Automatic Identification Techniques Research Articles

Automatic detection of inter-application permission leaks in Android applications

The Android® operating system builds upon already well-established permission systems but complements them by allowing application components to be reused within and across applications through a single communication mechanism, called the Intent mechanism. In this paper, we describe techniques that we developed for statically detecting Android application vulnerability to attacks that obtain unauthorized access to permission-protected information. We address three kinds of such attacks, known as confused deputy, permission collusion, and Intent spoofing. We show that application vulnerability to these attacks can be detected using taint analysis. Based on this technique, we developed PermissionFlow, a tool for discovering vulnerabilities in the byte code and configuration of Android applications. To enable PermissionFlow analysis, we developed a static technique for automatic identification of permission-protected information sources in permission-based systems. This technique identifies application programming interfaces (APIs) whose execution leads to permission checking and considers these APIs to be sources of taint. Based on this approach, we developed Permission Mapper, a component of PermissionFlow that improves on previous work by performing fully automatic identification of such APIs for Android Java® code. Our automated analysis of popular applications found that 56% of the most popular 313 Android applications actively use intercomponent information flows. Among the tested applications, PermissionFlow found four exploitable vulnerabilities. By helping ensure the absence of inter-application permission leaks, we believe that the proposed analysis will be highly beneficial to the Android ecosystem and other mobile platforms that may use similar analyses in the future.

Convolutive ICA-Based Forensic Speaker Identification Using Mel Frequency Cepstral Coefficients and Gaussian Mixture Models

Automatic speaker identification techniques are widely used nowadays in forensic applications, but its accuracy harshly drops when the voice of the speaker of interest is immersed in a recording containing more than one voice, common situation of investigations where the targets voice are obtained through ambient recordings. In forensic applications where microphones are hidden, such interferent sound sources in recordings are common and they degrade severely the performance of speaker identification techniques. In this paper, we propose a method to mitigate this problem by spatially separating the voice of each speaker using a Blind Source Separation technique called Convolutive Independent Component Analysis, and then applying the separated speech signals to a speaker identification system based on Mel Frequency Cepstral Coefficients and Gaussian Mixture Models. For identifying more than one speaker, the proposed system has a better accuracy than the state-of-the-art solutions.

Efficient Technique for Automatic Extraction and Identification of Text from an Image using Friend Pattern Chain and Euclidean Distance

A reference map acts as the fundamental source of information pertaining to various morphological features in studies related to Geographic information system (GIS). The feature set includes rivers, contours, transportation-network, national and international demarcation to name a few. Each of these features is represented on the reference map with the help of different color code to ease the task of visual interpretation while digitizing them. So reference map can be considered as superimposed layers of information casted on the terrain. One of the key features associated with a reference map is text demarking the landmarks and the rivers etc. These texts play an important role in identifying the land marks and rivers while performing studies related to drainage network or demography. These features are can possibly digitized in two ways either using traditional manual techniques which involves visually interpreting individual characters and then grouping it to form a name. This process takes demands increased effort, time and precision from the researchers responsible for digitizing the feature. Instead relying on the less effective traditional manual approach an efficient automatic extraction procedure can be developed that takes into consideration certain pre acquired knowledge for identifying the text. This proposed work introduces a rotation invariant text identifying procedure that relies on the combination of friend pattern chain concept and distance.

Gas monitoring data anomaly identification based on spatio-temporal correlativity analysis

Based on spatio-temporal correlativity analysis method, the automatic identification techniques for data anomaly monitoring of coal mining working face gas are presented. The asynchronous correlative characteristics of gas migration in working face airflow direction are qualitatively analyzed. The calculation method of asynchronous correlation delay step and the prediction and inversion formulas of gas concentration changing with time and space after gas emission in the air return roadway are provided. By calculating one hundred and fifty groups of gas sensors data series from a coal mine which have the theoretical correlativity, the correlative coefficient values range of eight kinds of data anomaly is obtained. Then the gas monitoring data anomaly identification algorithm based on spatio-temporal correlativity analysis is accordingly presented. In order to improve the efficiency of analysis, the gas sensors code rules which can express the spatial topological relations are suggested. The experiments indicate that methods presented in this article can effectively compensate the defects of methods based on a single gas sensor monitoring data.

