complexity

This article compares the quality and complexity of LPC, CELP, and MELP standard audio encoders. These standards are based on linear predictive and are used in sound (speech) processing. These standards are powerful high-quality speech coding methods that provide highly accurate estimates of audio parameters and are widely used in the commercial (mobile) and military (NATO) communications industries. To compare LPC, CELP, and MELP audio encoders in two male and female voice modes and four voice models: quiet, Audio recorded without sound by the microphone, MCE, office, and two noise models 1% and 05% were used. The simulation results show the complexity of MELP is higher than LPC and CELP in terms of both processor and memory requirements. The MELP analyzer requires 72% of its total processing time. This additional memory is, due to the vector quantization tables MELP uses for the linear spectral frequencies (LSFs) and the Fourier magnitude. Also, According to the quality comparison test using the MOS index, MELP has the highest score, followed by CELP and LPC

This article compares the quality and complexity of LPC, CELP, and MELP standard audio encoders. These standards are based on linear predictive and are used in sound (speech) processing. These standards are powerful high-quality speech coding methods that provide highly accurate estimates of audio parameters and are widely used in the commercial (mobile) and military (NATO) communications industries. To compare LPC, CELP, and MELP audio encoders in two male and female voice modes and four voice models: quiet, Audio recorded without sound by the microphone, MCE, office, and two noise models 1% and 05% were used. The simulation results show the complexity of MELP is higher than LPC and CELP in terms of both processor and memory requirements. The MELP analyzer requires 72% of its total processing time. This additional memory is, of course, due to the vector quantization tables that MELP uses for the linear spectral frequencies (LSFs) and the Fourier magnitude. Also, according to the quality comparison test using the MOS index, MELP has the highest score, followed by CELP and LPC.

Many different complex systems display emergent behavior, and quite a few of these systems have been studied in the past. The science of complexity, popularly known as chaos theory, deals with emergent systems in other fields. Designing emergence is something of a paradoxical task because one of the defining aspects of emergent behavior is that it occurs only after a system is put into motion. In this paper, we begin with the definition of complex systems. Then, we describe the continuum between strictly ordered systems and entirely chaotic ones and show that emergence takes place somewhere between the two. After that, we survey and show how gameplay emerges from the complex system. Our survey points out that three structural features of complex systems contribute to emergence: (a) active and interconnected elements; (b) feedback loops; and (c) interaction at different scales. To show the active and interconnected elements, we explain cellular automata as an example of simple systems that can produce emergence in games. Moreover, we described how a system can be stabilized/destabilized by feedback loops and how different behaviors may emerge in a system at different scales, along with particular games. In this survey, we identified seven classes of emergence that can be considered in games. These classes are Simple, Weak, Multiple, Strong, Cluster, Hub, and Complex Emergence. These classes are produced by different combinations of feedback loops and interactions among the elements of a system at different scales.

Optimization problems are becoming more complicated, and their resource requirements are rising. Real-life optimization problems are often NP-hard and time or memory consuming. Nature has always been an excellent pattern for humans to pull out the best mechanisms and the best engineering to solve their problems. The concept of optimization seen in several natural processes, such as species evolution, swarm intelligence, social group behavior, the immune system, mating strategies, reproduction and foraging, and animals’ cooperative hunting behavior. This paper proposes a new Meta-Heuristic algorithm for solving NP-hard nonlinear optimization problems inspired by the intelligence, socially, and collaborative behavior of the Qashqai nomad’s migration who have adjusted for many years. In the design of this algorithm uses population-based features, experts’ opinions, and more to improve its performance in achieving the optimal global solution. The performance of this algorithm tested using the well-known optimization test functions and factory facility layout problems. It found that in many cases, the performance of the proposed algorithm was better than other known meta-heuristic algorithms in terms of convergence speed and quality of solutions. The name of this algorithm chooses in honor of the Qashqai nomads, the famous tribes of southwest Iran, the Qashqai algorithm.

One of the main reasons for switching to the next generation of communication systems is the demand of increasing capacity and network connections. This goal can be achieved using massive multiple input - multiple output (massive-MIMO) systems in combination with Non-orthogonal multiple access (NOMA) technique. NOMA technology uses the successive interference cancellation (SIC) receiver to detect user’s signals which imposes an additional complexity on the system. In this paper, we proposed two methods to reduce the system complexity. The proposed method despite imperfect channel state information (CSI) in the receiver, there is not significantly reduction in the system performance. Since the computation of matrices inverse has a high computational complexity, we used the Neumann series approximation method and the Gauss-Seidel decomposition method to compute matrices inverse in the SIC receiver. Simulation results are provided at the end of the paper in terms of bit error rate (BER) at the receiver which show, these methods have lower computational complexity in comparison with the traditional methods while they cause a slight performance reduction in the SIC receiver. Also, we examined the increasing and decreasing value of imperfect channel state information in the system performance which shows the increasing value of imperfect channel state information, cause a slight performance reduction in SIC receiver.

This contribution gathers some of the ingredients presented during the Iranian Operational Research community gathering in Babolsar in 2019.It is a collection of several previous publications on how to set up an uncertainty quantification (UQ) cascade with ingredients of growing computational complexity for both forward and reverse uncertainty propagation.

Congestive heart failure (CHF) remains to be one of the major cardiovascular disorders in the world. Due to the prevalence of CHF related issues, it is prudent to seek out new prognostic predictors that would facilitate the prevention, monitoring, and treatment of the disease on a daily basis. A detection approach using entropy measures extracted from surface electrocardiograms (ECGs) and classification for congestive heart failure (CHF) is presented in this paper. Four different entropies are used: approximate entropy (ApEn), sample entropy (SampEn), permutation entropy (PE), and energy entropy (EE). These entropies are employed to evaluate the irregularity and complexity of ECG time series and discuss the viability of recognizing CHF patients from normal subjects. Student’s t-tests and receiver operating characteristic (ROC) plots show that among the four entropies, EE outperforms other three entropies. These tests also indicate the feasibility of using surface ECGs to effectively discriminate CHF patients from normal subjects.

Given an instance of the minimum cost flow problem, a version of the corresponding inverse problem, called the capacity inverse problem, is to modify the upper and lower bounds on arc flows as little as possible so that a given feasible flow becomes optimal to the modified minimum cost flow problem. The modifications can be measured by different distances. In this article, we consider the capacity inverse problem under the bottleneck-type and the sum-type weighted Hamming distances. In the bottleneck-type case, the binary search technique is applied to present an algorithm for solving the problem in O(nm log n) time. In the sum-type case, it is shown that the inverse problem is strongly NP-hard even on bipartite networks

The increasing of software’s quality is a significant issue that attracts the attention of many researchers in this field. They are looking for a modeling that meets the users trust and reliance on software. Therefore, the demand for high reliability software development has been increased as well. Having suitable design, increasing reliability is required. Coupling and cohesion are of among divide and conquer designs that by having useful choices, the complexity of software can be reduced. In this paper, the reliability, as an important quality parameter, will be evaluated with applying an approach based on fuzzy computing of cohesion and coupling. Of among other approaches, coupling and cohesion fuzzy computing are more accurate and focus on Myers Model. Since the rate of coupling and cohesion has impact on any non-Functional factors, the approach helps software engineering to calculate quality parameters with metrics and coefficient of accuracy. The policy system of premium of disable people, as the case study, has been chosen to show in object oriented system, how coupling and cohesion relations can be analyzed.