In this paper, we focus to find desired node position in indoor environments using a sequence of observations and user movement records. For this purpose, we first record the user's movements in indoor environments by defining a set of states and several matrices, which are Viterbi inputs. To record the fingerprints of the environment, we move across the entire coordinates of the building to collect and record the fingerprints of different places. In the online phase, we use the Weighted K-Nearest Neighbors (WKNN) Algorithm in parallel to check the accuracy of both WKNN and Viterbi Algorithms and to correct the WKNN behavior by Viterbi. During this phase, an experimental node is inserted into the environment and moves in the desired direction by determining the destination. The proposed method calculates the current location of the node and its most probable location in the next step. The results of the implementation and testing of the proposed Algorithm in the Faculty of Engineering, Arak University, show the optimal performance of the proposed idea for predicting the location and path of the node.

The parameters of a Hidden Markov Model (HMM) are transition and emis-sion probabilities. Both can be estimated using the Baum-Welch Algorithm. The process of discovering the sequence of hidden states, given the sequence of observations, is performed by the Viterbi Algorithm. In both Baum-Welch and Viterbi Algorithms, it is assumed that, given the states, the observations are independent from each other. In this paper, we first consider the direct dependency between consecutive observations in the HMM, and then use conditional independence relations in the context of a Bayesian network which is a probabilistic graphical model for generalizing the Baum-Welch and Viterbi Algorithms. We compare the performance of the generalized Algorithms with the commonly used ones in simulation studies for synthetic data. We finally apply these Algorithms on real data sets which are related to biological and inflation data. We show that the generalized Baum-Welch andViterbi Algorithms significantly outperform the conventional ones when sample sizes become larger.

Error correction capability of convolutional codes is improved by increasing code constraint length. However, increasing the constraint length results in high complexity of optimum Viterbi decoding Algorithm because the number of computations in Viterbi Algorithm is exponentially proportional to the constraint length. Consequently, decoding of high constraint length convolutional codes using Viterbi Algorithm may practically be impossible. Sub-optimum decoding Algorithms such as Fano and Stack Algorithms have been proposed to feasible fast sequential decoding of high constraint length convolutional codes. In this paper, we introduce different methods of implementing Fano and Stack Algorithms and propose some techniques to improve their speed and required memory. We compare the introduced implementations of the Algorithms in terms of error correction capability, decoding time and required memory. Furthermore, we use simulation results to show that if the communication error is low, Fano and Stack Algorithms can provide the same error correction capability as the optimum Viterbi Algorithm in very short decoding time.

Viterbi decoder is used for decoding data encoded using Convolution Forward Error Correction codes or data that suffers from inter-symbol interference. They occur in a large proportion of digital transmission. Viterbi decoders employed in digital wireless communications are complex and dissipate large power. The proposed method focuses on gate diffusion input (GDI) which is a low power technique of digital circuit design. Dynamic component of power is reduced in GDI technique as source of PMOS is not permanently connected to Vdd. It also reduces the latency of the circuit. The Viterbi decoder is implemented using GDI cell with 0.25 mm and 90 nm technology with 2.5 V Vdd. Frequency is varied from 15 MHz to 25 MHz. The outputs of the convolutional encoder designed for the constraint lengths K 4, 5, 6, 7 and rate are fed to the designed Viterbi decoder. The comparison results showed 29% reduction in power consumption and 66% reduction in area by using GDI circuit than the CMOS circuit.

This study focuses on the investigation of intelligent form-finding and vibration analysis of a triangular polyhedral tensegrity that is enclosed within a sphere and subjected to external loads. The nonlinear dynamic equations of the system are derived using the Lagrangian approach and the finite element method. The proposed form-finding approach, which is based on a basic genetic Algorithm, can determine regular or irregular tensegrity shapes without dimensional constraints. Stable tensegrity structures are generated from random configurations and based on defined constraints (nodes located on the sphere, parallelism, and area of upper and lower surfaces), and shape finding is performed using the fitness function of the genetic Algorithm and multi-objective optimization goals. The genetic Algorithm's efficacy in determining the shape of structures with unpredictable configurations is evaluated in two distinct scenarios: one involving a known connection matrix and the other involving fixed or random member positions (struts and cables). The shapes obtained from the Algorithm suggested in this study are validated using the force density approach, and their vibration characteristics are examined. The findings of the comparative study demonstrate the efficacy of the proposed methodology in determining the vibrational behavior of tensegrity structures through the utilization of intelligent shape seeking techniques.

