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Paper folding-and-punching tests are used to measure spatial abilities in humans. This paper presents a qualitative model for paper folding (QPF) and a computer game (Paper Folding Game) developed to apply and show the reasoning capabilities of the QPF model. This interactive game presents paper-folding activities intended to help users train and understand how to fold a paper to get a specific shape. Then, it presents paper-folding-and-punching tests to the players. The Paper Folding Game can automatically generate paper-folding-and-punching questions with varying degrees of difficulty depending on the number of folds and holes made, thus producing additional levels for training. The reasoning mechanisms in the QPF model are used by the Paper Folding Game to infer the right answer to each paper-folding-and-punching question. This reasoning capability allows the game to provide feedback to the players when they are wrong and also to create other plausible answers automatically, so that random question-answers are shown to the players in the master-mode. The Paper Folding Game has been implemented using Unity engine and it is available to download from GooglePlay and AppleStore for everyone to train their spatial reasoning skills.

Physical paper, in its various forms (e.g. books, leaflets, catalogues), is extensively used in everyday activities, despite any advancements in digital technology and ICT. Inspired by the popularity of this medium, several research efforts have envisioned and pursued a new era of interactive paper, however several challenges still remain to be addressed. On the other hand, recent advancements towards Ambient Intelligence (AmI) and Smart environments, bear the promise of seamless integration of the physical and digital worlds in an intuitive and user-friendly manner. This paper presents InPrinted, a systematic and generic framework supporting physical paper augmentation and user interaction in Ambient intelligence environments. InPrinted enables natural multimodal user interaction with any kind of printed matter in smart environments, providing context aware and anticipation mechanisms, as well as tools and interaction techniques that support the development of applications incorporating printed matter augmentation. InPrinted has been put to practice in the development of various systems, including the Interactive Maps system, facilitating interaction with printed maps as well as their augmentation with digital information. The results of an in-situ observation experiment of the Interactive Maps system are reported, highlighting that interacting with augmented paper is quite easy and natural, while the overall User Experience is positive.

In this paper, we aim to implement augmented reality (AR) on distant text documents or books. For this purpose, we propose a new paper-based AR system that can detect text documents in real scenes, markerize and identify them, estimate their relative 3D poses to the camera, and augment them with virtual contents. Unlike the previous paper-based AR systems (applicable to only close documents), the proposed system not only requires no detection of words or characters, but allows partial occlusions like the previous systems. In our experiments, the proposed system worked at 24 fps and could consistently achieve high identification rates for both occluded and unoccluded pages.

Paper checks may have complex background features including fine lines and patterns, which make forgery more difficult. Also, halftoning techniques are used to produce continuous tones and to prevent copies with void pantograph features. When these kinds of checks are scanned, Moire patterns may occur. These patterns make it difficult for customers to examine the scanned check images on ATM (Automated Teller Machine) displays. They also can decrease the classification accuracy of check recognition systems. In this paper, we propose an algorithm to enhance the perceptual quality of scanned check images by reducing the Moire patterns. The proposed algorithm consists of foreground extraction, Moire detection and Moire removal. Subjective image quality assessment was performed to evaluate the degree of improvement. Experimental results show that the proposed algorithm improves perceptual quality while maintaining check recognition accuracy.

International audience; This paper presents a novel method for image manipulation iden- tification of natural images in JPEG format. The image forgery detection technique is based on a signal-dependent noise model that is relevant to de- scribe a natural image acquired by a digital camera. This parametric model is characterized by two fingerprints which are used to falsification identification. The problem is cast in the framework of the hypothesis testing theory. For practical use, the Generalized Likelihood Ratio Tests (GLRT) are presented and their performance is theoretically established. There are different types of image forgery which have been considered in this paper for example re- sampling, Gaussian filtering and median filtering. Experiments with real and simulated images highlight the relevance of the proposed approach.

This paper considers the area of digital forensics (DF). One of the problem in DF is the issue of identification of digital cameras based on images. This aspect has been attractive in recent years due to popularity of social media platforms like Facebook, Twitter etc., where lots of photographs are shared. Although many algorithms and methods for digital camera identification have been proposed, there is lack of research about their robustness. Therefore, in this paper the robustness of digital camera identification with the use of convolutional neural network is discussed. It is assumed that images may be of poor quality, for example, degraded by Poisson noise, Gaussian blur, random noise or removing pixels’ least significant bit. Experimental evaluation conducted on two large image datasets (including Dresden Image Database) confirms usefulness of proposed method, where noised images are recognized with almost the same high accuracy as normal images.

Prostate cancer (PCa) has become the second most dreadful cancer in men after lung cancer. Traditional approaches used for treatment of PCa were manual, time consuming and prone to subjective errors. Thus, there is a need for a Computer aided diagnosis system (CADs) consisting of denoising, segmentation, and classification approaches for diagnosis of PCa. CADs may act as a second opinion for the medical experts and save their precious time used in manual analysis. Magnetic resonance imaging (MRI) is the commonly used modality, as it produces detailed and fine contrast images of internal organs for diagnosis of PCa, but it may contain a certain amount of rician and gaussian noise which is necessary to be denoising before segmentation and classification. Denoising offers several challenges such as suppressing of significance image details leading in inaccurate segmentation and classification for prediction of abnormality. Thus, improved denoising, segmentation, and classification approaches can overcome the challenges by analyzing the pitfalls in the state of the art. This paper presents the experimental analysis state of the art denoising and segmentation approaches to analyse their performance based on the values of Peak signal to noise ratio (PSNR), Mean squared error (MSE), Structured similarity index (SSIM), dice metric, area overlap and accuracy. Based on the experimental analysis it was analysed that anisotropic filter outperforms other filters for gaussian noise with PSNR of 28.29, MSE of 96.22 and SSIM of 0.64. Also, for the rician noise anisotropic filter outperforms others with PSNR of 28.06, MSE of 101.52 and SSIM of 0.01. Similarly, for the combined gaussian and rician noise, anisotropic filter outperform others with PSNR of 28.34, MSE of 95.13 and SSIM of 0.652. Further, the analysis of segmentation approaches such as contour and shape-based, region/atlas based, thresholding based, clustering based and deep learning based was performed. Amongst these approaches deep learning based segmentation was found to outperform with dice metric of 0.89 and area overlap of 0.80. Also, CNN based classification outperformed machine learning based Support vector machine (SVM), K nearest neighbour (K-NN) and Random forest (RF) with 94.55% sensitivity, 93.34% specificity, 95.45% accuracy. Finally, the paper discusses challenges and future scope based on analysis in the concerned field for diagnosis of PCa.

