Real-time Analysis Applications Research Articles

Read-safe snapshots: An abort/wait-free serializable read method for read-only transactions on mixed OLTP/OLAP workloads

This paper proposes Read-Safe Snapshots (RSS), a concurrency control method that ensures reading the latest serializable version on multiversion concurrency control (MVCC) for read-only transactions without creating any serializability anomaly, thereby enhancing the transaction processing throughput under mixed workloads of online transactional processing (OLTP) and online analytical processing (OLAP). Ensuring serializability for data consistency between OLTP and OLAP is vital to prevent OLAP from obtaining nonserializable results. Existing serializability methods achieve this consistency by making OLTP or OLAP transactions aborts or waits, but these can lead to throughput degradation when implemented for large read sets in read-only OLAP transactions under mixed workloads of the recent real-time analysis applications. To deal with this problem, we present an RSS construction algorithm that does not affect the conventional OLTP performance and simultaneously avoids producing additional aborts and waits. Moreover, the RSS construction method can be easily applied to the read-only replica of a multinode system as well as a single-node system because no validation for serializability is required. Our experimental findings showed that RSS could prevent read-only OLAP transactions from creating anomaly cycles under a multinode environment of master-copy replication, which led to the achievement of serializability with the low overhead of about 15% compared to baseline OLTP/OLAP throughputs under snapshot isolation (SI). The OLTP throughput under our proposed method in a mixed OLTP/OLAP workload was about 45% better than SafeSnapshots, a serializable snapshot isolation (SSI) equipped with a read-only optimization method, and did not degrade the OLAP throughput.

Toward Intelligent Monitoring in IoT: AI Applications for Real-Time Analysis and Prediction

Toward Intelligent Monitoring in IoT: AI Applications for Real-Time Analysis and Prediction

Corrections to “Toward Intelligent Monitoring in IoT: AI Applications for Real-Time Analysis and Prediction”

Corrections to “Toward Intelligent Monitoring in IoT: AI Applications for Real-Time Analysis and Prediction”

Enhanced selective detection of insecticide Profenofos with aid of a novel CeO2 NPs/rGO nanocomposite using bacterial derived bioflocculant polymer as reducing agent

Agricultural harvests with increased pesticide use are important in the development health toxins. In this study, we used an in-situ method to prepare a novel (CeO2 NPs/rGO) bionanocomposite from bioflocculant polymer reducing agent, which we then used to detect toxic organophosphorus insecticide - Profenofos (PFS). CeO2 NPs (Ce3+/Ce4+) strongly bind with rGO via π-π * electrostatic interaction, resulting in a synergistic effect that enhance the electroconductivity via electron transfer to the interlayers and can be used as an efficient electrocatalyst. Response surface methodology (RSM) was used to optimize the media composition for the production of bioflocculant polymer from Bacillus sp. and the media composition for the production of bioflocculant polymer. Various spectroscopic, microscopic techniques were used examine the structure, shape and functional properties of the bionanocomposite. This work provides an effective sustainable approach with a LOD of 0.004 μM, a wide response range 0.01–170 μM, superior selectivity and sensitivity of 2.63 μAμM–1cm–2. Furthermore, the proposed sensor has outstanding reproducibility and stability. The electrocatalytic demonstration of CeO2 NPs/rGO nanocomposite specifies its practical application in real-time analysis of a wide range of pesticides.

An Efficient Protocol for the Tag-information Sampling Problem in RFID Systems

Given a population S of N tags in an RFID system, the tag-information sampling problem is to randomly choose K distinct tags from S to form a subset T, and then inform each tag in T of a unique integer from {1,2,⋯ ,K}. This is a fundamental problem in many real-time analysis applications of RFID systems, as it enables rapidly selecting a small random tag subset T from a large tag population S and collecting the tag-information from T for analyzing purposes. However, existing protocols for this problem are far from satisfactory due to high communication costs. This paper aims at solving this problem by using a small communication cost. First, a lower bound on communication cost, denoted by Clb, is obtained for the tag-information sampling problem. Then, a protocol Ps is proposed for solving the studied problem, and is proved to have a communication cost within a factor of 2 of the lower bound Clb. Lastly, extensive simulation results not only verify the theoretical properties of the proposed protocol but also demonstrate its advantages in comparison with the state-of-art protocols.