Wavelet based intelligent techniques for automatic identification of motor vehicles: a comparative study

The vehicles of neighbouring states are more prone to violate Indian Motor Vehicles Act, 1988. An algorithm has been developed to automatically recognize the vehicles not registered in Jharkhand, a state of India for appropriate legal action using wavelet based intelligent techniques. The image of Vehicle Licence Plate (VLP) is extracted using Extended Hough Transform (EHT). Four features are obtained using four levels of decomposition using Haar wavelet and used as inputs to different classifiers. A comparative assessment regarding the efficacy of all classifiers indicates SVM to be the most suitable classifier for the intended automated VLP identification.

A pilot survey of 39 Victorian WWTP effluents using a high speed luminescent umu test in conjunction with a novel GC-MS-database technique for automatic identification of micropollutants

In 2007, samples of treated effluent were collected at point of discharge to the environment from 39 wastewater treatment plants (WWTPs) located across Victoria, Australia grouped by treatment type. Sample genotoxicity was assessed with a high-throughput luminescent umu test method using Salmonella typhimurium TL210 strain, with and without addition of a commercially available metabolic activation system. Samples were also screened using a gas chromatographic-mass spectrometric mass-structure database recognition method. A genotoxic response was observed in half of the samples tested without metabolic activation system (<LOR - 0.19 μg/L 4-nitroquinoline-N-oxide equivalents). On addition of metabolic activation system, 75% of samples elicited a genotoxic response, the majority of responses were stronger than without metabolic activation (<LOR - 2.97 μg/L benzo[a]pyrene equivalents). The type of WWTP had no effect on genotoxicity. A large number of chemicals were identified in the effluents, although none could be unambiguously tied to the genotoxicity observed. Chemicals observed in one or more effluents included food additives (e.g. dibenzylether), various alkyl phenols, tyre leachates (e.g. 2(3H)-benzothiazolone), antioxidants, flame retardants (e.g. tris(2-chloroethyl)phosphate), insect repellents (e.g. diethyltoluamide), stimulants (e.g. caffeine) and anticonvulsants (e.g. carbamazepine). Of the 451 pesticides screened, carbamate insecticides (e.g. bendiocarb, propoxur), plant growth regulators (e.g. propham) and herbicides (e.g. atrazine, metolachlor, simazine) were amongst the compounds observed.

Discovering Time-Constrained Sequential Patterns for Music Genre Classification

A music piece can be considered as a sequence of sound events which represent both short-term and long-term temporal information. However, in the task of automatic music genre classification, most of text-categorization-based approaches could only capture temporal local dependencies (e.g., unigram and bigram-based occurrence statistics) to represent music contents. In this paper, we propose the use of time-constrained sequential patterns (TSPs) as effective features for music genre classification. First of all, an automatic language identification technique is performed to tokenize each music piece into a sequence of hidden Markov model indices. Then TSP mining is applied to discover genre-specific TSPs, followed by the computation of occurrence frequencies of TSPs in each music piece. Finally, support vector machine classifiers are employed based on these occurrence frequencies to perform the classification task. Experiments conducted on two widely used datasets for music genre classification, GTZAN and ISMIR2004Genre, show that the proposed method can discover more discriminative temporal structures and achieve a better recognition accuracy than the unigram and bigram-based statistical approach.

The Relationship between near Infrared Spectroscopy and Surface Color of Eight Rosewoods

Rosewood is a kind of precious wood. Accurate wood species identification is a time consuming task for highly trained human experts. The development of cost effective techniques for automatic wood identification is a desirable goal. Wood color is one of the crucial factors to identify wood species. The color parameters (CIE L*, a* and b*) of eight rosewoods and the relationship between near infrared spectroscopy (NIR) and the color parameters were investigated in this paper. The results showed there was significant correlation between near infrared spectroscopy and color parameters. The correlation coefficients between laboratory-measured and NIR-predicted L*, a* and b* values were 0.93~0.99. The results will be helpful to develop a new method of rosewood identification through near infrared spectroscopy coupled with multivariate data analysis.