Detecting and classifying a brain tumor is a challenge that consumes a radiologist’s time and effort while requiring professional expertise. To resolve this, deep learning techniques can be used to help automate the process. The aim of this paper is to enhance the accuracy of brain tumor classification using a new layered architecture of deep neural networks rather than the current state-of-the-art algorithms. In this paper, we propose automated tumor classification by concatenating two convolutional neural network structures of layers and tuning the hyperparameters by utilizing Bayesian optimization. The proposed solution focuses on enhancing the accuracy of classifying tumors to increase the level of trust in the technologies employed in the medical field. The work is tested and evaluated to predict the classification of magnetic resonance imaging inputs and achieving a higher accuracy (97.37%) than other similar works, with accuracies between 84.19% and 96.13%, for the same dataset.

The goal of learning-based image super-resolution (SR) is to generate a plausible and visually high-resolution (HR) image from a single low-resolution (LR) input image. The SR is an important branch of image reconstruction that concentrates on the improvement of image resolution and the problem is severely underconstrained. It’s to rely on examples or some strong image priors to reconstruct the missing HR image details. The paper addresses the problem of learning the mapping functions is, projection matrix between the LR and HR images based on a dictionary of LR and HR exemplars. In this paper, we present a self-learning single image SR method, which restores an HR image from self-examples extracted from the LR input image itself without relying on additional external training images. This paper proposes a novel computationally to further improve the SR performance of single image SR method that learns multiple linear mappings (MLM) based on SR method functions anchored neighborhood regression (ANR) to directly transform LR feature subspaces into HR subspaces. Moreover, we utilize segmentation flipped and rotated with a matrix-vector of the self-examples to expand the internal patch space. Furthermore, we set up MLM from the input LR features to the desire HR outputs to obtain stable. In particular, we improved SR efficiency using optimization principal component analysis (OPCA) based on dimensionality reduction to generate from low-dimensional into high-dimensional with matrix-vector patch space. For our proposed model, there are three regularization parameters analysis that require to optimize the training and refine the outcome. Experimental result indicates that our approach comparison on the standard benchmark with state-of-the-art method validation the effectiveness of our proposed.

Automated segmentation of medical ultrasound (US) images is a challenging problem due to the complicated features of lesions, inconsistent lesions across individuals, and the high segmentation accuracy requirement. From recently published papers in this area, the active contour model (ACM) and machine learning method produce more accurate lesion segmentation results than previous methods. This paper proposes a novel image segmentation approach that integrates an ACM with a generalized linear model (GLM) and forms learning-structured inference. Compared with the GLM, the proposed method can solve the problems of initialization and the local minimum of the ACM. Furthermore, rather than using the ACM as a postprocessing tool, we integrate it into the training phase to fine-tune the GLM. This step allows the use of unlabeled data during training in a semisupervised setting. The integrated model requires only one image as the training set and is not as sensitive to labeled data as other methods. The proposed method is verified using US images, and the results show that the proposed method can produce accurate segmentation results.

In this paper, we present a whole new perspective on generating music. The method proposed in this paper is the first to be used which uses the frequency domain characteristics of pitch contour curve to generate music melody with long-term structure controllable. The music generated by this method has a good long-term structure that other basic music generation methods do not have. This method has great development potential and application ability, can be combined with other music generation methods, and improve the performance of long-term structure. This method firstly uses the neural network to fit the pitch contour curve in frequency domain, then combines the vocabulary matching method to perfect the detailed characteristics of melody generated in time domain and control the long-term trend of notes generated with respect to label information, finally generates music melody with real and controllable long-term structure. Through a large number of experiments, it can be seen that compared with the music generated based on the LSTM, the music generated by the proposed method has better long-term structure and has similar statistical characteristics.

This paper proposes a new rigid registration algorithm based on the rational quadratic kernel to align point sets with outliers and noise. First of all, the multi-source point sets may contain a lot of outliers and noise and the traditional registration algorithm cannot handle the outliers and noise efficiently, this paper introduces the rational quadratic kernel to the rigid registration problem, which can resist outliers and suppress noise to improve the registration accuracy. Secondly, based on the new registration model, we present an iterative closest point (ICP) algorithm and use Lagrange multiplier and the singular value decomposition (SVD) to compute the rigid transformation. Moreover, the effect of the parameter is discussed detailly and a useful parameter control method is introduced to increase the accuracy and robustness of registration. A series of experiments on simulations and real data demonstrate that the proposed algorithm is more precise and robust than other algorithms.

In the face of remote sensing images with diversified information, the recognition of remote sensing images only through local features or global features is limited. Traditionally, it is difficult to achieve good image modeling. To solve this problem, this paper proposes a recognition framework based on dual-channel deep learning(DCDL). The purpose of this framework is to mine global feature information and local feature information at the same time. On the first channel, this paper uses a multi-scale convolution residual network to perform local mining and residual calculations on the image to generate local features; secondly, the local attention mechanism is used to assign weight coefficients to the local features to make the information more contained. More features are more prominent; finally, after several times of mining features and assigning weights, more representative deep features are produced. On the other channel, we introduce the global attention mechanism to realize the weight coefficient distribution of global features; then use the multi-scale dilated convolution to expand the receptive field to obtain a larger range of feature information; then, use the Sigmoid function to achieve 0 to 1 for all features The weight distribution of, further expands the difference between global features; finally, the deep mining of features is realized through 2-layer convolution. In this paper, through the experimental results of the three sub-data sets in the NWPU-RESISC45 data set, we can see that our proposed algorithm has achieved higher recognition accuracy.

Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. In this paper, we hope to present a comprehensive review on ELM. Firstly, we will focus on the theoretical analysis including universal approximation theory and generalization. Then, the various improvements are listed, which help ELM works better in terms of stability, efficiency, and accuracy. Because of its outstanding performance, ELM has been successfully applied in many real-time learning tasks for classification, clustering, and regression. Besides, we report the applications of ELM in medical imaging: MRI, CT, and mammogram. The controversies of ELM were also discussed in this paper. We aim to report these advances and find some future perspectives.

