Search

In this paper, the implementation of artificial neural networks (multilayer perceptron [MLP] and radial base functions [RBF]) and the upgraded Markov chain model have been studied and performed to identify the human behavior patterns during rock, paper, and scissors game. The main motivation of this research is the design and construction of an intelligent robot with the ability to defeat a human opponent. MATLAB software has been used to implement intelligent algorithms. After implementing the algorithms, their effectiveness in detecting human behavior pattern has been investigated. To ensure the ideal performance of the implemented model, each player played with the desired algorithms in three different stages. The results showed that the percentage of winning computer with MLP and RBF neural networks and upgraded Markov model, on average in men and women is 59%, 76.66%, and 75%, respectively. Obtained results clearly indicate a very good performance of the RBF neural network and the upgraded Markov model in the mental modeling of the human opponent in the game of rock, paper, and scissors. In the end, the designed game has been employed in both hardware and software which include the Zana intelligent robot and a digital version with a graphical user interface design on the stand. To the best knowledge of the authors, the precision of novel presented method for determining human behavior patterns was the highest precision among all of the previous studies.

Examination is a vital role to measure the capabilities of students in their learning. Hence, generating question paper in an effective way is a decisive job for educators in educational institution. Using traditional method, it is monotonous and time consuming. Today, Autonomous Examination Paper (AEP) is used to produce exam paper. Many researchers have proposed effective AEPs to be used by educators. This paper aims to investigate about AEP development and to construct AEP in UiTM Cawangan Perlis. As a result, Ad-Hoc Question Paper Application (AQPA) has been developed using Fisher-Yates algorithm to generate questions for exam paper in the university. Evaluation based on Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) reveal that lecturers in the university manage to interact with AQPA and willing to use it as a tool to minimize their workload. However, more improvement must be done on AQPA to be an effective AEP. To conclude, AEP brings significance to educators and can be improved with the latest technology.

Paper waste in the mockups design with regular, irregular, and amorphous patterns is a critical problem in digital printing presses. Paper waste reduction directly impacts production costs, generating business and environmental benefits. This problem can be mapped to the two-dimensional irregular bin-packing problem. In this paper, an iterated local search algorithm using a novel neighborhood structure to detect overlaps between amorphous shapes is introduced. This algorithm is used to solve the paper waste problem, modeled as one 2D irregular bin-packing problem. The experimental results show that this approach works efficiently and effectively to detect and correct the overlaps between regular, irregular, and amorphous figures.

Imperfect Channel State Information at the Transmitter (CSIT) is inevitable in modern wireless communication networks, and results in severe multi-user interference in multi-antenna Broadcast Channel (BC). While the capacity of the multi-antenna (Gaussian) BC with perfect CSIT is known and achieved by Dirty Paper Coding (DPC), the capacity and the capacity-achieving strategy of the multi-antenna BC with imperfect CSIT remain unknown. Conventional approaches therefore rely on applying communication strategies designed for perfect CSIT to the imperfect CSIT setting. In this work, we break this conventional routine and make two major contributions. First, we show that linearly precoded Rate-Splitting (RS), relying on the split of messages into common and private parts and linear precoding at the transmitter, and successive interference cancellation at the receivers, can achieve larger rate regions than DPC in multi-antenna BC with partial CSIT. Second, we propose a novel achievable scheme, denoted as Dirty Paper Coded Rate-Splitting (DPCRS), that relies on RS to split the user messages into common and private parts, and DPC to encode the private parts. We show that the rate region achieved by DPCRS in Multiple-Input Single-Output (MISO) BC with partial CSIT is enlarged beyond that of conventional DPC and that of linearly precoded RS. Gaining benefits from the capability of RS to partially decode the interference and partially treat interference as noise, DPCRS is less sensitive to CSIT inaccuracies, networks loads and user deployments compared with DPC and other existing transmission strategies. The proposed DPCRS acts as a new benchmark and the best-known achievable strategy for multi-antenna BC with partial CSIT., accepted by IEEE transactions on communications for publication

Machine learning, a branch of artificial intelligence, is increasingly used in health research, including nursing and maternal outcomes research. Machine learning algorithms are complex and involve statistics and terminology that are not common in health research. The purpose of this methods paper is to describe three machine learning algorithms in detail and provide an example of their use in maternal outcomes research. The three algorithms, classification and regression trees, least absolute shrinkage and selection operator, and random forest, may be used to understand risk groups, select variables for a model, and rank variables’ contribution to an outcome, respectively. While machine learning has plenty to contribute to health research, it also has some drawbacks, and these are discussed as well. In order to provide an example of the different algorithms’ function, they were used on a completed cross-sectional study examining the association of oxytocin total dose exposure with primary cesarean section. The results of the algorithms are compared to what was done or found using more traditional methods.