Integration of samarium vanadate/carbon nanofiber through synergy: An electrochemical tool for sulfadiazine analysis

Antibiotic pollution causes worldwide concern due to its more apparent consequences, namely antibiotic resistance and destruction of the environment. Extensive use of antibiotics in human and veterinary drugs releases a significant amount of toxins into the sphere of living matter, causing adverse ecological impacts. This requires the design of new analytical protocols for the effective mitigation and monitoring of hazardous pharmaceutical products to reduce the environmental burden. Therefore, we present here the hydrothermal synthesis of samarium vanadate/carbon nanofiber (SmV/CNF) composite for the determination of sulfadiazine (SFZ). The synergistic effect arising from the combination of SmV and CNF accelerates charge transfer kinetics along with the creation of more surface-active sites that benefit effective detection. The structural and compositional disclosure indicates the high purity and superior attributes of the composite material that possesses the ability to improve catalytic performance. The proposed SmV/CNF sensor exhibits important static characteristics such as wide linear response ranges, low detection limit, high sensitivity and selectivity, and increased stability. To the best of our knowledge, this is the first report on the electrochemical performance of SmV/CNF, establishing its potential application in real-time analysis of environmentally hazardous contaminants.

Boosted-DEPICT: an effective maize disease categorization framework using deep clustering

Clustering of plant disease from digital images is an arduous task due to its dynamic nature and change of appearance under different environmental conditions. In most cases, the image captured in the real-time scenario is subjected to added noise, distortion, poor lighting conditions, and other potential factors that results in poor model performance during the process of discriminating between normal and disease-affected samples. It eventually maximizes the margin of the error rate, thereby leading to misclassification of disease of different varieties of plants in the database with other categories. This paper presents an effective deep clustering-based plant disease categorization algorithm, Boosted-Deep Embedded Regularized Clustering (DEPICT). This model integrates the convolutional autoencoder model with locality-preserving constraints and group sparsity into the network, which improves the embedded learning representation of the images. The PlantVillage and PDD image databases are accessed to develop this model for maize crop. The images are segmented by eliminating the background, cropped, augmented before model training. The performance of the system is evaluated by clustering accuracy and normalized mutual information. The proposed Boosted-DEPICT exhibits better performance, attains promising results with an accuracy of 97.73% and 91.25% on PV and PDD datasets, and outperforms state-of-the-art deep clustering algorithms. This system could be further enhanced by automating the entire process and transforming it into a mobile application for real-time analysis to gain instant results from any region.

Development of a ZnCdS@ZnS quantum dots-based label-free electrochemiluminescence immunosensor for sensitive determination of aflatoxin B1 in lotus seed.

In this study, we designed a ZnCdS@ZnS quantum dots (QDs)-based label-free electrochemiluminescence (ECL) immunosensor for sensitive determination of aflatoxin B1 (AFB1). A Nafion solution assembled abundant QDs on the surface of aAu electrode as ECL signal probes, with specially coupled anti-AFB1 antibodies as the capturing element. As the reduction reaction between S2O82- in the electrolyte and QDs on the electrode led to ECL emission, the decreased ECL signals resulting from target AFB1 in the samples were recorded for quantification. We evaluated electrochemical impedance spectroscopy and ECL measurements along each step in the construction of the proposed immunosensor. After systematic optimization of crucial parameters, the ECL immunosensor exhibited a good sensitivity, with a low detection limit of 0.01ng/mL for AFB1 in awide concentration range of 0.05-100ng/mL. Testing with lotus seed samples confirmed the satisfactory selectivity, stability, and reproducibility of the developed ECL immunosensor for rapid, efficient, and sensitive detection of AFB1 at trace levels in complex matrices. This study provides a powerful and universal analytical platform for a variety of analytes that can be used in broad applications for real-time analysis, such as food and traditional Chinese medicine safety testing, environmental pollution monitoring, and disease diagnostics. Graphical abstract Development of a ZnCdS@ZnS quantum dots based label-free electrochemiluminescence immunosensor for sensitive detection of aflatoxin B1 in lotus seed.

