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From 1880 to 2005, the Eastman Kodak Company manufactured black-and-white fiber-based gelatin silver paper in a wide variety of weights, grades, and formats. Kodak manufacturer records and sample books include details about Kodak paper surface characteristics and are an invaluable resource for understanding photographic paper materials. Using the extensive number of Kodak data books, manuals, and manufacturing records spread out in the collections of three Rochester, New York-based institutions—George Eastman Museum, University of Rochester Special Collections, and Image Permanence Institute—I created a chronological guide to Kodak photographic paper surface characteristics. This guide is not an approximate identification guide for Kodak papers but rather a resource that can be used to fill in gaps and propose questions about Kodak manufacturing history that is no longer easily accessible. The guide aims to help researchers, photography archivists and historians, conservators, collection managers, or anyone else interested in Kodak history gain access to a better understanding of photographic paper produced by Kodak from 1930 to 1955. The process for creating the guide is described in this article and may be used as a starting point for future research while also illuminating the importance of documenting and providing access to technological and material details about photographic objects.

The uniformity of paper sheets is a key factor which not only affects almost all physical, optical, and printing properties of paper sheets, but also influences the end users’ experience and perception. The uniformity of paper sheets is a great concern for paper manufacturers. However, the methods utilized to measure the uniformity of paper sheets are either performed offline in the quality control laboratory, or performed on line but not effective and fast enough considering the high production rates of paper making. It is imperative to explore effective ways which can be applied on line to achieve real time measurement of paper formation. In this paper, the uniformity of paper sheets was evaluated using K-means algorithm which is one of fastest clustering methods. A paper sample subjected to transmitted light was inspected to identify its uniformity according to the gray level of each pixel. The pixels were allocated into four clusters using K-means. The distribution of pixels of each cluster was analyzed, and the dispersion index was calculated to indicate the uniformity of the paper sample.

In this paper (This is a short paper accepted in the new ideas and work-in-progress section of SEFM 2017.) we introduce a technique to improve the efficiency of SAT calls in Bounded Model Checking (BMC) problems. The proposed technique is based on exploiting interpolation-based invariants as redundant constraints for BMC.

A summary of contributions made by significant papers from the first 25 years of the Field-Programmable Logic and Applications conference (FPL) is presented. The 27 papers chosen represent those which have most strongly influenced theory and practice in the field.

This paper considers an Adaptive Antenna Array (AAA), which weights are shared with those of a Feed-Forward (FF) part of the channel equalizer and the array output signal is combined with that of the Feed-Backward (FB) part of the equalizer. The array and this distributed equalizer operate as an indivisible adaptive filter, providing a good performance and an ability to receive a multipath data signal in the presence of external interference sources. The architecture of the array/equalizer and mathematical description of the Recursive Least Mean Squares (RLS), Least Mean Square (LMS) and Normalized LMS (NLMS) algorithms for weights calculation of the adaptive filter are presented. The simulation results demonstrate the efficiency of the proposed solution.

We investigate the performance of a watermarking system in which the encoder is forced to use a binning strategy based on antipodal binary-valued sequences. The use of antipodal binary random binning has several advantages, including the possibility of relying on simple and effective binary code constructions and the ease with which this kind of schemes can cope with amplitude scaling. By relying on a novel binning strategy, we derive a lower bound of the Gelfand-Pinsker capacity of the watermark channel when the encoder is forced to use an antipodal binary auxiliary random variable, showing that for low to moderate bit-rates, the bound coincides with Costa’s capacity. We exploit the properties of the new binning strategy, to develop a practical watermarking system and show that the new scheme outperforms previous constructions, exhibiting very good performance also in the presence of gain attack. Preliminary results on audio signals show that the new scheme retains its good performance also when used for the watermarking of real multimedia data.

SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments., Comment: Article source data is available here: https://github.com/scipy/scipy-articles

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.