Automatic defect identification on PWR nuclear power station fuel pellets

Abstract This article presents a new automatic identification technique of structural failures in nuclear green fuel pellet. This technique was developed to identify failures occurred during the fabrication process. It is based on a smart image analysis technique for automatic identification of the failures on uranium oxide pellets used as fuel in PWR nuclear power stations. In order to achieve this goal, an artificial neural network (ANN) has been trained and validated from image histograms of pellets containing examples not only from normal pellets (flawless), but from defective pellets as well (with the main flaws normally found during the manufacturing process). Based on this technique, a new automatic identification system of flaws on nuclear fuel element pellets, composed by the association of image pre-processing and intelligent, will be developed and implemented on the Brazilian nuclear fuel production industry. Based on the theoretical performance of the technology proposed and presented in this article, it is believed that this new system, NuFAS (Nuclear Fuel Pellets Failures Automatic Identification Neural System) will be able to identify structural failures in nuclear fuel pellets with virtually zero error margins. After implemented, the NuFAS will add value to control quality process of the national production of the nuclear fuel.

A New Fingerprint Matching Approach Using Level 2 and Level 3 Features

Fingerprint recognition is considered as one of the most popular and reliable techniques for automatic identification due to the well-known distinctiveness and persistence of fingerprints. In an automated fingerprint recognition system (AFRS), fingerprint matching is an important and essential step. The distortion and noise of given fingerprints and the inaccurate feature extraction make fingerprint matching a very difficult problem. With the advent of high-resolution fingerprint imaging techniques and the increasing demand for high security, sweat pores have been recently attracting increasing attention in automatic fingerprint recognition. Therefore, this paper takes fingerprint pore matching as an example to show the robustness of our proposed matching method to the errors due to fingerprint representation. This method using Onion Peeling algorithm of computational geometry, utilizes Level 3 features in conjunction with Level 2 features for matching. The experimental results on two databases of high-resolution fingerprints demonstrate that the proposed method can achieve much higher recognition accuracy compared with other state-of-the-art pore matching methods. The algorithm has been tested on two databases (NIST 9 database and FVC2006). We have measured the performance of our proposed algorithm in terms of receiver operating characteristics (ROC). For the NIST9 database, at ~11% false acceptance rate (FAR) the genuine acceptance rate (GAR) is ~ 92% and at ~0% FAR, observed GAR is ~74%. For the FVC2006 database at ~0% FAR the GAR observed is 71% and at ~19% FAR, GAR is 89%. It is concluded that with proposed technique we can have better GAR at low FAR with reduced computational complexity.

A new-generation automated warehousing capability

A novel and highly adaptable concept is presented whereby automated warehouses can be built based on a series of simple modules with their inherent feature of scalability and reconfigurability. A potential application example of such a warehousing system is modelled to indicate the level of capability that the concept can provide. Physical infrastructure and operational control events within the system are illustrated in the paper. Simulation results demonstrate that this type of automated warehousing system can simultaneously deliver large numbers of items from storage modules to assigned collection locations with minimal delay. The concept is readily applicable within the wider logistics sector. The system performance can be enhanced by deploying an integrated warehouse control and management mechanism using automatic identification and data capture techniques and wireless communication networks. A framework on application of these emerging technologies in order to achieve the desired coordinated functionality of automated warehouse operations is proposed in the paper.

