Phenetic Research Articles

PCA-Based Advanced Local Octa-Directional Pattern (ALODP-PCA): A Texture Feature Descriptor for Image Retrieval

This paper presents a novel feature descriptor termed principal component analysis (PCA)-based Advanced Local Octa-Directional Pattern (ALODP-PCA) for content-based image retrieval. The conventional approaches compare each pixel of an image with certain neighboring pixels providing discrete image information. The descriptor proposed in this work utilizes the local intensity of pixels in all eight directions of its neighborhood. The local octa-directional pattern results in two patterns, i.e., magnitude and directional, and each is quantized into a 40-bin histogram. A joint histogram is created by concatenating directional and magnitude histograms. To measure similarities between images, the Manhattan distance is used. Moreover, to maintain the computational cost, PCA is applied, which reduces the dimensionality. The proposed methodology is tested on a subset of a Multi-PIE face dataset. The dataset contains almost 800,000 images of over 300 people. These images carries different poses and have a wide range of facial expressions. Results were compared with state-of-the-art local patterns, namely, the local tri-directional pattern (LTriDP), local tetra directional pattern (LTetDP), and local ternary pattern (LTP). The results of the proposed model supersede the work of previously defined work in terms of precision, accuracy, and recall.

DBSCAN and TD Integrated Wi-Fi Positioning Algorithm

A density-based spatial clustering of applications with noise (DBSCAN) and three distances (TD) integrated Wi-Fi positioning algorithm was proposed, aiming to enhance the positioning accuracy and stability of fingerprinting by the dynamic selection of signal-domain distance to obtain reliable nearest reference points (RPs). Two stages were included in this algorithm. One was the offline stage, where the offline fingerprint database was constructed and the other was the online positioning stage. Three distances (Euclidean distance, Manhattan distance, and cosine distance), DBSCAN, and high-resolution distance selection principle were combined to obtain more reliable nearest RPs and optimal signal-domain distance in the online stage. Fused distance, the fusion of position-domain and signal-domain distances, was applied for DBSCAN to generate the clustering results, considering both the spatial structure and signal strength of RPs. Based on the principle that the higher resolution the distance, the more clusters will be obtained, the high-resolution distance was used to compute positioning results. The weighted K-nearest neighbor (WKNN) considering signal-domain distance selection was used to estimate positions. Two scenarios were selected as test areas; a complex-layout room (Scenario A) for post-graduates and a typical large indoor environment (Scenario B) covering 3200 m2. In both Scenarios A and B, compared with support vector machine (SVM), Gaussian process regression (GPR) and rank algorithms, the improvement rates of positioning accuracy and stability of the proposed algorithm were up to 60.44 and 60.93%, respectively. Experimental results show that the proposed algorithm has a better positioning performance in complex and large indoor environments.

The Route of Vaccine Administration Determines Whether Blood Neutrophils Undergo Long-Term Phenotypic Modifications.

Innate immunity modulates adaptive immunity and defines the magnitude, quality, and longevity of antigen-specific T- and B- cell immune memory. Various vaccine and administration factors influence the immune response to vaccination, including the route of vaccine delivery. We studied the dynamics of innate cell responses in blood using a preclinical model of non-human primates immunized with a live attenuated vaccinia virus, a recombinant Modified vaccinia virus Ankara (MVA) expressing a gag-pol-nef fusion of HIV-1, and mass cytometry. We previously showed that it induces a strong, early, and transient innate response, but also late phenotypic modifications of blood myeloid cells after two months when injected subcutaneously. Here, we show that the early innate effector cell responses and plasma inflammatory cytokine profiles differ between subcutaneous and intradermal vaccine injection. Additionally, we show that the intradermal administration fails to induce more highly activated/mature neutrophils long after immunization, in contrast to subcutaneous administration. Different batches of antibodies, staining protocols and generations of mass cytometers were used to generate the two datasets. Mass cytometry data were analyzed in parallel using the same analytical pipeline based on three successive clustering steps, including SPADE, and categorical heatmaps were compared using the Manhattan distance to measure the similarity between cell cluster phenotypes. Overall, we show that the vaccine per se is not sufficient for the late phenotypic modifications of innate myeloid cells, which are evocative of innate immune training. Its route of administration is also crucial, likely by influencing the early innate response, and systemic inflammation, and vaccine biodistribution.