Although fabrication of colorimetric paper-based analytical devices (PADs) has drawn increasing attention recently, the signal readout method is still a crucial challenge on the way to practical exploitations. We herein introduce an integration of digital image processing with a model PAD for easing and improving the signal readout procedure. The colorimetric detection mechanism of PAD relies on in-situ induced yellowish silver nanoparticles via the interaction of silver ions, poly(vinyl alcohol), and ammonia with isoniazid. The observed color value is related with the concentration of isoniazid. The proposed algorithm is based on mathematical morphology recognition, and minimizes the errors arising from manual area selection. Besides, it allows the recognition of both circle and square shapes in 96-well plate and A4 size array designs. Since this algorithm automatically provides the blank-corrected numerical matrixes and image profiles of red, green and blue channels, further investigations such as outlier classification, construction of regression/prediction models and calculation of detection limit can be easily performed. Comparison of signal readout results of the developed algorithm with ImageJ software demonstrates significant improvements in analysis speed, reproducibility, accuracy and color values. Therefore, application of the proposed algorithm is promising as a robust technique for practical applications.

At ASIACRYPT 2018, Castryck, Lange, Martindale, Panny and Renes proposed CSIDH, which is a key-exchange protocol based on isogenies between elliptic curves, and a candidate for post-quantum cryptography. However, the implementation by Castryck et al. is not constant-time. Specifically, a part of the secret key could be recovered by the side-channel attacks. Recently, Meyer, Campos, and Reith proposed a constant-time implementation of CSIDH by introducing dummy isogenies and taking secret exponents only from intervals of non-negative integers. Their non-negative intervals make the calculation cost of their implementation of CSIDH twice that of the worst case of the standard (variable-time) implementation of CSIDH. In this paper, we propose a more efficient constant-time algorithm that takes secret exponents from intervals symmetric with respect to the zero. For using these intervals, we need to keep two torsion points on an elliptic curve and calculation for these points. We implemented our algorithm by extending the implementation in C of Meyer et al. (originally from Castryck et al.). Then our implementation achieved 152.8 million clock cycles, which is about 29.03% faster than that of Meyer et al.

Background Electrocardiograms (ECGs) are routinely recorded and stored in a variety of paper or scanned image format. Current ECG recording machines record ECG on graph paper, also it provides digitized ECG signal along with automated cardiovascular diagnosis (CVD). However, such recording machines cannot analyse preserved paper ECG records as it requires input in terms of digitized signal. Therefore, it is important to extract ECG signal from these preserved paper ECG records using digitization method. There are different paper degradations that adversely affect digitization process. The purpose of this work is to perform an image enhancement and digitization of the degraded ECG images to extract continuous ECG signal. Methods In this paper, we propose entropy-based bit plane slicing (EBPS) algorithm in which pre-processing is done using dominant color detection and local bit plane slicing. Maximum entropy based adaptive bit plane selection is applied to the pre-processed image. Discontinuous ECG correction (DECGC) is then done to produce continuous ECG signal. Results The algorithm is tested on 836 different degraded paper ECG records obtained from various diagnostic centers. After analysis with 101 known ground truth ECG signals the accuracy, sensitivity, specificity and overall F-measure of ECG is 99.42%, 99.69%, 99.81% and 99.26% respectively. The RMS error and correlation between the extracted digitized signal and ground truth for 101 cases is 0.040 and 99.89% respectively. Conclusions The EBPS method is able to remove all types of degradation in paper ECG records to generate a uniform digitized signal. Instead of manual measurement and prediction from archived paper ECG records, automated prediction (using already existing cardiovascular diagnosis software) is possible with the help of extracted digitized signal obtained using proposed digitization method, which will also help retrospective cardiovascular analysis.

Analysis of images with deep learning and machine learning has recently show more accuracy in medical image processing. The paper review the major machine learning methods is used for the extraction of retinal fundus image. It describes how hardware has been modified according to time for retinal eye segmentation that device is helpful for the environment and also useful for humans The algorithms used for image extraction, clear and noise-free Images of different color scales are used to improve image quality. The paper gives a review of how researchers and scientists have been trying different methods to improve vessel segmentation in the future.

