Transparent memory tests with even repeating addresses for storage devices

The urgency of the problem of memory testing of modern computing systems is shown. Mathematical models describing the faulty states of storage devices and the methods used for their detection are investigated. The concept of address sequences (pA) with an even repetition of addresses is introduced, which are the basis of the basic element included in the structure of the new transparent march tests March _pA_1 and March _pA_2. Algorithms for the formation of such sequences and examples of their implementations are given. The maximum diagnostic ability of new tests is shown for the case of the simplest faults, such as constant (SAF) and transition faults (TF), as well as for complex pattern sensitive faults (PNPSFk). There is a significantly lower time complexity of the March_pA_1 and March_pA_2 tests compared to classical transparent tests, which is achieved at the expense of less time spent on obtaining a reference signature. New distance metrics are introduced to quantitatively compare the effectiveness of the applied pA address sequences in a single implementation of the March_pA_1 and March_pA_2 tests. The basis of new metrics is the distance D(A(j), pA) determined by the difference between the indices of repeated addresses A(j) in the sequence pA. The properties of new characteristics of the pA sequences are investigated and their applicability is evaluated for choosing the optimal test pA sequences that ensure the high efficiency of new transparent tests. Examples of calculating distance metrics are given and the dependence of the effectiveness of new tests on the numerical values of the distance metrics is shown. As well as in the case of classical transparent tests, multiple applications of new March_pA_1 and March_pA_2 tests are considered. The characteristic V(pA) is introduced, which is numerically equal to the number of different values of the distance D(A(j), pA) of addresses A(j) of the sequence pA. The validity of analytical estimates is experimentally shown and high efficiency of fault detection by the tests March_pA_1 and March_pA_2 is confirmed by the example of coupling faults for p = 2.

FORENSIC: an Online Platform for Fecal Source Identification

ABSTRACT Sewage overflows, agricultural runoff, and stormwater discharges introduce fecal pollution into surface waters. Distinguishing these sources is critical for evaluating water quality and formulating remediation strategies. With the falling costs of sequencing, microbial community-based water quality assessment tools are under development. However, their application is limited by the need to build reference libraries, which requires extensive sampling of sources and bioinformatic expertise. Here, we introduce FORest Enteric Source IdentifiCation (FORENSIC; https://forensic.sfs.uwm.edu/), an online, library-independent source tracking platform based on random forest classification and 16S rRNA gene amplicon sequences to identify in environmental samples common fecal contamination sources, including humans, domestic pets, and agricultural animals. FORENSIC relies on a broad reference signature database of Bacteroidales and Clostridiales, two predominant bacterial groups that have coevolved with their hosts. As a result, these groups demonstrate cohesive and reliable assemblage patterns within mammalian species or among species sharing the same diet/physiology. We created a scalable and extensible platform that we tested for global applicability using samples collected in distant geographic locations. This Web application offers a fast and intuitive approach for fecal source identification, particularly in sewage-contaminated waters. IMPORTANCE FORENSIC is an online platform to identify sources of fecal pollution without the need to create reference libraries. FORENSIC is based on the ability of random forest classification to extract cohesive source microbial signatures to create classifiers despite individual variability and to detect the signatures in environmental samples. We primarily focused on defining sewage signals, which are associated with a high human health risk in polluted waters. To test for fecal contamination sources, the platform only requires paired-end reads targeting the V4 or V6 regions of the 16S rRNA gene. We demonstrated that we could use V4V5 reads trimmed to the V4 positions to generate the reference signature. The systematic workflow we describe to create and validate the signatures could be applied to many disciplines. With the increasing gap between advancing technology and practical applications, this platform makes sequence-based water quality assessments accessible to the public health and water resource communities.

