This paper investigates the problem of estimating the direction of arrival considering colored noise at the sensors. To this aim, we model the recieved signal at each time instant with an AR system with rank equal to the number of sources and we simultaneously estimated signal and noise parameters by solving a quadratic eigenvalue problem. Simulation results demonstrate that utilizing root mean square error as the accuracy metric, the proposed method outperforms existing subspace-based approaches, particularly when the targets are close to eachother.

This paper investigates the problem of estimating the direction of arrival in non-uniform sensor noise. To this aim, The covariance matrix of the noise is estimated, and its effect is attenuated to obtain an improved covariance matrix of the observations. So, the problem can be solved using common subspace based methods for DOA estimation under uniform white noise assumption like the MUSIC algorithm. Simulation results confirm that the proposed method outperforms other existing subspace based methods when RMSE is utilized as the accuracy metric.

One of the most important needs in neuroimaging is brain dynamic source imaging with high spatial and temporal resolution. EEG source imaging estimates the underlying sources from EEG recordings, which provides enhanced spatial resolution with intrinsically high temporal resolution. To ensure identifiability in the underdetermined source reconstruction problem, constraints on EEG sources are essential. This paper introduces a novel method for estimating source activities based on spatio-temporal constraints and a dynamic source imaging algorithm. The method enhances time resolution by incorporating temporal evolution of neural activity into a regularization function. Additionally, two spatial regularization constraints based on L 1 and L 2 norms are applied in the transformed domain to address both focal and spread neural activities, achieved through spatial gradient and Laplacian transform. Performance evaluation, conducted quantitatively using synthetic datasets, discusses the influence of parameters such as source extent, number of sources, correlation level, and SNR level on temporal and spatial metrics. Results demonstrate that the proposed method provides superior spatial and temporal reconstructions compared to state-of-the-art inverse solutions including STRAPS, sLORETA, SBL, dSPM, and MxNE. This improvement is attributed to the simultaneous integration of transformed spatial and temporal constraints. When applied to a real auditory ERP dataset, our algorithm accurately reconstructs brain source time series and locations, effectively identifying the origins of auditory evoked potentials. In conclusion, our proposed method with spatio-temporal constraints outperforms the state-of-the-art algorithms in estimating source distribution and time courses.

Recently, sparse signal and image recovery have shown significant promise in different biomedical fields. In this paper, we introduce a novel method to recover structured-sparse biomedical signals and images in a hierarchical Bayesian framework. The proposed method promoted sparse distribution using a Bernoulli–Laplace prior. In addition to sparse prior, we consider cluster prior on sparsity patterns. To implement the Bayesian inference, we use an MCMC technique to sample the target posterior distribution. Using generated samples, the model parameters and hyperparameters are estimated in an unsupervised scheme. Finally, the estimation procedure has been completed using the MAP estimator. Our method solves the inverse problem automatically without needing to alter the parameters manually. We have used synthetic data sets with several sparsity scenarios to explore the proposed algorithm, which outperforms the existing recovery methods, e.g., CoSaMP, BCS, EBSBL, and Cluss. Finally, through experiments using different real-life biomedical data (EMG and ECG signals and MR images), the superiority of the proposed method is demonstrated. This study demonstrates that using a combination of Bernoulli–Laplace and structured prior on sparsity patterns can recover structured-sparse biomedical signals and images precisely.

Polar codes are a class of block codes which are widely used in communication networks. Polar codes have been utilized in the fifth generation of enhanced mobile broadband systems (5G) due to their performance in error correction and also their low instinct complexity in implementation. However when they come in very large blocks, their efficient implementation in reasonable time is challenging. The automatic code generator (ACG) tools are useful and essential in these cases, when the design process should be error prone and time consuming. This paper presents an error prone and fast ACG tool to generate the VHDL description code at gate level for Polar decoders in multimedia communication systems according to user adjusted parameters including code-length, code-rate and quantization width, called PoCH tool. The PoCH constructs the Polar decoder using the design SNR and Bhattacharyya parameters which are set by the user, or are provided by a file consisting of the frozen bits location. The PoCH can generate the Polar decoder for four famous algorithms including SC, SSC, Fast-SSC and Fast-SSC + BiREP algorithms. To validate the performance of the PoCH tool, the block counts is presented for each generated code for various code-length, code-rate and Bhattacharyya parameters. Finally, the time consumed by the tool to generate Polar channel decoders for each algorithm is compared.

