Footprint Features Research Articles

Parametric analysis and potential evaluation of thermoelectric generator driven by solar energy and radiative sky cooling

Solar energy and radiative cooling (RC)-driven thermoelectric generator (TEG) technology, which harnesses the extraterrestrial heat and cold sources from the sun and deep universe, is attracting increasing attention. However, existing studies on solar TEG (STEG) and RC TEG (RCTEG) only focus on either solar spectrum or infrared spectrum. To investigate the TEG performance considering full spectrum’s influence, a thermal-electrical coupled model based on thermal resistance theory is developed. Results indicate the TEG with ideal selective absorber (ISA) provides the best power generation performance, with a power generation of around 2.06 W/m2 and a temperature difference of about 3.18 K. The maximum power generation is 2.89 and 2.87 W/m2 for the ISA and ideal broadband absorber (IBA) systems when the bottom cooling temperature is 275 K, respectively. For every 100 W/m2 increase in solar irradiance, there is a temperature difference increment of about 0.31 K. The IBA and ideal selective absorber and emitter (ISAE) systems are capable of generating electricity continuously, ensuring a 24-hour power supply. Moreover, the radiative cooling capacity provided by the ISAE system is greater than that of the RCTEG system. The proposed STEG system has promising application potential for power generation in view of its carbon footprint features.

Building function classification in Nanjing, China, using deep learning

AbstractThe functional classification of buildings is important for creating and managing urban zones and assisting government departments. Existing building function classification methods are mainly designed for remote sensing imagery or zones in vector maps. These methods cannot be used for the single buildings in large‐scale vector maps. In this study, a learning strategy for multiple features and context information is developed to detect a single building function in a vector map. First, multiple features are extracted for each building based on local and regional structures. Then, a graph convolutional network, GraphSAGE, is introduced to analyze the modeled graph and building footprint features through supervised learning. Experiments show that the framework can learn local and contextual building information with the ability to distinguish different building functions. When classifying the building function, the proposed method performed better than other machine learning methods, such as random forest and support vector machines.

A compact five-element printed MIMO antenna with pattern and polarization diversity for 2.45 GHz WLAN applications

A five-element pattern and polarization (linear/circular) diversity multiple-input-multiple-output (MIMO) antenna system has been presented in this article for application in 2.45 GHz WLAN. The proposed configuration consists of a circularly polarized (CP) patch antenna element and four monopole antenna elements placed orthogonal to each other. Broadside CP characteristics are achieved by using a square patch antenna with circular chamfered corners and central axis feed. Simple rectangular monopole antenna elements with microstrip line feed produce four linearly polarized (LP) end-fire directive radiation patterns. It achieves a bandwidth (BW) of 24.1% and 5.1% for the end-fire LP antenna element and broadside CP antenna element (corresponding axial ratio BW is 1.64%), respectively, with minimum port isolation of 15 dB. The ECC and CLL are below 0.03 and 0.5 bits/s/Hz, respectively, showing good MIMO performance. Due to its simple design, compact footprint (93 × 93 mm2), planar structure, and competitive features, the proposed antenna is suited for practical WLAN applications.

A texture feature based approach for person verification using footprint bio-metric

Biometrics is the study of unique characteristics present in the human body such as fingerprint, palm-print, retina, iris, footprint, etc. While other traits have been explored widely, only a few people have been considered the foot-palm region, despite having unique properties. Prior work has explored the foot shape features using length, width, major axis, minor axis, centroid, etc. but they are not reliable for personal verification due to similarity in the physical composition of two persons. It increases the demand for more unique features based on the footprint. Footprint texture features coming from creases of foot palm are unique and permanent like palmprint texture features. Hence the main objective of the paper is to investigate various kinds of texture feature techniques. These techniques will be further used in correct extraction of footprint features. After extraction of footprint features a detailed experimental analysis is performed to discover the uniqueness in foot texture. It is further utilized to test its viability as a human recognition trait. We describe a detailed feature extraction and classification technique applied to a collected footprint data-set. For feature extraction, we use three techniques: Gray Level Co-occurrence Matrix (GLCM), Histogram Oriented Gradient (HOG), and Local Binary Patterns (LBP). Feature classification is performed using four techniques: Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Ensemble Subspace Discriminant (ESD). GLCM provides less accuracy, while HOG generates a big feature vector which takes more execution time. LBP provides a trade-off between the accuracy and the execution time. Detailed quantitative experiments show: GLCM with LDA provides an accuracy of $$88.5\%$$ , HOG with Fine-KNN achieves $$86.5\%$$ accuracy and LBP with LDA achieves the accuracy of $$97.9\%$$ .

