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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.

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.

In the paper we present an approach to the automatic detection and identification of important elements in paper documents. This includes stamps, logos, printed text blocks, signatures and tables. Presented approach consists of two stages. The first one includes object detection by means of AdaBoost cascade of weak classifiers and Haar-like features. Resulting image blocks are, at the second stage, subjected to verification based on selected features calculated from recently proposed low-level descriptors combined with certain classifiers representing current machine-learning approaches. The training phase, for both stages, uses bootstrapping, i.e., integrative process, aiming at increasing the accuracy. Experiments performed on large set of digitized paper documents showed that adopted strategy is useful and efficient.

Recently, virus diseases, such as SARS-CoV, MERS-CoV, and COVID-19, continue to emerge and pose a severe public health problem. These diseases threaten the lives of many people and cause serious social and economic losses. Recent developments in information technology (IT) and connectivity have led to the emergence of Internet of Things (IoT) and Artificial Intelligence (AI) applications in many industries. These industries, where IoT and AI together are making significant impacts, are the healthcare and the diagnosis department. In addition, by actively communicating with smart devices and various biometric sensors, it is expanding its application fields to telemedicine, healthcare, and disease prevention. Even though existing IoT and AI technologies can enhance disease detection, monitoring, and quarantine, their impact is very limited because they are not integrated or applied rapidly to the emergence of a sudden epidemic. Especially in the situation where infectious diseases are rapidly spreading, the conventional methods fail to prevent large-scale infections and block global spreads through prediction, resulting in great loss of lives. Therefore, in this paper, various sources of infection information with local limitations are collected through virus disease information collector, and AI analysis and severity matching are performed through AI broker. Finally, through the Integrated Disease Control Center, risk alerts are issued, proliferation block letters are sent, and post-response services are provided quickly. Suppose we further develop the proposed integrated virus disease control model. In that case, it will be possible to proactively detect and warn of risk factors in response to infectious diseases that are rapidly spreading worldwide and strengthen measures to prevent spreading of infection in no time.

Deep neural networks (DNNs) have been extensively studied in medical image segmentation. However, existing DNNs often need to train shape models for each object to be segmented, which may yield results that violate cardiac anatomical structure when segmenting cardiac magnetic resonance imaging (MRI). In this paper, we propose a capsule-based neural network, named Seg-CapNet, to model multiple regions simultaneously within a single training process. The Seg-CapNet model consists of the encoder and the decoder. The encoder transforms the input image into feature vectors that represent objects to be segmented by convolutional layers, capsule layers, and fully-connected layers. And the decoder transforms the feature vectors into segmentation masks by up-sampling. Feature maps of each down-sampling layer in the encoder are connected to the corresponding up-sampling layers, which are conducive to the backpropagation of the model. The output vectors of Seg-CapNet contain low-level image features such as grayscale and texture, as well as semantic features including the position and size of the objects, which is beneficial for improving the segmentation accuracy. The proposed model is validated on the open dataset of the Automated Cardiac Diagnosis Challenge 2017 (ACDC 2017) and the Sunnybrook Cardiac Magnetic Resonance Imaging (MRI) segmentation challenge. Experimental results show that the mean Dice coefficient of Seg-CapNet is increased by 4.7% and the average Hausdorff distance is reduced by 22%. The proposed model also reduces the model parameters and improves the training speed while obtaining the accurate segmentation of multiple regions. Supplementary Information The online version contains supplementary material available at 10.1007/s11390-021-0782-5.

The outbreak of the new type of coronavirus pneumonia (COVID-19) has caused a huge impact on the world. In this case, only by adhering to the prevention and control methods of early diagnosis, early isolation, and early treatment, can the spread of the virus be prevented to the greatest extent. This article uses artificial intelligence–assisted medical imaging diagnosis as the research object, combines artificial intelligence and CT medical imaging diagnosis, introduces an intelligent COVID-19 detection system, and uses it to achieve COVID-19 disease screening and lesion evaluation. CT examination has the advantages of fast speed and high accuracy, which can provide a favorable basis for clinical diagnosis. This article collected 32 lung CT scan images of patients with confirmed COVID-19. Two professional radiologists analyzed the CT images using traditional imaging diagnostic methods and artificial intelligence–assisted imaging diagnostic methods, and the comparison showed the gap between the two methods. According to experiments, CT imaging diagnosis assisted by artificial intelligence only takes 0.744 min on average, which can save a lot of time and cost compared with the average time of 3.623 min for conventional diagnosis. In terms of comprehensive test accuracy, it can be concluded that the combination of artificial intelligence and imaging diagnosis has extremely high application value in COVID-19 diagnosis.