Polar codes are a class of block channel coding that can theoretically achieve channel capacity. The Polar codes are used in 5G applications due to superior error correction performance. Throughput and energy efficiency are important issues for efficient hardware implementation of Polar codes in real-world applications. This paper presents a high-throughput and energy-efficient pipeline architecture for a fast simplified successive cancellation decoding algorithm. In this architecture, the searching method in the Polar code data bit positions is improved which leads to ignoring computing all of the bits in the ordinary approach. This results in having efficient length for each node type which can make a tradeoff in latency and T/P. Furthermore, the supernode which helps to decrement the latency of the decoder are discussed and used to improve the decoder. Utilizing the supernodes, the number of pipeline stages decreases with a negligible corruption in the maximum frequency and throughput. The Polar decoder is implemented on Xilinx kintex-7 xc7k325t-2ffg900c. The achieved number of pipeline stages is 118 and the maximum clock frequency is 284 MHz on the target FPGA chip. The throughput of the decoder is up to 290.81 Gb/s and the latency is 415ns. The energy per bit is 3.14 pJ/bit. The implementing results confirm that the proposed decoder significantly improves the throughput and energy per bit in comparison with the related pipeline decoders.

Emotion plays a predominant role in external situations or events in daily life. Different emotions display different connectivity patterns through related information processing. Electroencephalography (EEG)-based emotional recognition is a controversial subject in the field of affective computing. However, EEG recordings are mixed-signals and cannot show the exact information about active sources from different emotional states. In this paper, we propose a method for emotion discrimination based on the source connectivity method. Features are extracted as connectivity patterns in different frequency bands based on emotion-based reconstructed EEG sources using sLORETA. In order to identify most related brain regions to emotions, we identify data-driven spatially compact regions, called regions of interest (ROIs), based on the reconstructed neural activity. Also, to estimates ROI time series with the intrinsic non-stationarity of neural activity, an iterative dynamic approach is used. The method explicitly contains a dynamic constraint, considering the neural activity evolution for advance connectivity analysis. Throughout this study, we consider three connectivity measures widely applied to emotion recordings including, iCoh, PLV, and WPLI. In the next step, the connectivity patterns are used as features to discriminate emotion states by training an SVM classifier. The performance of the proposed method is assessed over a real high-resolution emotional database. This study reveals that the proposed method can identify meaningful connection features between main emotion-related brain regions, leading to higher interpretability and accuracy. Our results demonstrate that the ROI-wise iCoh features enhance the average of accuracy up to 83.84% in comparison with raw EEG features (71.70%).

In this paper, hardware investigations show the effects of the hidden and exposed node problems in visible light communication (VLC) networks. Furthermore, the request to send/clear to send (RTS/CTS) mechanism, as a physical layer-independent solution, is used for solving the hidden node problem in VLC networks. A VLC hardware system, called VLCIoT, which was designed and implemented in our laboratory based on PHY I of the IEEE 802.15.7 standard, is used for the physical layer. The medium access control layer of the IEEE 802.15.7 is considered for the network. We implement the multiple access protocol of the IEEE 802.15.7 and the RTS/CTS mechanism on the microcontroller of each device using the C programming language. Goodput, message loss ratio, fairness, average delay, energy efficiency, network load, frame size, number of hidden nodes, and number of contending nodes are the evaluated parameters. The results show that for the IEEE 802.15.7 increase of the hidden nodes decreases the goodput and energy efficiency and increases the message loss ratio and the average delay. But, using the RTS/CTS mechanism, the hidden nodes do not affect the goodput, the message loss ratio, the energy efficiency, and the average delay because this mechanism solves the hidden node problem. The results show that the RTS/CTS mechanism increases the saturation goodput by 300%, decreases the message loss ratio by 94%, decreases the average delay by 50%, and increases the energy efficiency by 300%. The results show that at higher network load, higher data frame size, and more contending nodes, the performance of the RTS/CTS mechanism is better. Also, in symmetric networks, in which all nodes are hidden, or no hidden node exists in the network, fairness is better than the asymmetric networks, in which some nodes are hidden while others are not.

This study aims to investigate implementing EM and FCM algorithms for skin color extraction. The capabilities of three well-known color spaces, namely, RGB, HSV, and YCbCr for skin-tone extraction are assessed by using statistical modeling of skin tones using EM and FCM algorithms. The results show that utilizing a Gaussian mixture model for parametric modeling of skin tones using EM algorithm works well in HSV color space when all three components of the color vector are used. In spite of discarding the luminance components in YCbCr and HSV color spaces, EM algorithm provides the best results. The results of the detailed comparisons are explained in the conclusion.

