Pattern Vectors Research Articles

Signal analysis and classification of a novel active brain-computer interface based on four-category sequential coding

In order to increase the number of instruction sets for the active brain-computer interface (BCI), a novel experimental paradigm based on the sequential coding of motor imagery and speech imagery is proposed in this paper. By dividing one motor imagery and one speech imagery into time series, four imagination tasks are obtained: 1) motor imagery; 2) speech imagery; 3) motor imagery first and then speech imagery; 4) speech imagery first and then motor imagery. After analyzing the temporal, frequency and spatial features of electroencephalography (EEG) signals, four types of signals are classified by a multi-classification model based on time sequence. In this model, feature extraction and classification are accomplished by common spatial pattern (CSP) and support vector machine (SVM), respectively. 12 subjects participate in this experimental paradigm, and their average classification accuracy is 68.94%. The classification results are much higher than random probability, so the proposed experimental paradigm is feasible and valuable. The experimental paradigm based on sequential coding can effectively increase the number of instruction sets of active BCIs, so the practicability of BCIs is also improved.

Detection and localization of image tampering in digital images with fused features

SummaryIn digital forensics, image tamper detection and localization have attracted increased attention in recent days, where the standard methods have limited description ability and high computational costs. As a result, this research introduces a novel picture tamper detection and localization model. Feature extraction, tamper detection, as well as tamper localization are the three major phases of the proposed model. From the input digital images, a group of features like “Scale‐based Adaptive Speeded Up Robust Features (SA‐SURF), Discrete Wavelet Transform (DWT) based Patched Local Vector Pattern (LVP) features, HoG feature with harmonic mean based PCA and MBFDF” are extracted. Then, with this extracted feature strain the “optimized Convolutional Neural Network (CNN)” will be trained in the tamper detection phase. Since it is the key decision‐maker about the presence/absence of tamper, its weighting parameters are fine‐tuned via a novel improved Sea‐lion Customized Firefly algorithm (ISCFF) model. This ensures the enhancement of detection accuracy. Once an image is recognized to have tampers, then it is essential to identify the tamper localization. In the tamper localization phase, the copy‐move tampers are localized using the SIFT features, splicing tampers are localized using the DBN and the noise inconsistency is localized with a newly introduced threshold‐based tamper localization technique. The simulation outcomes illustrate that the adopted model attains better tamper detection as well as localization performance over the existing methods.

New Results on Classification Modeling of Noisy Tensor Datasets: A Fuzzy Support Tensor Machine Dual Model

In this article, classification problems for a class of tensor datasets with a noisy environment are investigated. To address such issues, a novel fuzzy support tensor machine (FSTM) dual model with robustness is established. First, for each input sample in the noisy tensor dataset, we define three kinds of fuzzy membership functions, such as linear, cosine, and exponential forms. In particular, the reconstruction process from one-dimensional (1-D) vector data to third-order tensor data is also derived in the Appendix. Second, the original optimization model of an FSTM on fuzzy membership is designed by constructing the vector pattern of the traditional support vector machine (SVM) models into a tensor pattern. Next, by introducing the Lagrangian multiplier method and tensor-Tucker decomposition method to the original FSTM model, an FSTM dual model without tensor inner product operation is obtained for the first time. Such a dual model with tensor-Tucker decomposition form can avoid conservativeness caused by the vectorization of tensor data in the traditional SVM model. Furthermore, an FSTM classifier is derived by the designed numerical algorithm, and the classification generalization error bound of the FSTM model with a general form is developed. It is worth noting that a linear least-squares FSTM (LLS-FSTM) equation with tensor-Tucker decomposition is also designed in the Appendix to further reduce the slightly time-consuming problem of the solving the dual optimization model FSTM. Finally, two numerical examples are presented to verify the feasibility and validity of the derived FSTM classifier.

PERANCANGAN STOCK ILLUSTRATION BERTEMA BEACH HOLIDAY DI FLORTO STUDIO

In implementing the MBKM work/internship practice, Florto Studio became the place the author chose as a partner for the internship. Florto Studio itself is a creative company that provides visual communication design services. Florto Studio is located at Jalan Singasari Gg. Grouse No. 5 Peguyangan, North Denpasar District, Denpasar City. During the MBKM internship program at Florto Studio, the author has been given the task of working on various illustration and design projects. One of the projects entrusted to the author and currently in the process of being worked on is the design of a stock illustration with a Beach Holiday theme for Florto Studio. The design for this Beach Holiday-themed Stock Illustration begins with creating a concept by brainstorming, creating a mood board, creating a Thumbnail Design, loading Tight Tissue, and creating a Mockup for the final Beach Holiday-themed stock illustration. Some media designs that the author has done include unit illustration vector designs, greeting cards, patterns, and web banners.