Application specific instruction-set processors (ASIP) are a programmable and flexible alternative of traditional finite state machine (FSM) controlled register-transfer level (RTL) designs for multimode basedband systems. In this paper, we present two ASIPs for small scale multiple-input multiple-output (MIMO) wireless communication systems that demonstrate the soundness and effectiveness of ASIPs for this type of applications. The first ASIP is programmed with multiple MIMO symbol detection algorithms for 4 × 4 systems. The supported detection algorithms are minimum mean-square error (MMSE), two variants of the selective spanning with fast enumeration (SSFE) and K-best list sphere detection (LSD). The second ASIP supports MMSE and zero-forcing dirty paper coding (ZF-DPC) algorithms for a base station (BS) with 4 antennas and for 4 users. Both ASIPs are based on transport triggered architecture (TTA) and are programmed with a retargetable compiler with high level language to meet the time-to-market requirements. The detection and precoding algorithms can be switched in the respective ASIPs based on the error-rate requirements. Depending on the algorithms, MIMO detection ASIP delivers 6.16—66.66 Mbps throughput at a clock frequency of 200 MHz on 90 nm technology. The precoder ASIP provides a throughput of 52.17 and 51.95 Mbps for MMSE and ZF-DPC precoding respectively at a clock frequency of 210 MHz on 90 nm technology.

Salient object detection is a hot spot of current computer vision. The emergence of the convolutional neural network (CNN) greatly improves the existing detection methods. In this paper, we present 3MNet, which is based on the CNN, to make the utmost of various features of the image and utilize the contour detection task of the salient object to explicitly model the features of multi-level structures, multiple tasks and multiple channels, so as to obtain the final saliency map of the fusion of these features. Specifically, we first utilize contour detection task for auxiliary detection and then utilize use multi-layer network structure to extract multi-scale image information. Finally, we introduce a unique module into the network to model the channel information of the image. Our network has produced good results on five widely used datasets. In addition, we also conducted a series of ablation experiments to verify the effectiveness of some components in the network.

Annually, lots of research papers are published in scientific journals around the world. The knowledge of the status of research is a prerequisite for research planning and policy making. This type of knowledge could be gained through a scientometrics study on the published literature that analyzes research products in a scientific field. Always healthcare was a permanent concern of researchers and also rapidly expanding field of Big Data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It leads attracting attention from academia, industry and even governments around the world to “Big data in Healthcare”. Therefore, this paper has done a meta-analysis on published researches methodology in this field in the period of 2008–2018. Statistical finding shows the “Meta-analysis and evidence” is the most used methodology in published papers. We applied data mining techniques for predicting using methodologies in the various databases to achieving knowledge discovery in the field. Naïve Bayes classifier in RapidMiner has been applied and results show eight main categories for words used in papers while “Developing methods to evaluate of care” averagely is the most intended using methodology for publishing papers and “Agent-based modeling” in nature is most using methodology and could be better predicted.

Satellite imagery is changing the way we understand and predict economic activity in the world. Advancements in satellite hardware and low-cost rocket launches have enabled near-real-time, high-resolution images covering the entire Earth. It is too labour-intensive, time-consuming and expensive for human annotators to analyse petabytes of satellite imagery manually. Current computer vision research exploring this problem still lack accuracy and prediction speed, both significantly important metrics for latency-sensitive automatized industrial applications. Here we address both of these challenges by proposing a set of improvements to the object recognition model design, training and complexity regularisation, applicable to a range of neural networks. Furthermore, we propose a fully convolutional neural network (FCN) architecture optimised for accurate and accelerated object recognition in multispectral satellite imagery. We show that our FCN exceeds human-level performance with state-of-the-art 97.67% accuracy over multiple sensors, it is able to generalize across dispersed scenery and outperforms other proposed methods to date. Its computationally light architecture delivers a fivefold improvement in training time and a rapid prediction, essential to real-time applications. To illustrate practical model effectiveness, we analyse it in algorithmic trading environment. Additionally, we publish a proprietary annotated satellite imagery dataset for further development in this research field. Our findings can be readily implemented for other real-time applications too.

