recognizing the target SNP. Furthermore, the core of SNP-RFLPing version 2 is used to enhance the mining work for restriction enzymes based on the latest REBASE. Twenty-five SNPs with mismatch PCR-RFLP screened from 288 SNPs in human SLC6A4 gene are used to appraise the TLBOMPD. Also, the computational results are compared with those of the GAMPD. In the future, the usage of the mutagenic primers in the wet lab needs to been validated carefully to increase the reliability of the method. The TLBOMPD is implemented in Java and it is freely available at http://tlbompd.googlecode.com/. © 2016 IEEE. Source

Fehske A.,TU Dresden | Klessig H.,University of Mobile | Voigt J.,Radioplan | Fettweis G.,Adaptive Computing
IEEE Communications Magazine | Year: 2014

In 4G cellular networks, both the adaptation of data rates to current interference conditions due to adaptive modulation and coding as well as a frequency reuse of one mandate precise techniques to estimate cell capacities and cell loads in order to accurately predict the quality of service delivered to end users. Such estimation happens ideally already during the network planning phase and is further required for selfoptimization at runtime. Classic flow-level techniques to estimate cell loads, capacities, and related quality of service metrics assume static and worst case interference, which is analytically simple, but may produce considerable errors and lead to disadvantageous planning and optimization results. Appropriate models where individual cells are coupled through interference are rendered analytically intractable. This article first introduces basic flow-level modeling techniques and then reviews recent results in the field of flow-level network models, which allow the actual loads and capacities in interference-coupled wireless networks to be bound and closely approximated. We discuss trade-offs between accuracy and numerical complexity of different techniques and identify a model based on the notion of average interference as the most practically relevant. Simulation results for a large scenario based on a real network illustrate its applicability to practical network planning. © 2014 IEEE. Source

Liang Y.-W.,Research in Motion | Ikhlef A.,University of British Columbia | Gerstacker W.,University of Mobile | Schober R.,University of British Columbia
IEEE Transactions on Wireless Communications | Year: 2011

In this paper, we consider filterandforward beamforming (FFBF) for twoway relay networks employing singlecarrier transmission over frequencyselective channels. In FFBF, the relay nodes filter the received signal using finite impulse response (FIR) or infinite impulse response (IIR) filters. For the processing at the transceivers, we investigate two different cases: (1) simple slicing without equalization and (2) linear equalization (LE) or decisionfeedback equalization (DFE). For the first case, we optimize FIR FFBF filters, respectively, for maximization of the minimum transceiver signaltointerferenceplusnoise ratio (SINR) subject to a relay transmit power constraint and for minimization of the total relay transmit power subject to two quality of service (QoS) constraints. We show that both problems can be transformed into a convex secondorder cone programming (SOCP) problem, which can be efficiently solved using standard tools. For the second case, we optimize IIR and FIR FFBF filters for maxmin optimization of the SINR, and for transceivers with zeroforcing LE, also for minimization of the sum meansquared error (MSE) at the equalizer outputs of both transceivers. Leveraging results from FFBF for oneway relaying, we establish an upper and an achievable lower bound for the maxmin problem and an exact solution for the sum MSE problem. Since the gap between the upper and the lower bound for the maxmin problem is small, a closetooptimal solution is obtained. Our simulation results reveal that the performance of FFBF without equalization at the transceivers crucially depends on the slicer decision delay and transceivers with slicers can closely approach the performance of transceivers with equalizers provided that the FFBF filters are sufficiently long and a sufficient number of relays is deployed. © 2011 IEEE. Source

Sharma R.K.,University of Mobile | Rawat D.B.,Georgia Southern University
IEEE Communications Surveys and Tutorials | Year: 2015

Cognitive radio (CR) is regarded as an emerging technology, which equips wireless devices with the capability to adapt their operating parameters on the fly based on the radio environment, to utilize the scarce radio frequency spectrum in an efficient and opportunistic manner. However, due to the increasingly pervasive existence of smart wireless devices in cognitive radio networks (CRNs), CR systems are vulnerable to numerous security threats that affect the overall performance. There have been many significant advances on security threats and countermeasures in CRNs in the past few years. Our main goal in this paper is to present the state-of-The-art research results and approaches proposed for CRN security to protect both unlicensed secondary users and licensed primary users. Specifically, we present the recent advances on security threats/attacks and countermeasures in CRNs focusing more on the physical layer by categorizing them in terms of their types, their existence in the CR cycle, network protocol layers (exploited during their activities and defense strategies), and game theoretic approaches. The recent important attacks and countermeasures in CRNs are also summarized in the form of tables. We also present recommendations that can be followed while implementing countermeasures to enhance CRN security. With this paper, readers can have a more thorough understanding of CRN security attacks and countermeasures, as well as research trends in this area. © 1998-2012 IEEE. Source

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