Clustered vector textures

Repetitive vector patterns are common in a variety of applications but can be challenging and tedious to create. Existing automatic synthesis methods target relatively simple, unstructured patterns such as discrete elements and continuous Bézier curves. This paper proposes an algorithm for generating vector patterns with diverse shapes and structured local interactions via a sample-based representation. Our main idea is adding explicit clustering as part of neighborhood similarity and iterative sample optimization for more robust sample synthesis and pattern reconstruction. The results indicate that our method can outperform existing methods on synthesizing a variety of structured vector textures. Our project page is available at https://phtu-cs.github.io/cvt-sig22/.

POp: Parameter Optimization of Differentiable Vector Patterns

AbstractProcedural materials are extensively used in computer graphics, since they provide editable, resolution‐independent representation of textures. However, tuning the parameters of procedural generators to achieve a desired result remains time‐consuming for users. Recently, inverse procedural material algorithms have been developed, exploiting differentiable rendering methods to find the parameters of a procedural model that match a target image. These approaches focus on raster textures. We propose pOp, a practical method for estimating the parameters of vector patterns, that are formed by collections of vector shapes arranged by an arbitrary procedural program. In our approach, patterns are defined as arbitrary programs, that control the translation, rotation and scale or vector graphics elements. We support elements typical of vector graphics, namely points, lines, circle, rounded rectangles, and quadratic Bèzier drawings, in multiple colors. We optimize the program parameters by automatically differentiating the signed distance field of the drawing, which we found to be significantly more reliable than using differentiable rendering of the final image. We demonstrate our method on a variety of cases, representing the variations found in structured vector patterns.

Blood meal analysis of Culicoides species associated with livestock in West Bengal, India

Knowledge gaps exist on the feeding pattern and host range of bluetongue virus vectors, Culicoides species, associated with livestock in India. Adult midges were trapped with ultraviolet light traps at 13 household farms adjacent to human biotope. Host DNA was isolated from individual females (n=101; blood engorged-82, gravid-4 and parous-15) and subjected to PCR amplification targeting CytB and 16S rRNA gene fragments followed by sequencing of amplified DNA samples. However, DNA sequences from only 71 individuals (70.3%) comprising of 10 Culicoides species were obtained. Blood meal analysis revealed at least 10 species that fed on five mammalian hosts including humans, but surprisingly none tested positive for birds. Results revealed that Culicoides innoxius tested positive for four not previously recognized species indicating a potential role as a vector species. Likewise, Culicoides shortti and Culicoides hegneri preferred goat and cattle respectively as hosts, whereas Culicoides palpifer preferred cattle along with buffalo as hosts, which is being reported for the first time. This is the first document on DNA-based blood meal identification and feeding preference of Culicoides midges associated with livestock in India.

Impact of self adaptive-elephant herding optimization towards neural network for facial emotion recognition

FACIAL expression is one of the most efficient, universal and fundamental indicators to identify their emotions and intentions in humans. Various experiments have already been performed on automatic Facial Emotion Recognition (FER) owing to useful significance in medical diagnosis, stress monitoring for drivers, sociable robots, and other human-computer interface devices. Here, this proposed framework consists of two processes namely; “(i) proposed feature extraction and (ii) classification”. Here, a major novelty relies in the initial phase (i.e. feature extraction phase), where the Proposed Local Vector Pattern (Proposed- LVP) based features are extracted. In addition to the proposed-LVP, the other Discrete Wavelet Transform (DWT) and Gray Level Co-occurrence Matrix (GLCM) based features are also extracted. Besides, the Principal Component Analysis (PCA) method is used for reducing the dimension of the features. Further, they are subjected to classification process, where Optimized Neural Network (NN) is used. More particularly, a new Improved Elephant Herding Optimization (EHO) model termed as Self Adaptive-EHO (SA-EHO) is used to train the NN model via selecting the optimal weights. At last, the proposed work performance is computed over the other traditional systems with respect to the positive measures like “accuracy, sensitivity, specificity and precision”; negative measures like “False Positive Rate (FPR), False Negative Rate (FNR) and False Discovery Rate (FDR)”; other measures like “Negative Predictive Value (NPV), F1-score and Matthew’s Correlation Coefficient (MCC)”, respectively.