This survey explores how Deep Learning has battled the COVID-19 pandemic and provides directions for future research on COVID-19. We cover Deep Learning applications in Natural Language Processing, Computer Vision, Life Sciences, and Epidemiology. We describe how each of these applications vary with the availability of big data and how learning tasks are constructed. We begin by evaluating the current state of Deep Learning and conclude with key limitations of Deep Learning for COVID-19 applications. These limitations include Interpretability, Generalization Metrics, Learning from Limited Labeled Data, and Data Privacy. Natural Language Processing applications include mining COVID-19 research for Information Retrieval and Question Answering, as well as Misinformation Detection, and Public Sentiment Analysis. Computer Vision applications cover Medical Image Analysis, Ambient Intelligence, and Vision-based Robotics. Within Life Sciences, our survey looks at how Deep Learning can be applied to Precision Diagnostics, Protein Structure Prediction, and Drug Repurposing. Deep Learning has additionally been utilized in Spread Forecasting for Epidemiology. Our literature review has found many examples of Deep Learning systems to fight COVID-19. We hope that this survey will help accelerate the use of Deep Learning for COVID-19 research.

Gradient Boosted Decision Trees (GBDT’s) are a powerful tool for classification and regression tasks in Big Data. Researchers should be familiar with the strengths and weaknesses of current implementations of GBDT’s in order to use them effectively and make successful contributions. CatBoost is a member of the family of GBDT machine learning ensemble techniques. Since its debut in late 2018, researchers have successfully used CatBoost for machine learning studies involving Big Data. We take this opportunity to review recent research on CatBoost as it relates to Big Data, and learn best practices from studies that cast CatBoost in a positive light, as well as studies where CatBoost does not outshine other techniques, since we can learn lessons from both types of scenarios. Furthermore, as a Decision Tree based algorithm, CatBoost is well-suited to machine learning tasks involving categorical, heterogeneous data. Recent work across multiple disciplines illustrates CatBoost’s effectiveness and shortcomings in classification and regression tasks. Another important issue we expose in literature on CatBoost is its sensitivity to hyper-parameters and the importance of hyper-parameter tuning. One contribution we make is to take an interdisciplinary approach to cover studies related to CatBoost in a single work. This provides researchers an in-depth understanding to help clarify proper application of CatBoost in solving problems. To the best of our knowledge, this is the first survey that studies all works related to CatBoost in a single publication.

This position paper describes the context, the goal, the strategy and the tactics of the ERATO MMSD project (2016–2022). The project aims at enhanced quality assurance measures for industry products like cars. In doing so, we follow a recent trend and exploit formal methods, a body of mathematical techniques originally developed for computer systems. However, there are fundamental gaps in application of formal methods to industry products: additional concerns in industry products such as continuous dynamics of physical components and quantitative measures such as probability, time, and cost make problems fundamentally different from those about software. Formal methods that accommodate these concerns is an active research area, which shows that it is a hard problem. There are several successful theoretical developments in this direction. They typically combine one individual technique with one specific concern, such as hybrid automata that extend automata with continuous dynamics. Our project aims to contribute to this hard problem in a unique way. In our project we will take a unique metamathematical strategy to bridging the gaps: instead of creating one technique for each concern, we want to find a meta-level theory that describes how to develop such techniques for many potential concerns in general. Through this strategy, together with our emphasis on real-world applications in industry, we expect a new prototype of applied mathematics will emerge. In this prototype, abstraction and genericity—characteristics of modern mathematics that are not often associated with application—are turned into crucial advantages in applications.

The explosive growing number of data from mobile devices, social media, Internet of Things and other applications has highlighted the emergence of big data. This paper aims to determine the worldwide research trends on the field of big data and its most relevant research areas. A bibliometric approach was performed to analyse a total of 6572 papers including 28 highly cited papers and only papers that were published in the Web of ScienceTM Core Collection database from 1980 to 19 March 2015 were selected. The results were refined by all relevant Web of Science categories to computer science, and then the bibliometric information for all the papers was obtained. Microsoft Excel version 2013 was used for analyzing the general concentration, dispersion and movement of the pool of data from the papers. The t test and ANOVA were used to prove the hypothesis statistically and characterize the relationship among the variables. A comprehensive analysis of the publication trends is provided by document type and language, year of publication, contribution of countries, analysis of journals, analysis of research areas, analysis of web of science categories, analysis of authors, analysis of author keyword and keyword plus. In addition, the novelty of this study is that it provides a formula from multi-regression analysis for citation analysis based on the number of authors, number of pages and number of references.

