Speckle Noise Detection and Removal for Laser Speech Measurement Systems

Laser speech measurement is a new sound capture technology based on Laser Doppler Vibrometry (LDV). It avoids the need for contact, is easily concealed and is ideal for remote speech acquisition, which has led to its wide-scale adoption for military and security applications. However, lasers are easily affected by complex detection environments. Thus, speckle noise often appears in the measured speech, seriously affecting its quality and intelligibility. This paper examines all of the characteristics of impulsive noise in laser measured speech and proposes a novel automatic impulsive noise detection and removal method. This method first foregrounds noise using decorrelation based on a linear prediction (LP) model that improves the noise-to-signal ratio (NSR) of the measured signal. This makes it possible to detect the position of noise through a combination of the average short-time energy and kurtosis. The method not only precisely locates small clicks (with a duration of just a few samples), but also finds the location of longer bursts and scratches (with a duration of up to a hundred samples). The located samples can then be replaced by more appropriate samples whose coding is based on the LP model. This strategy avoids unnecessary processing and obviates the need to compromise the quality of the relatively large fraction of samples that are unaffected by speckle noise. Experimental results show that the proposed automatic speckle noise detection and removal method outperforms other related methods across a wide range of degraded audio signals.

Analysis of CO2 Emissions in the Whole Production Process of Coal-Fired Power Plant

The linear programming (LP) model has been used to identify a cost-effective strategy for reducing CO2 emissions in power plants considering coal washing, pollutant removal, and carbon capture processes, thus CO2 emissions in different production processes can be obtained. The direct emissions (combustion emissions and desulfurization emissions) and indirect emissions (pollutant removal, coal washing, and carbon capture) of CO2 were all considered in the LP model. Three planning periods were set with different CO2 emission control desirability to simulate CO2 emissions of the different reduction requirements. The results can reflect the CO2 emissions across the whole production process of a coal-fired power plant overall. The simulation results showed that for a coal-fired power plant containing two 1000 MW ultra super-critical sets, when the desirability was 0.9, the CO2 total emissions were 2.15, 1.84, and 1.59 million tons for the three planning periods. The research results suggest that the methodology of LP combined with fuzzy desirability function is applicable to represent the whole production process of industry sectors such as coal-fired power plants. The government policy makers could predict CO2 emissions by this method and use the results as a reference to conduct effective industrial and energy structure adjustment.

Linear Programming Optimization Techniques for Addis Ababa Public Bus Transport

Anbessa City Bus Service Enterprise (ACBSE) is the only public enterprise that provides transport services in the city of Addis Ababa. The enterprise uses a fixed bus schedule system to serve passengers in more than 125 routes. However, the current bus assignment and scheduling system are becoming a challenge in the company’s operational performances. The objective of this paper is to develop an optimum bus assignment method using linear programming (LP). After a thorough analysis of the existing bus scheduling system, the LP model is developed and used to determine the optimal number of busses for each route in four shifts. The output of the LP-model is then validated with the performances of the existing systems. The findings of the study showed that the new model reveals better performances on the operating costs, bus utilization, and trips and distance covered compared with the existing scheduling system. The bus utilization is improved by the new system and cut costs on the one hand and improves the service quality to passengers on the other hand. The authors recommended the enterprise to adopt the new bus assignment system so that busses can be assigned based on the demand distribution of passengers for each route at a given shift.

A Linear Programming Relaxation DEA Model for Selecting a Single Efficient Unit with Variable RTS Technology

The selection-based problem is a type of decision-making issue which involves opting for a single option among a set of available alternatives. In order to address the selection-based problem in data envelopment analysis (DEA), various integrated mixed binary linear programming (MBLP) models have been developed. Recently, an MBLP model has been proposed to select a unit in DEA with variable returns-to-scale technology. This paper suggests utilizing the linear programming relaxation model rather than the MBLP model. The MBLP model is proved here to be equivalent to its linear programming relaxation problem. To the best of the authors’ knowledge, this is the first linear programming model suggested for selecting a single efficient unit in DEA under the VRS (Variable Returns to Scale) assumption. Two theorems and a numerical example are provided to validate the proposed LP model from both theoretical and practical perspectives.