Automatic Lineament Extraction Method in Mineral Exploration Using CANNY Algorithm and Hough Transform

Copper mineralization in Kahang porphyry mining district as a tectonically active region in the center of Iran, at the middle of Urmia–Dokhtar magmatic assemblage (UDMA), has been drastically controlled by structural lineaments. Determination of these footprint features is a good guide to identify the location of ore occurrences. One way to recognize lineaments is to process satellite imagery along with geophysical data. Although the visual extraction of lineaments from such data set is the most common method, automatic methods for detecting lineaments can highly reduce user errors and runtime. The most efficient automated methods in this regard are those that simultaneously take edge detection filters with lineament extraction algorithms into consideration. In this work, the CANNY algorithm was employed as an edge-detector filter at first and later Hough transform was used to extract linear features from satellite imagery and geophysical magnetometry data. The proposed methods were implemented on the high-resolution imagery data collected by QuickBird satellite along with ground-based magnetometry data to extract the shallow and deep-seated lineaments. After investigating and plotting this structural controller in a map, the dominant orientation was in the NE–SW direction perpendicular to the UDMA. Generated lineament density map also indicated that the eastern, southeast, and western portions of the area had high potential for porphyry copper mineralization.

Swarm intelligent hyperdization biometric

At the last decade the importance of biometrics has been clearly configured due to its important in the daily life that starts from civil applications with security and recently terrorizing. A Footprint recognition is one of the effective personal identifications based on biometric measures. The aim of this research is to design a proper and reliable left human footprint biometrics system addressed (LFBS). In addition, to create a human footprint database which it is very helpful for numerous use such as during authentication. The existing footprint databases were very rare and limited. This paper presents a sturdy combined technique which merges between Image Processing with Artificial Intelligent technique via Bird Swarm Optimization Algorithm (BSA) to recognize the human footprint. The use of (BSA) enhance the performance and the quality of the results in the biometric system through feature selection. The selected features was treated as the optimal feature set in standings of feature set size. The visual database was constructed by capturing life RGB footprint images from nine person with ten images per person. The visual dataset images was pre-processed by successive operations. Chain Code is used with footprint binary image, then statistical features which represent the footprint features. These features were extracted from each image and stored in Excel file to be entered into the Bird Swarm Algorithm. The experimental results show that the proposed algorithm estimates, excellent results with a smaller feature set in comparison with other algorithms. Experimental outcomes show that our algorithm achieves well-organized and accurate result about 100% accuracy in relation with other papers on the same field.

Estimation of centroid, ensembles, anomaly and association for the uniqueness of human footprint features

Present study proposes an approach for the estimation of the distinctiveness of human footprints under the machine learning environment. In this system, a sum of 880 raw footprints have been segmented to get the 21 features for ensemble learning. All the features have been analysed for computation of minimum, mean grey value, median, maximum, standard deviation, kurtosis, and skewness for footprint dataset. The G-means clustering offers centroid information of footprint features. A set of 10 ensembles has analysed for surrogate footprint attributes. Ten anomaly models were created for anomaly scores among these features. The association of features gives the uniqueness of the human footprints for personal identification through fuzzy rules for every set of ensembles. As a consequence, centroid, ensembles, anomaly, and affiliation proved the individuality of human footprints.