Due to the increasing cost of data storage and computation, more and more graphs (e.g., web graphs, social networks) are outsourced and analyzed in the cloud. However, there is growing concern on the privacy of these outsourced graphs at the hands of untrusted cloud providers. Unfortunately, simple label anonymization cannot protect nodes from being re-identified by adversary who knows the graph structure. To address this issue, existing works adopt the k-automorphism model, which constructs $$(k-1)$$ symmetric vertices for each vertex. It has two disadvantages. First, it significantly enlarges the graphs, which makes graph mining tasks such as subgraph matching extremely inefficient and sometimes infeasible even in the cloud. Second, it cannot protect the privacy of attributes in each node. In this paper, we propose a new privacy model (k, t)-privacy that combines the k-automorphism model for graph structure with the t-closeness privacy model for node label generalization. Besides a stronger privacy guarantee, the paper also optimizes the matching efficiency by (1) an approximate label generalization algorithm TOGGLE with $$(1+\epsilon )$$ approximation ratio and (2) a new subgraph matching algorithm PGP on succinct k-automorphic graphs without decomposing the query graph.

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.

The field of pipelined FFT hardware architectures has been studied during the last 50 years. This paper is a survey that includes the main advances in the field related to architectures for complex input data and power-of-two FFT sizes. Furthermore, the paper is intended to be educational, so that the reader can learn how the architectures work. Finally, the paper divides the architectures into serial and parallel. This classification puts together those architectures that are conceived for a similar purpose and, therefore, are comparable.

This paper presents a theory for the power transfer efficiency of printed circuit board coils to integrated circuit coils, with focus on load-dependence for low-power single-chip systems. The theory is verified with electromagnetic simulations modelled on a 350 nm CMOS process which in turn are verified by measurements on manufactured integrated circuits. The power transfer efficiency is evaluated by on-chip rectification of a 151 MHz signal transmitted by a spiral coil on a printed circuit board at 10 mm of separation to an on-chip coil. Such an approach avoids the influence of off-chip parasitic elements such as bond wires, which would reduce the accuracy of the evaluation. It is found that there is a lower limit for the load below which reducing the power consumption of on-chip circuits yield no increase in voltage generated at the load. For the examined process technology, this limit appears to lie around 56 k$$\Omega$$ Ω . The paper is focused on the analysis and verification of the theory behind this limit. We relate the results presented in this work to the application of wireless single-chip temperature monitoring of power semiconductors and conclude that such a system would be compatible with this limit.

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.

The paper presents a method of optimizing frequency characteristics of filter banks in terms of their implementation in digital CMOS technologies in nanoscale. Usability of such filters is demonstrated by frequency-interleaved (FI) analog-to-digital converters (ADC). An analysis filter present in these converters was designed in switched-current technique. However, due to huge technological pitch of standard digital CMOS process in nanoscale, its characteristics substantially deviate from the required ones. NANO-studio environment presented in the paper allows adjustment, with transistor channel sizes as optimization parameters. The same environment is used at designing a digital synthesis filter, whereas optimization parameters are input and output conductances, gyration transconductances and capacitances of a prototype circuit. Transition between analog s and digital z domains is done by means of bilinear transformation. Assuming a lossless gyrator-capacitor (gC) multiport network as a prototype circuit, both for analysis and synthesis filter banks in FI ADC, is an implementation of the strategy to design filters with low sensitivity to parameter changes. An additional advantage is designing the synthesis filter as stable infinite impulse response (IIR) instead of commonly used finite impulse response (FIR) filters. It provides several dozen-fold saving in the number of applied multipliers.. The analysis and synthesis filters in FI ADC are implemented as filter pairs. An additional example of three-filter bank demonstrates versatility of NANO-studio software.

