Signal and Data Processing

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The processing of point clouds is one of the growing areas in machine vision. With the advent of inexpensive depth sensors, there has been a great interest in point clouds to detect three-dimensional objects. In general, 3D object recognition methods are alienated into two classes: local and global feature-based methods.  In global feature-based methods, the entire shape of the model is described, while in local methods, the geometric properties of the local area around a point are used to obtain the characteristic of the point. Unlike global methods, local methods do not entail any segmentation and they are more robust to clutter and occlusion. The local feature-based methods extract some geometric features from local surfaces around specific points named keypoints. The geometric features of a keypoint are encoded into a feature descriptor. How to describe the environment around a keypoint is the main challenge of these methods. The commonly used local feature-based methods often are sensitive to noise, varying mesh resolution, and rigid transformation. To overcome such disadvantages, in this paper, a new local feature descriptor based on the Mercator projection is proposed. The Mercator projection is one of the most popular 3D to 2D projections that can preserve true distance, direction, and relative longitude and latitude between any two points in point clouds. To evaluate, the proposed method has been compared with several state-of-the-art descriptor methods. The superiority of this method over other methods is shown by using the criteria of square Root Mean Square Error (RMSE), Recall versus 1-Precision Curve (RPC), and registration correction, rotation, and translation errors, and it is proved that this method has good descriptiveness power and it is robust to noise and varying mesh resolution.

Introduction
In this paper, we propose a new local descriptor to provide robust and precise geometric features. The geometric features are extracted using the Mercator projection of the neighborhood sphere. Our contributions are as follows: (1) The proposed descriptor directly learns from the point clouds (2) using the proposed method, there is only one representation for each point so the problem of multiple representations of a point is addressed. Also, the Mercator projection has many properties that make it appropriate for data representations in a point cloud. (3) It can accurately describe the geometric properties around a point. (3) The Mercator projection is a conformal projection so it preserves true distances, directions, and relative longitudes and latitudes. (4) It keeps small element geometry, which means Mercator projection preserves the shapes of small regions. 

The proposed method
Given a query point p, a sphere of radius r is centered at p for determining the neighbor points. Then Mercator projection is used for mapping the sphere into a plane with considering the Local reference frame (LRF) as previously suggested by Tombaret al. (2010b). The Mercator projection is a cylindrical projection that was proposed by G. Mercator in 1569. In this projection, the surface of a sphere is mapped into a plane. It preserves true distances, directions, and relative longitudes and latitudes. The Mercator projection for each point is identified using two following equations:

(2)

where λ is the  longitude and φ is the  latitude of a point  in the sphere, and (x,  y) represents corresponding point  in the Cartesian map. For extracting images as the input of the Siamese network, we need ranges for achieved x and  y. The variable x is in the interval [−π,  π] but range of y is different for the Mercator projection of each keypoint. As a result, the minimum and maximum of the variable y for all neighbor points are considered as the range of y, then a histogram 30 × 30 is measured. The Mercator projections of all neighbors are defined and the number of points  in each bin counted. Then we normalize the histogram by dividing each bin by the total number of neighbor points, it causes more robustness to noise and mesh resolution.

Results and discussion
The performance of the proposed method is evaluated on the Bologna (Tombari et al., 2010c) and John Burkardt in terms of RMSE, RPC and registration correction rate, rotation and translation errors. The proposed outperforms other methods in term of RPC also the results show that the method is robust to noise, rigid transformation and varying mesh resolution.