Low Latency, Online Processing of the High-Bandwidth Bunch-By-Bunch Observation Data From the Transverse Feedback System in the LHC

During long shutdown 2 (2019-2020) the transverse observation system (ADTObsBox) in the LHC will undergo a substantial upgrade. The purpose of this upgrade is to allow for true low latency, online processing of the 16 data-streams of transverse bunch-by-bunch, turn-by-turn positional data provided by the transverse feedback system in the LHC (ADT). This system makes both offline and online analysis of the data possible, where the emphasis will lie on online analysis, something that the older generation was not designed to provide. The result of the analysis is made available for accelerator physicists, machine operators, and engineers working with LHC. The new system allows users to capture buffers of various lengths for later analysis just like the older generation and it provides a platform for real-time analysis applications to directly capture the data with minimal latency while also providing a heterogeneous computing platform where the applications can utilize CPUs, GPUs and dedicated FPGAs. The analysis applications include bunch-by-bunch instability analysis and passive bunch-by-bunch tune extraction to name a few. The ADTObsBox system uses commodity server technology in combination with FPGA-based PCIe I/O cards. This paper will cover the design and status of the I/O cards, server, firmware, driver, analysis applications and results of early performance testing.

New method to analyze random telegraph signals in resistive random access memories

A new technique to analyze random telegraph noise (RTN) is proposed. It has been used for the analysis of current versus time measurements performed on Ni/HfO2/Si-n+-based resistive random access memories. The method allows us to study current-time traces with a massive number of data without losing the capability of dealing with background noise and discriminating the active defects responsible for current fluctuations. A comparison of this algorithm with the previous ones is given in terms of computing time and RTN description accuracy. The computing efficiency and the validity of the model have been proved, and therefore, it is feasible to propose applications for real time analysis making use of this new algorithm.

A supercomputing framework for the evaluation of real-time analysis and optimization techniques

Generic framework to massively generate synthetic distributed system models.Automatic application of real-time analysis and optimization techniques.Extensible framework independent of any particular metamodel or real-time tool.Supercomputer usage enables large studies for a better validation of techniques.Different generation methods covering a variety of system characteristics. The evaluation of new approaches in the analysis and optimization of real-time systems usually relies on synthetic test systems. Therefore, the development of tools to create these test systems in an efficient way is highly desirable. It is usual for these evaluations to be constrained by the processing power of current personal computers. For example, in order to assess whether a specific technique generally performs better than others or whether the improvement observed is constrained to a limited set of circumstances, a vast set of examples must be tested, making the execution infeasible in a common PC. In this paper, we present a framework that defines the building blocks of a tool to enable the validation of real-time techniques, through the efficient execution of massive evaluations of event-driven synthetic distributed systems. Its main characteristic is that it can leverage the computing power of a supercomputer to perform large studies that otherwise could not be performed with a PC. The framework also defines different generation methods so that the generated systems can cover a wide range of characteristics that can be present in different application domains. As a case study, we also implement this framework based on a previously developed prototype.

