Linear spectral mixture analysis (SMA) has been used extensively in remote sensing studies to estimate the sub pixel composition of spectral mixtures. The mathematical solution of the mixing problem is to resolve a set of linear equations using least squares approaches. The lack of ability to account for temporal and spatial variability between and among endmembers has been acknowledged as a major shortcome of conventional SMA approaches applying a linear mixture model using a set of fixed endmembers. Also, if endmembers are highly correlated, the matrix will become non-orthogonal, the inversion will be unstable and the inverse or estimated fractions will become highly sensitive to random errors (e. g., noise). In this paper, we present a new BAND SELECTION method that comprises a BAND prioritization and a BAND de-correlation. The BAND prioritization will prioritizes all BANDs according to the reduced spectral variability of endmembers which will be used for unmixing. BANDs are then selected on the basis of their associated priorities. Since the BAND prioritization does not consider as spectral correlation, a BAND de-correlation using the angles between BANDs are being applied to de-correlate prioritized BANDs. It is shown that the proposed BAND SELECTION method effectively eliminates a great number of insignificant BANDs. Surprisingly, the experimental results on real and synthetic data sets show that with a proper BAND SELECTION less than 0. 2 of the total number of BANDs can achieve comparable performance using all BANDs.

such images provide valuable information from the earth surface objects. Target detection (TD) is a fast growing research filed in processing hyperspectral images. In recent years, developing target detection algorithms has received growing interest in hyperspectral images. The aim of TD algorithms is to find specific targets with known spectral signatures. Nevertheless, the enormous amount of information provided by hyperspectral images increases the computational burden as well as the correlation among spectral BANDs. Besides, even the best TD algorithms exhibit a large number of false alarms due to spectral similarity between the target and background especially at subpixel level in which the size of target of interest is smaller than the ground pixel size of the image. Thus, dimensionality reduction is often conducted as one of the most important steps before TD to both maximize the detection performance and minimize the computational burden. However, in hyperspectral image analysis few studies have been carried out on dimension reduction or BAND SELECTION for target detection in comparison to the hyperspectral image classification field. Otherwise BAND SELECTION has great impact on remote sensing processing because of its effect on dimension reduction and reducing computational burden of hyperspectral image processing by selecting of optimum BANDs subset.This paper presents a simple method to improve the efficiency of subpixel TD algorithms based on removing bad BANDs in a supervised manner. The idea behinds the proposed method is to compare field and laboratory spectra of desired target for detecting bad BANDs. Since the laboratory spectrum of targets is measured under standard conditions with the minimized level of noise and atmospheric effects, they can be considered as ideal spectrum. On the other hand, the recorded field-based reflectance spectrum are affected by surrounding objects such as vegetation cover and atmospheric affects specially water vapor absorption. Obviously, the spectrum becomes progressively noisier at longer wavelengths due to reduction of radiance of the illumination source, i.e., the sun. In this way, bad BANDs can be observed in the field based spectrum when comparing with the laboratory spectrum of the target of interest. Based on fitting a normal distribution to laboratory-field spectral difference of all corresponding BANDs, best of them will be select and introduce to target detection methods.In this study for our evaluation, the proposed method is compared with six popular BAND SELECTION methods implemented in PRtools and False alarm parameter for validation is used in this study. Comparison is done using two well-known sub-pixel TD algorithms, the adaptive coherence estimator (ACE) and the constrained energy minimization (CEM), in the target detection blind test dataset. This dataset includes two HyMap radiance and reflectance images of Cooke City in Montana, USA. The images are obtained by an airborne HyMap sensor which has 126 spectral BANDs and a ground sample distance of 3m. This dataset includes 10 sub-pixel targets located in an open grass region. Experimental results show that the proposed method improves the efficiency of the ACE and CEM comparing with other BAND SELECTION methods used. Between of all target detection experiments only in 12 percent results destroyed. Moreover, high speed, simplicity, low computational burden, and time consuming are the advantages of the proposed method.