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Data clustering is one of the main steps in data mining, which is responsible for exploring hidden patterns in non-tagged data. Due to the complexity of the problem and the weakness of the basic clustering methods, most studies today are guided by clustering ensemble methods. Diversity in primary results is one of the most important factors that can affect the quality of the final results. Also, the quality of the initial results is another factor that affects the quality of the results of the ensemble. Both factors have been considered in recent research on ensemble clustering. Here, a new framework for improving the efficiency of clustering has been proposed, which is based on the use of a subset of primary clusters, and the proposed method answers the above questions and ambiguities. The selection of this subset plays a vital role in the efficiency of the assembly. Since evolutionary intelligent algorithms have been able to solve the majority of complex engineering problems, this paper also uses these intelligent methods to select subsets of primary clusters. This selection is done using three intelligent methods (genetic algorithm, simulation annealing and particle swarm optimization). In this paper a clustering ensemble method is proposed which is based on a subset of primary clusters. The main idea behind this method is using more stable clusters in the ensemble. The stability is applied as a goodness measure of the clusters. The clusters which satisfy a threshold of this measure are selected to participate in the ensemble. For combining the chosen clusters, a co-association based consensus function is applied. A new EAC based method which is called Extended Evidence Accumulation Clustering, EEAC, is proposed for constructing the Co-association Matrix from the subset of clusters. Experimental results on several standard datasets with normalized mutual information evaluation, Fisher and accuracy criteria compared to Alizadeh, Azimi, Berikov, CLWGC, RCESCC, KME, CFSFDP, DBSCAB, NSC and Chen methods show the significant improvement of the proposed method in comparison with other ones.
 Keywords: Clustering Ensemble, local optimization, evolutionary algorithm, correlation matrix, diversity.

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Epilepsy is a chronic disorder of brain function caused by abnormal and excessive electrical neurons discharge in the brain. Seizures cause disturbances in consciousness that occur without prior notice, so their prediction ability, based on EEG data, can reduce stress and improve quality of life. An epileptic patient EEG data consists of five parts: Ictal, Inter-Ictal, pre-Ictal, Post-Ictal, and IT (seconds before Ictal onset). The purpose of predicting an attack is to detect the period of pre-ictal or IT to create warnings for medical procedures that are actually determined hours or minutes before ictal and do not necessarily mean the exact time of ictal [4]. The aim of many studies has been to identify the pre-ictal period based on EEG data. However, the problem of reliable prediction of epileptic seizures remains largely unsolved [5].
 EEG and IEEG data types are used in detection and predicting methods. Due to the fact that artifacts and noises have a greater effect on EEG than IEEG, if there is IEEG, it has been tried to use it [6, 7]. Seizure warning methods that have a clinical application are generally based on the use on EEG [8].