The aim of this concept paper is the description of a new tool to support institutions in the implementation of targeted countermeasures, based on quantitative and multi-scale elements, for the fight and prevention of emergencies, such as the current COVID-19 pandemic. The tool is a cloud-based centralized system, a multi-user platform that relies on artificial intelligence (AI) algorithms for the processing of heterogeneous data, which can produce as an output the level of risk. The model includes a specific neural network which is first trained to learn the correlations between selected inputs, related to the case of interest: environmental variables (chemical&ndash, physical, such as meteorological), human activity (such as traffic and crowding), level of pollution (in particular the concentration of particulate matter) and epidemiological variables related to the evolution of the contagion. The tool realized in the first phase of the project will serve later both as a decision support system (DSS) with predictive capacity, when fed by the actual measured data, and as a simulation bench performing the tuning of certain input values, to identify which of them led to a decrease in the degree of risk. In this way, we aimed to design different scenarios to compare different restrictive strategies and the actual expected benefits, to adopt measures sized to the actual needs, adapted to the specific areas of analysis and useful for safeguarding human health, and we compared the economic and social impacts of the choices. Although ours is a concept paper, some preliminary analyses have been shown, and two different case studies are presented, whose results have highlighted a correlation between NO2, mobility and COVID-19 data. However, given the complexity of the virus diffusion mechanism, linked to air pollutants but also to many other factors, these preliminary studies confirmed the need, on the one hand, to carry out more in-depth analyses, and on the other, to use AI algorithms to capture the hidden relationships among the huge amounts of data to process.

Intrusion Detection System (IDS) has been an effective way to achieve higher security in detecting malicious activities for the past couple of years. Anomaly detection is an intrusion detection system. Current anomaly detection is often associated with high false alarm rates and only moderate accuracy and detection rates because it’s unable to detect all types of attacks correctly. An experiment is carried out to evaluate the performance of the different machine learning algorithms using KDD-99 Cup and NSL-KDD datasets. Results show which approach has performed better in term of accuracy, detection rate with reasonable false alarm rate.

This article describes how with the advent of computer-based technology, there is movement from manual to automated systems for different aspects of the education system. Testing is an essential part of teaching process that helps academics in classifying the level of students and evaluating the outcomes of their teaching process. The testing process requires a large amount of attention and professionalism. Automated Test Paper Generation is a system applying the shuffling algorithm in designing different sets of questions without repetition and duplication. It helps the faculty in developing and designing exams with a particular level of difficulty required in evaluating the students by using the utility-based agent. The system includes a knowledge base of many questions' types that are linked to a test engine where the faculty can specify the type and the difficulty level of the exam and then the system will assemble the exam and produce the output as electronic or paper-based. Questions will be picked randomly from the knowledge database. This automated system provides cost saving and time efficient solutions.

Multivariate cryptography plays an important role in post-quantum cryptography. Many signature schemes, such as Rainbow remain secure despite the development of several attempted attack algorithms. However, most multivariate signature schemes use relatively large public keys compared with those of other post-quantum signature schemes. In this paper, we present an approach for constructing a multivariate signature scheme based on matrix multiplication. At the same security level, our proposed signature scheme has smaller public key and signature sizes compared with the Rainbow signature scheme.

Free-falling paper shapes exhibit rich, complex and varied behaviours that are extremely challenging to model analytically. Physical experimentation aids in system understanding, but is time-consuming, sensitive to initial conditions and reliant on subjective visual behavioural classification. In this study, robotics, computer vision and machine learning are used to autonomously fabricate, drop, analyse and classify the behaviours of hundreds of shapes. The system is validated by reproducing results for falling discs, which exhibit four falling styles: tumbling, chaotic, steady and periodic. A previously determined mapping from a non-dimensional parameter space to behaviour groups is shown to be consistent with these new experiments for tumbling and chaotic behaviours. However, steady or periodic behaviours are observed in previously unseen areas of the parameter space. More complex hexagon, square and cross shapes are investigated, showing that the non-dimensional parameter space generalizes to these shapes. The system highlights the potential of robotics for the investigation of complex physical systems, of which falling paper is one example, and provides a template for future investigation of such systems. The dynamics of paper shapes in free fall are still not fully understood, despite being discussed for more than 150 years. Collecting large amounts of data has the potential to give us new insights and a robotics system could generate and analyse data in large quantities.

In this paper, we investigate the fast downlink beamforming for full-dimension multiple-input multiple-output systems under correlated Rician channels. Under the assumption that the base station (BS) has only statistical channel state information (CSI), we decouple each user’s beamforming vector and derive their optimal beamforming vector through the maximization of the average signal-to-leakage-plus-noise ratio (SLNR) lower bound. Then, to reduce the computation time, a model-driven deep learning (DL)-based beamforming algorithm is proposed, as well as a data-driven algoriothm for comparison. In the model-driven DL-based beamforming algorithm, the process of obtaining the beamforming vector is separated into two parallel neural networks which are constructed and trained independently. The proposed algorithms can achieve similar ergodic rate as the optimal beamforming algorithm with much less computation time, and the model-driven algorithm requires less computing resource than the data-driven algorithm.