Estimation of centroid, ensembles, anomaly and association for the uniqueness of human footprint features

Estimation of centroid, ensembles, anomaly and association for the uniqueness of human footprint features

Identification of sex from footprint dimensions using machine learning: a study on population of Punjab in Pakistan

BackgroundLikewise the fingerprints and palm prints, footprints are also helpful in solving a crime puzzle; however, very few studies have been reported targeting the identification of sex-based upon footprint features. Therefore, the present study aims at the identification of sex using footprint features from the population of Punjab, Pakistan. The foot measurements, i.e., toe length ratio, individual toe lengths, foot breadth, and foot index, are used as features for the identification of sex. Footprint samples were collected from 280 volunteers (142 males and 138 females) from all over Punjab (age range 18–50 years). A sex identification method is proposed in this study employing various machine learning algorithms, i.e., Naïve Bayes, J48, Random Forest, Random Tree, and REP Tree, and compared them.ResultsThe designed model was cross-validated using 10-fold cross-validation. The results demonstrated the varying accuracy of the machine learning algorithms, using different combinations of footprint features. However, the Naïve Bayes algorithm demonstrated an accuracy of 87.8%, for sex identification, using the combination of toe length and foot indexes.ConclusionsIt is concluded that by using a combination of toe length and foot indexes and employing the Naïve Bayes algorithm, sex can be identified more accurately as compared to the other methods.

Mammal responses to the human footprint vary across species and stressors

A rapidly expanding human footprint – comprised of anthropogenic land-use change and infrastructure - is profoundly affecting wildlife distributions worldwide. Cumulative effects management (CEM) is a regional approach that seeks to manage combined effects of the human footprint on biodiversity across large spatial scales. Challenges to implementing this approach include a lack of ecological data at large spatial scales, the high cost of monitoring multiple indicators, and the need to manage multiple footprints across industries. To inform development of effective CEM, we used large mammals as indicators to address the following questions: a) do species respond more strongly to individual footprint features or to cumulative effects (combined area of all footprint types, measured as total footprint), b) which features elicit the strongest responses across species, and c) are the direction of responses to footprint consistent? We used data from 12 years of snowtrack surveys (2001–2013) in the boreal forest of Alberta, coupled with regional footprint and landcover data, to develop generalized linear mixed-effects models relating the relative abundance of five boreal mammals [gray wolf (Canis lupus), Canada lynx (Lynx canadensis), coyote (Canis latrans), white-tailed deer (Odocoileus virginianus) and moose (Alces alces)] to individual and cumulative effects of the human footprint. We found that across species the strongest responses were to agriculture, roads, and young cutblocks (<10 years), suggesting these as potential priority stressors to address within CEM. Most species also responded to total footprint, indicating that in the absence of detailed information on individual features, this coarse measure can serve as an index of cumulative effects. There was high variability in direction and magnitude of responses across species, indicating that community-level responses are likely and should be considered within CEM planning.

Pareto Evolutionary Optimization of Joint Network Design and Pricing Strategies Related to Emissions in Urban Networks

In the current social, physical, economical and environmental circumstances of scarcity, the design and management of vital lifelines like transportation systems, especially within the metropolitan context, are subject to the implementation of multiple objectives in a unified framework. Thus, one of the most important issues is the identification of optimal trade-offs among crucial objectives both from the designer's as well as from the users' perspective. In the current study a comprehensive framework for estimating optimal interrelations and dilemmas among emissions-related carbon footprint and other (social- and economic-related) features of urban road networks design and operation are presented and analyzed, based on techniques of multi-objective and hierarchical mathematical programming with equilibrium constraints, solved by suitable hybridization of evolutionary algorithms. The results of the proposed optimization methodological approach provide the Pareto Frontier of solutions, which corresponds to optimal trade-offs amongst multiple objectives. The computational experience from the application of the proposed methodological approach on a part of a realistic urban network is presented, providing evidence on the applicability as well as on the computational burden of such transportation design paradigms, but most importantly, on the dilemmas emerging in sustainable design and planning of transportation systems.