Omnidirectional video quality assessment (OVQA) helps to evaluate the viewers’ visual experience and promotes the development of omnidirectional video. The perceived quality of omnidirectional video is affected not only by the video content and distortion, but also by the viewing directions of viewer’s preference. At present, there are some quality assessment methods for omnidirectional video, but most of them are full-reference (FR). Compared with the FR method, the no-reference (NR) method becomes more difficult due to the lack of the reference video. In this paper, a NR OVQA based on generative adversarial networks (GAN) is proposed, which is composed of the reference video generator and the quality score predictor. Generally, a reference image/video is distorted by some distortion types, and each distortion type has some distortion levels. To the best of our knowledge, there are some NR methods using GAN to generate the reference images/videos for quality assessment. In these methods, the reference images/videos is generated by GAN when the distorted images/videos, which are from a distortion type but with different distortion level, are input into the GAN. In order to achieve an accurate quality assessment, the generated reference images/videos, which are from a distortion type but with different distortion level, are expected to have as similar quality as possible with each other. However, the distorted images/videos are independent for GAN, and the GAN will generate a little different reference images/videos for these distorted images/videos. This issue is not considered in the existing GAN-based methods. To solve this issue, we introduced a level loss in OVQA. For the quality score predictor, as a further contribution of this paper, the viewing direction of the omnidirectional video is incorporated to guide the quality and weight regression. The publicly available dataset is used to evaluate the proposed method. The experimental results indicate the effectiveness of the proposed method.

The innovation of convolutional neural networks motivated the study on the many aspects of image processing and object detection. One topic of interest in the field of object detection is human detection and the human pose estimation scheme. Human pose estimation scheme enables computer vision with a higher accuracy of identifying human motion and movement. Pose estimation scheme has been applied in making 2D images into 3D representation. This paper aims to determine the distance between the camera and the human subject with real-time application which takes advantage of existing pose estimation schemes. The distance scaling applied in the paper showed high performance of about 0.27 m error from the actual distance.

The crowding in bus is an important factor affecting passenger satisfaction and bus dispatching level. However, how to use video images to detect crowding accurately is a difficult problem. In this paper, firstly, an image sample library is established based on the evaluation standard of crowding in bus, which contains 16346 sample images. Then, Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) are used to extract the texture features of the image in bus. Then, a rough classification method of crowding based on Support Vector Machine (SVM) is proposed. At the same time, in order to improve the accuracy of rough classification of crowding, the optimization effects of grid search algorithm, particle swarm optimization algorithm and genetic algorithm on SVM parameters are compared. The results show that the optimization effect of genetic algorithm is the best, and the accuracy rate is 93.20%. Finally, for the problem that the SVM method is not ideal in the fine classification of crowding, this paper proposes a new method based on ResNet. SGD, Adadelta and Adam are selected to optimize the parameters of ResNet model. The accuracy of the optimal Adam algorithm reaches 96.22%, which effectively solves the problem of the fine classification of crowding in bus.

Recently, social media are the most popular way of consuming news for people due to their fast, low cost, and easy accessibility. Unfortunately, in order to provide financial, political, or personal interests on social media, a large amount of fake news is intentionally produced that contains false information. Although fake news detection is a very important problem to avoid negative effects, efficient studies on this issue are limited. More efficient models are required in order to obtain better solutions with respect to different metrics for fake news detection. In this paper, a novel model was proposed that uses optimization methods for fake news detection. In addition, an improved Salp Swarm Optimization (SSO) based on a nonlinear decreasing coefficient and oscillating inertia weight was proposed to find the best optimum solution for fake news detection for the first time. The standard SSO, Grey Wolf Optimization (GWO) which is one of the most recent swarm intelligence algorithms, and two new adaptive SSO methods were modeled to detect fake news for the first time in this study. These methods were tested over four different real-world fake news data sets to verify the performance of the algorithms proposed in this paper. Furthermore, Friedman test was conducted to distinguish the differences among these methods. The obtained results prove that the proposed new model is significantly superior to standard SSA and GWO on the real-world fake news data sets.

Stock market is a dynamic and volatile market that is considered as time series data. The growth of financial data exposed the computational efficiency of the conventional systems. This paper proposed a hybrid deep learning model based on Long Short- Term Memory (LSTM) and Artificial Bee Colony (ABC) algorithm. ABC is best fit for hyper parameter selection for deep LSTM models and maintains the equilibrium of exploitation and exploration issues. Handling a large volume of multidimensional reviews from social media is a major challenge. This paper evolves the multiple aspects of market sentiments and uses the reliable Big data platform Hadoop ecosystem and its services to compute sentiment polarity index. The ABC-LSTM hybrid model is validated with other core and hybrid models with evolutionary algorithms as Differential Evolution (DE) and Genetic Algorithm (GA). For the experiments, 10 years of historical datasets and social media reviews of IT sector funds Apple Inc. (AAPL), Microsoft corporation (MSFT) and Intel corporation (INTL) from NASDAQ GS, an American stock exchange are considered to validate hybrid forecasting models. Proposed algorithm ABC-LSTM is used to tune the hyperparameters (window size, LSTM units, dropout probability, epochs, batch size and learning rate) and evaluated through Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) as loss function. Performance analysis proves that with sentiment polarity, ABC optimized LSTM obtains improved forecasting accuracy over its counterpart models.

CNN(Convolutional Neural Networks) is a hot topic in the field of pattern recognition., especially in the field of image recognition. And ResNet(Residual Networks) is a special kind of CNN. Compared with the general CNN structure, ResNet introduces the residual unit with an identity mapping. Identity mapping allows the deep layers to directly learn the data received by the shallow layers, which reduces the difficulty of network convergence to a certain extent. As a result, ResNet has a better learning ability, has achieved good performance in various types of image recognition work. The essence of the residual network is to fuse two types of features from different receptive fields, using the fused features instead of the output features of the previous layer as the learning object. But the implementation of feature fusion in original ResNet is adding the two features with equal weights. And this method ignores the fact that the contribution of features from different levels to the learning of the network may not be the same. In this paper, we introduce a feature-inspired gating strategy in the residual unit of ResNet, which allows the network giving different weights to different features, so that the implementation of the feature fusion can be transformed from adding features with equal weights into weighted summation with different weights. And through experiments, we proved that ResNet with gating strategy proposed in this paper can obtain higher recognition accuracy than original ResNet.