Academic research is crucial to the development of science and technology and is an important factor that affects national strength. When writing an academic research paper, a rhetorical structure is typically used to present the paper’s ideas, but this task is quite difficult for junior researchers. To solve this problem, some studies have adopted text mining to assist with the writing, but the existing methods still require human intervention to generate sentences. Recently, due to the increasing maturity of deep learning technology and the ability to address the problem of automatic text generation, progress has been made in this area. The highly complex deep learning operations can correctly generate sequences and find correlations between sequences. When a user provides a few keywords and key sentences, the proposed algorithm can generate an introduction section for the user. The results show that the generated introduction is more coherent, clearer, and more fluent than existing summarization methods. In addition, the method proposed in this study improves the accuracy compared with traditional text extraction methods. The manuscript produced by this study has been evaluated to show that the study can produce a comprehensive introduction compared with previous studies.

Recently, the vector dirty paper coding (DPC) achievable rate region has been shown to be the capacity region of a multiple-input multiple-output Gaussian broadcast channel (MIMO GBC). With DPC, the multiuser interference noncausally known at the transmitter can be completely removed. In this paper, we present a vector DPC structure for MIMO GBC. It is a generalization of the single antenna superposition dirty paper coding for the scalar Gaussian dirty paper problem proposed by Bennatan et al. In a theoretical random code setting, this construction is shown to be able to achieve the promised rate performance of the MIMO GBC. We also implement it with existing vector quantizer and capacity-achieving channel coding. Combined with iterative decoding, a design example validates the effectiveness of our methods.

A generalization of the problem of writing on dirty paper is considered in which one transmitter sends a common message to multiple receivers. Each receiver experiences on its link an additive interference (in addition to the additive noise), which is known noncausally to the transmitter but not to any of the receivers. Applications range from wireless multi-antenna multicasting to robust dirty paper coding. We develop results for memoryless channels in Gaussian and binary special cases. In most cases, we observe that the availability of side information at the transmitter increases capacity relative to systems without such side information, and that the lack of side information at the receivers decreases capacity relative to systems with such side information. For the noiseless binary case, we establish the capacity when there are two receivers. When there are many receivers, we show that the transmitter side information provides a vanishingly small benefit. When the interference is large and independent across the users, we show that time sharing is optimal. For the Gaussian case we present a coding scheme and establish its optimality in the high signal-to-interference-plus-noise limit when there are two receivers. When the interference is large and independent across users we show that time-sharing is again optimal. Connections to the problem of robust dirty paper coding are also discussed., Comment: To appear, IT. Trans. on Information Theory. Modified to incorporate reviewer's comments. Also includes some additional results for the Gaussian case

Reversible computing is a version of computing in which the computational system to a degree is reversible. An essential circumstance for reversibility of a computational version is that the relation of the mapping states of transition features to their successors have to always be one-to-one. Reversible logic has emerged as an change design technique to the conventional logic, ensuing in decrease energy consumption and lesser circuit area. In this paper, a review of an efficient architecture of the reversible FIR filter structure is presented. For reaching low electricity, reversible good judgment mode of operation is implemented inside the design. Vicinity overhead is the tradeoff inside the proposed layout. From the synthesis consequences, the proposed low electricity FIR filter architecture offers power saving when as compared to the conventional layout. The vicinity overhead is for the proposed structure.

Recycling wastewater of the pulping and paper-making industry are widely considered for clean production, which heavily rely on the timely and accurate monitoring in paper-making wastewater treatment processes. A novel predicting model based on genetic-deep belief network algorithm was proposed to improve the predictive accuracy and reliability for process monitoring. Considering the deep belief networks (DBN) as a deep learning model is aiming to describe the relationship among variables in a complex process modeling, genetic algorithm (GA) was employed to reduce the input variables dimensionality, simplify the network structure and overcome the dynamic characteristic difficulties of process data in monitoring. Compared with DBN and back propagation neural network (BPNN), the GA-DBN effectively achieved a better predictive accuracy than other tests models in complex wastewater treatment processes. The value of the coefficient of determination of GA-DBN model is increased by 1.71–1.86% and 5.21–9.32%, respectively. The GA-DBN model can be structured with fewer variables or samples to describe the complex paper-making wastewater treatment process, obtaining the better model performance and predictive accuracy.

In this paper, we study a multiple access monostatic backscatter communication (BackCom) system, where one reader talks to multiple backscatter nodes (BNs). We propose to use nonorthogonal multiple access (NOMA) to improve the efficiency of BackCom system. Specifically, the reader uses the feedback signal during the training phase to separate the multiple BNs into two groups. The reader then randomly pairs the BNs from these two groups to implement NOMA. We propose to set the reflection coefficients for these two groups differently to further distinguish the reflected signal power for the paired BNs. To characterize the network performance, we derive an analytical expression for the success rate which shows the percentage of bits that can be successfully decoded in one time slot. We also present the design guideline of the reflection coefficients for the two groups to maximize the system performance. Our numerical results show that increasing the readers transmit power has little impact on the system performance when the channel condition on the transmission link is less severe. Instead, the proper choice of the reflection coefficients for the two groups of BNs can greatly enhance the BackCom system.