Automatic Building Detection for Multi-Aspect SAR Images Based on the Variation Features

Multi-aspect synthetic aperture radar (SAR) images contain more information available for automatic target recognition (ATR) than images from a single view. However, the sensitivity to aspect angles also makes it hard to extract and integrate information from multi-aspect images. In this paper, we propose a novel method based on the variations features to realize automatic building detection in the image level. First, to get a comprehensive description of target variation patterns, statistical characteristic variances are derived from three representative and complementary categories. Then, these obtained features are fused and put in the K-means classifier for prescreening, whose results are used as the training sets in supervised classification later to avoid manual labeling. Second, for more precise detection performance, finer features in vector forms are obtained by principal component analysis (PCA). The variation patterns of these feature vectors are explored in two different manners of correlation and fluctuation analyses and processed by separate support vector machines (SVMs) after fusion. Finally, the independent SVM detection results are fused according to a maximum probability rule. Experiments conducted on two different airborne data sets demonstrate the robustness and effectiveness of the proposed method, in spite of significant target signature variabilities and cluttered background.

Blood-feeding patterns of Anopheles vectors of human malaria in Malawi: implications for malaria transmission and effectiveness of LLIN interventions

BackgroundAccess to human hosts by Anopheles mosquitoes is a key determinant of vectorial capacity for malaria, but it can be limited by use of long-lasting insecticidal nets (LLINs). In Malawi, pyrethroid-treated LLINs with and without the synergist piperonyl butoxide (PBO) were distributed to control malaria. This study investigated the blood-feeding patterns of malaria vectors and whether LLINs containing pyrethroid and PBO led to a reduction of human blood feeding than those containing only pyrethroids.MethodsMosquitoes were sampled inside houses from May 2019 through April 2020 by aspiration, pyrethrum spray catch, and light trap methods in two sites. One site (Namanolo, Balaka district) had LLINs containing only pyrethroids whereas the other (Ntaja, Machinga district) had LLINs with both pyrethroids and PBO. Anopheles species, their blood-meal host, and infection with Plasmodium falciparum were determined using PCR methods.ResultsA total of 6585 female Anopheles were sampled in 203 houses. Of these, 633 (9.6%) were blood-fed mosquitoes comprising of 279 (44.1%) Anopheles arabiensis, 103 (16.3%) Anopheles gambiae 212 (33.5), Anopheles funestus, 2 (0.3%), Anopheles parensis and 37 (5.8%) were unidentified Anopheles spp. Blood meal hosts were successfully identified for 85.5% (n = 541) of the blood-fed mosquitoes, of which 436 (81.0%) were human blood meals, 28 (5.2%) were goats, 11 (2.0%) were dogs, 60 (11.1%) were mixed goat-human blood meals, 5 (0.9%) were dog–human, and 1 was a mixed dog-goat. Human blood index (fraction of blood meals that were humans) was significantly higher in Namanolo (0.96) than Ntaja (0.89). Even though human blood index was high, goats were over-selected than humans after accounting for relative abundance of both hosts. The number of infectious Anopheles bites per person-year was 44 in Namanolo and 22 in Ntaja.ConclusionAlthough LLINs with PBO PBO may have reduced human blood feeding, access to humans was extremely high despite high LLIN ownership and usage rates in both sites. This finding could explain persistently high rates of malaria infections in Malawi. However, this study had one village for each net type, thus the observed differences may have been a result of other factors present in each village.

Image splicing detection using discriminative robust local binary pattern and support vector machine

PurposeThis paper aims to propose a novel splicing detection method using a discriminative robust local binary pattern (DRLBP) with a support vector machine (SVM). Reliable detection of image splicing is of growing interest due to the extensive utilization of digital images as a communication medium and the availability of powerful image processing tools. Image splicing is a commonly used forgery technique in which a region of an image is copied and pasted to a different image to hide the original contents of the image.Design/methodology/approachThe structural changes caused due to splicing are robustly described by DRLBP. The changes caused by image forgery are localized, so as a first step, localized description is divided into overlapping blocks by providing an image as input. DRLBP descriptor is calculated for each block, and the feature vector is created by concatenation. Finally, features are passed to the SVM classifier to predict whether the image is genuine or forged.FindingsThe performance and robustness of the method are evaluated on public domain benchmark data sets and achieved 98.95% prediction accuracy. The results are compared with state-of-the-art image splicing finding approaches, and it shows that the performance of the proposed method is improved using the given technique.Originality/valueThe proposed method is using DRLBP, an efficient texture descriptor, which combines both corner and inside design detail in a single representation. It produces discriminative and compact features in such a way that there is no need for the feature selection process to drop the redundant and insignificant features.