Real-time analysis application for identifying bursty local areas related to emergency topics

Since social media started getting more attention from users on the Internet, social media has been one of the most important information source in the world. Especially, with the increasing popularity of social media, data posted on social media sites are rapidly becoming collective intelligence, which is a term used to refer to new media that is displacing traditional media. In this paper, we focus on geotagged tweets on the Twitter site. These geotagged tweets are referred to as georeferenced documents because they include not only a short text message, but also the documents’ posting time and location. Many researchers have been tackling the development of new data mining techniques for georeferenced documents to identify and analyze emergency topics, such as natural disasters, weather, diseases, and other incidents. In particular, the utilization of geotagged tweets to identify and analyze natural disasters has received much attention from administrative agencies recently because some case studies have achieved compelling results. In this paper, we propose a novel real-time analysis application for identifying bursty local areas related to emergency topics. The aim of our new application is to provide new platforms that can identify and analyze the localities of emergency topics. The proposed application is composed of three core computational intelligence techniques: the Naive Bayes classifier technique, the spatiotemporal clustering technique, and the burst detection technique. Moreover, we have implemented two types of application interface: a Web application interface and an android application interface. To evaluate the proposed application, we have implemented a real-time weather observation system embedded the proposed application. we used actual crawling geotagged tweets posted on the Twitter site. The weather observation system successfully detected bursty local areas related to observed emergency weather topics.Electronic supplementary materialThe online version of this article (doi:10.1186/s40064-015-0817-x) contains supplementary material, which is available to authorized users.Electronic supplementary materialThe online version of this article (doi:10.1186/s40064-015-0817-x) contains supplementary material, which is available to authorized users.

Radio Astronomical Monitoring in Virtual Environment

We present StarWatch , our application for real-time analysis of radio astronomical data in Virtual Environment. Serving as an interface to radio astronomical databases or being applied to live data from the radio telescopes, the application supports various data filters measuring signal-to-noise ratio (SNR), Doppler's drift, degree of signal localization on celestial sphere and other useful tools for signal extraction and classification. Originally designed for the database of narrow band signals from SETI Institute (setilive.org), the application has been recently extended for the detection of wide band periodic signals, necessary for the search of pulsars. We will also address the detection of week signals possessing arbitrary waveforms and present several data filters suitable for this purpose.

The Use of In‐line Quantitative Analysis to Follow Polymer Processing

AbstractIn this work it is presented three applications of real time analysis during extrusion process using an optical device developed by our research group, which applies the concepts of light extinction. Monitoring of polymer blends morphology takes place to infer data concerned to dispersed phase size and concentration. The detector also enables information about melting temperature of polymer during extrusion and the level of viscous heating, and the exfoliation step during processing of a polymer‐clay nanocomposite.

ReTiS: a real-time simulation platform. Analysis of real-time applications

This paper presents ReTiS, a simulation tool for distributed real-time application analysis. Simulation concerns the operational architecture and takes into account the software (executable code of real-time tasks and real-time operating system) and the hardware (set of processors and networks). In this paper we present two mechanisms. The first enables the tracking of data dependencies across a distributed system. The second permits the detection of task switching and the analysis of a task's stack (usage and corruption). These mechanisms do not rely on a particular hardware or operating system nor do they require any application or operating system code modification. We show their interest for the analysis of the temporal behaviour of real-time applications in a distributed system.

Recent progress in thin film growth analysis by multichannel spectroscopic ellipsometry

In this article we review recent advances in instrumentation for multichannel spectroscopic ellipsometry (SE) and its applications in real time analysis of thin film growth. An established approach for multichannel SE utilizes the rotating polarizer configuration to provide 50- to 100-point spectra in the ellipsometric angles and the polarized reflectance for photon energies ranging from 1.5 to 4.5 eV, with a minimum acquisition time of 15 ms. In a recent advance, the upper photon energy limit in rotating-polarizer multichannel SE has been extended to 6.5 eV for the study of wide bandgap thin films. In addition, multichannel SE in the rotating-compensator configuration has also been developed recently. This new instrument configuration provides spectra in the four parameters that define the Stokes vector of the light beam reflected from the film surface, with a minimum acquisition time of 25 ms. The additional information in the Stokes vector allows one to assess sample non-idealities that lead to depolarization of the incident beam upon reflection. Real time SE data analysis procedures have been developed to handle a variety of problems in thin film deposition including (i) nucleation and bulk layer growth on smooth and rough substrates, (ii) nucleation and bulk layer growth on rough substrates accompanied by modification of the substrates, and (iii) graded layer growth with rough surfaces. Illustrative examples from our studies of amorphous semiconductor materials and devices demonstrate these capabilities.