Online Signature Verification Based on a Single Template via Elastic Curve Matching

Person verification using online handwritten signatures is one of the most widely researched behavior-biometrics. Many signature verification systems typically require five, ten, or even more signatures for an enrolled user to provide an accurate verification of the claimed identity. To mitigate this drawback, this paper proposes a new elastic curve matching using only one reference signature, which we have named the curve similarity model (CSM). In the CSM, we give a new definition of curve similarity and its calculation method. We use evolutionary computation (EC) to search for the optimal matching between two curves under different similarity transformations, so as to obtain the similarity distance between two curves. Referring to the geometric similarity property, curve similarity can realize translation, stretching and rotation transformation between curves, thus adapting to the inconsistency of signature size, position and rotation angle in signature curves. In the matching process of signature curves, we design a sectional optimal matching algorithm. On this basis, for each section, we develop a new consistent and discriminative fusion feature extraction for identifying the similarity of signature curves. The experimental results show that our system achieves the same performance with five samples assessed with multiple state-of-the-art automatic signature verifiers and multiple datasets. Furthermore, it suggests that our system, with a single reference signature, is capable of achieving a similar performance to other systems with up to five signatures trained.

Complex analysis and monitoring of the environment based on earth sensing data

We study a problem of complex analysis and monitoring of the environment based on Earth Sensing Data, with the emphasis on the use of hyperspectral images (HSI), and propose a solution based on developing algorithmic procedures for HSI processing and storage. HSI is considered as a two-dimensional field of pixel signatures. Methods are proposed for evaluating the similarity of a HSI pixel signature with a reference signature, via simple alignment transformations: identical; amplitude scaling; shift along y-axis; and a combination of the last two. A clustering / recognition method with self-learning is proposed, which determines values of the transformation parameters that ensure the alignment of the current pixel signature with the reference signature. Similarity with the reference is determined by a standard deviation value. A HSI compression method with controlled losses has been proposed. The method forms a basis via accumulating reference signatures and represents the rest of the signatures by parameters matching them with the already detected class-reference signature. In an experiment with the GSI f100520t01p00-12 data of the AVIRIS spectrometer, the method provided a 2 % loss and compression coefficients of the original HSI ranging from 43 to 165 for various types of alignment transformation, while not requiring archiving and thus maintaining active access to the HSI and using the list of references as an analogue of the HSI palette. An algorithm for the formation of dense groups of detectable objects (for example, oil spots) and their nonconvex contouring, controlled by 4 parameters, is proposed. A pilot version of the concept of geographic information system (GIS) and an appropriate database management system (DBMS) was built and implemented, which provides monitoring and is based on the prioritized processing and storage of the HSI, which serve as a data source for the system. A laboratory complex with new algorithms for processing and storing the GSE is introduced into the structure of the system.

Analysis and object markup of hyperspectral images for machine learning methods

The development of the nominal sequence of steps for analyzing the HSI proposed by Landgrebe, which is necessary in the context of the appearance of reference signature libraries for environmental monitoring, is discussed. The approach is based on considering the HSI pixel as a signature that stores all spectral features of an object and its states, and the HSI as a whole - as a two-dimensional signature field. As a first step of the analysis, a procedure is proposed for detecting a linear dependence of signatures by the magnitude of the Pearson correlation coefficient. The main apparatus of analysis, as in Landgrebe sequence, is the method of principal component analysis, but it is no longer used to build classes and is applied to investigate the presence in the class of subclasses essential for the applied area. The experimental material includes such objects as water, swamps, soil, vegetation, concrete, pollution. Selection of object samples on the image is made by the user. From the studied images of HSI objects, a base of reference signatures for classes (subclasses) of objects is formed, which in turn can be used to automate HSI markup with the aim of applying machine learning methods to recognize HSI objects and their states.

Cubic Bezier Curve Approach for Automated Offline Signature Verification with Intrusion Identification

Authentication is a process of identifying person’s rights over a system. Many authentication types are used in various systems, wherein biometrics authentication systems are of a special concern. Signature verification is a basic biometric authentication technique used widely. The signature matching algorithm uses image correlation and graph matching technique which provides false rejection or acceptance. We proposed a model to compare knowledge from signature. Intrusion in the signature repository system results in copy of the signature that leads to false acceptance. Our approach uses a Bezier curve algorithm to identify the curve points and uses the behaviors of the signature for verification. An analyzing mobile agent is used to identify the input signature parameters and compare them with reference signature repository. It identifies duplication of signature over intrusion and rejects it. Experiments are conducted on a database with thousands of signature images from various sources and the results are favorable.