Numerous studies have been performed to detect and predict seizures. The methods of signal processing and feature extraction are same in detection and prediction, but the difference is that, in detection, ictal and inter-ictal periods are compared, while in prediction, pre-ictal or IT and inter-ictal periods are being compared. Some algorithms use data modeling to extract features. References [13, 14], the coefficients AR model for the EEG data is obtained with least squares estimator, then the model coefficients are classified by SVM binary classification. In the article [15] the non-Gaussian EEG is considered using the ARIMA model (Autoregressive integrated moving average). In references [16, 17], predictions are performed based on the dynamic model with hidden variable and the sparse LVAR model, respectively. Also other features such as Mean Phase Coherency [18-20], Lag Synchronization Index to compare phase Synchronization between irregular oscillations [8,21], eigenspectra of space-delay correlation and covariance matrices [22], Largest Lyapunov Exponent [23, 25], decorrelation time, Hjorth parameters such as mobility and complexity, power spectrum in frequency bands, spectral edge frequency, the four statistical moments: mean, variance, kurtosis, skewness and there are features based on entropy and probability [6, 26-29]. Empirical mode decomposition (EMD) and wavelet transform methods have also been used to extract the feature [2, 30, 31, 37]. In articles [32, 33], the Cepstrum method has been used on short time multi channels EEG and IEEG in different patient states. Cepstrum is used to extract slow and periodic changes in speech that can be used to detect the ictal period from the inter-ictal, and has also been used to linearize the EEG [34]. In the paper [33], Cepstrum coefficients of multi-channel EEG are calculated and the 9 first coefficients are considered, then calculates the velocity and acceleration of the desired coefficients and uses a neural network to detect an epileptic seizure. The method of this paper was improved in 2014. In this way, first the signal energy and coefficients of Cepstrum are calculated and then the same process is followed. The accuracy values ​​of velocity and acceleration coefficients in this study were 89.7% - 98.7% and 98.9% - 99.9%, respectively [32].
 In this study, the period of IT was detected in patients with temporal lobe epilepsy (TLE), which is the most common type of epilepsy [38]. For this purpose, two long term EEG channels LTM (long term monitoring) with a sampling rate 256, which are facing each other have been used. First, the desired signal is considered by the moving window with a length if 5 seconds and 80% overlap. The desired signal is normalized and its linear trend is removed and band-pass filtered (220 order FIR filter, cutoff at 6-20 Hz). Then the filtered date will de decomposed using discrete wavelet transform with 6-levels and Daubechies4 mother wavelet. In this step we will have 12 outputs. Next, by windowing of 500 samples and 75% overlap, the AR model with 8 order is applied to outputs. Cepstrum method can be used to detect regular and periodic changes in the ictal period of the EEG signal. According to this feature, the Cepstrum coefficients of the data window are calculated and the first coefficient of each window is considered. By applying a median filter to the 12 outputs of the previous stage, the current period of the first channel is compared to the background period of the same channel and the second channel, and the same is done for the second channel. This method reduces artifact error and inter-attack discharges. Finally, the signal is averaged by the moving window and the positive envelope of the curve is calculated. Given that we will eventually have 12 outputs, 12 threshold values are obtained for a patient’s training data, then these values are checked on the test data.
 The proposed method was reviewed on a proposed model of adult epilepsy as well as 10 patients with long-term EEG data without artifact removal. Accuracy and average prediction time were 92% and 18.5 seconds, respectively. The algorithm performed better than other methods. Another advantage of the algorithm is the ability to reduce artifacts, while many studies have used short-term data without artifacts. Artifacts are located at different frequencies, which frequency analysis is performed by wavelet transform. Because two channel artifacts are unequal at the same time and in the same channel at different times, the artifacts are reduced by comparing the channels to each other. Algorithm testing on more patients is recommended to confirm the performance of the algorithm clinically.  