Increasing population and food demand, disproportionate cultivation and annual production of various agricultural products with market needs and low productivity of the agricultural sector and the loss of water and soil resources have made it necessary to determine and implement the country's optimal cropping pattern. In this study, due to the limitations and problems of classical methods in order to reduce processing time and improve the quality of solutions, the Multi-Objective Chaotic Particle Swarm Optimization was used to determine the optimal cultivation pattern of Sistan plain in optimal conditions and deficit irrigation. The results of the Multi-Objective Chaotic Particle Swarm Optimization for the dominant cultures in the region showed that the current cropping pattern of the region is not optimal and with the implementation of the proposed model, the profit per unit area under cultivation will increase. The results of application of deficit irrigation during different growing periods of wheat, barley, alfalfa, sorghum, watermelon and grapes showed that applying deficit irrigation in this plain is not a good strategy and therefore only a full irrigation strategy is recommended. The results of sensitivity analysis of the model showed that at low prices, farmers reaction is less and at higher prices more reaction to price changes and with increasing prices, the program efficiency is lower.

One of the basic topics in hydrological and river engineering studies is flood routing.Flood flooding is common in multi-tributary rivers and rivers without intermediate basin statistics. Therefore, to achieve the determination of slopes and cross-sections in all sections of the river, the Muskingum hydrological model is a useful method that helps to save information on the depth and flow of the flood at any time by saving time and money. To specify. In this study, the nonlinear parameters of the new Muskingum model are optimized based on the fly Algorithm (MA). In this non-linear model of Muskingum, which has eight parameters, the recovery coefficient γ is used, which has more or less values ​​than the number of peaks discharged in the output hydrograph.To evaluate the performance of Muskingum's new nonlinear model with the new MA Algorithm, the Wilson and Weisman-Lewis case study has been used by many previous researchers for validation.The results of the MA Algorithm for Wilson and Weissman-Lewis rivers show the minimization of the residual squares (SSQ) as the objective function, which is 3.21 for the Wilson River and 68722 for the Weissman River. The results of this study showed that the proposed model has high accuracy in estimating the output discharge values.

Improving phoneme recognition has attracted the attention of many researchers due to its applications in various fields of speech processing. The recent research achievements show that using deep neural network (DNN) in speech recognition systems significantly improves the performance of these systems. There are two phases in the DNN-based phoneme recognition systems including training and testing. Most previous research works have attempted to improve training phases such as training Algorithms, different types of network, network architecture and feature type. However, in this work, we focus on the test phase, which is related to the generation of phoneme sequence that is also essential to achieve a good phoneme recognition accuracy. Past research works have used Viterbi Algorithm on hidden Markov model (HMM) to generate phoneme sequences. We address an important problem associated with this method. In order to deal with the problem of considering geometric distribution of state duration in HMM, we use real duration probability distribution for each phoneme with the aid of hidden semi-Markov model (HSMM). We also represent each phoneme with only one state to simply use phoneme duration information in HSMM. Furthermore, we investigate the performance of a post-processing method that corrects the phoneme sequence obtained from the neural network based on our knowledge about phonemes. The experimental results obtained using the Persian FarsDat corpus show that using the extended Viterbi Algorithm on HSMM achieves phoneme recognition accuracy improvements of 2. 68% and 0. 56% over the conventional methods using Gaussian mixture model-hidden Markov models (GMM-HMMs) and Viterbi on HMM, respectively. The postprocessing method also increases the accuracy compared to before its application.

Objective: Analyzing the behavior of Tehran Stock Market, based on the daily asset return for the duration between 1387 and 1397 has been the main aim of this research. Methods: Tehran Stock Market daily asset return can be considered as a time-series and therefore all existing models can be applied to it. Considering the distributional and temporal properties of such series, it can be shown that the series is stationary. Hence, Hidden Semi-Markov Model, which is widely used for analyzing time series, could be employed to analyze this series. Results: Based on Kolmogorov-Smirnov test and Akaike and Bayesian indices, the best density function for the series is a three parameter Gussian Mixture. Moreover, employing three-state Hidden Semi-Markov Model would be the suitable method for modeling. In addition, it was found that Tehran Stock Market followed three states namely bull, bear, and sidewalk and the definitions for such states have been given, while the probability of being in each state has also been provided. Conclusion: Tehran Stock Market was in sidewalk state around half of the analyzed duration and the luckiest state after both bear and bull states was sidewalk. The market almost never came to bull state after the bear state. Moreover, the chance of getting into bear state from sidewalk was three times more than the chance of getting into the bull market.