With the frequent interaction and cooperation between different disciplines in recent years, the number of research papers associated with multiple subjects increased. Correspondingly, some of the existing literatures belong to a single discipline, while others may simultaneously involve more than 2 subjects. At this time, the traditional single-label text classification is not conducive to people obtaining comprehensive and cutting-edge research papers in real life. Thus, it’s of great importance to conduct a multi-label classification of research papers effectively. This paper tests the performance of multi-label learning tasks with text data obtained from the Kaggle website. Firstly, lemmatization and Term Frequency-Inverse Document Frequency (TF-IDF) are used for feature extraction in the pre-processing part. The critical information of text content is statistically analysed, and text content is converted into numerical and high-dimensional vector space. As the traditional single-label classification algorithm is not suitable for the above problem, this paper adopts the Multi-Label K-Nearest Neighbour (ML-KNN) algorithm framework for classification. Experimental results report that the ML-KNN algorithm has achieved better results in multi-label text classification problems than a traditional multi-label algorithm, which proves the effectiveness of the ML-KNN algorithm for text data prediction with multiple subjects. Moreover, the work in this paper is analysed and summarized.

For the stacked paper placed parallel to each other, the characteristics of the side texture image are analyzed, and an algorithm for counting compensation combined with the overall information is proposed. Considering that the most algorithms only deal with one-dimensional information, it is easy to generate the blind area of counting, resulting in false detection and missing detection. On this basis, this paper counts the edges of the paper in combination with the overall image information. This method detects the binarized side image of the paper at the suspected position by using a rectangular template to eliminate the false detection position and recover the missed detection position. The experimental results show that the accuracy of the algorithm is significantly improved, and the counting error is controlled within the required range.

This brief presents a random number generator (RNG) based on irregular sampling of regular waveform method where the irregular signal is obtained by combining Fibonacci-Galois ring oscillators with an XOR gate. The RNG is implemented on a FPGA (field-programmable gate array). The regular waveform generated by the digital clock manager of the FPGA, is sampled at times corresponding to certain number of rising edges of the irregular signal, and the resulting bit stream is subjected to statistical tests of randomness. It is demonstrated that the resulting bit sequence from the proposed RNG satisfies NIST 800-22 test suit and Rabbit and SmallCrush batteries from TestU01 library without any need for post-processing such as Von Neumann or XOR. A comparison between the methods regular sampling of irregular waveform and irregular sampling of regular waveform is given in terms of robustness against external interference. The impact of selection of Fibonacci-Galois polynomials is discussed. Using digital design flow for TSMC 65nm process, an asic implementation of the proposed RNG is given having 1115 gates and 4.811 mW estimated power.

This paper studies the design of fabric drape prediction model based on BP neural network, and predicts the static and dynamic drape coefficients according to the number of warp and weft yarns, density of warp and weft yarns, weight per square meter and thickness of fabric. The effect of the number of intermediate layer neurons on the accuracy of neural network was studied. The influence of activation function on neural network prediction and initial value on local optimization. The simulation results show that the prediction results of fabric drape by BP neural network are good and meet the expected requirements.

The work is devoted to estimation of the amplitude frequency characteristics of the optical measuring system that implements method of phase triangulation. Method based on the analysis of images during spatial modulation of an optical source encoding a binary Gray code and on the spatial-frequency analysis of the image observed by the photo detector.

We deal with the waveform design problem for the radar-communications coexistence (RCC) that ensures low levels of waveform correlations and meanwhile implements efficient information embedding (IE) via the fast-time waveform transmission. To tackle the problem, we propose two unimodular waveform designs, in which the integrated sidelobe level (ISL) is evaluated to find the optimal waveform correlations for the radar transmission and the attributes of waveform phases with large degrees of freedom are exploited to embed communication symbols. The first design is an offline strategy simply using the phase initialization for waveform generation to embed communication symbols. It requires only a predefined database that maps the waveform initialization to the emitted version of it, which gives the lowest ISL of the waveform. The second design serves as an online strategy employing the phase difference between any two waveform elements for the implementation of IE, and its accuracy to approaching desirable phases is elaborately transformed into a quadratic form and jointly designed with the minimization of ISL. We formulate this design as a nonconvex optimization problem, whose compound objective involves the outer product of the waveform vector that is to be designed. Moreover, we apply the majorization-minimization technique to solve the formulated problem, based on which we also devise an efficient algorithm that iterates with a closed-form solution. Simulation results verify the effectiveness of the proposed designs for the RCC.