 

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Speech recognition is a subfield of artificial intelligence that develops technologies to convert speech utterance into transcription. So far, various methods such as hidden Markov models and artificial neural networks have been used to develop speech recognition systems. In most of these systems, the speech signal frames are processed uniformly, while the information is not evenly distributed in all of them. Auditory experiments have also shown that the human brain pays more attention to information-rich areas. By focusing on these areas instead of uniform processing, the brain can more robustly recognize speech in intrinsic and environmental speech variations such as speaker and noise. In contrast, the performance of most speech recognition systems degrades dramatically in these conditions. Therefore, to boost speech recognition systems' robustness, some researchers have focused on developing speech recognition systems by modeling these informative parts of the speech signal named landmarks. Similarly, in this article, we implemented a landmark-based system to obtain a robust Persian speech recognition system inspired by human brain perception. We also conducted neural networks-based variation compensation methods to boost its performance.
In this article, acoustic landmarks are classified into two categories of events and states with the following definitions. Events are defined as areas of the speech signal in which the spectral characteristics change drastically while their length does not change a lot. The transition areas between some adjacent pairs of phones (phones' borders) are primarily selected as events. States are also defined as areas of the speech signal that spectral characteristics do not change significantly. Here the nuclei of phones are considered as the states. Previous research, linguistic sources, and implementation results have been used to determine the Persian language's appropriate landmarks. Finally, a set of 313 landmarks was selected and used in our acoustic landmarks-based phone recognition system. 
The neural network structure used to recognize acoustic landmarks is a feed-forward fully connected structure with ReLU function in its hidden layers and a linear function in its final layer. The number of layers and neurons of this structure has been determined experimentally. The best structure is composed of 5 fully connected layers with 1000 neurons per layer. In this study, instead of considering 313 neurons to express each of the 313 landmarks, a heuristic labeling method is used to reduce the number of output neurons and utilize the shared information between the landmarks. The landmark recognition model slides on the speech feature sequence in the test phase to produce the output landmark sequence. Finally, to convert the obtained landmark sequence to a phone sequence, three rule-based post-processing steps are performed. 
Variabilities are among the essential quality degradation sources in speech recognition; therefore, we proposed two approaches to reduce them and boost phone recognition quality in our landmark-based system. To this aim, we have utilized the nonlinear filtering characteristic of neural networks by implementing four neural network schemes. In scheme 1, a feed-forward neural network is first trained to map training landmarks to their corresponding well-recognized samples. Then this structure can act as a nonlinear filter before the landmark recognition block. In scheme 2, a unified structure is simultaneously trained to learn landmark labels and the filtering part. In both of these schemes, we used a recursive loop to increase the chance of attractor manipulation in the structures. In scheme 3, a recursive loop is added to one hidden layer. This loop acts as an input variability simulator and forces the network to recognize the input data and its variations correctly. Finally, in scheme four, a deep attractor neural network-based structure is proposed to shape the structure’s hidden layer components so that it can compensate for variabilities.
The experiments are implemented on a Persian database named Farsdat, and the results are reported using phone error rate (PER) criteria. From every 25-millisecond speech frame, an acoustic feature called LHCB is extracted and combined with delta and delta-delta features of that frame. Every frame's features are concatenated with fourteen adjacent frames and are finally fed to our neural network-based landmark extraction model. The best-trained model obtained the PER of 21.74% on test data. Using scheme one to four, we achieved an absolute PER decrease by 0.39, 0.58, 0.43 and 1.30 percent, respectively. Comparing our landmark-based system's performance with other Persian phone recognition systems shows that this method could perform efficiently as a Persian phone recognition system. 
In our future works, we intend to compare our acoustic-based phone recognition system's performance with conventional methods such as CTC in noisy conditions. Besides, it seems that acoustic landmarks can be used to create an alignment of the input speech sequence and the output transcription. Therefore, we will present a combination of CTC-based methods and acoustic landmarks to utilize acoustic landmarks' complementary information. This information might boost the performance and speed of CTC-based speech recognition methods, particularly in low resource languages.

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In special purpose circuits, the amount of energy consumed and the speed of operation are the main challenges. There are wide researches and methods to improve the performance of these types of circuits. One of these methods is to use a Residue Number System (RNS). In the RNS, there are a number of modules (channels) as a set to represent the number and perform parallel arithmetic operations. The most famous set is the 3-modlui set {2ⁿ-1, 2ⁿ, 2ⁿ +1}. The form of modules to the power of 2 makes it easier to perform binary computational operations. To use this system, you need to perform conversion operations from binary to residue (forward conversion) and residue to binary (reverse conversion). The greater the number of modules (channels) in the set, the higher the degree of parallelism of computational operations. In contrast, more complex forward and reverse conversion circuits are required. The overhead of conversion computing can reduce the efficiency of using this system, unless the number of consecutive operations is large enough to cover the conversion overhead time. In this paper, based on 3-moduli set {2ⁿ-1, 2ⁿ, 2ⁿ +1} evaluation, it was determined that for how many consecutive addition or multiplication operations, the use of RNS operations leads to greater speed. In this paper, we evaluate the carry propagation adder as the most popular adder and parallel prefix adder as the high speed adder. Also, the parallel block multiplier circuit was used to evaluate the multiplication operations. First, modular adder/multiplier, binary, and forward and reverse conversion circuits were implemented and synthesized. We used Synopsys Design Compiler, K-2015.06 version and 45nm technology. The results show that if the carry propagation adder is used, in modules with a width of more than 8 bits (n≥8), if the number of consecutive operations is at least 4, it will speed up the calculations. Likewise, in the multiplication operation and parallel prefix addition, the number of sequences is reduced to two.