Cephalometric Variability Among Siblings: A Pilot Study.

To determine whether multiple siblings resemble one another in their craniofacial characteristics as measured on cephalometric radiographs. This study was conducted retrospectively using the Forsyth Moorrees twin sample. A total of 32 families were included, each with ≥4 postpubertal siblings, totaling 142 subjects. Only 1 monozygotic twin was included per family. Headfilms were digitized, skeletal landmarks were located, and 6 parameters that indicated sagittal jaw relationships and vertical status were measured. Diverse statistical approaches were used. Dixon's Q-test detected outliers in a family for a given parameter. Manhattan Distance quantified similarity among siblings per parameter. Scatter plots visually displayed subject's measure relative to the mean and standard deviation of each parameter to assess the clinical relevance of the differences. A total of 11 families (34.4%) had no outliers on any parameter, 13 families (40.6%) had outliers on 1 parameter, and 8 families (25%) had outliers on ≥2 parameters. We identified 29 individuals with at least 1 outlying measure (20.4%). Among these, only 2 individuals (1.4%) were significantly different from their siblings for more than 1 measurement. Although the majority of the families did not demonstrate any statistical outlier, the ranges of the measurements were clinically relevant as they might suggest different treatment. For example, the mean range of SNB (Sella-Nasion-B point) angles was 7.23°, and the mean range of MPA was 9.42°. Although families are generally not dissimilar in their craniofacial characteristics, measurements from siblings cannot be used to predict the measurements of another sibling in a clinically meaningful way.

Multi-Objective Bee Swarm Optimization Algorithm with Minimum Manhattan Distance for Passive Power Filter Optimization Problems

Harmonic distortion in power systems is a significant problem, and it is thus necessary to mitigate critical harmonics. This study proposes an optimal method for designing passive power filters (PPFs) to suppress these harmonics. The design of a PPF involves multi-objective optimization. A multi-objective bee swarm optimization (MOBSO) with Pareto optimality is implemented, and an external archive is used to store the non-dominated solutions obtained. The minimum Manhattan distance strategy was used to select the most balanced solution in the Pareto solution set. A series of case studies are presented to demonstrate the efficiency and superiority of the proposed method. Therefore, the proposed method has a very promising future not only in filter design but also in solving other multi-objective optimization problems.

Retracted] Research on Fast Compensation Algorithm for Interframe Motion of Multimedia Video Based on Manhattan Distance

To improve the video quality, aiming at the problems of low peak signal‐to‐noise ratio, poor visual effect, and low bit rate of traditional methods, this paper proposes a fast compensation algorithm for the interframe motion of multimedia video based on Manhattan distance. The absolute median difference based on wavelet transform is used to estimate the multimedia video noise. According to the Gaussian noise variance estimation result, the active noise mixing forensics algorithm is used to preprocess the original video for noise mixing, and the fuzzy C‐means clustering method is used to smoothly process the noisy multimedia video and obtain significant information from the multimedia video. The block‐based motion idea is to divide each frame of the video sequence into nonoverlapping macroblocks, find the best position of the block corresponding to the current frame in the reference frame according to the specific search range and specific rules, and obtain the relative Manhattan distance between the current frame and the background of multimedia video using the Manhattan distance calculation formula. Then, the motion between the multimedia video frames is compensated. The experimental results show that the algorithm in this paper has a high peak signal‐to‐noise ratio and a high bit rate, which effectively improves the visual effect of the video.