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 Coronavirus disease (COVID-19) is an infectious disease caused by the newly discovered Severe Acute Respiratory Syndrome Coronavirus two (SARS-CoV-2). Most of the people do not have the acquired immunity to fight this virus. There is no specific treatment or medicine to cure the disease. The effects of this disease appear to vary from individual to individual, right from mild cough, fever to respiratory disease. It also leads to mortality in many people. As the virus has a very rapid transmission rate, the entire world is in distress. The control and prevention of this disease has evolved as an urgent and critical issue to be addressed through technological solutions. The Healthcare industry therefore needs support from the domain of artificial intelligence (AI). AI has the inherent capability of imitating the human brain and assisting in decision-making support by automatically learning from input data. It can process huge amounts of data quickly without getting tiresome and making errors. AI technologies and tools significantly relieve the burden of healthcare professionals. In this paper, we review the critical role of AI in responding to different research challenges around the COVID-19 crisis. A sample implementation of a powerful probabilistic machine learning (ML) algorithm for assessment of risk levels of individuals is incorporated in this paper. Other pertinent application areas such as surveillance of people and hotspots, mortality prediction, diagnosis, prognostic assistance, drug repurposing and discovery of protein structure, and vaccine are presented. The paper also describes various challenges that are associated with the implementation of AI-based tools and solutions for practical use.

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.

The number of mobile applications downloaded recently from Google stores increase steadily. It is believed that mobile applications market revenues will grow fast and receive more attention from software developers. Even though, hardware technologies and operating systems have made a big contribution to improve the battery capacity, power consumption is one of the most important and crucial non-functional properties of mobile applications. Many approaches have been dedicated for analyzing and optimizing the power consumption at different levels. This paper proposes a method for analyzing energy leaks of Android application at the design phase. First, it introduces a formalization of an event-based power consumption model. After that, the paper presents a method to translate this model to Event-B notations. Based on the target model, it is able to check if the application leads to energy leaks or violates the power consumption constraints based on formal proofs. Finally, the motivating examples are shown for the illustration purpose.

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.

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.

The Internet of Things is a rapidly evolving technology in which interconnected computing devices and sensors share data over the network to decipher different problems and deliver new services. For example, IoT is the key enabling technology for smart homes. Smart home technology provides many facilities to users like temperature monitoring, smoke detection, automatic light control, smart locks, etc. However, it also opens the door to new set of security and privacy issues, for example, the private data of users can be accessed by taking control over surveillance devices or activating false fire alarms, etc. These challenges make smart homes feeble to various types of security attacks and people are reluctant to adopt this technology due to the security issues. In this survey paper, we throw light on IoT, how IoT is growing, objects and their specifications, the layered structure of the IoT environment, and various security challenges for each layer that occur in the smart home. This paper not only presents the challenges and issues that emerge in IoT-based smart homes but also presents some solutions that would help to overcome these security challenges.

Extracting shape-related features from a given query subsequence is a crucial preprocessing step for chart pattern matching in rule-based, template-based and hybrid pattern classification methods. The extracted features can significantly influence the accuracy of pattern recognition tasks during the data mining process. Although shape-related features are widely used for chart pattern matching in financial time series, the intrinsic properties of these features and their relationships to the patterns are rarely investigated in research community. This paper aims to formally identify shape-related features used in chart patterns and investigates their impact on chart pattern classifications in financial time series. In this paper, we describe a comprehensive analysis of 14 shape-related features which can be used to classify 41 known chart patterns in technical analysis domain. In order to evaluate their effectiveness, shape-related features are then translated into rules for chart pattern classification. We perform extensive experiments on real datasets containing historical price data of 24 stocks/indices to analyze the effectiveness of the rules. Experimental results reveal that the features put forward in this paper can be effectively used for recognizing chart patterns in financial time series. Our analysis also reveals that high-level features can be hierarchically composed from low-level features. Hierarchical composition allows construction of complex chart patterns from features identified in this paper. We hope that the features identified in this paper can be used as a reference model for the future research in chart pattern analysis.