In recent years, ultra-broadband communication using densification of millimeter-wave (mmWave) smallcells has attracted attention owing to its ability to accommodate increasing mobile data traffic. To make full use of mmWave access, we need to deploy ultra-broadband backhauling lines such as 10Gbps Ethernet everywhere at the expense of very high installation costs. The mmWave Edge Cloud system proposed in this paper is a solution to this problem. Based on achievable user context information e.g. mobility and traffic demand, user data can be transmitted and stored in storage (prefetching) installed at the smallcell site in advance. By using this, mmWave access can bring out sufficient performance even with low-capacity backhaul. For the analysis, we have developed a new mobility model, traffic model, and prefetching algorithm. Numerical results showed that the proposed method with 1Gbps-backhaul and prefetching can achieve 95% of the ideal system rate while reduce the average access latency by a factor of 67% as compared to the case without prefetching.

The objective of this paper was to provide an automatic engine to classify and locate information using natural language. The proposal integrates a set of two algorithms to extract information from different repositories using their own open APIs and creates a knowledge database using a natural language approach using a Bayesian algorithm to classify and a second algorithm to clean the paper. Putting said techniques together derived in a strong alternative which reach common gaps in classification and location of information including avoid the use of the whole paper to get information and not only the information introduced at the moment of upload the paper in the digital library. The proposal was oriented to classify and locate research papers in order to better describe this contribution, however, findings could be applicable to a vast range of scenarios. An adaptation of the popular methodology Crisp-DM was used to evaluate the performance of the algorithm obtaining good results in classifying, searching, and feeding the knowledge base.

Intrusion Detection System (IDS) has been an effective way to achieve higher security in detecting malicious activities for the past couple of years. Anomaly detection is an intrusion detection system. Current anomaly detection is often associated with high false alarm rates and only moderate accuracy and detection rates because it’s unable to detect all types of attacks correctly. An experiment is carried out to evaluate the performance of the different machine learning algorithms using KDD-99 Cup and NSL-KDD datasets. Results show which approach has performed better in term of accuracy, detection rate with reasonable false alarm rate.

With today’s eye-tracking technologies, it is possible to analyze cognitive processes such as reading. There are many studies on that topic. We have studied some of these studies that analyze eye movements recording during reading by various machine learning algorithms.

Most paper-based 3D color printers are plagued by the problem of slow printing speed, which is caused by the double-sided coloration for A4 office paper during the printing process. In order to resolve this issue, we propose a new fast and reliable coloration algorithm using geometric information in STL file, given the existing slicing methods. During the entire process, the colored surface is determined by D value that represents the positive or negative slope of layer in the 3D model after slicing. Colored inks with corresponding permeability are used to print single-sided or double-sided to accelerate the printing speed. For the given 3D models, the efficiency of the speedup is discussed and qualitatively analyzed. The results show that the proposed algorithm can increase the printing speed of paper-based 3D color printers, based on the existing materials and apparatus, at a lower cost than the common approaches.

Traditional data mining algorithms are exceptional at seeing patterns in data that humans cannot, but are often confused by details that are obvious to the organic eye. Algorithms that include humans “in-the-loop” have proved beneficial for accuracy by allowing a user to provide direction in these situations, but the slowness of human interactions causes execution times to increase exponentially. Thus, we seek to formalize frameworks that include humans “over-the-loop”, giving the user an option to intervene when they deem it necessary while not having user feedback be an execution requirement. With this strategy, we hope to increase the accuracy of solutions with minimal losses in execution time. This paper describes our vision of this strategy and associated problems.

The next generation of seismic imaging algorithms will use full wavefield migration, which regards multiple scattering as indispensable information. These algorithms will also include autonomous velocity-updating in the migration process, called joint migration inversion. Full wavefield migration and joint migration inversion address industrial requirements to improve the images of highly complex reservoirs as well as the industrial ambition to produce these images more automatically (automation in seismic processing). In these vision papers on seismic imaging, full wavefield migration and joint migration inversion are formulated in terms of a closed-loop, estimation algorithm that can be physically explained by an iterative double-focusing process (full wavefield Common Focus Point technology). A critical module in this formulation is forward modelling, allowing feedback from the migrated output to the unmigrated input (‘closing the loop’). For this purpose, a full wavefield modelling module has been developed, which uses an operator description of complex geology. Full wavefield modelling is pre-eminently suited to function in the feedback path of a closed-loop migration algorithm. ‘The Future of Seismic Imaging’ is presented as a coherent trilogy of papers that propose the migration framework of the future. In Part I, the theory of full wavefield modelling is explained, showing the fundamental distinction with the finite-difference approach. Full wavefield modelling allows the computation of complex shot records without the specification of velocity and density models. Instead, an operator description of the subsurface is used. The capability of full wavefield modelling is illustrated with examples. Finally, the theory of full wavefield modelling is extended to full wavefield reverse modelling (FWMod−1), which allows accurate estimation of (blended) source properties from (blended) shot records.