High spectral resolution of hyperspectral images in form of very narrow and constant within visible and infrared spectral ranges has brought the technology of remote hyperspectral measurement into spotlight in order to detect objects as well as earthly phenomena. In this field، most methods have been presented with the purpose of improving the accuracy of image classification and there have been scarce researches on target detection (TD). Supervised TD problem can be considered as a one-class classification problem between the target and non-target pixels using training data from the target class only. However، a spectral signature of the target sample obtained using field or laboratory measurements is the only training data available for TD. A substantial number of BANDs lead to heavy computational costs along with Hughes Effect on hyperspectral image processing. Hence، in recent years much attention has been paid to reduction of computational complexity in the processing of hyperspectral images. In comparison to the classification field، few studies have been done on dimension reduction or BAND SELECTION for target detection in hyperspectral images. A chief reason behind this is the shortage or absence of training samples of the desired target in background images. In order to solve this problem، a method is introduced based on target simulation. Recently، target simulation method has been used for creating artificial sub-pixel targets on hyperspectral images in order to investigate the performance of sub-pixel target detection (STD) algorithms. But in this paper target simulation has been used as method for optimum BAND SELECTION. In this method for STD several simulated training samples، created by means of target spectrum implantation in the image. For optimal BAND SELECTION after achieving sufficient implanted targets as training data، searching strategy in hyperspectral image space is of high account. Once simulated targets are created، optimal BANDs are selected via Particle Swarm Optimization (PSO) Algorithm. To make the optimization algorithms exploit well in search space، their cost functions must be well defined. So one of contributions of this study is defining a new cost function for optimization algorithms used for selecting optimal BANDs. In the next stage، based on the optimal BANDs selected، the Local ACE is applied on the image to obtain detection result. ACE algorithm، one of the most useful and common algorithms for detection of sub-pixel and full pixel targets. But local ACE is commonly used to detect sub-pixel targets in hyperspectral images. In this version of the ACE algorithm، instead of using the mean and covariance of the entire image، just the neighbouring window pixels are used. The output of this stage will be TD map that by applying a threshold in a detection map can determine whether the pixels are target or not. In order to evaluate and study the ability of the introduced algorithm، Target Detection Blind Test (TDBT) of Hymap dataset and Hyperion dataset from Botswana have been used. False alarm rate was used as touchstone to evaluate the results between the outputs of different methods. Compared to some PSO-based algorithms such as maximum-submaximum-ratio (MSR) and correlation coefficient (CC)، an evolutionary method such as genetic algorithm (GA)، and the use of full BAND، the proposed method was able to improve the results by 46% in all cases where it was possible to decrease false alarm for the searched target.

Colored dissolved organic matter (CDOM) is an important measure of water quality. CDOM can reduce the amount of light in water layers, disrupt the biological activity of photosynthesis, and inhibit the growth of phytoplankton populations that are essential for the aquatic food chain. Contrary to conducted research to date, which uses a specific wavelength, in this paper, we first examined the possibility of using visible portion of the spectrum to determine CDOM at 254-443 nm (254, 260, 350, 375, 400, 412, 440, 443 nm) in Landsat 8 . we then selected the most appropriate BAND ratios to measure CDOM at measurable wavelengths using the SVR algorithm (the parameters of which have been optimized using the genetic algorithm). It is noteworthy that in this study, the ratio of Coastal to red BANDs (), blue to red (), and the ratio of green to red BANDs () were considered for CDOM retrieval. Based on the results, considering the coefficient of determination ( = 0.71) and the amount of errors (MSE = 1.161 , RMSE = 1.077  and MAE = 0.946 ), it was concluded that the ratio of green to red BANDs in Landsat 8 is the most suitable choice for determining the colored dissolved organic matter. Moreover, according to the results from this study, the measurement of CDOM (440) is the most appropriate index for evaluating the quality of lake water resources in terms of their concentrations.

The In-BAND Full-Duplex (IBFD) technique is attracting a great deal of attention thanks to its ability to simultaneously transmit and receive the information over the same frequency BAND. IBFD has the potential to double the spectral efficiency and system capacity, compared to conventional Half-Duplex (HD) schemes. However, strong self-interference caused by simultaneous transmission and receiption over the same frequency BAND, renders the feasibility of IBFD in practice. Recently, advanced self-interference cancellation techniques are proposed to address this issue. In this paper, an IBFD relay network with Simultaneous Wireless Information and Power Transmission (SWIPT) is introduced that applies multiple relays. We propose a joint antenna-and-relay SELECTION scheme that decreases the complexity and increases the ergodic capacity. We provide simulation results for the outage probability and the ergodic capacity. The results show that the IBFD system outperforms the HD system, for a certain range of self-interference power.

