Open- and closed-loop multiobjective optimal strategies for HIV therapy using NSGA-II

In this paper, multiobjective open- and closed-loop optimal treatment strategies for HIV/AIDS are presented. It is assumed that highly active antiretroviral therapy is available for treatment of HIV infection. Amount of drug usage and the quality of treatment are defined as two objectives of a biobjective optimization problem, and Nondominated Sorting Genetic Algorithm II is used to solve this problem. Open- and closed-loop control strategies are used to produce optimal control inputs, and the Pareto frontiers obtained from these two strategies are compared. Pareto frontier, resulted from the optimization process, suggests a set of treatment strategies, which all are optimal from a perspective, and can be used in different medical and economic conditions. Robustness of closed-loop system in the presence of measurement noises is analyzed, assuming various levels of noise.     

Rapid optimal scheduling for time-multiplex switches using acellular automaton

Many time-multiplex switching systems require that the incoming traffic be scheduled to avoid conflict at the switch output (two or more users converging simultaneously upon a single output). Optimal scheduling provides a means to assign traffic on demand such that either blocking probability is minimized (unbuffered system) or packet waiting time is minimized (buffered system). However, computation of an optimal schedule for switches of a reasonable size (i.e. N=100) may require many seconds or even minutes, whereas the traffic demand may vary much more rapidly. Since the computation time varies as O(N²), the problem becomes readily intractable for large N. This computational bottleneck is overcome by using a scheduling algorithm which is run on a simple special-purpose parallel computer (cellular automaton). A schedule is produced in O(N) time if signal propagation time in the automaton is considered negligible, and therefore increases in computation speed by several orders of magnitude should be possible; the time to compute a schedule for a 1000-input switch would be measured in milliseconds rather than minutes     

An optimal scheduling algorithm for testing interconnect using boundary scan

Systems will soon be built with ICs that conform with the IEEE 1149.1 boundary scan architecture. Due to the hierarchical nature of such systems, they may contain many boundary scan chains. These chains can be used to test the system, subsystem, and board interconnect. To reduce test time, the application of test vectors to these scan chains must be carefully scheduled. This article deals with problems related to finding an optimal schedule for testing interconnect. This problem is modeled using a directed graph. The following results are obtained: (1) upper and lower bounds on interconnect test time; (2) necessary and sufficient conditions for obtaining an optimal schedule when the graph is acyclic; (3) sufficient condition for obtaining an optimal schedule when the graph is cyclic; and (4) an algorithm for constructing an optimal schedule for any graph.This work was supported by Defense Advanced Research Projects Agency and monitored by the Office of Naval Research under contract No. N00014-87-K-0861. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the U.S. Government.     

Static and dynamic characteristics analysis of all-opticalwavelength conversion using directionally coupled semiconductor opticalamplifiers

We analyze the wavelength-converting operation of directionally coupled semiconductor optical amplifiers (DCSOA's) with respect to their static and dynamic characteristics. A complete dynamic DCSOA model based on the modified time-dependent transfer-matrix method is developed. This model accurately considers longitudinal variations of carrier and photon densities, gain, refractive index, coupling coefficients, and lateral optical fields. The extinction ratio, modulation bandwidth, and frequency chirping of DCSOA-based wavelength converters are investigated. For most characteristics, the DCSOA scheme has advantages over cross-gain modulation. Furthermore, by optimizing the injection current into the DCSOA, even better performance can be attained     

基于不精确信息的Pareto 最优路径搜索

将不精确状态信息下两可加约束优化路径搜索问题,建模为最大概率两可加约束路径(MP-TACP)问题,并首次提出了解决MP-TACP问题的算法MP-POC。MP-POC采用了预计算与在线计算相结合的方式,既使得算法有较快的响应速度,又能够减小不精确状态信息对算法性能的影响。MP-POC定义了连接界及方差界,并利用这些界信息以及启发式前瞻信息,使得算法具有较小的平均计算代价。另外,MP-POC在搜索可行路径时,只搜索Pareto最优路径,在不影响解的质量同时,极大地减小了搜索空间。大量仿真实验表明,在不精确状态信息下,MP-POC不但具有高的找到可行路径的成功率,而且响应速度比当前在线算法快得多。     