Facing a great deal of Lifelog data and complex information, how to extract valuable information and making it easily understood for users is the most urgent and important issue. This paper introduces a method to find personal trajectories from a Lifelog dataset named informative lifelogging which the author collected over the past 8 years. After introducing the Lifelog system and the data structure, this paper propose a method to find personal trajectory in the dataset. Experiment result in the last chapter shows the correctness of this method. The method proposed in this paper provides some way of managing and retrieving the Lifelog data efficiently.

In this paper, SFF (Segmentation Facilitate Feature) technique is proposed to find the junction path to segment touched components based on the seed pixel selected among candidate pixels. Handwritten Recognition system has number of applications like reading postal address, filling forms, reading bank cheques, offering several challenges. In practice, constitute of the word images get touched in handwritten data due to variability in stroke, shortage of space which make the individual character extraction from the word image more complicated. Segmentation of individual in a word image requires a technique that takes care of the variability of writing. This paper proposed the SFF (Segmentation Facilitate Feature) technique to find seed pixel among candidate pixels based on 3-neighbouring pixels. It is used to find junction pixels which form a junction path to segregate the touched component. The junction path is selected to avoid the issues arising due to artifacts or deletion of components features. For experimentation, 1840 legal amount words containing touching components are used. The above number includes 250 words from benchmark database (ICDAR) and 1590 words are gathered from 15 different writers. On implementing, SFF (Segmentation Facilitate Feature) technique on the above mentioned database, 89.9% accuracy is achieved and a higher accuracy level 96.2% is achieved when performed on 1000 words containing two touching consonants.

Due to the automatic feature extraction procedure via multi-layer nonlinear transformations, the deep learning-based visual trackers have recently achieved a great success in challenging scenarios for visual tracking purposes. Although many of those trackers utilize the feature maps from pre-trained convolutional neural networks (CNNs), the effects of selecting different models and exploiting various combinations of their feature maps are still not compared completely. To the best of our knowledge, all those methods use a fixed number of convolutional feature maps without considering the scene attributes (e.g., occlusion, deformation, and fast motion) that might occur during tracking. As a pre-requisition, this paper proposes adaptive discriminative correlation filters (DCF) based on the methods that can exploit CNN models with different topologies. First, the paper provides a comprehensive analysis of four commonly used CNN models to determine the best feature maps of each model. Second, with the aid of analysis results as attribute dictionaries, an adaptive exploitation of deep features is proposed to improve the accuracy and robustness of visual trackers regarding video characteristics. Third, the generalization of proposed method is validated on various tracking datasets as well as CNN models with similar architectures. Finally, extensive experimental results demonstrate the effectiveness of proposed adaptive method compared with the state-of-the-art visual tracking methods.

In the COVID-19 pandemic, telehealth plays a significant role in the e-healthcare. E-health security risks have also risen significantly with the rise in the use of telehealth. This paper addresses one of e-health’s key concerns, namely security. Secret sharing is a cryptographic method to ensure reliable and secure access to information. To eliminate the constraint that in the existing secret sharing schemes, this paper presents Tree Parity Machine (TPM) guided patients’ privileged based secure sharing. This is a new secret sharing technique that generates the shares using a simple mask based operation. This work considers addressing the challenges presents in the original secret sharing scheme. This proposed technique enhances the security of the existing scheme. This research introduces a concept of privileged share in which among k number of shares one share should come from a specific recipient (patient) to whom a special privilege is given to recreate the original information. In the absence of this privileged share, the original information cannot be reconstructed. This technique also offers TPM based exchange of secret shares to prevent Man-In-The-Middle-Attack (MITM). Here, two neural networks receive common inputs and exchange their outputs. In some steps, it leads to full synchronization by setting the discrete weights according to the specific rule of learning. This synchronized weight is used as a common secret session key for transmitting the secret shares. The proposed method has been found to produce attractive results that show that the scheme achieves a great degree of protection, reliability, and efficiency and also comparable to the existing secret sharing scheme.

Bus passenger flow information is very important as a reference data for bus company line optimization, schedule scheduling basis, and passenger travel mode arrangement. With the development of image processing technology, it has become a current research trend to count passenger flow with the help of surveillance video of passengers getting on and off the bus. The specific research contents of this paper based on video image detection and statistics of passengers are as follows:(1) Collect head target image samples through a variety of ways, including 3960 positive head target samples and 4150 negative head target samples, which together constitute the head target feature database. (2) Established a head target detection model based on deep learning. First, the labeling of the head target training data set is completed. Then, after 15,000 iterations of model training, the YOLOv3 head target detection network model was obtained, with a recall rate of 92.12% and an accuracy rate of 89.71%. (3) A multi-target matching tracking algorithm based on the combination of Cam-shift and YOLOv3 is proposed. First, the Cam-shift algorithm is used to track the head target. Secondly, the head target tracking data and the YOLOv3 detection data are combined to solve the problem of drift during the tracking of the Cam-shift algorithm through the data association matching method based on the minimum distance, and then combined with the time constraint, a passenger location information judgment rule is proposed. Optimize the error and missed detection in the process of head target detection and tracking, and improve the reliability of passenger trajectory tracking. (4) A statistical algorithm for the detection of passengers getting on and off the bus is proposed. First, the trajectory of passengers in the bus boarding and disembarking area is analyzed, and a process for judging passengers’ boarding and boarding behavior is proposed. At the same time, a passenger position information judgment rule is proposed according to the different situations of whether there are new passengers or missing passengers, so as to optimize the problem of wrong detection and missing detection in the process of head target detection and tracking. (5) Finally, experiments are carried out in actual bus scenes and simulation scenes. The experiment proves that the statistical algorithm for the detection of passengers getting on and off the bus proposed in this paper has good detection, tracking and statistics effects in bus scenes and simulation scenes.

Image encryption mechanisms provide confidentiality and concealment of information (image) in transmission over the alleyway, susceptible to prevalent invasions. With escalating threats in cybersecurity, various cryptography techniques were projected by researchers. The abundance of such mechanisms requires systematic investigation so that that appropriate method can be selected for diverse applications. An efficient encryption technique must analyze all the parameters in an ideal situation and practical cases. Numerous survey papers available in the literature for experimental comparison are devoid of many probable attacks such as anti occlusion attack, chosen-plaintext attack, jpeg compression, etc. This paper provides a qualified study of almost all basic, traditional, chaotic, lightweight, quantum, fractal, and qubit based encryption methods under the influence of prevalent intimidation (Salt & Pepper, Gaussian, Poisson, Rotation attack, chosen-plaintext attack, known-plaintext attack, and so on). We thus carried out an experimental and theoretical investigation based on statistical, differential, and quantitative analysis. To measure the efficacy, all the mechanisms are implemented in MATLAB-2014 and provide the standard deviation to each metric. It is observed that the Quantum and Qubit based algorithms are superlative in comparison to others under the majority of extensive threats due to their sensitive behaviour towards initial conditions and highly random behaviour.