Purpose: Managers are one of the important elements of an organization, for this reason, in order to draw the future of the organization, it is necessary for the planners to specify the conditions of their SELECTION and appointment. Therefore, the current research has been done with the aim of identifying and analyzing the components of selecting future principals.Method: In this research, comparative and benchmarking method is used as a prospective approach. This approach is based on the belief that today's advanced organizations/countries can be considered as a model for the future of another organization/countries in their respective subjects. For this, first, the fields of comparison and benchmarking were determined using Brody's four-step comparison method; then the countries of Canada, Finland, Australia, South Africa, and Japan were selected according to the qualitative balance value in the international advanced TEAMS test, human development index, life quality index(health, instruction, and welfare), education quality index, and other scientific-scholarly indexes; finally, by extracting the criteria for the SELECTION and appointment of principals through content analysis and comparison with Iran, the proposed framework for Iran has been presented.Findings: A total of 61 components for the SELECTION of secondary school principals were identified from among the studies conducted in the selected countries in this article. By extracting the commonalities and differences of each of the components among the countries, it was found that the highest index of manager SELECTION and appointment belongs to Japan and the lowest one is related to Finland.Conclusion: There are similarities between the components of SELECTION of principals of secondary schools in Iran and selected countries. In Iran, special attention should be paid to important components such as adherence to religious principles, appropriate personality traits, creativity and innovation, motivation to develop capabilities, professional growth, power of supervision and accountability, social image, leader skills, and purposefulness and foresight.

In this paper, uplink secure transmission in a non-orthogonal multiple access (NOMA) network is investigated by SELECTION of the users for data transmission to the base station (BS) and also jammers with the capability of energy harvesting. In fact, each frame has two phases. In the first phase, jammers harvest energy from BS and in the second phase, the selected users transmit their data to BS using NOMA technique while selected jammer emits the artificial noise for confusing the eavesdropper. In fact, the problem is maximizing the secrecy throughput by SELECTION of the users for uplink data transmission to BS in each frequency channel and suitable jammers to make the artificial noise for eavesdropper with constraints on the secrecy outage probability (SOP) and connection outage probability (COP). The problem is solved based on the convex optimization methods and Karush-Kuhn-Tucker (KKT) conditions. An algorithm is proposed for solving the problem and the system performance is evaluated. Simulation results present that the proposed algorithm has the better performance for the throughput and security of the network in comparison with the benchmark algorithms in different situations and scenarios.

The aim of this study was a strain-improvement program for Trichoderma reesei PTCC 5142 by using a combination of UV light and NTG (N-methyl-N'-nitro-N-nitrosoguanidine) for enhanced cellulase production. Following mutagenesis after several rounds, mutant A6: 2 was selected from a total of 6500 colonies. Results obtained after 4 days were: Enzyme activity 1.26 U/ml and 0.82 U/ml for exoglucanase and endoglucanase, respectively. The comparative results showed increased production exoglucanase and endoglucanase by mutant A6: 2 than Trichoderma reesei PTCC 5142 to amount 130% for exoglucanase and 156% for endoglucanase.

Multi-label classification aims at assigning more than one label to each instance. Many real-world multi-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature SELECTION is a common approach to tackle this issue by choosing prominent features. Multi-label feature SELECTION is an NP-hard approach, and so far, some swarm intelligence-based strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence algorithm based on the binary algorithm of particle swarm optimization and a novel local search strategy has been proposed to select a set of prominent features. To this aim, features are divided into two categories based on the extension rate and the relationship between the output and the local search strategy to increase the convergence speed. The first group features have more similarity to class and less similarity to other features, and the second is redundant and less relevant features. Accordingly, a local operator is added to the particle swarm optimization algorithm to reduce redundant features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed algorithm compared to other algorithms presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different classification criteria of multi-label classification and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.