Detecting congestion in DEA by solving one model

Presence of input congestion is one of the key issues that results in lower efficiency and performance in Decision Making Units (DMUs). So, determination of congestion is of prime importance, and removing it improves performance of DMUs. One of the most appropriate methods for detecting congestion is Data Envelopment Analysis (DEA). Since the output of inefficient units can be increased by keeping the input constant through projecting on the weak efficiency frontier, it is unnecessary to determine the congested inefficient DMUs. Therefore, in this case we solely determine congested vertex units. Towards this aim, only one LP model in DEA is proposed and the status of congestion (strong congestion and weak congestion) obtained. In our method, a vertex unit under evaluation is eliminated from the production technology, and then, if there exists an activity that belongs to the production technology with lower inputs and higher outputs compared with omitted unit, we say vertex unit evidences congestion. One of the features of our model is that by solving only one LP model and with easier and fewer calculations compared to other methods, congested units can be identified. Data set obtained from Japanese chain stores for a period of 27 years is used to demonstrate the applicability of the proposed model and the results are compared with some previous methods.

OPTIMIZATION OF THE CROPPING PATTERN IN CENTRAL YASIN DISTRICT GHIZER, GILGIT-BALTISTAN

In this research study Linear Programming (LP) Model is applied to determine the maximum profit from the three major crops (wheat, maize and alfalfa) in Central Yasin District Ghizer, where agriculture is the main source of income, however, due to population growth the agricultural land is used to construct buildings, which has minimized the land for agricultural use. In such a challenging condition a method to suggest the cultivation of a maximum profitable crop is the need of time. Therefore, LP model is an appropriate method to decide the profitable crop and to maximize the profit. The study was conducted employing quantitative method of research, with the purpose to compare the maximum profit per annum from the major crops, and to determine the beneficial crop among the three major crops in Central Yasin. All the farmers of the study area who grow the crops were the target population of the study and samples from the target population were selected using random sampling technique. Data was collected by conducting survey in the study area, where self-made questionnaires were distributed among the samples individually and responses were noted on the spot. The raw data is organized and analyzed using Microsoft Excel for LP model formulation. The result showed that the average land used for the cultivation of wheat, maize and alfalfa in the study area is 0.924 acres, 0.664 acres and 0.75 acres respectively. Furthermore, results of the LP model showed that alfalfa is the optimal solution rather than wheat and maize, and it (alfalfa) gives an average annual net profit of Rs.195634.488.

Efficiency and super-efficiency under inter-temporal dependence

In this paper, a linear programming (LP) model for measuring the relative efficiency of a decision-making unit (DMU) under inter-temporal dependence of data is proposed. Necessary and sufficient conditions are derived for identification of dynamically efficient paths. Furthermore, an LP model is proposed to estimate the super-efficiency of the dynamically efficient paths using an extended version of the modified MAJ model (Saati et al., Ric. Oper. 31 (2001) 47–59). To evaluate the applicability of the proposed method in a banking sector example, this method is employed for ranking some branches of the Iranian commercial bank.

A Methodology and Case Study to Assess SCOR-Make Agility Measures Under Uncertainties

An assessment of agility is not an easy task since agility has been differently defined in literature, and it is not convenient to measure by experiment in practice. The objective of this paper is to propose a methodology to assess the agility performance under uncertainties based on level 1 of SCOR-Make process metric including the upside make flexibility (AG1.1), upside make adaptability (AG1.2), and downsize make adaptability (AG1.3). The proposed methodology consists of predictive models, which are a deterministic linear programming (LP) model and LP model with uncertainties, and algorithms to assess the agility measures. A case study of a bottled-water factory is conducted to demonstrate the application of the proposed methodology. The case study shows that the proposed methodology can effectively determine the agility measures. It can also be adapted to answer other agility related practical questions that are different from the SCOR definition.

Optimizing Dietary Plan to Develop Brain Functions

The purpose of this paper is to show how linear programming model can help to design dietary plan in developing brain functions for the children undertaken by dietician in health care. For the development of the whole body and mental development, child nutrition is very important. In this paper, the constraints of problem, specified objective, structured mathematical model are detailed. The required data are sought and systematic review was done by searching engine with words like nutrition, food, diet, etc. In this paper, food items such as fish, egg, tomato, orange, peanut and oats corresponding to the nutrients are considered and calculated. Excel Solver is used to solve the LP model. From these results, the children can achieve by following the dietary plan.