The multi-dimensional characteristics of public opinion in online education lead to the difficulty of data cross-dimensional mining. To solve this problem, this paper designs a cross-dimensional data mining model of public opinion in online education based on fuzzy association rules. Based on the public opinion subject, object, and ontology to analyze the characteristics of public opinion in online education, Yaahp software is used to calculate the influence factor weight of public opinion in online education. According to the weight analysis results, the relationship between the dimensions of various public opinion data is clarified by using data semantic association. This paper introduces the fuzzy set theory into the database and uses crawlers to obtain public opinion data and stores them in the database, to complete the data preprocessing through distributed text preprocessing, feature selection distributed computing, and text vectorization distributed computing. Taking the cloud computing platform as the core, the cross-dimension mining model of public opinion in online education data is constructed according to the dimension correlation analysis and preprocessing results. The simulation results show that the model has the advantages of wide range, fast speed, and high accuracy, and can provide data support for education reform.

The main goal of this paper is to design an efficient 2D FIR digital filter for digital image processing and digital signal processing applications. To optimize filter speed, area and power different multipliers like array, Wallace tree, Booth and Vedic are used in the design of filters. Among these multipliers, Vedic multiplier reduces the partial products in multiplication. This increases the speed of the multiplication process. Vedic multiplier is based on ancient mathematics and uses a sutra called “Urdhva Tiryabhyam”. This paper, proposes two methods to optimize speed, area and power. The first method uses a predictor block in Vedic multiplier. This helps in predicting the outputs with the help of previous outputs. In the second method Reusable Logic blocks are used in Vedic multiplier. This method occupies only less number of LUTs and slice registers. The 2D FIR digital filter is designed and implemented using the two proposed multipliers and the state of art multipliers. The experimental results show that the 2D FIR digital filter using Vedic multiplier with predictor has 71.07% less EDP and using Vedic multiplier with Reusable Logic has 89.5% less EDP compared to conventional Vedic multiplier. The 2D FIR filter designed using Verilog HDL, synthesized, simulated and implemented using Xilinx FPGA. Comparison of simulation results show that 2D FIR filter implemented using the proposed methods optimizes the speed, area and power of the filter.

In order to suppress leakage current caused in the traditional multi-cells series Li-ion battery pack protection system, a new battery voltage transfer method is presented in this paper, which uses the current generated in the transfer process of one of the batteries to compensate for the leakage of itself and other cells except the top cell. Based on the 0.18 µm Bipolar and CMOS and DMOS technology, this proposed circuit has been integrated into a 7-cells Li-ion battery charge and discharge protection chip successfully. The experimental results of cell balancing confirm that the balancing algorithm can help with the shortage of the voltage transfer circuit. Furthermore, analysis of 20 samples shows that the new method can greatly suppress the battery leakage. Compared with the traditional solution, the voltage transfer circuit proposed in this paper greatly reduces the leakage current, which helps to extend the service life of the battery.

OCR (Optical Character Recognition) for scanned paper invoices is very challenging due to the variability of 19 invoice layouts, different information fields, large data tables, and low scanning quality. In this case, table structure recognition is a critical task in which all rows, columns, and cells must be accurately positioned and extracted. Existing methods such as DeepDeSRT only dealt with high-quality born-digital images (e.g., PDF) with low noise and apparent table structure. This paper proposes an efficient method called CluSTi (Clustering method for recognition of the Structure of Tables in invoice scanned Images). The contributions of CluSTi are three-fold. Firstly, it removes heavy noises in the table images using a clustering algorithm. Secondly, it extracts all text boxes using state-of-the-art text recognition. Thirdly, based on the horizontal and vertical clustering algorithm with optimized parameters, CluSTi groups the text boxes into their correct rows and columns, respectively. The method was evaluated on three datasets: i) 397 public scanned images; ii) 193 PDF document images from ICDAR 2013 competition dataset; and iii) 281 PDF document images from ICDAR 2019’s numeric tables. The evaluation results showed that CluSTi achieved an F1-score of 87.5%, 98.5%, and 94.5%, respectively. Our method also outperformed DeepDeSRT with an F1-score of 91.44% on only 34 images from the ICDAR 2013 competition dataset. To the best of our knowledge, CluSTi is the first method to tackle the table structure recognition problem on scanned images.