Interactive Visual Optimization and Analysis for RFID Benchmarking

Radio frequency identification (RFID) is a powerful automatic remote identification technique that has wide applications. To facilitate RFID deployment, an RFID benchmarking instrument called aGate has been invented to identify the strengths and weaknesses of different RFID technologies in various environments. However, the data acquired by aGate are usually complex time varying multidimensional 3D volumetric data, which are extremely challenging for engineers to analyze. In this paper, we introduce a set of visualization techniques, namely, parallel coordinate plots, orientation plots, a visual history mechanism, and a 3D spatial viewer, to help RFID engineers analyze benchmark data visually and intuitively. With the techniques, we further introduce two workflow procedures (a visual optimization procedure for finding the optimum reader antenna configuration and a visual analysis procedure for comparing the performance and identifying the flaws of RFID devices) for the RFID benchmarking, with focus on the performance analysis of the aGate system. The usefulness and usability of the system are demonstrated in the user evaluation.

An improved automatic detection method for earthquake-collapsed buildings from ADS40 image

Earthquake-collapsed building identification is important in earthquake damage assessment and is evidence for mapping seismic intensity. After the May 12th Wenchuan major earthquake occurred, experts from CEODE and IPSC collaborated to make a rapid earthquake damage assessment. A crucial task was to identify collapsed buildings from ADS40 images in the earthquake region. The difficulty was to differentiate collapsed buildings from concrete bridges, dry gravels, and landslide-induced rolling stones since they had a similar gray level range in the image. Based on the IPSC method, an improved automatic identification technique was developed and tested in the study area, a portion of Beichuan County. Final results showed that the technique’s accuracy was over 95%. Procedures and results of this experiment are presented in this article. Theory of this technique indicates that it could be applied to collapsed building identification caused by other disasters.

L-VIRT: Range-free 3-D localization of RFID tags based on topological constraints

RFID is an automatic identification technique that stores and remotely retrieves data on small devices called tags. This technology enables tracking of people and objects, supporting the conception of pervasive networks if identities are linked in real-time to their locations. We propose L-VIRT, a 3-D localization method for RFID tags based only on connectivity information. Virtual landmarks are combined with defined topological constraints to localize tags. A relaxation of the constraints is introduced to attenuate the effect of reading errors and, thus, to increase the precision of the localization. Simulation results show a fine-grained accuracy in free space or moderately obstructive environments, and a satisfactory accuracy in very cluttered environments. Moreover, the use of additional mobile readers enables rapid and large localization error reduction in sparse deployments of static readers.

A traceability system for beef products based on radio frequency identification technology in China

Radio frequency identification (RFID) technology is an automatic identification and data capture (AIDC) technique and it is the representative technology for handling beef traceability. In this paper we offer a complete solution including: frequency, identifier information system, information system, data organisation, tag reclaimation and control technology, from three segments of the beef production process in China. From the farm to the slaughterhouse, electronic identification ear tag technology was used to identify the individual animal. From the slaughterhouse to the processing plant, gambrel identification was used to transfer the carcass information from the “old ear tag” to a “new ear tag”. Last, gambrel RFID was converted to UCC/EAN‐128 barcode labelling with a wireless electronic scale. The whole solution was evaluated successfully in Beijing.

Text-Independent Writer Identification and Verification Using Textural and Allographic Features

The identification of a person on the basis of scanned images of handwriting is a useful biometric modality with application in forensic and historic document analysis and constitutes an exemplary study area within the research field of behavioral biometrics. We developed new and very effective techniques for automatic writer identification and verification that use probability distribution functions (PDFs) extracted from the handwriting images to characterize writer individuality. A defining property of our methods is that they are designed to be independent of the textual content of the handwritten samples. Our methods operate at two levels of analysis: the texture level and the character-shape (allograph) level. At the texture level, we use contour-based joint directional PDFs that encode orientation and curvature information to give an intimate characterization of individual handwriting style. In our analysis at the allograph level, the writer is considered to be characterized by a stochastic pattern generator of ink-trace fragments, or graphemes. The PDF of these simple shapes in a given handwriting sample is characteristic for the writer and is computed using a common shape codebook obtained by grapheme clustering. Combining multiple features (directional, grapheme, and run-length PDFs) yields increased writer identification and verification performance. The proposed methods are applicable to free-style handwriting (both cursive and isolated) and have practical feasibility, under the assumption that a few text lines of handwritten material are available in order to obtain reliable probability estimates.