A proportional fair scheduling strategy using multiobjective gradient-based African buffalo optimization algorithm for effective resource allocation and interference minimization

The increased usage of Internet of Things (IoT) applications in several areas, like healthcare, agriculture, and business, has aggravated mobile traffic issues to a large extent. The deployment of 5G technology has resulted in increased traffic globally. These coherent devices, on the other hand, use the internet to fine-tune the quality of service in order to provide scalability, anonymity, and accessibility. Despite its numerous virtues, it is bound to encounter issues with interference management, fairness, throughput, and computational complexities. In this paper, a novel Multi-Objective Gradient-based African Buffalo Optimization (MOGABO) algorithm is developed to handle proportional fairness scheduling, improve interference management, increase throughput, and reduce computational complexities in cellular communication systems, particularly device-to-device (D2D) communication. The simulation analysis is done against other approaches such as the Hungarian technique, DDDPG technique, heuristic technique, and SC-FDMA technique demonstrates that this method has improved system fairness over the existing approaches. Our method maximizes throughput, and for 500 UE, the throughput is 10.4 Mbps, with an average queuing delay of 24 ms. As a result, when compared to the existing method, MOGABO method offers a 1.2% increase in throughput. Thus, our method offers better coverage and throughput for D2D cellular communication with the reduction of computational complexities.     

Image completion using efficient belief propagation via priority scheduling and dynamic pruning.

In this paper, a new exemplar-based framework is presented, which treats image completion, texture synthesis, and image inpainting in a unified manner. In order to be able to avoid the occurrence of visually inconsistent results, we pose all of the above image-editing tasks in the form of a discrete global optimization problem. The objective function of this problem is always well-defined, and corresponds to the energy of a discrete Markov random field (MRF). For efficiently optimizing this MRF, a novel optimization scheme, called priority belief propagation (BP), is then proposed, which carries two very important extensions over the standard BP algorithm: "priority-based message scheduling" and "dynamic label pruning." These two extensions work in cooperation to deal with the intolerable computational cost of BP, which is caused by the huge number of labels associated with our MRF. Moreover, both of our extensions are generic, since they do not rely on the use of domain-specific prior knowledge. They can, therefore, be applied to any MRF, i.e., to a very wide class of problems in image processing and computer vision, thus managing to resolve what is currently considered as one major limitation of the BP algorithm: its inefficiency in handling MRFs with very large discrete state spaces. Experimental results on a wide variety of input images are presented, which demonstrate the effectiveness of our image-completion framework for tasks such as object removal, texture synthesis, text removal, and image inpainting.     

Reduction of dynamic linewidth in single-frequency semiconductor lasers

Carrier density oscillations and frequency chirping of pulse code modulated single frequency semiconductor lasers are shown to be greatly reduced by use of a small current step in the leading edge of the drive pulse. For a 100 km single mode fibre link the dispersion power penalty at 1.55 ?m resulting from dynamic line broadening is predicted to be reduced from 3.7 to 1.0 dB by use of the modified current pulse.     

半导体光放大器的超快动态增益特性

提出了一种包括载流子密度脉动(CDP)、载流子加热(CH)和光谱烧孔(SHB)效应在内的半导体光放大器(SOA)的时域动态模型。利用该模型分析了半导体光放大器中的增益饱和、超快增益动态以及光脉冲在增益饱和半导体光放大器中的波形畸变,其中重点考虑了超短脉冲的情况。模拟计算表明,对于10ps量级以下的短脉冲,分析半导体光放大器的动态增益特性时,不能忽略载流子加热和光谱烧孔等带内超快非线性效应的影响。     

Synthesis of pipelined DSP accelerators with dynamic scheduling

To construct complete systems on silicon, application specific DSP accelerators are needed to speed up the execution of high throughput DSP algorithms. In this paper, a methodology is presented to synthesize high throughput DSP functions into accelerator processors containing a datapath of highly pipelined, bit-parallel hardware units. Emphasis is put on the definition of a controller architecture that allows efficient run-time schedules of these DSP algorithms on such highly pipelined data paths. The methodology is illustrated by means of an image encoding filter bank     

Sensitivity aware NSGA-II based Pareto front generation for the optimal sizing of analog circuits