At present, Data remains as the most important part of human life. The future of data generation is manipulated through different data analysis techniques. But every day it is becoming much more difficult. Due to current growth of technology, People are generating huge amount of uncontrollable data. Because of that text summarization became important to reduce the volume of the data and extracts the required useful information. This review based paper discusses about different text summarization techniques and algorithms along with their accuracy and efficiency. So that the researchers can easily understand the concept of text summarization and find their expected information in a fast pace.

In this paper we propose a statistical approach for the optimization of the phased codes used in radar. By considering each code as a realization of a random variable we show that we can build good codes if the distribution of the random variable is suitably chosen. We then consider sequences generated by the skew tent map and we show that for some values of the bifurcation parameter the invariant probability density coincides with the desired one. To confirm this idea we compute the maximal peak of the ambiguity function outside zero relative to the bifurcation parameter of the skew tent map.

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.

We consider private information retrieval (PIR) for distributed storage systems with noncolluding nodes where data is stored using a non maximum distance separable (MDS) linear code. Recently, it was shown that when data is stored using certain non-MDS codes, the MDS-PIR capacity can be achieved, and is indeed the capacity of the system. In this paper, for storage codes not belonging to this class, we present a heuristic algorithm for their decomposition into punctured subcodes and a PIR protocol based on these punctured subcodes. The code decomposition is guided by the generalized Hamming weights of the storage code. We show that the proposed PIR protocol can achieve a larger PIR rate than that of all existing PIR protocols.

For constructing a span-lateral inhibition neural network (S-LINN) with optimal architecture and parameters for actual application, an improved self-organizing optimization approach is proposed in this paper to tune the architecture and parameters simultaneously. This self-organization pruning algorithm can prune insignificant hidden neurons automatically through building a modified significance index function to evaluate the significance of hidden neurons. A preprocessing training of the initial network with assumed redundant hidden neurons will be allowed in the tuning process. A subsequent learning after the self-organization pruning process is also implemented to optimize the parameters of pruned network. The proposed self-organizing approach has been tested on nonlinear dynamic system identification benchmark problem. Simulation results demonstrate that the proposed method has good exploration and exploitation capabilities in terms of searching the optimal structure and parameters for S-LINN.

Quantum Approximate Optimization Algorithm (QAOA) provides approximate solution to combinatorial optimization problems. It encodes the cost function using a $p$ -level quantum circuit where each level consists a problem Hamiltonian followed by a mixing Hamiltonian. Despite the promises, few real-world applications (besides the pedagogical MaxCut problem) have exploited QAOA. The success of QAOA relies on the classical optimizer, variational parameter setting, and quantum circuit design and compilation. In this study, we implement QAOA and analyze its performance for a broader Quadratic Unconstrained Binary Optimization (QUBO) formulation to solve real-word applications such as, partially occluded object detection problem. Furthermore, we analyze the effects of above influential factors on QAOA performance. We propose a 3-level improvement of hybrid quantum-classical optimization for object detection. We achieve more than 13X execution speedup by choosing L-BFGS-B as classical optimizer at the first level and 5.50X additional speedup by exploiting parameter symmetry and more than 1.23X acceleration using parameter regression at the second level. We empirically show that the circuit will achieve better fidelity by optimally rescheduling gate operations (especially for deeper circuits) at the third level.

This paper investigates the user selection problem of successive zero-forcing preceded multiuser multiple-input multiple- output (MU-MIMO) downlink systems, in which the base station and mobile receivers are equipped with multiple antennas. Assuming full knowledge of the channel state information at the transmitter, dirty paper coding (DPC) is an optimal preceding strategy, but practical implementation is difficult because of its excessive complexity. As a suboptimal DPC solution, successive zero-forcing DPC (SZF-DPC) was recently proposed; it employs partial interference cancellation at the transmitter with dirty paper encoding. Because of a dimensionality constraint, the base station may select a sub- set of users to serve in order to maximize the total throughput. The exhaustive search algorithm is optimal; however, its computational complexity is prohibitive. In this paper, we develop two low-complexity user scheduling algorithms to maximize the sum rate capacity of MU-MIMO systems with SZF-DPC. Both algorithms add one user at a time. The first algorithm selects the user with the maximum product of the maximum column norm and maximum eigenvalue. The second algorithm selects the user with the maximum product of the minimum column norm and minimum eigenvalue. Simulation results demonstrate that the second algorithm achieves a performance similar to that of a previously proposed capacity-based selection algorithm at a high signal-to-noise (SNR), and the first algorithm achieves performance very similar to that of a capacity-based algorithm at a low SNR, but both do so with much lower complexity.