Historical Lessons on ID Technology and the Consequences of an Unchecked Trajectory

Abstract This paper traces the use of identification techniques throughout the ages and focuses on the growing importance of citizen identification by governments. The paper uses a historical approach beginning with manual techniques such as tattoos, through to more recent automatic identification (auto‐ID) techniques such as smart cards and biometrics. The findings indicate that identification techniques born for one purpose have gradually found their way into alternate applications, and in some instances have been misused altogether. There is also strong evidence to suggest that governments are moving away from localized identification schemes to more global systems based on universal lifetime identifiers.

Real-Time Estimation of Origin-Destination Matrices with Partial Trajectories from Electronic Toll Collection Tag Data

The origin-destination (O-D) matrix of a traffic network is usually estimated from link traffic counts combined with a sample survey. Partially observed vehicle trajectories obtained with vehicle reidentification or automatic vehicle identification techniques such as electronic tags provide a new data source for real-time O-D matrix estimation. However, because of incomplete sampling, accurate estimation of O-D matrices from these data is not trivial. A statistical model was developed for such data, and an unbiased estimator of the O-D matrix was derived based on the method of moments. With further exploitation of the sound statistical model, the bootstrap standard error estimate of the O-D matrix estimator was also developed. The algorithm can be computed quickly and performs well under simulation compared with simpler estimators. Applied to data from vehicles with electronic toll collection tags in the San Francisco Bay Area, the algorithm produces a realistic time series of the hourly O-D matrix. The r...

Real-Time Estimation of Origin–Destination Matrices with Partial Trajectories from Electronic Toll Collection Tag Data

The origin–destination (O-D) matrix of a traffic network is usually estimated from link traffic counts combined with a sample survey. Partially observed vehicle trajectories obtained with vehicle reidentification or automatic vehicle identification techniques such as electronic tags provide a new data source for real-time O-D matrix estimation. However, because of incomplete sampling, accurate estimation of O-D matrices from these data is not trivial. A statistical model was developed for such data, and an unbiased estimator of the O-D matrix was derived based on the method of moments. With further exploitation of the sound statistical model, the bootstrap standard error estimate of the O-D matrix estimator was also developed. The algorithm can be computed quickly and performs well under simulation compared with simpler estimators. Applied to data from vehicles with electronic toll collection tags in the San Francisco Bay Area, the algorithm produces a realistic time series of the hourly O-D matrix. The relationship of the proposed estimator with similar methods in the literature was also studied and extension of the methods to general, more complex networks is discussed.

Assessing forest metrics with a ground-based scanning lidar

A ground-based scanning lidar (light detection and ranging) system was evaluated to assess its potential utility for tree-level forest mensuration data extraction. Ground-based-lidar and field-mensuration data were collected for two forest plots: one located within a red pine (Pinus resinosa Ait.) plantation and another in a mixed deciduous stand dominated by sugar maple (Acer saccharum Marsh.). Five lidar point cloud scans were collected from different vantage points for each plot over a 6-h period on 5 July 2002 using an Optech Inc. ILRIS-3D laser imager. Field- validation data were collected manually over several days during the same time period. Parameters that were measured in the field or derived from manual field measures included (i) stem location, (ii) tree height, (iii) stem diameter at breast height (DBH), (iv) stem density, and (v) timber volume. These measures were then compared with those derived from the ILRIS-3D data (i.e., the lidar point cloud data). It was found that all parameters could be measured or derived from the data collected by the ground-based lidar system. There was a slight systematic underestimation of mean tree height resulting from canopy shadow effects and suboptimal scan sampling distribution. Timber volume estimates for both plots were within 7% of manually derived estimates. Tree height and DBH parameters have the potential for objective measurement or derivation with little manual intervention. However, locating and counting trees within the lidar point cloud, particularly in the multitiered deciduous plot, required the assistance of field-validation data and some subjective interpretation. Overall, ground-based lidar demonstrates promise for objective and consistent forest metric assessment, but work is needed to refine and develop automatic feature identification and data extraction techniques.