This paper deals with multiobjective analog circuit optimization taking into consideration performance sensitivity vis-a-vis parameters' variations. It mainly considers improving computation time of the inloop optimization approaches by including sensitivity considerations in the Pareto front generation process, not as a constraint, but by involving it within the used metaheuristic evolution process. Different approaches are proposed and compared. NSGA-II metaheuristic is considered. The proposed sensitivity aware approaches are showcased via two analog circuits, namely, a second generation CMOS current conveyor and a CMOS voltage follower. We show that the proposed ideas considerably alleviate the long computation time of the process and improve the quality of the generated front, as well.     

A novel clustering and optimal resource scheduling in vehicular cloud networks using MKMA and the CM-CSO algorithm

Various opportunities aimed at the employment of delay-sensitive applications in the vehicular environment are presented by the vehicular cloud (VC). Contrary to diverse wireless networks, VC networks possess exceptional features among others, namely, shorter transmission time along with a higher dynamic topology. Although integration with the cloud offers higher storage along with computation capabilities, it as well entails restricted resource availability. The restrictions on the number of resources serve as a challenge in servicing the applications with their necessary quality of service (QoS) guarantees as the number of service requests for applications keeps on augmenting with diverse circumstances. Thus, the need for an effective scheduling methodology arises to decide the sequence of servicing application requests and successful utilization of a broadcast medium, along with data transmission. To do efficient resource scheduling on VC networks, an optimization algorithm, namely, the crossover and mutation (CM)-centered chicken swarm optimization (CSO) is proposed and implemented with the help of a publicly available dataset. Initially, the VC infrastructure is initialized and some vehicle information is extracted as features. Next, the Brownian motion-centered bacteria foraging optimization (BM-BFO) algorithm chooses the essential features. Centered on the chosen features, the vehicles are clustered using the modified K-means algorithm. Next, as for the cloud server's virtual machines (VMs), the resource information is extracted. Lastly, the CM-CSO algorithm carries out the optimal scheduling in the VC by means of the clustered features of vehicles and features of the VM. The proposed techniques' findings are scrutinized and analogized to the other prevailing methodologies to confirm that the proposed work performs effectively and gives optimal resource allocation (RA) to the VC.     

Analysis of the dynamic response of multielement semiconductor lasers

We present a derivation of the dynamic response of a semiconductor laser consisting of more than one active element. We show that the amplitude and phase of the modulated cavity adiabatically follows the complex resonance of the composite cavity; and using this relation, plus linearized carrier equations, we calculate the parameters characterizing the modulation response of the composite system. In the process, "effective" differential gain constants and linewidth enhancement factors arise which take the place of the corresponding parameters in single-element lasers. In the case of a two-section laser, we show that frequency chirping under modulation is present except under special conditions; we identify those conditions and show how chirping can be avoided.     

Algorithms for optimal scheduling and management of hidden Markovmodel sensors

The author considers a hidden Markov model (HMM) where a single Markov chain is observed by a number of noisy sensors. Due to computational or communication constraints, at each time instant, one can select only one of the noisy sensors. The sensor scheduling problem involves designing algorithms for choosing dynamically at each time instant which sensor to select to provide the next measurement. Each measurement has an associated measurement cost. The problem is to select an optimal measurement scheduling policy to minimize a cost function of estimation errors and measurement costs. The optimal measurement policy is solved via stochastic dynamic programming. Sensor management issues and suboptimal scheduling algorithms are also presented. A numerical example that deals with the aircraft identification problem is presented     

Near optimal scheduling of data aggregation in wireless sensor networks

Due to the large-scale ad hoc deployments and wireless interference, data aggregation is a fundamental but time consuming task in wireless sensor networks. This paper focuses on the latency of data aggregation. Previously, it has been proved that the problem of minimizing the latency of data aggregation is NP-hard [1]. Many approximate algorithms have been proposed to address this issue. Using maximum independent set and first-fit algorithms, in this study we design a scheduling algorithm, Peony-tree-based Data Aggregation (PDA), which has a latency bound of 15R + Δ ? 15, where R is the network radius (measured in hops) and Δ is the maximum node degree. We theoretically analyze the performance of PDA based on different network models, and further evaluate it through extensive simulations. Both the analytical and simulation results demonstrate the advantages of PDA over the state-of-art algorithm in [2], which has a latency bound of 23R + Δ ? 18.     