A spiral laser scanning routine for powder bed fusion inspired by natural predator-prey behaviour

ABSTRACT Additive manufacturing by laser powder bed fusion (LPBF) requires process parameters that consider both local and global thermal gradients to address material and component quality issues caused by non-uniform heating and cooling. Here, we develop an analogy between natural predator-prey behaviour and heat flow in LPBF, and consequently derive a spiral pattern that is translated into a discrete vector pattern compatible with LPBF. A power optimisation routine is applied to the spiral pattern to maintain constant melt pool depth, and the simulated thermal histories are compared to well-established zig-zag and helix scan patterns. From these results, we propose that the spiral pattern reduces the spatial variation of temperature, while a larger area remains above a specified threshold temperature at the end of the scan. Consequently, the spiral pattern may be promising for printing crack-prone materials, and for parameter optimisation of LPBF overall, and should be experimentally validated in future work.

Diagnosis and Fault Detection of Rotor Bars in Squirrel Cage Induction Motors Using Combined Park’s Vector and Extended Park’s Vector Approaches

The induction motor (IM) is considered to be one of the most important types of motors used in industries. A sudden failure in this machine can lead to unwanted downtime, with consequences in costs, product quality, and safety. Over the last decade, several methods and techniques have been proposed to diagnose and detect faults in induction machines. In this paper, we present the development of a new algorithm based on the combination of both the Park’s vector approach (PVA) and the extended Park’s vector approach (EPVA) for broken rotor bars (BRBs) fault detection and identification. This fault can be detected using the PVA by monitoring the thickness and orientation of the park’s vector pattern and using EPVA by identifying specific spectral components related to the fault. For ability evaluation of our suggested algorithm, simulations and experiments are conducted and presented. The obtained results demonstrate that the proposed algorithm is accurate and effective and can be extensively used in IM fault detections and identifications.

Index Encoding and Antenna Selection in Multiuser Precoder Index Modulation MIMO Communication

Index modulation (IM) offers energy efficient solutions to communication systems by altering the on/off status of entities of the system. This work presents a multiuser (MU) IM-based system operating in a multiple-input multiple-output (MIMO) channel, named Multiuser Precoder Index Modulation (MU-PIM-MIMO), in which the choice of the IM-precoder matrices, responsible for assigning zero or nonzero values to the information vector, is a source of information. System model is specified for Zero-Forcing and Block Diagonalization channel precoders, as well as additional mechanisms, such as user notification and channel estimation. Numerical results show that MU-PIM-MIMO systems can offer attractive tradeoff between detection performance and spectral efficiency. Metrics for selection of the most favorable information bearing positions (IBP) patterns of the information vector, based on the maximization of the signal-to-noise ratio and on the maximization of the achievable rate, are developed in order to offer further improvements in system performance. Additionally, a scenario where the number of transmit antenna elements exceeds the number of radiofrequency chains at the base station is considered, and optimal and computational efficient ways to select the IBP patterns and the active transmit antennas are proposed. Simulation results evidence the effectiveness of the strategies.

High Accuracy Numerical Simulation of Non-Isothermal Viscoelastic Polymer Fluid Past a Cylinder

The collocated grid finite volume CLEAR (coupled and linked equations algorithm revised) method is applied to solve the governing equations of viscous and XPP (eXtended Pom-Pom) viscoelastic fluids. The high accuracy AVLSMART schemes for the convection terms of momentum and constitutive equations are constructed based on the deferred correction method. Firstly, the incompressible viscous fluids past a cylinder at different Reynolds are simulated to verify the validity of the developed numerical method. Then, the isothermal and non-isothermal XPP viscoelastic fluid past a cylinder is studied numerically, giving the distribution patterns of velocity vectors, stress components, stretch and temperature. Especially, the effects of Weissenberg on the horizontal velocity, normal stresses and stretch are analyzed. The results obtained in this research will provide the theoretical foundation for accurately predicting fiber reinforced viscoelastic polymer dynamic filling process in complex cavities.

Classification of breast cancer images using completed local ternary pattern and support vector machine

Breast cancer is the major cause of deaths in women compared to other cancers. Though early detection of breast cancer reduces cancer deaths, it is a challenging task for physicians. Local binary pattern (LBP) and local ternary pattern (LTP) techniques are widely applied in texture classification applications. Since LBP is more sensitive to noise in texture classification, it needs to be improved for achieving better results. Though LTP is more robust to noise, there are few drawbacks. Completed LBP and completed local binary count techniques achieve good accuracy for texture classification, but they inherit few drawbacks of LBP. In this paper, completed LTP operator is applied on breast cancer images for better classification accuracy than LBP and completed LBP operators, by extracting sign and magnitude components. Experimental results based on breast cancer database show that the proposed technique achieved better classification accuracy than existing similar approaches.