Emotion recognition based on Electroencephalogram (EEG) has attracted much attention in brain-computer interaction. However, most existing methods usually focus on amplitude and spectrum of the EEG signal, leading to sub-optimal performances due to the insufficiency in modelling the complex intrinsic information of neural integration. To address this issue, this paper proposes to capitalize on the largely neglected phase synchronization (PS) between EEG channels which reflects the intrinsic rhythmic interactions between different channels in neural integration. Specifically, this paper develops a simple and novel feature extraction method which calculates the PS based sparse representation features to analyze emotion states. First, the EEG phase synchronization indexes (PSI) of all channel pairs are estimated as features to distinguish different emotions, since certain topographical maps on PSI reveal specific emotion states. Then principal component analysis is performed to eliminate redundant and noisy features in PSI. Finally, Sparse Representation based Classification (SRC) furtherly emphasize emotion-related features and restrain useless features. For the benchmark affective EEG dataset DEAP, the proposed method based on no-overlapping EEG features achieve an average accuracy of 94.5%, 87.61%, and 67.04% for the classification tasks respectively on two, three and four emotions, demonstrating the superiority over state-of-the-art emotion classification methods.

Devanagari script is the most widely used script in India and other Asian countries. There is a rich collection of ancient Devanagari manuscripts, which is a wealth of knowledge. To make these manuscripts available to people, efforts are being done to digitize these documents. Optical Character Recognition (OCR) plays an important role in recognizing these documents. Convolutional Neural Network (CNN) is a powerful model that is giving very promising results in the field of character recognition, pattern recognition etc. CNN has never been used for the recognition of the Devanagari ancient manuscripts. Our aim in the proposed work is to use the power of CNN for extracting the wealth of knowledge from Devanagari handwritten ancient manuscripts. In addition, we aim is to experiment with various design options like number of layes, stride size, number of filters, kenel size and different functions in various layers and to select the best of these. In this paper, the authors have proposed to use deep learning model as a feature extractor as well as a classifier for the recognition of 33 classes of basic characters of Devanagari ancient manuscripts. A dataset containing 5484 characters has been used for the experimental work. Various experiments show that the accuracy achieved using CNN as a feature extractor is better than other state-of-the-art techniques. The recognition accuracy of 93.73% has been achieved by using the model proposed in this paper for Devanagari ancient character recognition.

Shadow play is an ancient traditional folk art in China and a precious Chinese artistic treasure. However, with the continuous diversification of entertainment forms, traditional shadow play art is gradually fading out of sight and even facing the possibility of having no successor. A fast method of creating digital shadow puppets based on parameterized templates is proposed in this paper. Adopting this method can transform the materials of a shadow character designed by an artist into a digital shadow puppet that can be interactively controlled by hand gestures. This paper classifies digital shadow puppets based on an analysis and summary of traditional shadow play art and designs the corresponding parameterized template for each type. In addition, a control method for shadow puppets based on the Leap Motion device is designed and implemented in this paper in accordance with common human hand gestures and the requirements for controlling shadow puppets. Based on the above work, we design and implement a platform for digital shadow story creation and performance and test the proposed method on this platform. The experimental results show that the method proposed in this paper can quickly create digital shadow puppets that can be interactively controlled and allow shadow plays to be performed based on gesture control. The research ideas and achievements of this paper help promote the development of digital shadow play technology and have great significance to the inheritance and development of traditional shadow play art.

Currently, embedded systems can be found everywhere in quotidian life. In the development of embedded systems, information security is one of the important factors. Encryption is an efficient technique to protect information against attacks. However, because of constraints, existing encryption functions are not compatible and do not agree with real-time applications in embedded systems. In this paper, an improved cryptographic approach with a high level of security and high speed is put forward. Our work uses an efficient version of a hybrid scheme comprising an Advanced Encryption Standard (AES) - Elliptic Curve Cryptography (ECC) for medical image encryption, which combines the benefits of the symmetric AES to speed-up data encryption and asymmetric ECC in order to secure the interchange of a symmetric session key. The contribution of this paper consists of the following two main points: First, we put forward an optimized ECC hardware architecture to respect the compromise between area, power dissipation, and speed. Thus, we primarily utilize only two multipliers to develop the Point Addition (PA) block and the Point Doubling (PD) block, which reduces time complexity. Then, a 32-bit multiplier and a 32-bit inverter architecture based on shifts and XORs are proposed to reduce power consumption and area occupancy. Second, for image encryption, we primarily propose to modify the AES by eliminating the mix-columns transformation and replacing it with a permutation based on the shifts of columns, which decreases time complexity while maintaining the Shannon diffusion and the confusion principle. Then, an adjustment of the rearrangement of the general structure is given to enhance the entropy value. The global cryptosystem is implemented using a co-design approach where the modified AES runs on the NIOS II processor, and the scalar ECC multiplication is designed as a hardware accelerator. The suggested cryptographic system spends much less execution time, which is a significant factor for being applied in practice. Security analysis is successfully performed, and our experiments prove that our proposed technique provides the basics of cryptography with more simplicity and correctness. In fact, the results of the evaluation prove the effectiveness, rapidity and high security of the suggested algorithm.

The prediction of electricity consumption is a vital foundation for smart energy management. Since the consumption of power varies with different appliances, better forecasting of power and peak demand is an essential accomplishment for the proper planning and development of the power generation and distribution system. This forecast analysis helps the service providers and the government to understand the lifestyle of the customers. The existing prediction and forecasting models are not meeting the standard requirements and moreover difficult to apply in practice. The forecast says that the boom of electric vehicles will increase the demand of electricity globally by 3% for the upcoming year. There exists a number of machine learning algorithms for classification and decision making. But the accuracy of the exiting methods have shown inferior performance in terms of prediction which leads to inefficient decision making in the quantity of electricity generation. This paper proposes the use of random forest supervised learning model to forecast the consumption of power and identify the level of peak demand. The large smart meter dataset collected at varying seasons of the year is fed to the random forest classifier technique for better analysis and forecasting. This approach outperforms in terms of accuracy, stability and generalization. In addition, this paper investigates the existing models and compares the performance with those models. The performance analysis shows that this model performs better than the other investigated models with performance accuracy of 95.67% and enhanced accuracy of precision and recall.