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Wireless Body Area Network (WBAN) is a pioneer trend in healthcare technology. Since any cyber-attack on a WBAN could jeopardize the patient's health, securing the WBAN plays a crucial role in healthcare applications. An intrusion detection system (IDS), as a second-line defense, is one of the security methods in computer networks. In this paper, a new IDS has been presented which is able to detect denial of service (DoS) attacks in a WBAN. In the proposed IDS, a genetic algorithm is used to select features of collected data, in a way that increases the performance of the IDS and as a result the WBAN. Then, using support vector machine and k nearest neighbor techniques, the data classification is performed to detect DoS traffic from regular data traffic. Simulation results indicate that the proposed IDS has effective performance with a 90% detection rate.

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Data clustering is one of the main tasks of data mining, which is responsible for exploring hidden patterns in unlabeled data. Due to the complexity of the problem and the weakness of the basic clustering methods, today most of the studies are directed towards clustering ensemble methods. Although for most datasets, there are individual clustering algorithms that provide acceptable results, but the ability of a single clustering algorithm is limited. In fact, the main purpose of clustering ensemble is to search for better and more stable results, using the combination of information and results obtained from several initial clustering. In this paper, a clustering ensemble-based method will be proposed, which, like most evidence accumulation methods, has two steps: 1- building a simultaneous participation matrix and 2- determining the final output from the proposed participation matrix. In the proposed method, some other information will be used in addition to the clustering of the samples to construct the simultaneous participation matrix. This information can be related to the degree of similarity of the samples, the size of the initial clusters, the degree of stability of the initial clusters, etc. In this paper, the clustering problem is defined as an explicit optimization problem by the mixed Gaussian model and is solved using the simulated annealing algorithm. Also, an evolutionary method based on simulated annealing will be presented to determine the final output from the proposed simultaneous participation matrix. The most important part of the evolutionary method is to determine the objective function that guarantees the final output will be of high quality. The experimental results show that the proposed method is better than other similar methods in terms of different clustering quality evaluation criteria.
 

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Nowadays, wireless sensor networks (WSNs) have found many applications in a variety of topics. The main purpose of these networks is to measure environmental phenomena and to send read data in multi-hop paths to the sink to be exploited by users. The most important challenge in WSNs is to minimize energy consumption in sensor batteries and increase network lifetime. One of the most important techniques for reducing energy consumption in WSNs is the compressive sensing (CS) technique. CS reduces network energy consumption by reducing data transmission in the network and increasing the network lifetime. The use of CS technique in a WSN results in the production of different models of CS signals. These models are based on spatial, temporal and spatio-temporal sensors readings. On the other hand, in order to overcome the challenge of energy consumption, the exact recognition of energy resources in the network is essential. 
Energy consumption in a sensor node can be divided into two parts: (a) the energy used for computing; and (b) the energy consumed by the communication. The energy used for the computing consists of three components: 1. sensor energy consumption (data reading), 2. background energy consumption, and 3. energy consumption for processing. The power consumption of the communication includes the following: 1. energy consumption for data transmission; 2. energy consumption for data receiving; 3. energy consumption for sending messages; and 4. energy consumption for receiving messages. Hence, the existence of a model for analyzing energy consumption in a CS-based WSN is necessary. Several models have been developed to analyze energy consumption in a WSN, but there is not a complete model for analyzing energy consumption in a CS-based WSN.
 In this paper, we study all energy consumption components mentioned above in a CS-based WSN and present a complete model for energy consumption analysis. This model can optimize the design of CS-based WSNs energy efficiency improvement approach. To evaluate the proposed model, we use this model to analyze energy consumption in the compressive data gathering technique which is a CS-based data aggregation method. Using this model can optimize the design of CS-based WSNs.