The rapid development of information technology accelerates the modernization of distance education. To realize the unified organization and management of learning resources as well as improve the utilization rate of resources, this paper proposes the design of a mobile online education resource sharing system from the perspective of man-machine cooperation. This paper analyzes the main body and scope of collaboration, constructs a man-machine collaborative resource sharing model with large-scale man-machine cooperation as the main model; defines the system design principle, comprehensively considers the software layering idea and user usage, and determines the overall framework including client, presentation layer and business logic layer; divides resource sharing into three stages: production, registration, audit and release, and uses “centralized management”, to protect the intellectual property rights of distance education resources. The role access control mechanism is used to manage user rights, and the settlement incentive algorithm is introduced to protect the intellectual property rights of distance education resources. Our experimental results show that the system can be used as a key technology to promote the development of distance education information, improve the efficiency of resource sharing, and enhance the enthusiasm of users to contribute educational resources.

Arithmetic logic unit (ALU), a core component of a processor, is one of the thrust areas of the current research. Presently, ALU is designed by transistor-based CMOS technique and its individual components are placed in different layers. The current design is affected by the limitations of Moore’s law and design complexity. At present, ‘Quantum cellular automata electro-spin (QCA-ES)’ technology is widely accepted technology as an alternative of ‘CMOS’ to minimize the above discussed problems. In this research paper, the design of a novel multilayer portable, dynamic, fault-tolerant, power-efficient, thermally stable reversible ALU is proposed which is explored through QCA-ES. All the arithmetic and logical components of ALU are separately placed in different layers. Area density, delay, fault tolerance and thermal stability are investigated. A specific type of gate, known as reversible gate (modified 3:3 ‘TSG’ gate), is used in this proposed design with QCA technology to get the optimized design ALU with low occupied area, complexity, delay and power dissipation. Investigation of a fault-free design and saturated amplitude level (of output) change with respect to temperature increment in the proposed device are also discussed in this paper. Not only the thermal stability (up to 6 k temperature) but also an investigation on cell complexity of the 100% fault-free (against multiple cell-omission, cell-displacement, cell-orientation change and extra cell deposition), multilayer nano-device is represented in this work. ‘QCA-Designer’ software is used in this research work to design and develop layout of the proposed components in quantum-field and find out the occupied area, delay and complexity of proposed design. ‘QCA-Pro’ software is used for getting the value of dissipated power.

In practical applications of the motor imagery-based brain–computer interface (BCI) system, the differences in electroencephalogram (EEG) signal manifestation and corresponding rhythm ranges in different individuals pose a significant challenge. The corresponding EEG features in different frequency bands differ; therefore, personalized screening must be conducted to obtain information that is conducive to the classification of EEG signals for different motor imageries. Also, in current BCI system, to obtain more information, multi-channel electrodes are often used to collect EEG signals, but also increasing the complexity of calculation. In this paper, a personalized feature extraction method based on filter bank and elastic net and a personalized channel selection based on Deep Belief Network to obtain a classification accuracy similar to or even higher than using all channels is proposed. Compared with the typically used feature extraction and classification algorithms, this method obtains higher calculation rates and recognition accuracy and provides a theoretical reference for the practical application of BCI systems. The shortcomings of the common spatial pattern (CSP) algorithm are addressed. The major contribution of this paper is the flexible screening of feature vectors and channels containing more classification information based on individual differences, thereby preventing the manual adjustment of specific frequency ranges in traditional CSP for performing feature extraction and avoiding inputting all channels. In the case study, the highest test accuracy reaches 86.94%.

With the development for the technology of mobile edge computing (MEC) and the grave situation for the shortage of global energy, the problem of computation offloading in a cloud computing framework is getting more attention by network managers. In order to improve the experience quality of users and increase the energy efficiency of the system, we focus on the issue of task offloading strategy in MEC system. In this paper, we propose a task offloading strategy in the MEC system with a heterogeneous edge. By considering the execution and transmission of tasks under the task offloading strategy, we present an architecture for the MEC system. We establish a system model composed of M/M/1, M/M/c and M/M/ $$\infty$$ queues to capture the execution process of tasks in local mobile device (MD), MEC server and remote cloud servers, respectively. Moreover, by trading off the average delay of tasks, the energy consumption level of the MD and the offloading expend of the system, we construct a cost function for serving one task and formulate a joint optimization problem for the task offloading strategy accordingly. Furthermore, under the constraints of steady state and proportion scope, we use the Lagrangian function and the corresponding Karush–Kuhn–Tucker (KKT) condition to obtain the optimal task offloading strategy with the minimum system cost. Finally, we carry out numerical experiments on the MEC system to investigate the influence of system parameters on the task offloading strategy and to obtain the optimal results. The experiment results show that the task offloading strategy proposed in this paper can balance the average delay, the energy consumption level and the offloading expend with the optimal allocation ratio.