Enhanced Generalized Regression Neural Network for Soil Moisture Estimation Over the Qinghai-Tibet Plateau

Soil moisture (SM) is a critical parameter in maintaining the balance of water cycle and energy budgets between climate system and the Earth's environment. Generalized regression neural network (GRNN) has been substantially verified as a powerful model for SM estimation due to the ability of capturing complex, non-linear relationships between predictors and responses. However, GRNN builds a full adjacency matrix using Gaussian kernel, which is computationally expensive and may ignore the local structure. In addition, it is laborious to optimize the “spread” parameter. To overcome the above issues, we propose an enhanced generalized regression neural network (EGRNN) for SM estimation, where two main adaptations are made. On the one hand, the City block distance instead of the Euclidean distance is used for building Gaussian kernel. On the other hand, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbors ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -NN) is adopted to yield an empirically sparse adjacency matrix. As the key advantage, the proposed EGRNN weakens the sensitivity to outliers since large differences are weighted more heavily by using Euclidean distance than City block distance. Another advantage is that EGRNN models more local and discriminant information in the pattern layer since only the data points within neighbors are connected by using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -NN. Experiments conducted in the Qinghai-Tibet Plateau (QTP) demonstrate that; 1) EGRNN outperforms the other four neural network models, with R = 0.9485 and RMSE = 0.0325 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ; 2) It can well capture spatial-temporal dynamics and has higher consistent with the in-situ measurements; 3) It adapts well to different in-situ networks and has better generalization performance.

Prototypical Network Based on Manhattan Distance

Few-shot Learning algorithms can be effectively applied to fields where certain categories have only a small amount of data or a small amount of labeled data, such as medical images, terrorist surveillance, and so on. The Metric Learning in the Few-shot Learning algorithm is classified by measuring the similarity between the classified samples and the unclassified samples. This paper improves the Prototypical Network in the Metric Learning, and changes its core metric function to Manhattan distance. The Convolutional Neural Network of the embedded module is changed, and mechanisms such as average pooling and Dropout are added. Through comparative experiments, it is found that this model can converge in a small number of iterations (below 15,000 episodes), and its performance exceeds algorithms such as MAML. Research shows that replacing Manhattan distance with Euclidean distance can effectively improve the classification effect of the Prototypical Network, and mechanisms such as average pooling and Dropout can also effectively improve the model.

BOEW: A Content-Based Image Retrieval Scheme Using Bag-of-Encrypted-Words in Cloud Computing

Content-based Image Retrieval (CBIR) techniques have been extensively studied with the rapid growth of digital images. Generally, CBIR service is quite expensive in computational and storage resources. Thus, it is a good choice to outsource CBIR service to the cloud server that is equipped with enormous resources. However, the privacy protection becomes a big problem, as the cloud server cannot be fully trusted. In this paper, we propose an outsourced CBIR scheme based on a novel bag-of-encrypted-words (BOEW) model. The image is encrypted by color value substitution, block permutation, and intra-block pixel permutation. Then, the local histograms are calculated from the encrypted image blocks by the cloud server. All the local histograms are clustered together, and the cluster centers are used as the encrypted visual words. In this way, the bag-of-encrypted-words (BOEW) model is built to represent each image by a feature vector, i.e., a normalized histogram of the encrypted visual words. The similarity between images can be directly measured by the Manhattan distance between feature vectors on the cloud server side. Experimental results and security analysis on the proposed scheme demonstrate its search accuracy and security.

Latent fingerprint enhancement and matching using intuitionistic type-2 fuzzy

Latent fingerprints are obtained from crime places by law enforcement and forensic agencies to identify the suspect. The latent fingerprints have vague ridge structures and various overlapping valley-based structures that result in low image quality. Furthermore, background noise, low contrast, and low information content make feature extraction difficult. To address these challenges, we propose a novel intuitionistic type-2 fuzzy relation for enhancing image quality. And to improve the matching score, a crossing number method is applied to extract minutiae points. The matching score is calculated using four different distance algorithms: Manhattan distance, Euclidean distance, Earth mover's distance, and chi-square distance. The proposed methodology is evaluated using three datasets IIITD, FVC-2004-1, and FVC-2004-2. The experimental results show that the proposed method yields satisfactory results, with the chi-square distance achieving the highest accuracy. The proposed method can be used by law enforcement and forensic agencies to reduce the workload.