Activity recognition represents the task of classifying data derived from different sensor types into one of predefined activity classes. The most popular and beneficial sensors in the area of action recognition are inertial sensors such as accelerometer and gyroscope. Convolutional neural network (CNN) as one of the best deep learning methods has recently attracted much attention to the problem of activity recognition, where 1D kernels capture local dependency over time in a series of observations measured at inertial sensors (3-axis accelerometers and gyroscopes) while in 2D kernels apart from time dependency, dependency between signals from different axes of same sensor and also over different sensors will be considered. Most convolutional neural networks used for recognition task are built using convolution and pooling layers followed by a few number of fully connected layers but large and deep neural networks have high computational costs. In this paper, we propose a new architecture that consists solely of convolutional layers and find that with removing the pooling layers and instead adding strides to convolution layers, the computational time will decrease notably while the model performance will not change or in some cases will even improve. Also both 1D and 2D convolutional neural networks with and without pooling layer will be investigated and their performance will be compared with each other and also with some other hand-crafted feature based methods. The third point that will be discussed in this paper is the impact of applying fast fourier transform (FFT) to inputs before training learning algorithm. It will be shown that this preprocessing will enhance the model performance. Experiments on benchmark datasets demonstrate the high performance of proposed 2D CNN model with no pooling layers.

This paper presents a novel encryption scheme based on genetic operations and Mixed Linear-Nonlinear Coupled Logistic Map Lattice (MLNCML) spatiotemporal chaotic system. The initial values of the MLNCML system are related to the plain-image. Therefore, this scheme is sensitive to the plaintext and keys. For the simplicity and efficiency of the encryption process, expanded XOR (eXOR) operation and genetic operations are applied in the scheme. Spatiotemporal chaotic system can resist dynamical degradation and periodic phenomena, which offers good randomness for encryption. Moreover, the eXOR operation in this paper can effectively avoid the chosen-plaintext attack of traditional XOR operation. The DNA mutation and crossover operations are controlled by chaotic sequences, which overcome man-in-the-middle attack in the traditional DNA addition and subtraction operation. The experimental results indicate that the proposed scheme has good security to resist common attacks.

Due to the huge flow of data and complications in mutable characteristics of the data, Distributed Denial of Services attacks existed in the Multimedia Internet of Things. Attacks over the IoT have become an increasing menace in recent time, which tries to hack or illegally tamper the streaming data available over the networks. On the other hand, there has been an increase in volume in research contributions to effectively counter these attacks and implement a strong defense mechanism. There have been numerous algorithms and frameworks implemented in recent times that are intelligent and soft computing-based. These evolution-based algorithms play a vital role in self-adapting the system under attack towards increasing and new types of attacks which are increasing day by day. One such area of soft computing algorithms investigated in this paper is the Artificial Neural Network or popularly known as ANNs. It works analogously to the biological neurons in the human body. In this paper, we systematically explain the ANN-based network model to counteract the DDoS attacks in the Multimedia Internet of Things, architecture, and implementation of ANNs, the experimental investigations and findings which help in drawing an inference of ANN-based defense models.

Weber’s law reveals the relationship between human perception and perceptual stimuli. Inspired by the theory, this paper designs a multi-level convolution correlation feature statistic method for image retrieval. Firstly, the difference between a central pixel and its neighbors is described by Weber’s law through computing the differential excitation of image. Then, a multi-level saliency map is obtained by binary transformation and convolution operation. Thirdly, to exploit spatial correlation information of the image, a pixels pair-wise correlation and hierarchy statistic model is constructed. Finally, all intermediate features are concatenated into one histogram, which includes salient color and texture features. Extensive experiments demonstrate the proposed method of this paper has excellent performance.

Matching images of a scene with high precision is a core component in real time applications such as image-based navigation of unmanned aerial vehicles, terminal guidance of missiles. The aim of the research discussed in this paper is to develop a scene matching algorithm of high accuracy, robust to the variations between the images compared with scope for parallelism. After a study of state-of-the-art scene matching algorithms, a local feature-based approach was chosen to meet the stated requirements. The paper discusses the techniques for modules of the algorithm chosen and the methodology implemented after a metric study with a dynamic threshold based nearest neighbour distance ratio matching strategy (DT-NNDR). The metrics and the proposed strategy were tested for the matching of planar images from Affine covariant features group, unmanned aerial vehicle videos from a national defence agency, and satellite images from Google. The performance of strategy was compared with the existing NN-DR strategy with an experimental optimal threshold. The overall precision of the proposed strategy is 88%, making it suitable for applications which require high value of true positive.

Object recognition is a key research area in the field of image processing and computer vision, which recognizes the object in an image and provides a proper label. In the paper, three popular feature descriptor algorithms that are Scale Invariant Feature Transform (SIFT), Speeded Up Robust Feature (SURF) and Oriented Fast and Rotated BRIEF (ORB) are used for experimental work of an object recognition system. A comparison among these three descriptors is exhibited in the paper by determining them individually and with different combinations of these three methodologies. The amount of the features extracted using these feature extraction methods are further reduced using a feature selection (k-means clustering) and a dimensionality reduction method (Locality Preserving Projection). Various classifiers i.e. K-Nearest Neighbor, Naive Bayes, Decision Tree, and Random Forest are used to classify objects based on their similarity. The focus of this article is to present a study of the performance comparison among these three feature extraction methods, particularly when their combination derives in recognizing the object more efficiently. In this paper, the authors have presented a comparative analysis view among various feature descriptors algorithms and classification models for 2D object recognition. The Caltech-101 public dataset is considered in this article for experimental work. The experiment reveals that a hybridization of SIFT, SURF and ORB method with Random Forest classification model accomplishes the best results as compared to other state-of-the-art work. The comparative analysis has been presented in terms of recognition accuracy, True Positive Rate (TPR), False Positive Rate (FPR), and Area Under Curve (AUC) parameters.