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ABSTRACT
Nowadays, we are witnessing financial markets becoming more competitive, and banks are facing many challenges to attract more deposits from depositors and increase their fee income. Meanwhile, many banks use performance-based incentive plans to encourage their employees to achieve their short-term goals. In the meantime, fairness in the payment of bonuses is one of the important challenges of banks, because not paying attention to this issue can become a factor that destroys the motivation among employees and prevents the bank from achieving its short-term and mid-term goals. This article is trying to tackle the problem of optimizing the coefficients of branch performance evaluation indicators based on their business environment in one of the state banks of Iran. In this article, a two-objective genetic algorithm is proposed to solve the problem.
This article is comprised of four main sections. The first section is dedicated to the problem definition which is what is our meaning of optimizing the importance coefficients of branches based on the business environment. The second section is about our proposed solution for the defined problem. In the third section, we are comparing the performance of the proposed two-objective genetic algorithm on the defined problem with the performance of four well-known multi-objective algorithms including NSGAII, SPEAII, PESAII, and MOEA/D. And finally, the set of ZDT problems which is a standard set of multi-objective problems is taken into account for evaluating the general performance of the proposed algorithm comparing four well-known multi-objective algorithms.
Our proposed solution for solving the problem of optimizing branch performance coefficients includes two main steps. First, identifying the business environment of the branches and second, optimizing the coefficients with the proposed two-objective genetic algorithm. In the first step, the k-means clustering algorithm is applied to cluster branches with similar business environments. In the second step, to optimize the coefficients, it is necessary to specify the fitness functions. The defined problem is a two-objective problem, the first objective is to minimize the deviation of the real performance of the branches from the expected performance of them, and the second objective is to minimize the deviation of the coefficients from the coefficients determined by the experts. To solve this two-objective problem, a two-objective genetic algorithm is proposed.
In this article, two approaches are adopted to compare the proposed solution performance. In the first stage, the results of applying the proposed two-objective genetic algorithm have been compared with the results of applying four well-known multi-objective genetic algorithms on the problem of optimizing the coefficients. The results of this comparison show that the proposed algorithm has outperformed the other compared methods based on the S indicator and run time, and it is also ranked second after the NSGAII algorithm in terms of the HV indicator.
Finally, for evaluating the performance of the proposed algorithm with other well-known methods, the set of ZDT problems including ZDT1, ZDT2, ZDT3, ZDT4, and ZDT6 has also been taken into consideration. At this stage, the performance of the proposed algorithm has been compared with the four mentioned algorithms based on four key indicators, including GD, S, H, and run time. The results show, the proposed algorithm has outperformed significantly in terms of run time in all five ZDT problems. In terms of GD indicator, the performance of our proposed algorithm is located in the first or second rank among all considered algorithms. In addition, in terms of S and H indicators in many cases, the proposed algorithm outperformed the other well-known algorithms.

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The development of information technology and its use in the health system has taken many measures to protect and promote human health, however, the world still faces long-term threats and recurrence of infectious diseases.
Understanding the dynamics of infectious diseases is important in controlling the disease because the network and the mode of impact of infectious diseases are very complex. The management of infectious diseases can also be considered as a complex social system due to the fact that has many complexities (such as dimensions, parameters, interactions, behaviors and rules), for this reason, the approach of the present study is a multifaceted understanding of the spread of infectious diseases. To design the present model, an intelligent system with a combination of mathematical, machine learning and epidemiological dimensions is proposed.
The disease studied in this study, due to its importance and prevalence, is Covid 19.
In this study, with the approach of complex systems and using the Internet of Things and machine learning methods, an algorithm was presented that uses environmental and individual variables to predict the probability of disease in an individual. Therefore, this research can improve the prevention of infectious diseases by filling some of the gaps in 3 sections: 1- Re-emergence of infectious diseases and the potential of IoT and AI, 2- Speed of dissemination and importance of real-time tracking, and 3- Budget and cost.
The evaluation of the algorithm in this study was determined by two criteria of sensitivity and specificity.
The results of the proposed algorithm for predicting Covid 19 disease showed an accuracy of more than 98%. Sensitivity above 98% was also obtained. Which is very important for the diagnosis of Covid disease 19 and shows the low number of false negatives in the test results.
Therefore, the proposed model, combined with the Internet of Things and machine learning, can cause early diagnosis and prevent the spread of the Covid-19 disease with high specificity and sensitivity.