Evaluation the influence of distance-based K-means method for detecting moving vehicles

Detecting moving vehicles is one of important elements in the applications of Intelligent Transport System (ITS). Detecting moving vehicles is also part of the detection of moving objects. K-Means method has been successfully applied to unsupervised cluster pixels for the detection of moving objects. In general, K-Means is a heuristic algorithm that partitioned the data set into K clusters by minimizing the number of squared distances in each cluster. In this paper, the K-Means algorithm applies Euclidean distance, Manhattan distance, Canberra distance, Chebyshev distance and Braycurtis distance. The aim of this study is to compare and evaluate the implementation of these distances in the K-Means clustering algorithm. The comparison is done with the basis of K-Means assessed with various evaluation paramaters, namely MSE, PSNR, SSIM and PCQI. The results exhibit that the Manhattan distance delivers the best MSE, PSNR, SSIM and PCQI values compared to other distances. Whereas for data processing time exposes that the Braycurtis distance has more advantages.

Optimisation of attribution processes using novel pixel-value based colour detection approach

Conventional CNN-based colour detection mechanisms are not optimal due to colour overlap, for example, the CNN model can find it difficult to distinguish shades of red and orange. The proposed approach is aimed at optimizing colour detection in attribution processes using a superior, objective and mathematical approach. The proposed approach allows you to detect a much larger range of shades of colour while increasing colour detection accuracy and reducing the time it takes to predict colour without model training. The proposed approach performs better than the conventional CNN-based supervised machine learning colour detection owing to its enhanced quantifiability, increased degree of quantisation of the colour palette (i.e., an improved colour range that can be detected) and lower the complexity of time. The approach employs fundamental concepts of image processing to isolate pixels that best represents the object’s colour from the region of interest (RoI), following which a mode filter is applied to the RGB pixel values of the pixels lying in the RoI to obtain a single set of [R, G, B] values that best represents the colour of the object. The mapping of this set of RGB values to the name of colour defined in a “Colour Taxonomy” is performed using Euclidean or Manhattan distance, depending upon the application. The accuracy of colour prediction is enhanced and computing time is greatly reduced. The current approach is aimed to be more accurate, more granular in detection (i.e., allows you to detect a much larger range of shades of colour and more time-effective than CNN-based colour detection and manual tagging thus greatly helping optimize attribution processes.

Securing Copyright Using 3D Objects Blind Watermarking Scheme

Recently, securing Copyright has become a hot research topic due to rapidly advancing information technology. As a host cover, watermarking methods are used to conceal or embed sensitive information messages in such a manner that it was undetectable to a human observer in contemporary times. Digital media covers may often take any form, including audio, video, photos, even DNA data sequences. In this work, we present a new methodology for watermarking to hide secret data into 3-D objects. The technique of blind extraction based on reversing the steps of the data embedding process is used. The implemented technique uses the features of the 3-D object vertex’ discrete cosine transform to embed a grayscale image with high capacity. The coefficient of vertex and the encrypted picture pixels are used in the watermarking procedure. Additionally, the extraction approach is fully blind and is dependent on the backward steps of the encoding procedure to get the hidden data. Correlation distance, Euclidean distance, Manhattan distance, and the Cosine distance are used to evaluate and test the performance of the proposed approach. The visibility and imperceptibility of the proposed method are assessed to show the efficiency of our work compared to previous corresponding methods.