The influence of Internet of Things (IoT) is growing exponentially in the consumer patterns and will continue to increase in the coming years. With millions of interconnected devices over the internet, IoT is currently running into its monumental security and centralization issues that can be resolved using Blockchain. This paper aims to demonstrate the use of a smart contract on a private Ethereum Blockchain (also known as permission less Blockchain) to check whether a user holds enough tokens to utilize a service. Private in this context implies a private instance of the Ethereum implementation in which we make use of the same security and principles of the Ethereum main chain on a personal network of connected IoT devices which improves the overall privacy and security of the system than the traditional schemes. The proposed model consists of a target IoT device controlled by a Raspberry Pi, running a client application that makes decisions based on the state of the smart contract. With this environment, we are able to achieve the following goals: (1) no data leaves the private network (2) all services are employed with transparency and accountability (3) only registered devices can issue requests for using a service. Although the demonstration is made on a home network, the model presented in this paper can be utilized in commercial environments where any service can be availed from a wide array of smart devices through a mobile application.

In this paper, we recognize serial numbers on banknotes using deep learning. The samples used in this paper are digital images which have been preprocessed with data labelling and data augmentation, e.g., scaling transformation, etc. The algorithms based on deep learning are proposed and have the stability for serial number recognition with complex backgrounds. In this paper, a pipeline of deep neural networks is established for the recognition of banknote serial numbers. Because high reliability is more important than accuracy in financial applications, DenseNet is set forth as the primary classifier, the scaling transformation of SegLink is put forward to locate the characters, the detection rate is up to 95.80%. A convolutional neural network with residual attention model is proposed for serial number recognition, the precision is up to 97.09%.

In this paper, a chaos-based neural synchronization has been proposed for the development of the public-key exchange protocol. A special neural network structure called Tree Parity Machine (TPM) is used for neural synchronization. Two TPMs accept the common input and different weight vector and update the weights using neural learning rule by exchanging their output. In some steps, it results in complete synchronization, and the weights of the two TPMs become identical. These identical weights serve as a secret key. There is, however, hardly some investigation to investigate the randomness of the common input vector used in the synchronization process. In this paper, logistic Chaos system based Tree Parity Machine (CTPM) is proposed. For faster synchronization, this proposed CTPM model uses logistic chaos generated random common input vector. This proposed CTPM model is faster and has better security than TPM with the same input, output, and hidden neurons. This proposed technique has been passed through a series of parametric tests. The results have been compared with some recent techniques. The results of the proposed technique have shown effective and robust potential.

User-Generated Content (UGC) is turning into the predominant type of internet traffic. Content popularity prediction plays a pivotal role in managing this large-scale traffic. As a result, popularity prediction is increasingly becoming an important area of research in computer networking. Generally, popularity prediction methods are classified into two groups, namely, feature-driven and early-stage. While feature-driven methods predict content popularity before publication, early-stage methods monitor early content popularities to forecast the future. Many papers have shown that early-stage popularity prediction performs better than feature-driven methods. In this paper, we improve the performance of early-stage popularity prediction by first, classifying the data into several clusters using k-means clustering with Pearson correlation distance, and then, training a Deep-Belief Network (DBN) for each cluster. We evaluate our method using a dataset of YouTube videos and show that using a generative model such as DBN for time series prediction significantly improves the performance. Numerical results indicate that our proposed method outperforms other state-of-the-art methods by reducing Mean Absolute Percentage Error (MAPE) and mean Relative Square Error (mRSE) by up to 47.86% and 25.18%.

The real-time solicitations of biometric systems have been extensively used for several things with the growing necessities of higher security level. There are numerous biometric traits used for person identification. In recent years, iris biometric trait become very popular and efficient in many security applications. However, biometric systems are prone to presentation attack. This attack is carried out by using spoofing of any biometric modality and present as a real trait. The objective of this paper is to present a broad and well thought-out overview of the effort that has been conceded out over the preceding years in the field of iris anti-spoofing. Spoofing of an iris image can be done by paper print and contact lens etc. So, it is essential to discover fake iris up to its core level. In this survey, we have discussed various research work done in the past on the topic of different classes of presentation attack and summarized the state-of-the-art methods in a structural way. We have also discussed the future direction in the field of iris liveness detection.

In this paper we introduce a cavity reconstructing algorithm for 3D surface scans (CRASS) developed for filling cavities in point clouds representing human body surfaces. The presented method uses Bezier patches to reconstruct missing data. The source of input data for the algorithm was an 8-directional structured light scanning system for the human body. Typical 3D scan representing human body consists of about 1 million points with average sampling density of 1 mm. The paper describes the complete scan processing pipeline: data pre-processing, boundary selection, cavity extraction and reconstruction, and a post-processing step to smooth and resample resulting geometry. The developed algorithm was tested on simulated and scanned 3D input data. Quality assessment was made based on simulated cavities, reconstructed using presented method and compared to original 3D geometry. Additionally, comparison to the state-of-the-art screened Poisson method is presented. Values’ ranges of parameters influencing result of described method were estimated for sample scans and comprehensively discussed. The results of the quantitative assessment of the reconstruction were lower than 0,5 of average sampling density.

Soil classification is one of the major affairs and emanating topics in a large number of countries. The population of the world is rising at a majorly rapid pace and along with the increase in population, the demand for food surges actively. Typical techniques employed by the farmers are not adequate enough to fulfill the increasing requirements and therefore they have to hinder the cultivating soil. For proper crop yield, farmers should be aware of the correct soil type for a particular crop, which affects the increased demand for food. There are various laboratory and field methods to classify soil, but these have limitations like time and labor-consuming. There is a requirement of computer-based soil classification techniques which will help farmers in the field and won’t take a lot of time. This paper talks about different computer-based soil classification practices divided into two streams. First is image processing and computer vision-based soil classification approaches which include the conventional image processing algorithms and methods to classify soil using different features like texture, color, and particle size. Second is deep learning and machine learning-based soil classification approaches, such as CNN, which yields state-of-the-art results. Deep learning applications mostly diminish the dependency on spatial-form designs and preprocessing techniques by facilitating the end-to-end process. This paper also presents some databases created by the researchers according to the objective of the study. Databases are created under different environmental and illumination conditions, using different appliances such as digital cameras, digital camcorder, and a smartphone camera. Also, evaluation metrics are briefly discussed to layout some graded measures for differentiation. This review serves as a brief guide to new researchers in the field of soil classification, it provides fundamental understanding and general knowledge of the modern state-of-the-art researches, in addition to skillful researchers considering some dynamic trends for future work.