The next generation seismic imaging algorithms will consider multiple scattering as indispensable information, referred to as Full Wavefield Migration. In addition, these algorithms will also include autonomous velocity updating in the migration process, referred to as Joint Migration Inversion. Full wavefield migration and joint migration inversion address the industrial needs of improving images of very complex reservoirs as well as the industry ambition of producing these images in a more automatic manner (‘automation in seismic processing’). In this vision paper on seismic imaging, full wavefield migration and joint migration inversion are formulated in terms of a closed-loop, estimation algorithm that can be physically explained by an iterative double focusing process (full wavefield Common-Focus-Point technology). A critical module in this formulation is forward modelling, allowing feedback from migrated output to unmigrated input (‘closing the loop’). For this purpose a full wavefield modelling module has been developed that utilizes an operator description of complex geology. Full wavefield modelling module is pre-eminently suited to function in the feedback path of a closed-loop migration algorithm. ‘The Future of Seismic Imaging’ is presented as a coherent trilogy, proposing in three consecutive parts the migration framework of the future. In part I it was shown that the proposed full wavefield modelling module algorithm differs fundamentally from finite difference modelling, as velocities and densities need not be provided. Instead, full wavefield modelling module uses an operator description of the subsurface. In Part II it was shown how the theory of Primary Wavefield Migration can be extended to Full Wavefield Migration by correcting for elastic transmission effects and by utilizing multiple scattering. In Part III it is shown how the full wavefield migration technology can be extended to Joint Migration Inversion, allowing full wavefield migration of blended data without knowledge of the velocity. Velocities are part of the joint migration inversion output, being obtained by an operator-driven parametric inversion process. The potential of the proposed joint migration inversion algorithm is illustrated with numerical examples.

A novel approach is here presented to embed a low-priority data stream into a high-priority data stream for OFDM wireless communications. The main improvement with respect to conventional hierarchical modulations is that the low-priority data stream can be decoded from the received subcarrier symbols without any previous equalization in case of multipath fading channel. As a consequence, the expected performance is independent of the channel estimation method used to equalize the channel. The low-priority signal is superimposed on the high-priority symbols using a dirty paper coding technique. As a result of the analysis of the proposed method, several improvements to reduce the interference on the high-priority symbols are introduced. The performance of our scheme has been compared with that of a conventional hierarchical modulation using DVB-T system parameters, showing a considerable improvement for multipath fading channels.

Based on the analysis of the types and characteristics of Chinese traditional paper-cut patterns, we propose a new algorithm for extracting and vectoring the patterns. On the contour extraction, the algorithm uses a grayscale, binary, contour tracking method. On the vectorization, we improved the segment Displaced curve segments so that you can make the final contour paper cutting pattern vector into a control point and a curve. Using this algorithm, we can quickly get the vector contour of the pattern of manual cutting. Further, we can carry on the processing, such as filling, deformation, combination, animation control, so as to complete the core function of the paper cutting animation software. The algorithm is also suitable for the extraction and the vector of common bitmap image, although the algorithm is put forward for the particularity of the paper cutting patterns.

Interference pre-cancellation as in the “writing onto dirty paper” channel crucially depends on the transmitter having exact knowledge of the way in which input and channel state combine to produce the channel output. The presence of even a small amount of uncertainty in such knowledge, gravely hampers the ability of the encoder to pre-code its transmissions against the channel state. This is particularly disappointing as it implies that interference pre-coding in practical systems is effective only when the channel estimates have very high precision, a condition which is generally unattainable in wireless environments. In this paper we show that state decoding, instead of state pre-cancellation, can be approximately optimal for a channel with discrete states when only partial channel knowledge is available. More specifically, we consider a variation of the “writing onto dirty paper” channel in which a discrete-valued state sequence is multiplied by a fast fading process and derive conditions on the fading distribution for which state decoding closely approaches capacity. This channel model is a special case of the Gelf'and-Pinsker channel and our results show an instance of this problem in which state decoding is approximately optimal.