An Experiment in Design and Analysis of Real-Time Applications

In the paper some experiences of joining two methodologies, which were originally independently developed in different institutions, with the goal to overcome the possible discrepancies due to the separate design of the hardware and the software part of an embedded real-time application are presented. Based on Multiprocessor PEARL, Specification PEARL has been developed in FERI, Maribor. Hardware and system architecture of an application can be described and gradually refined. Application software can be designed using LACATRE tool, developed at INSA, Lyon. Decisions about the application design taken in each tool have influence to the ones taken in the other, thus allowing for parallel design of both parts. Both designs are subsequently verified and eventually joined for feasibility estimation by co-simulation. The application program is coded using the ObjectPEARL language. The real-time system design cycle is closed by the execution time analysis and measurements upon which it is then considered about further program and/or hardware part reconfiguration. This feature is supported by the specific compiler, which includes the execution time analyser. The article reports on the work that was done in the framework of the PROTEUS project in co-operation of the teams from FERI Maribor, Slovenia, and INSA de Lyon, France.

Whey Changes during Processing Determined by Near Infrared Spectroscopy

ABSTRACTWe assessed the potential application of near infrared (NIR) spectrophotometry using a remote optic fiber probe for the development of calibrations. We evaluated the heat denaturation of whey proteins, the chemical composition of a whey protein concentrate (WPC) during production, and the degree of hydrolysis (DH) of whey proteins during trypsin hydrolysis. Partial least square (PLS) calibrations were developed for these applications and were related to a variety of products and processing conditions, so as to increase their reliability. NIR has potential applications in real time analysis for whey processing lines.

A comparative study and assessment of doppler ultrasound spectral estimation techniques part II: Methods and results

Various alternative spectral estimation methods are examined and compared in order to assess their possible application for real-time analysis of Doppler ultrasound arterial signals. Specifically, five general frequency domain models are examined, including the periodogram, the general autoregressive moving average (ARMA) model which has the autoregressive (AR) and moving average (MA) models as special cases, and Capon's maximum likelihood spectral model. A simulated stationary Doppler signal with a known theoretical spectrum was used as the reference test sequence, and white noise was added to enable various signal/noise conditions to be created. The performance of each method representative of each spectral model was assessed using both qualitative and quantitative schemes that convey information related to the bias and variance of the spectral estimates. Three integrated performance indices were implemented for quantitative analysis. The relative computational complexity for each algorithm was also investigated. Our results indicate that both the AR(Yule-Walker) and ARMA(singular value decomposition) models of orders (8) and (4, 4), respectively, show good agreement with the theoretical spectrum, and yield estimates with variances considerably less than the Fast Fourier Transform (FFT). Preliminary results obtained with these methods using a clinical, non-stationary Doppler signal supports these observations.

The “4X” real-time digital signal processor

We have realized a real-time digital signal processor for applications in real-time analysis and synthesis of sound and music. The 4X processor works as a peripheral connected to a host computer. A main feature of the 4X processor is its modularity. It consists of up to 16 elementary processors working in parallel. Each of these processors contains a data memory, an address memory, a microprogram memory, a function memory, a multiplier, and an arithmetic and logic unit. All the computations are done in 24-bit fixed point arithmetic. The 4X processor also contains 256 programmable clocks and a large dual buffer memory for recording and reproduction of signals to and from the host computer disk. The programming of the 4X, which is done in high level languages (FORTRAN and C), allows an easy programmation of the different possible structures of the machine, as oscillators, envelope generators, filters and so on. These capabilities make it possible to implement all known audio synthesis and processing techniques. The 4X processor is now used for musical composition, synthesis of aircraft noise for flight simulators, synthesis of underwater noise of submarines, audio mixing, and psychoacoustic stimulus generation.