Every woman spends her life with a cyclic occurrence of the menstrual cycle of approximately 28 days. During a menstrual cycle, there are many physical, psychological and behavioural changes in women that affect the autonomic activities of the heart. However, health management is an important factor to be considered as it influences the entire quality of women life. Thus, Heart Rate Variability (HRV) analysis is an appropriate tool to examine the physiological effects of the menstrual cycle in young healthy women. So, in this paper, Detrended Fluctuation Analysis method (DFA) is used for analyzing the HRV in the phases of the menstrual cycle. In DFA, there is a limitation of abrupt jumps in detrended profile that leads to the inaccurate detection of properties of HRV. In order to overcome the limitations of abrupt jumps in the DFA method, a novel method is proposed in this paper. The trend in proposed method is created by averaging the integrated HRV time series of each window in its respective time scale. Then, cubic interpolation is done for estimating new data points within each averaged segment. Finally, proposed trend is used to estimate scaling exponent function. The proposed method detects the properties of HRV variations accurately among phases of the menstrual cycle when applied on self-recorded dataset.

Camouflaged communication using a media is known as Steganography. It is different than encryption as the presence of message is also concealed in case of steganography. The message however can be encrypted before hiding in a media. Detection of concealed exchange being carried out or unraveling the details of such transmission is known as Steganalysis. Steganalysis can be detected by classifying the given media file as cover media file or stego media file. Blind steganalysis detects presence of hidden content without any knowledge about the cover media file and steganography algorithm used. Steganalysis plays a vital role in forensics of various media such as text, audio, image, video and network packets. Machine learning techniques have been widely used for steganalysis in literature. These techniques use a three step approach consisting of Feature Extraction, Training and Testing Phases. Deep learning techniques, a subset of machine learning techniques are preferred by researchers over machine learning techniques as (i) they consist of Training and Testing Phases with the feature extraction step done automatically, (ii) they give better accuracy when trained with huge amount of data. This paper proposes novel multi class blind steganalysis technique for images. Convolutional Neural Network (CNN) is one of the best known architecture used with image steganalysis. But as the depth of CNN architecture increases, problem of vanishing descent arise which affects the accuracy. In order to solve the problem of the vanishing/exploding gradient in CNN, concept called Residual Network which use a technique called skip connections is being used. The skip connection skips training from few layers and connects directly to the output. A deep residual network helps to automatically capture complex statistical features of images and preserve weak stego signal in image content making it suitable for multi class blind steganalysis. This paper uses deep residual network for multi-class blind steganalysis. Proposed DRN has been successfully applied for multi class blind steganalysis in spatial and JPEG images. Experimental results demonstrate proposed network is comparable to state or art techniques present in literature.

There is always a need for an updated systematic review of a special subject area because of its importance for the researchers and the interested audience. This paper presents a review of medical image authentication (MIA) which is an interesting application of medical image watermarking techniques. The main objectives of medical image authentication techniques are protecting the medical images from tampering and verifying their integrity. Over the years many MIA schemes have been presented for different purposes such as detecting tampering in the medical images, localizing the tampered region, and recovering the tampered region. This paper starts by providing some basic information about the medical image watermarking techniques and their requirements, followed by a brief display for the previous review papers in this field which proves the necessity to present this review paper. Then, an overview of MIA process and its domain-based concepts are presented. Thereafter, the MIA schemes are classified according to their objectives and the domain of embedding the watermarks. The most used performance evaluation measures and metrics in the state-of-the-art are also reviewed in this paper followed by the conclusions and some recent trends of this interesting research field.

In this paper, a new scrambling algorithm is proposed to reduce the correlation between the image pixels in three directions. In this paper, this algorithm is called ladder scrambling, and the image is scrambled multiple times in the form of diagonal lines. In terms of diffusion, DNA coding is used, and the secondary processing of the image is performed through DNA coding to change the pixel value of the image. The final encrypted image is affected by three conditions: the number of scrambling algorithms, the selection of DNA coding rules, and the key of the chaotic system. Theoretical analysis and experimental results show that the algorithm is safe and effective.

Recently, data hiding techniques have become very popular in several vital applications, especially in telemedicine. The reason for this is their ability to give good results such as high embedding capacity while preserving visual image quality as much as possible after extracting the hidden secret message. In earlier studies, many researchers have achieved the goal of reversible data hiding (RDH) algorithm. All these methods have achieved excellent results on standard and natural images. However, in the case of medical images, especially color medical images, we face the problem of how to preserve the visual quality of image contents while achieving the goals of RDH in avoiding the loss of patient data or the distortion of the diagnosing image. In this paper, we proposed a secure data hiding method using a hyper chaotic map and left-most embedding strategy. The proposed methods are hybrid, where it is applied in the DCT frequency domain and encrypted domain together as presented here. This gives a higher embedding rate and higher visual image quality than existing methods without any loss or distortion of both hidden secrets data and reconstructed image. The novelty of this paper is to embed the desired secret data in each quantized block of DCT using (8-bit LMSB) strategy for embedding process. We tested our algorithm on both color medical images and standard color images of different sizes and different formats. We evaluated the performance of our algorithm on the basis of the quality metrics MSE, PSNR, BER, SSIM, Correlation, Symbol Error Rate, additional quality evaluation metrics, execution time, and different types of geometric and signal attacks. All of these parameters are demonstrated and represented in this proposed work in detail.

The main focus of this paper is the separation of underdetermined convolutive blind speech in a multi-speaker environment. We present a method based on mask prediction in the time-frequency domain. Firstly, depending on the sparsity of signals in the time-frequency (TF) domain, we extimate speakers’ masks by clustering the relative absolute and Hermitian angle features extracted from the frequency components of the mixtures. Speech separation algorithms that are based on the sparsity and disjoint orthogonality of the speech signals in the time-frequency domain are not efficient when more than one source is active. Hence, in this paper, the cluster centers are estimated mostly based on the TF units that probably have only one active source. The correlations between the estimated masks, belonging to adjacent frequency bins, are leveraged to solve the permutation problem. To increase the accuracy, we have zeroed the value of masks at the TF unit without any active source. Moreover, in clustering, we employ a weighting function to consider the parts of masks that probably contains just one active source. Finally, in order to decrease the musical noise of the separated signals and improve their quality, sparse filters in the time-domain are utilized to re-estimate the separated signals. Performance of the proposed method is evaluated by a number of simulated and real speech signals. The simulated experiments were performed using a public dataset and Roomsim simulator. Compared the proposed method with some conventional algorithms, we observed that our separation method is more accurate than other approaches.