In the previous decade, the mixing between chaotic supposition and cryptography frames considers a significant field of data security. Chaos-based image encryption is given a lot of attention in the exploration of data security moreover a great deal of picture encrypting calculations have been proposed concerning chaotic maps. Because of some inveterate highlights of media like information limit and high information excess, the encryption of images not quite the same as that of texts; accordingly it is hard to deal with them by conventional encryption strategies. This paper presents a short review of robust digital watermarking systems that used chaotic algorithms such as Logistic, Tent, Baker, Hyper, Fibonacci, and Arnold maps for encryption of the data presented in several years.

A Mean shift algorithm is a clustering & unsupervised algorithm. It is also called as “Mode seeking Algorithm”. A mean-shift algorithm used for image processing and also in computer vision. It doesn't require specified clusters like k-mean. Basically it has two types BMS & MS. By studying these two, it looks like BMS is slightly better than MS because it changes the dataset in each iteration with computed value. It's nonparametric algorithm and it can control no. of the cluster because the mean shift is much slower and much time complex and output depend on windows size. (Windows size is trivial). The BMS is an accelerated version which uses the original data only in the first step, then re-smoothes previous estimates. A BMS does not predefined shape on data cluster. To overcome such a situation, we can use the BMS and K-Medoid algorithm because k-medoid is partitioning technique of a cluster. It has better scalability for the larger datasets and much efficient so using these two we can build mean shift algorithm faster than before.

The conventional approach to pre-process data for compression is to apply transforms such as the Fourier, the Karhunen-Loeve, or wavelet transforms. One drawback from adopting such an approach is that it is independent of the use of the compressed data, which may induce significant optimality losses when measured in terms of final utility (instead of being measured in terms of distortion). We therefore revisit this paradigm by tayloring the data pre-processing operation to the utility function of the decision-making entity using the compressed (and therefore noisy) data. More specifically, the utility function consists of an Lp-norm, which is very relevant in the area of smart grids. Both a linear and a non-linear use-oriented transforms are designed and compared with conventional data pre-processing techniques, showing that the impact of compression noise can be significantlv reduced.

We propose a multistage differentiable method to select convolutional channels and construct light neural networks from a heavy network for inference on a subset of a big data set. The selection proceeds backward in layers and utilizes sparse penalty to diversify channel scores. The resulting light network gains sizable accuracy over the baseline heavy network.

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.

Dirty paper coding (DPC) allows a transmitter to send information to a receiver in the presence of interference that is known (non-causally) to the transmitter. The original version of DPC was derived for the case where the noise and the interference are statistically independent Gaussian random sequences. More recent works extended this approach to the case where the noise and the interference are mutually independent and at least one of them is Gaussian. In this letter we further extend the DPC scheme by relaxing the Gaussian and statistical independence assumptions. We provide lower bounds on the achievable data rates in a DPC setting for the case of possibly dependent noise, interference and input signals. Also, the interference and noise terms are allowed to have arbitrary probability distributions. The bounds are relatively simple, are phrased in terms of second-order statistics, and are tight when the actual noise distribution is close to Gaussian., Published in the IEEE Wireless Communications Letters

This paper investigates how much the rate region of the two-user Gaussian interference channel can be enlarged by allowing the two source nodes to cooperate. Two cooperative transmission schemes are proposed, based on dirty-paper coding and the assumption that the radio bandwidth is partitioned into two parts, and each part is utilized by one source node. The achievable rate regions and the outage performance of these two schemes are compared with the simplified Han-Kobayashi scheme, which is an efficient coding scheme for the interference channel. Simulation results show that in some channel realizations, the rate region of the Han-Kobayashi scheme is a subset of the rate regions of our two proposed cooperative transmission schemes. Furthermore, a significant gain in outage performance can be obtained, as the cooperative schemes have twice the diversity order of the simplified Han-Kobayashi scheme. While both cooperative schemes are able to yield large diversity gain, one of them can be implemented by simple decoder. Besides, it has an efficient algorithm for maximizing its weighted sum rate, and can be extended easily to the multi-channel case.