A study in dynamic neural control of semiconductor fabricationprocesses

This paper describes a generic dynamic control system designed for use in semiconductor fabrication process control. The controller is designed for any batch silicon wafer process that is run on equipment having a high number of variables that are under operator control. These controlled variables include both equipment state variables such as power, temperature, etc., and the repair, replacement, or maintenance of equipment parts, which cause parameter drift of the machine over time. The controller consists of three principal components: 1) an automatically updating database, 2) a neural-network prediction model for the prediction of process quality based on both equipment state variables and parts usage, and 3) an optimization algorithm designed to determine the optimal change of controllable inputs that yield a reduced operation cost, in-control solution. The optimizer suggests a set of least cost and least effort alternatives for the equipment engineer or operator. The controller is a PC-driven software solution that resides outside the equipment and does not mandate implementation of recommendations in order to function correctly. The neural model base continues to learn and improve over time. An example of the dynamic process control tool performance is presented retrospectively for a plasma etch system. In this study, the neural networks exhibited overall accuracy to within 20% of the observed values of .986, .938, and .87 for the output quality variables of etch rate, standard deviation, and selectivity, respectively, based on a total sample size of 148 records. The control unit was able to accurately detect the need for parts replacements and wet clean operations in 34 of 40 operations. The controller suggested chamber state variable changes which either improved performance of the output quality variables or adjusted the input variable to a lower cost level without impairment of output quality     

优化反射率以增强半导体激光器输出功率

在考虑了俄歇效应的情况下，用行波速率方程组研究了半导体激光器的输出功率与端面反射率的关系。给出了在不同偏置电流下使激光器输出端输出功率最大所需要的端面反射率。利用主动监控法在１．３μｍＩｎＧａＡｓＰ半导体激光器端面上完成了这种功率增强膜的镀制，在所选择的６０ｍＡ工作电流处，镀膜后激光器的输出功率增加了１３０％。     

On generalized optimal scheduling of high data-rate bursts in CDMA systems

In a code-division multiple access (CDMA)-based wireless communication system, forward link is power limited and reverse link is interference limited. With power control and statistical multiplexing, voice services can be supported reasonably well. However, for high data-rate services, a more comprehensive scheduling mechanism is needed in order to achieve a high capacity while satisfying the forward and reverse link constraints. We formulate the high data-burst scheduling as a integer programming problem using a generic CDMA system model. We also suggest an optimal algorithm for generating scheduling solutions. With cdma2000 system details plugged in the proposed algorithm, it is found that our algorithm considerably outperforms several fast heuristics, including equal sharing, first-come-first-served, longest delay first, and shortest burst first.     

Evolutionary multiobjective optimization of cellular base station locations using modified NSGA-II

In this paper, various parameters of cellular base station (BS) placement problem such as site coordinates, transmitting power, height and tilt angle are determined using evolutionary multiobjective algorithm to obtain better compromised solutions. The maximization of service coverage and minimization of cost are considered as conflicting objectives by satisfying inequality constraints such as handover, traffic demand and overlap. For the purpose of simulation, a 15 × 15 Km² synthetic test system is discretized as hexagonal cell structure and necessary simulations are carried out to calculate receiving field strength at various points. The path loss is calculated using Hata model. To improve the diversity and uniformity of the obtained nondominated solutions, controlled elitism and dynamic crowding distance operators are introduced in non-dominated sorting genetic algorithm-II (NSGA-II) and are designated as modified NSGA-II (MNSGA-II). The optimal placement for BS is determined using MNSGA-II and NSGA-II. The effect of maximum number of function evaluations, handover and overlap on the performances of the algorithms is studied. A better distributed Pareto-front is obtained in MNSGA- II when compared with NSGA- II. The results reveal that, increasing of overlap percentage not only increases the coverage but also increases the overlap and handover error. The coverage percentage is indirectly proportional to the number of antennas involved in the handover constraint. The simulation results reveal that the proposed technique is more suitable for real-world BS placement problem.