Семейства изолиний в пространстве с прямоугольной метрикой

The paper is devoted to planar linear sets generated by linear conditions which are realized for rectangular or Manhattan metrics. The aim of this paper is to give an approach to researching the properties of linear conditions defined on Manhattan plane. Linear conditions are written as a finite sum of products Manhattan distances and real numerical factors. The paper presents a constructive method to solve the next general geometric problem. Let finite sets of linear figures (segments of line, polygons) are given on Manhattan plane at general positions. Find a planar set corresponding to given linear condition based on the given sets. The set of broken lines corresponding to given conditions generates a family of isolines for given sets. Constructive algorithm to build the family is suggested. The algorithm is based on the rectangular Hanan lattice and calculation numerical values in each cell. Some theorems applied to these cases are formulated in the paper.

Median location problem with two probabilistic line barriers: Extending the Hook and Jeeves algorithm

&lt;p style='text-indent:20px;'&gt;We consider a median location problem in the presence of two probabilistic line barriers on the plane under rectilinear distance. It is assumed that the two line barriers move on their corresponding horizontal routes uniformly. We first investigate different scenarios for the position of the line barriers on the plane and their corresponding routes, and then define the visibility and invisibility conditions along with their corresponding expected barrier distance functions. The proposed problem is formulated as a mixed-integer nonlinear programming model. Our aim is to locate a new facility on the plane so that the total weighted expected rectilinear barrier distance is minimized. We present efficient lower and upper bounds using the forbidden location problem for the proposed problem. To solve the proposed model, the Hooke and Jeeves algorithm (HJA) is extended. We investigate various sample problems to test the performance of the proposed algorithm and appropriateness of the bounds. Also, an empirical study in Kingston-upon-Thames, England, is conducted to illustrate the behavior and applicability of the proposed model.&lt;/p&gt;

Varying coefficient linear discriminant analysis for dynamic data

Linear discriminant analysis (LDA) is an important classification tool in statistics and machine learning. This paper investigates the varying coefficient LDA model for dynamic data, with Bayes’ discriminant direction being a function of some exposure variable to address the heterogeneity. We propose a new least-square estimation method based on the B-spline approximation. The data-driven discriminant procedure is more computationally efficient than the dynamic linear programming rule [21]. We also establish the convergence rates for the corresponding estimation error bound and the excess misclassification risk. The estimation error in L2 distance is optimal for the low-dimensional regime and is near optimal for the high-dimensional regime. Numerical experiments on synthetic data and real data both corroborate the superiority of our proposed classification method.

CASPIAN: A method to identify chromatin topological associated domains based on spatial density cluster

With the development of Hi-C technology, the detection of topologically associated domains (TADs) boundaries plays an important role in exploring the relationship between gene structure and expression. However, a method that can identify accurate TAD boundaries from the Hi-C contact matrix with different resolutions is currently lacking. We proposed a method named CASPIAN that can identify chromatin TAD boundaries based on the spatial density clustering algorithm. CASPIAN requires few parameters to call TADs. This method is realized using the hierarchical density-based clustering method HDBSCAN, where the distance of pairwise bins is calculated based on three distance metrics (Euclidean, Manhattan, and Chebyshev distance metric) to adapt to the characteristics of the Hi-C contact matrix generated from simulation experiments or normalized methods. Our results show that, same as standard methods (e.g., Insulation Score, TopDom), CASPIAN can enrich factors related to promoting the gene expression, such as CTCF, H3K4me1, H3K4me3, RAD21, POLR2A, and SMC3. We also calculated the approximate proportion of various factors anchored at the TAD boundaries to observe the distribution of these factors surrounding the TAD boundaries. In conclusion, CASPIAN is an easy method to explore the relationship between transcription factors and TAD boundaries. CASPIAN is available online (https://gitee.com/ghaiyan/caspian).