Most of the scientific research engines utilize the same way to rank scientific research papers (SRP), this way is highly depend on the citation network. The retrieved results of these engines for any keyword contain old papers with the highest citation value. In this paper, we proposed a new and easy to implement ranking algorithm for scientific research papers known as Paper Time Ranking Algorithm (PTRA). PTRA is a new ranking algorithm that depends on three factors to rank its results; paper age, citation index and publication venue. To construct and prove our ranking algorithm we created a web crawler that crawl different scientific search papers databases in the world to collect the information that PTRA needs. Unfortunately, some of this information was missing, such as, the impact factor of the journals. To collect this information, we created another crawler that search the Internet for these journals impact factors. However, to prove our ranking algorithm results, we have compared PTRA with Google scholar ranking algorithm. We used Google scholar, since it has more than 50 million papers and its own system can gather and collect papers in a faster way. Our comparison results show that, citation index has the highest impact on Google scholars ranking algorithm results. Unlike PTRA that gains the paper age a higher impact on the ranked results. However, PTRA depends on the citation index and publication venue to rank the results but with less impact than the paper age.

(Invited Paper) Abstract—In this paper we present an alternative approach to addressing the problem of scattering reduction for radar targets, which have recently been dealt with by using the Transformation Optics (TO) algorithm which typically calls for the use of Metamaterials (MTMs) that are inherently narrowband, dispersive and highly sensitive to polarization as well as to the incident angle. The present design utilizes realistic lossy materials that can be conveniently fabricated in the laboratory, and are wideband as well as relatively insensitive to polarization and incident angle of the incoming wave. A modified interpretation of the TO algorithm is presented and is employed the design of RCS-reducing absorbers for arbitrarily shaped targets, and not just for canonical shapes, e.g., cylinders, for which cloaks have been designed by using the TO. The paper also briefly examines the topic of performance enhancement of absorbers by using graphene materials and embedded Frequency Structure Surfaces (FSSs). We begin by presenting the design procedure for planar absorbers, and then examine how well those designs perform for arbitrarily-shaped objects. Finally, we discuss how the planar design can be modified by tailoring the material parameters of the coating for specific object shapes. A number of test cases are included as examples to illustrate the application of the proposed design methodology, which is a modification of the classical TO paradigm.

Online examination system is built on internet applications,aimed at achieving information management of examinations. Online examination system in the generation of test papers, the submission and approval can be done automatically on the network. We proposes a new heuristic genetic test paper algorithm, and the proposed algorithm is applied to the online examination system. From the experimental results,the improved algorithm has achieved satisfactory performance.

We study a dirty paper channel model where the input is constrained to belong to a PAM constellation. In particular, we provide lower bounds on the capacity as well as explicit coding schemes for the binary-input dirty- paper channel. We examine the case of causal as well as non-causal side information.

Sensoria is an IST project funded by the EU as Integrated Project (IP) in the 6th Framework Programme (FP6) as part of the Global Computing Initiative (GC). It started on September 2005 and ended on February 2010.

Advanced map-matching algorithms use location and heading of GPS points along with geometrical and topological features of digital road networks to find the road segment on which the vehicle is moving. However, GPS errors sometimes impede map-matching algorithms in finding the correct segment, especially in dense and complicated parts of the network, such as near intersections with acute angles or on close parallel roads. In this paper an artificial neural network (ANN) approach is explored to improve the segment identification accuracy of map-matching algorithms. The proposed ANN is continuously trained by using the horizontal shift imposed on GPS points and once it is trained, it will be used to correct raw GPS points before inputting them into the map-matching algorithm. Integrating the proposed ANN enabled an existing map-matching algorithm to find the correct segments for some of the GPS points where the original map-matching algorithm had failed to do so.

Most works covering the topic of transfer learning propose an algorithm to solve a given domain adaptation problem, then test the algorithm using real-world datasets. A test with a real-world dataset represents a single transfer learning test condition, which partially measures an algorithm's performance. Previous research has placed little emphasis on developing a comprehensive and uniform test for transfer learning algorithms. With this in mind, a test framework is proposed, comprising of distortion profiles which define a comprehensive test suite. The unique contribution of this paper is the definition of a test framework that measures a more complete profile of a transfer learning algorithm's capability, facilitating the identification of relative poor and good performance areas. As a proof of concept, the test framework is used to test a homogeneous transfer learning algorithm. The test framework will be the basis for a number of future applications.

Cryptographic techniques are necessary for the security of Adhoc Network. These cryptographic Algorithms are obligatory for protection of the user data so that only the permitted user are allowed to access it. This review paper outlines the comparison of various algorithms i.e AES, Rijndael AES and Pipelined AES. These algorithms estimate the performance on the basis of data throughput and clock frequency.