Study of Biometric Identification Method Based on Naked Footprint

The scale of deployment of biometric identity-verification systems has recently seen an enormous increase owing to the need for more secure and reliable way of identifying people. Footprint identification which can be defined as the measurement of footprint features for recognizing the identity of a user has surfaced recently. This study is based on a biometric personal identification method using static footprint features viz. friction ridge / texture and foot shape / silhouette. To begin with, naked footprints of users are captured; images then undergo pre processing followed by the extraction of two features; shape using Gradient Vector Flow （ GVF ） snake model and minutiae extraction respectively. Matching is then effected based on these two features followed by a fusion of these two results for either a reject or accept decision. Our shape matching feature is based on cosine similarity while the texture one is based on miniature score matching. The results from our research establish that the naked footprint is a credible biometric feature as two barefoot impressions of an individual match perfectly while that of two different persons shows a great deal of dissimilarity. Normal 0 false false false IN X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Doi : 10.12777/ijse.5.2.29-35 How to cite this article: King , R.R. and Xiaopeng , W . (2013). S tudy of Biometric I dentification Method Based on Naked Footprint . International Journal of Science and Engineering , 5(2),18-24. Doi: 10.12777/ijse.5.2.29-35 ] Normal 0 false false false IN X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

Enhancement in Footprint Image using Diverse Filtering Technique

Footprint identification is the measurement of footprint features for recognizing the identity of a user. Footprint is universal, easy to capture and does not change across time. The image enhancement is carried out to obtain an accurate image. One of the main objectives of any image filter is enhancing the pictorial information of the image. Hence spatial domain techniques are used which directly process the pixel array of the given input image. In this paper, we have used various filtering techniques to enhance the image. Compare to other techniques proves to be better for accuracy.

A Wet Interdune Dinosaur Trampled Surface in the Jurassic Navajo Sandstone, Coyote Buttes, Arizona: Rare Preservation of Multiple Track Types and Tail Traces

Abstract A distinctive, disturbed surface with numerous soft-sediment impressions occurs within a wet interdune interval of Jurassic Navajo Sandstone at the Coyote Buttes along the Arizona-Utah border. These high-density impressions are interpreted as footprints that comprise a dinosaur trampled surface. This surface displays an unusual combination of multiple overlapping track types and sizes, distinct to modified footprint features that include claws and toes and rare tail traces. The trampled surface covers ∼3000 m2 with an average density of ∼12 impressions/m2 in its main extent. Although modern water collection and biofilms typical of weathering potholes or pits are superimposed on this surface, the primary origin of the impression features are trace fossil structures formed prior to lithification. Four criteria distinguish the impressions as vertebrate in origin: (1) large—up to several tens of centimeters—repeating identifiable foot morphologies; (2) impression floors surrounded by soft-sediment ma...

Mechanical and thermal behaviours of grinding acoustic emission

Grinding burn is a common phenomenon of thermal damage that has been one of the main constraints in grinding in respect of high efficiency and quality. Many attempts have tried to utilise Acoustic Emission (AE) technique for identifying grinding burn. However, the AE features of grinding burn are relatively weak and easily merged by other AE sources. This paper presents an investigation of the AE features in terms of grinding mechanical and thermal behaviour. A series tests of single grit scratching, tension testing and laser irradiation were carried out to simulate grinding process in order to separate the mechanical and thermal characteristics of grinding acoustic emission. By means of the Wavelet Packet Transform (WPT), AE features that represent grinding burn temperature can successfully be extracted. These AE footprint features can provide a firm foundation for monitoring and analysing grinding burn.

Cholinergic agonist-induced binding of adrenomedullary nuclear proteins to the rat preproenkephalin promoter

Cholinergic receptors are well-recognized modulators of transmitter release and biosynthesis with newly identified actions mediated through trans-acting DNA binding proteins affecting gene expression at cis-acting regulatory elements. In the case of the rat preproenkephalin (ppEnk) gene, we have identified changes in ppEnk DNA binding proteins detected by gel mobility shift assay that correlate with a cholinergic induction of ppEnk RNA levels. Antibody competition experiments implicate Fos-like and Jun-like transcription factors in this phenomenon. Methylation interference footprinting identified two cholinergic-inducible enhancements in the regions −155 to −149 and −185 to −173 (BETA1 site). An additional footprinted region at the BETA2 site (−137 to −132) was unaffected by cholinergic agonist treatment. These footprint features are significantly different from those obtained from the striatum after identical cholinergic drug treatments. We speculate that basal as well as cholinergic agonist responsive transcription factors account for tissue-specific differences in ppEnk gene expression. Further characterization of these cholinergic agonist-inducible events will require functional analysis of footprinted domains to evaluate the in vivo role of these putative gene regulatory elements.