Network monitoring has been traditionally conducted using Simple Network Management Protocol (SNMP): a network monitoring protocol that allows network administrators to keep track of every node in the network and ensure that it behaves correctly. This paper presents the Constrained Monitored Protocol (CoMP): a lightweight resource-efficient alternative to SNMP that targets the low-end devices of the Internet of Things (IoT). These devices are characterised by severe resource constraints in terms of memory, processing power, and bandwidth. Moreover, they are often energy-constrained as well, powered either by small batteries or energy harvesting. While SNMP does work with these devices, it has an unnecessary overhead resulting in a waste of resources that could otherwise be used for some other task, or to save energy. Furthermore, this paper proposes a cross-protocol CoMP-SNMP proxy that operates at the border router of the resource-constrained network and enables the efficient monitoring of resource-constrained IoT devices using CoMP from existing SNMP-based network monitoring infrastructures.

In this information age, with the emergence of organizations, the number of various resources on the Internet of Things is also increasing. Generally, different users have different access permissions to different resources and most of the existing schemes have realized access control. But most of them are rough and not feasible in many organizations. Moreover, traditional access control schemes adopted a central entity or a trusted third party to centrally manage users’ permissions and access information, which can easily lead to single point of failure. Therefore, a kind of secure, trusted, and fine-grained access control is urgently needed in some large-scale organizations or institutions that maintain thousands of IoT devices. In order to solve the above problems, in this paper, we propose a blockchain-based high-efficiency access control framework called BHE-AC to achieve secure and efficient access to resources for users. In BHE-AC, a registration model is designed to register users and resources, and it can evaluate the ability value for users according to their attributes which is an essential factor when requesting resources. Besides, we represent a blockchain-based token requesting mechanism to compare the capability values of users with requested resources; meanwhile, the mechanism also allows users to request multiple resources at the same time, which can avoid repeated requests. Then, an unforgeable token indicates the users’ access to a specific resource is granted to a requester only if a requester meets the access requirements; from then on, the user can use a token to invoke the resource. Finally, security analysis and experiments of our framework are given in our paper. The experimental results show that BHE-AC can achieve low cost and efficient access.

Automatic classification of musical instruments from audio relies heavily on datasets of acoustic recordings of the instruments to train models of those instruments. To do this, precise labels of the instrument’s events are mandatory. Also, it is very difficult to obtain such labels, especially in polyphonic performances. OpenMic-2018 is a polyphonic dataset created specifically with the aim to train instrument models. However, this dataset is based on weak and incomplete labels. The automatic classification of sound events, based on the VGGish bottleneck layer as proposed before by the AudioSet, implies the classification of only one second at a time, making it hard to find the label of that exact moment. To answer this question, this paper proposes PureMIC, a new strongly labeled dataset (SLD) that isolates 1000 single instrument clips manually labeled. Moreover, the proposed model classifies clips over time and also enhances the labeling robustness of a high number of unlabeled samples in OpenMIC-2018 due to its ability of classification over time. In the paper we disambiguate and report the automatic labeling of previously unlabeled samples. The proposed new labels achieve a mean average precision (mAP) of 0.701 for OpenMIC test data, outperforming its baseline (0.66). The code is released online so that the research community can replicate and follow the proposed implementation.

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.

This paper presents a wide frequency range three-stage voltage-controlled ring oscillator in CNTFET technology. The advantages of CNTFETs are the high speed of charge carriers, high signal to noise ratio, small size and ballistic transport. Therefore in comparison with MOSFETs, they have a higher frequency, and can operate at a wide frequency range with a very low phase noise if forward bulk bias and active inductor techniques are simultaneously used in the oscillators that employ CNTFETs. In this paper, the Stanford CNTFET model is implemented in Verilog-A, and the proposed CNT ring oscillator is simulated using ADS software over the 50–500 GHz frequency range. The phase noise of the oscillator is − 136 dBc/Hz at 1 MHz offset, which is suitable for PLL applications.

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.