Recently, wireless sensor networks have employed the concept of mobile agent to reduce energy consumption and obtain effective data gathering. Typically, in data gathering based on mobile agent, it is an important and essential step to find out the optimal itinerary planning for the mobile agent. However, single-agent itinerary planning suffers from two primary disadvantages: task delay and large size of mobile agent as the scale of the network is expanded. Thus, using multi-agent itinerary planning overcomes the drawbacks of single-agent itinerary planning. Despite the advantages of multi-agent itinerary planning, finding the optimal number of distributed mobile agents, source nodes grouping, and optimal itinerary of each mobile agent for simultaneous data gathering are still regarded as critical issues in wireless sensor network. Therefore, in this article, the existing algorithms that have been identified in the literature to address the above issues are reviewed. The review shows that most of the algorithms used one parameter to find the optimal number of mobile agents in multi-agent itinerary planning without utilizing other parameters. More importantly, the review showed that theses algorithms did not take into account the security of the data gathered by the mobile agent. Accordingly, we indicated the limitations of each proposed algorithm and new directions are provided for future research.

For a binary code, the function-call graph (FCG) reflects its capability, structure and intrinsic relations. In this work, we propose a FCG matching algorithm based on Hungarian algorithm which makes matching between graphs of large scale possible. Also, optimizations are proposed to improve the efficiency and accuracy of FCG matching algorithm. Finally, a series of experiments are conducted to show that FCG matching is an effective method and has huge application potentiality in software and security analysis.

Paper is an underrated medium in multimedia technology. Rather than showing printed information only, it can also be used for creating real paper models from virtual objects by means of glue and scissors. This requires to compute a planar cut-out sheet from a 3D model. However, faces forming a model in 3D will often overlap once unfolded. While 3D space leaves much possibility for a non-intersecting topology of faces, they tend to intersect easily in the planar projection of the cut-out sheet. But overlaps are not the only problem. In some cases, neighboring faces enclose such a small angle that they are difficult to cut out and glue together. Unfolding a virtual model involves to decide which edges of a 3D model to cut first. A carefully chosen order can help to avoid the above mentioned problems in many cases. Finding the optimal order, however, is an NP-complete problem. In this paper, we develop and analyze a number of strategies that generate sheets which can be crafted easily and which yield high quality results.

Algorithms are based on some influential principles like Survival of the Fittest and with some natural phenomena in Genetic Inheritance. The key for searching the solution in improved function optimization problems are based only on Selection and Mutation operators. This paper reflects on Survival selection schemes specifically like Truncate Selection, Proportionate Selection, Tournament Selection and Ranking Based Selection. In this paper we calculate the best fittest value among the populations which is generated.

The study first summarizes the characteristics of various intelligent algorithms such as improved genetic algorithm, differential evolution algorithm and ant colony algorithm adopted in test paper generation, and then proposes the parallel evolution of swarm based on ideas of shared intelligent algorithm and dynamic multi branches tree algorithm, so as to improve searching speed and achieve the effect of short-time optimization. During forming optimal individuals, classified training and repeated recognition by virtue of dynamic multi branches tree can not only avoid premature appearance but also get strong convergence. In addition, when the constraints change, the existing knowledge can be inherited. Facts have shown that this algorithm has certain theoretical significance and reference value to the development of intelligent test paper generation algorithm.

Radio-wave propagation through wireless channel is a complicated phenomenon characterized by fading which is the result of multipath propagation. In wireless communication system, random process associated with fading channels can usually characterized by their PDF (Probability Density Function) and CDF (Cumulative Distribution Function). Signal fading can drastically affect the performance of terrestrial communication systems. Several statistical models are available for describing the fading envelope of the received signal in which Rayleigh, Rician and Nakagami are the most frequently applied models. Higher-order statistics such as Level Crossing Rate (LCR) & Average Fade Duration render (AFD) insight into signals which is not always available at lower orders.

Highly realistic visualization becomes crucial in numerous applications such as: medical imaging, simulation and entertainment systems. In this article, we describe the image/video capture system aimed for autostereoscopic multiview displays, as well as view interpolation and image restoration algorithms used to enhance the image quality. We evaluate the proposed algorithms on a head tracking based stereoscopic multiview display.

In this paper we present a survey of the work on type-based useless-code elimination for higher-order functional programs. After some historical remarks on the motivations and early approaches we give an informal but complete account of the techniques and results developed at the Computer Science Department of the University of Torino. In particular, we focus on the fact that, for each of the type-based techniques developed, there is an optimal program simplification.

According to the problem that conventional laminated paper counting algorithms have some unavoidable shortcomings such as high dependence on the quality of laminated paper and noise sensitivity, we propose a laminated paper counting algorithm based on compressive sensing (CS) and Hough transform (HT). In the proposed algorithm, the over-complete dictionary which is created by dispersing the Hough transform space of straight lines acts as the sparse matrix. Making use of high degree of sparse nature of the laminated paper image, we can obtain the accurate result through using CS theory. Experimental results on simulation images and laminated paper images have shown that our proposed algorithm can effectively restrain noise of the laminated paper image, and will get accurate experimental results with fewer CS measurements.