Classification of breast cancer images using completed local ternary pattern and support vector machine

Classification of breast cancer images using completed local ternary pattern and support vector machine

The Influence of Menstrual Cycle on Bioimpedance Vector Patterns, Performance, and Flexibility in Elite Soccer Players.

To examine whether menstrual cycle affects body composition and bioimpedance vector analysis (BIVA) patterns, jumping and sprinting ability, and flexibility in elite soccer players. A total of 20 elite female soccer players (age: 23.8 [3.4]y, height: 1.63 [0.04]m, body mass: 61.4 [5.9]kg, and body mass index: 22.5 [2.4]kg/m2) were monitored during the early follicular and ovulatory phase across 2 consecutive menstrual cycles. Bioimpedance analysis was performed using foot-to-hand technology, and total body water and fat mass were determined by specific equations developed for athletes. Bioelectrical resistance and reactance were adjusted according to the BIVA procedures and plotted as a vector within the resistance-reactance graph. In addition, countermovement jump, 20-m sprint, and sit and reach were assessed. A time effect (P < .05) was found for body mass, total body water, bioelectrical resistance and reactance, and flexibility. Specifically, body mass increased (P = .021) along with a gain in total body water (P = .001) from the ovulatory to the early follicular phase, while it decreased from the early follicular to the ovulatory phase during the second menstrual cycle. The BIVA vector shortened during the early follicular phases (P < .001). No change in jumping and sprinting capacity was observed (P > .05). Flexibility was impaired during the early follicular phases (P < .05). Specific bioelectrical impedance analysis and BIVA procedures are able to detect menstrual cycle-induced changes in body composition in elite soccer players. The early follicular phase resulted in fluid accumulations and BIVA vector shortening. In addition, while menstrual cycle did not affect performance, a fluctuation in flexibility was observed.

Variación temporal de la abundancia de mosquitos (Diptera: Culicidae) en el Bosque Atlántico subtropical de Brasil

Understanding the temporal variation in abundance patterns of vector or potential vector species, as well as the abiotic factors associated with these patterns, enables the creation of strategic measures aimed at preventing diseases spread by these organisms. Here, we evaluate through circular and linear analyses the temporal variation in abundance patterns of native mosquito species and verify how abiotic factors (temperature and rainfall) have influenced these patterns. For this purpose, fortnightly collections were carried out during one year in a remnant of the Atlantic Forest in Southern Brazil. We collected 1891 specimens belonging to 24 species. From those, 10 species were abundant enough to evaluate temporal variation in abundance patterns. We found different periods of occurrence for different species of mosquitoes and the distribution of abundances also differed throughout the year. Temperature was the main abiotic factor associated with temporal variation in abundance patterns presented by mosquito species. In this way, the application of these analyses is particularly interesting for the understanding of temporal variation in abundance patterns and subsequent adoption of preventive strategies for organisms of medical importance such as mosquitoes.

Highly Discriminative Physiological Parameters for Thermal Pattern Classification.

Infrared Thermography (IRT) is a non-contact, non-intrusive, and non-ionizing radiation tool used for detecting breast lesions. This paper analyzes the surface temperature distribution (STD) on an optimal Region of Interest (RoI) for extraction of suitable internal heat source parameters. The physiological parameters are estimated through the inverse solution of the bio-heat equation and the STD of suspicious areas related to the hottest spots of the RoI. To reach these values, the STD is analyzed by means: the Depth-Intensity-Radius (D-I-R) measurement model and the fitting method of Lorentz curve. A highly discriminative pattern vector composed of the extracted physiological parameters is proposed to classify normal and abnormal breast thermograms. A well-defined RoI is delimited at a radial distance, determined by the Support Vector Machines (SVM). Nevertheless, this distance is less than or equal to cm due to the maximum temperature location close to the boundary image. The methodology is applied to 87 breast thermograms that belong to the Database for Mastology Research with Infrared Image (-). This methodology does not apply any image enhancements or normalization of input data. At an optimal position, the three-dimensional scattergrams show a correct separation between normal and abnormal thermograms. In other cases, the feature vectors are highly correlated. According to our experimental results, the proposed pattern vector extracted at optimal position cm reaches the highest sensitivity, specificity, and accuracy. Even more, the proposed technique utilizes a reduced number of physiological parameters to obtain a Correct Rate Classification (CRC) of 100%. The precision assessment confirms the performance superiority of the proposed method compared with other techniques for the breast thermogram classification of the -.