In this paper, a visible light communication (VLC) system for indoor Internet of Things (IoT) applications, called VLCIoT, is proposed. The proposed system is based on type I of the IEEE 802.15.7 standard physical (PHY) layer. The PHY I is provided for low data rate applications from 10 to 100 kb/s, which looks suitable for the typical IoT applications. The on-off keying suggested modulation scheme by the PHY I that is simple and requires low-cost hardware for implementation is considered. The implemented VLCIoT system is robust against indoor ambient light interference. Using the frequency division multiple access, several VLC networks can operate at different frequencies in the vicinity of each other without interference. The data rate of VLCIoT is up to 115.2 kb/s, and the bit error ratio of the system is very low. This system is designed for indoor, which for this purpose operates well up to 7 m distances. In this paper, a figure of merit (FoM) is proposed, in which the most important parameters for IoT applications are considered. A comprehensive comparison of VLCIoT to other suitable VLC systems for IoT applications is performed. The results show that the VLCIoT achieves the best FoM and is suitable for indoor IoT applications.

Face detection is one of the most challenging and long-studied areas in computer vision. In real-world, images are exposed to the noise and degradation. In this paper, we investigate the robustness of two networks namely SSD and Faster R-CNN in confrontation with salt and pepper noise, Gaussian blur, as well as JPEG compression. Our experiments are conducted on the well-known Wider Face dataset. These experiments show that the Faster R-CNN is more robust against Gaussian blur, while SSD is much more sensitive to the edges. On the other hand, SSD is more robust against reduced-quality JPEG compressed images. The reason should be due to the sensitivity of Faster R-CNN to the texture of the objects. Moreover, our experiments demonstrated that both networks have a relatively similar resistance under salt and pepper noise.

Social networks have become the main infrastructure of today’s daily activities of people during the last decade. In these networks, users interact with each other, share their interests on resources and present their opinions about these resources or spread their information. Since each user has a limited knowledge of other users and most of them are anonymous, the trust factor plays an important role on recognizing a suitable product or specific user. The inference mechanism of trust in social media refers to utilizing available information of a specific user who intends to contact an unknown user. This mostly occurs when purchasing a product, deciding to have friendship or other applications which require predicting the reliability of the second party. In this paper, first the raw data of the real world dataset, Epinions, is examined, and the feature vector is calculated for each pair of social network users. Next, fuzzy logic is incorporated to rank the membership of trust to a specific class, according to two-, three- and five-classes classification. Finally, to classify the trust values of users, three machine learning techniques, namely Support Vector Machine (SVM), Decision Tree (DT), and k-Nearest Neighbors (kNN), are used instead of traditional weighted sum methods, to express the trust between any two users in the presence of a special pattern. The results of simulation show that the accuracy of the proposed method reaches to 91%, and unlike other methods, does not decrease by increasing the number of samples.

In this study heavy-tailed distributions including Pareto, Weibull, Levy, and Log-normal distributions are proposed to model the DCT coefficients of real-world sources. It will be shown that the proposed heavy-tailed distributions are more accurate to model non-strictly sparse sources rather than Laplace and Cauchy distributions. Maximum Likelihood Estimation (MLE) method is utilized to parameter estimation of the proposed distributions. Finally, the Shannon Lower Bound (SLB) on the rate distortion function of each model is calculated, accordingly a framework is provided that could be used to assess the rate distortion performance of any coding scheme that is supposed to compress DCT coefficients as outcome of a sparse source.

We propose a new source connectivity method by focusing on estimating time courses of the regions of interest (ROIs). To this aim, it is necessary to consider the strong inherent non-stationary behavior of neural activity. We develop an iterative dynamic approach to extract a single time course for each ROI encoding the temporal non-stationary features. The proposed approach explicitly includes dynamic constraints by taking into account the evolution of the sources activities for further dynamic connectivity analysis. We simulated an epileptic network with a non-stationary structure; accordingly, EEG source reconstruction using LORETA is performed. Using the reconstructed sources, the spatially compact ROIs are selected. Then, a single time course encoding the temporal non-stationarity is extracted for each ROI. An adaptive directed transfer function (ADTF) is applied to measure the information flow of underlying brain networks. Obtained results demonstrate that the contributed approach is more efficient to estimate the ROI time series and ROI to ROI information flow in comparison with existing methods. Our work is validated in three drug-resistance epilepsy patients. The proposed ROI time series estimation directly affects the quality of connectivity analysis, leading to the best possible seizure onset zone (SOZ) localization verified by electrocorticography and post-operational results.