Numerous problems require an on-line treatment. The variation of the problem instance makes it harder to solve: an algorithm used may be very efficient for a long period but suddenly its performance deteriorates due to a change in the environment. It could be judicious to switch to another algorithm in order to adapt to the environment changes.

Based on the image processing method for counting on paper the most critical extraction problem on the paper stripes, use a 2D Gaborl filtering method based on the edge of the paper processing, trying to solve the problem. The method is firstly established template Gabor filters of different frequencies, the arrangement of the original paper filter, edge images are arranged strong regularity, and then sampling pretreatment paper image gray pixel curve on the paper a row direction. Finally, according to the gray scale pixels curves were obtained using a gray value differential algorithm to count the amount of paper based on. Although the Gabor filter is greatly affected by the quality of the paper, so this method can not guarantee the application range of paper counting, but through the experiments verify the validity and accuracy of the method.

This paper proposes an approach to detect mistaken foldings in computer-aided origami by using a single top-view camera. The position of the origami paper is continually tracked by matching the shape of the paper with the state model of the folding process. Further, the change of the shape is also grasped, and the folder’s mistake is pointed out online. In this paper, first, we define the change ratio of the shape of the paper to measure the progress of foldings. Next, a criterion expressing the degree of difference from the correct shape is introduced to detect the mistake. Finally, threshold measurements for those criteria are investigated. Several experimental results showed the validity of our approach.

In this paper, we design a new coded cooperation protocol utilizing superposition modulation together with iterative decoding/detection algorithms. The aim of the proposed system is to apply “dirty paper coding” theory in the context of half-duplex relay systems. In the proposed system, the node transmits a superposed signal which consists of its own coded information and other node's re-coded information. The destination node detects and decodes the signal using the received signals at two continuous time-slots with iterative decoding algorithm. Moreover, the destination node detects the received signal using the results of decoding, iteratively. This paper provides the outage probability of the proposed system under the assumption that the proposed system can ideally perform dirty paper coding, and it is shown from the comparison between outage probabilities and simulated results that the proposed system can get close to the dirty paper coding theory.

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

The problem of communication over binary dirty paper (DP) using nested polar codes is considered. An improved scheme, focusing on low delay, short to moderate blocklength communication is proposed. Successive cancellation list (SCL) decoding with properly defined CRC is used for channel coding, and SCL encoding without CRC is used for source coding. The performance is compared to the best achievable rate of any coding scheme for binary DP using nested codes. A well known problem with nested polar codes for binary DP is the existence of frozen channel code bits that are not frozen in the source code. These bits need to be retransmitted in a second phase of the scheme, thus reducing transmission rate. We observe that the number of these bits is typically either zero or a small number, and provide an improved analysis, compared to that presented in the literature, on the size of this set and on its scaling with respect to the blocklength when the power constraint parameter is sufficiently large or the channel crossover probability sufficiently small., Accepted to ISIT 2019

Polar codes natively lack the flexibility that is desired for practical application. Namely, Arikan’s polar code definition can only achieve code lengths that are powers of two. Rate-matching techniques, known as puncturing and shortening, have been applied to polar codes to grant a flexible block length. By considering polarizing kernels of alternate dimensions, Multi-kernel polar codes improve natural block length flexibility. With the recent advent of the 3GPP 5th generation New Radio specification, there now exists an industry standard for length-flexible polar codes. This paper outlines various state-of-the-art flexible polar coding schemes, such as puncturing, shortening, and multi-kernel construction, and evaluates their efficacy with respect to the newly designed 3GPP standard. Simulations and an in-depth analysis are presented.

With the development of unmanned aerial vehicle (UAV) systems, multi-UAV cooperation has attracted noticeable attention. In response to the communication constraints faced in UAV swarm coordination, both the lazy and the eager strategies were proposed to enable swarm-wide reliable information exchange to further behavior coordination for UAV swarms. However, these two algorithms are only evaluated in a fixed and homogeneous network scenario. Hence, how to choose the proper information exchange strategy for a UAV swarm in realistic dynamic and heterogeneous network environments remains an open while interesting problem. Therefore, in this paper, we first evaluate the convergence and payload cost of both strategies for robotic swarms in realistic network scenarios. Then we propose a novel online adaptive information exchange strategy by adopting single relay selection schemes to ensure low payload and fast convergence in various network environments. Numerical results reveal our novel strategy performs well across different network scenarios in terms of convergence and payload cost, showing its robustness, adaptive capability and potential applications in UAV swarms.