An SSPVEP Brain-Computer Interface Using a Small Amount of Flicker Stimuli

Brain-computer interface based on steady-state visual evoked potential (SSVEP-BCI) has been widely concerned because of its highest SNR of EEG signals. This kind of BCIs needs to modulate the control instructions with visual stimuli. However, a large number of visual stimuli will induce strong visual fatigue, and occupy a large area of screen space. As a result of the above disadvantages, the further application of this kind of BCIs can not be accepted by most users. It has been verified that 40 control instructions can be encoded with 20 flicker stimuli. The visual fatigue caused by SSVEP-BCIs can be alleviated by reducing the number of flicker stimuli. However, whether it is possible to encode the same number of control instructions with fewer flicker stimuli or not still remains unknown. This study will show the performance of the SSPVEP-BCIs after further removing some redundant flicker stimuli. We divide the proposed BCI system into four subsystems, and the performance of each subsystem will be shown in this paper. 12 flickering disks were used as visual stimuli to stimulate the brain to produce SSVEP responses. 12 targets were set on the flickering disks, and 28 targets were set alternately between the flicker stimuli, ensuring that always exist 1 to 2 flicker stimuli near each target. Manhattan distance based task-related component analysis (MTRCA) was used to process and decode the EEG signals in this paper. The performance within each of the subsystems was investigated and compared. When the sampling time was set to 4.6 s, the average accuracy of all subjects reaches 77.12&#x0025;. This result shows that the 40-target BCI system based on proposed stimulation paradigm is available to the vast majority of users. Meanwhile, the accuracy of three subsystems is greater than 80&#x0025; when the sampling time is 4.6 s. This result shows that the performance of SSPVEP stimulation paradigm is accepted in the application scenarios which only require a small number of instructions. In this paper, the research of SSPVEP paradigm is improved. It is revealed that using less flicker stimuli can still stimulate the brain to produce classifiable SSVEP responses. The conclusion of this study can guide the following study and application of SSPVEP-BCIs.

Word-Level and Pinyin-Level Based Chinese Short Text Classification

Short text classification is an important branch of Natural Language Processing. Although CNN and RNN have achieved satisfactory results in the text classification tasks, they are difficult to apply to the Chinese short text classification because of the data sparsity and the homophonic typos problems of them. To solve the above problems, word-level and Pinyin-level based Chinese short text classification model is constructed. Since homophones have the same Pinyin, the addition of Pinyin-level features can solve the homophonic typos problem. In addition, due to the introduction of more features, the data sparsity problem of short text can be solved. In order to fully extract the deep hidden features of the short text, a deep learning model based on BiLSTM, Attention and CNN is constructed, and the residual network is used to solve the gradient disappearance problem with the increase of network layers. Additionally, considering that the complex deep learning network structure will increase the text classification time, the Text Center is constructed. When there is a new text input, the text classification task can be quickly realized by calculating the Manhattan distance between the embedding vector of it and the vectors stored in the Text Center. The Accuracy, Precision, Recall and F1 of the proposed model on the simplifyweibo_4_moods dataset are 0.9713, 0.9627, 0.9765 and 0.9696 respectively, and those on the online_shopping_10_cats dataset are 0.9533, 0.9416, 0.9608 and 0.9511 respectively, which are better than that of the baseline method. In addition, the classification time of the proposed model on simplifyweibo_4_moods and online_shopping_10_cats is 0.0042 and 0.0033 respectively, which is far lower than that of the baseline method.

Adaptive Defect Detection for 3-D Printed Lattice Structures Based on Improved Faster R-CNN

To detect the internal small defects of three-dimensional (3D) printed lattice structural samples, this study proposes an adaptive defect detection method based on a faster region-based convolutional neural networks (Faster R-CNN) structure. A K-medoids clustering algorithm, which adopts the Manhattan distance to calculate clustering centers, is used to adaptively select the optimal preset anchors from 3D printed lattice structures obtained via a computed tomography slices dataset. Based on these preset anchors, an improved defect detection model for over-melt based on Faster R-CNN is constructed. To improve the generalization ability, data augmentation methods are used for the computed tomography slices and a fine-tuning strategy is used for Faster R-CNN. The experimental results show that the defects of the lattice structure can be effectively detected. The adaptive defect detection model achieves the expected average precision of 93.4%. The feasibility of the adaptive defect detection method is thereby verified.