In this paper, a channel drop ring resonator filter based on two dimensional photonic crystal is proposed which is suitable for all optical communication systems. The multilayer of silicon rods in the center of resonant ring enables one to adjust resonant wavelength of the ring and enhance power coupling efficiency between ring and waveguide. Refractive index and radius of multilayer rods inside the ring are important factors which help one to enhance the desired output parameters. The proposed structure is capable of presenting high quality factor near 1937 in conjunction with 0.8 nm pass band. The high coupling efficiency 99% is another advantage of the proposed filter.

Cloud computing is an Internet based computing environment, where storage and computing resources are assigned dynamically among users according to their needs, using the virtualization technology. Virtualization is an underlying infrastructure of cloud computing, and has led to certain security problems during the development of cloud computing. One essential but formidable task in cloud computing is to detect malicious attacks and their types. Due to increasing incidents of cyber-attacks, design and implementation of effective intrusion detection systems to protect the security of information systems is crucial. In this paper, a host-based intrusion detection system (H-IDS) for protecting virtual machines in the cloud environment is proposed. To this end, first, important features of each class are selected using logistic regression and next, these values are improved using the regularization technique. Then, various attacks are classified using a combination of three different classifiers: neural network, decision tree and linear discriminate analysis with the bagging algorithm for each class. The proposed model has been trained and tested using the NSL-KDD data set with an implementation in the Cloudsim software. Simulation results compared to other methods shows acceptable accuracy of about 97.51 for detecting attacks against normal states.

In this paper, a polarization-insensitive temperature sensor based on photonic crystal fiber is proposed. Using finite element method, coupling between core and defected clad is analyzed. A high refractive index temperature sensitive liquid is infiltrated into the air holes of the second cladding ring. Numerical simulation shows as the phase matching between core and defect-clad super modes satisfied, resonance and peak confinement loss happen. A blue shift in resonance wavelength is obtained when temperature increases. Excellent linearity, simplicity and symmetrical structure, FOM of -0.272/degrees C and especially the same sensitivity of -1.96 nm/degrees C for both X and Y polarizations make the peak loss wavelength more detectable. (C) 2018 Elsevier GmbH. All rights reserved.

In our previous work, ''robust transmission of scalable video stream using modified LT codes'', an LT code with unequal packet protection property was proposed. It was seen that applying the proposed code to any importance-sorted input data, could increase the probability of early decoding of the most important parts when enough number of encoded symbols is available at the decoder's side. In this work, the performance of the proposed method is assessed in general case for a wide range of loss rate, even when there are not enough encoded symbols at the decoder's side. Also in this work the degree distribution of input nodes is investigated in more detail. It is illustrated that sorting input nodes in encoding graph, as what we have done in our work, has superior advantage in comparison with unequal input node selection method that is used in traditional rateless code with unequal error protection property.

The ultimate goal of pattern recognition is to discriminate different classes with minimum misclassification rate. The feature vector used in classification should be as short as possible to reduce the algorithm complexity and informative enough to be able to discriminate complicated patterns. In this regard, dimensionality reduction methods are utilized to reduce the raw feature vector length and also to make the features more discriminative. In this paper, a face detection scheme is proposed by using discrete cosine transform (DCT) features in Bayesian discriminating features (BDF) classifier. Low redundancy of DCT features, optimal reconstruction property of Hotelling transform as the dimensionality reduction method, and the minimum error rate of Bayesian classifier, all in all, bring about a high detection rate in the proposed scheme. Various experiments, performed on different databases, certify that using more informative feature vectors results in a higher dimensionality reduction and improves the classifier's detection rate.

Principal component analysis (PCA) is an effective tool for dimension reduction in classification approaches. Bayesian discriminating features (BDF) is a classifier which effectively utilizes this tool. In this classifier, any of the M largest eigenvalues of the training patterns' covariance matrix are individually involved in classification while the arithmetic average of the remaining eigenvalues take part just as a single parameter. In this paper, by suggesting a new classifier, effect of the number of involved eigenvalues in classification performance is studied. In the suggested classifier we ignore the arithmetic average that is utilized in BDF. Our experiments verify that increasing M does not lead to an ongoing increase in classifier's detection rate in both BDF and the proposed one. However, by over-increasing M, the dependency of classifiers' parameters to the training samples increases which could reduce the performance of the classifiers when they come to make decision about new samples. Furthermore, experimental results verify that arithmetic average of the remaining eigenvalues in BDF improves the classifier performance only when an appropriate number of eigenvalues is selected; hence, ignoring the arithmetic average, as done in proposed classifier, could provide a better performance rather than BDF.