Once the sparse representation-based classifier (SRC) was raised, it achieved a more outstanding performance than typical classification algorithm. Normally, SRC algorithm adopts \(l_1\)-norm minimization method to solve the sparse vector, and its computation complexity increases correspondingly. In this paper, we put forward a compressed sensing reconstruction algorithm based on residuals. This algorithm utilizes the local sparsity within figures as well as the non-local similarity among figure blocks to boost the performance of the reconstruction algorithm while remaining a median computation complexity. It achieves a superior recognition rate in the experiments of Yale facial database.

This paper proposes a feature selection approach, based on improved Discrete Particle Swarm Optimization (DPSO), to solve the “dimension disaster” problem in data classification; it is named Progressive Binary Particle Swarm Optimization (PBPSO). This feature selection approach is highly problem-dependent and influenced by the locations of particles. It adopts the principle of “partial retention - change - reduction of duplication - update” in the process of selection, and defines a new fitness function describing the correlation between the features and class labels. Experimentation was conducted using of the KDDCup99 data set to evaluate our proposed PBPSO. The experimental results show that 14 features were selected from the original data space with 41 features. Three classic classifiers, namely J48, Naive Bayes and ID3, were then used to further evaluate the performance of the selected features. The classification accuracy rates on the different classifiers achieved using the selected feature subset are similar to those achieved using the original feature set. The training time is, however, significantly reduced. In comparison with other similar algorithms, including Genetic Algorithm GA and Greedy Algorithm FGA. The results show that the PBPSO extracts fewer features, achieves slightly higher classification accuracy, and less time consuming in terms of model training. It has been demonstrated that the PBPSO enhances the practicability of certain classification algorithms in handling high-dimensional data.

In multi-label classification a datapoint can be labelled with more than one class at the same time. A common but trivial approach to multi-label classification is to train individual binary classifiers per label, but the performance can be improved by considering associations between the labels, and algorithms like classifier chains and RAKEL do this effectively. Like most machine learning algorithms, however, these approaches require accurate hyperparameter tuning, a computationally expensive optimisation problem. Tuning is important to train a good multi-label classifier model. There is a scarcity in the literature of effective multi-label classification approaches that do not require extensive hyperparameter tuning. This paper addresses this scarcity by proposing CascadeML, a multi-label classification approach based on cascade neural network that takes label associations into account and requires minimal hyperparameter tuning. The performance of the CasecadeML approach is evaluated using 10 multi-label datasets and compared with other leading multi-label classification algorithms. Results show that CascadeML performs comparatively with the leading approaches but without a need for hyperparameter tuning.

In this paper we introduce a spectral control chart that is designed to detect the onset of cyclic behaviour in a process, even in the presence of multiple cycles. This new spectral control chart is based on the periodogram test proposed by Bolviken (1983a, b). While no more difficult to implement than the traditional spectral control based on Fisher's test statistic, this new control chart shows improvement in detecting the presence of compound periodicity, which the chart based on Fisher's test is not designed to handle. This is assessed using Monte Carlo simulations to estimate and compare the average run lengths of several spectral control charts. In addition, the spectral control charts are applied to paper production data, published by Pandit & Wu (1993), in which the stock flow and paper thickness are monitored. The application of the new spectral control chart to the stock flow process detects out-of-control behaviour that is not found using standard control charts. This behaviour, in tur...

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.

This Special Issue of Algorithms is of a different nature than other Special Issue in the journal, which are usually dedicated to a particular subjects in the area of algorithms [...]

Cell tracking automatically in time-lapse image sequences is important for understanding the dynamic pattern of micro-cell. In this paper, we present a novel method for tracking cell with shape feature based on the delta generalized labeled multi-Bernoulli (delta-GLMB) filter which is of great research significance. The delta-GLMB filter with cell shape parameters can improve the tracking accuracy. This approach is evaluated and compared with raw detection using the generalized optimal sub-pattern assignment (GOSPA) metric on real N2DH-SIM cell sequences. Experiment results show that the delta-GLMB filter can provide the shape information as well as the better estimation than raw detection and KTH method.