scholarly journals Detection of a wide range of viruses and viroids collected in Nara Prefecture fields by mRNA-Seq and evaluation of the detection system

2020 ◽  
Vol 62 (0) ◽  
pp. 39-45
Author(s):  
Shunsuke Asano ◽  
Kandai Yoshida ◽  
Yoshihiko Hirayama
Keyword(s):  
Detection System ◽  
Wide Range ◽  
Nara Prefecture

Research on System Architecture to Provide Maximum Security, End User Device Independency and User Centric Control over Content in Cloud

2013 ◽  
Vol 4 (3) ◽  
pp. 38-52
Author(s):  
Sai Manoj Marepalli ◽  
Razia Sultana ◽  
Andreas Christ
Keyword(s):  
Cloud Computing ◽  
Detection System ◽  
Cloud Service ◽  
Service Level ◽  
Mobile Technologies ◽  
Communication Structure ◽  
Layered Architecture ◽  
Wide Range ◽  
The Impact ◽  
Cloud User

Cloud computing is the emerging technology providing IT as a utility through internet. The benefits of cloud computing are but not limited to service based, scalable, elastic, shared pool of resources, metered by use. Due to mentioned benefits the concept of cloud computing fits very well with the concept of m-learning which differs from other forms of e-learning, covers a wide range of possibilities opened up by the convergence of new mobile technologies, wireless communication structure and distance learning development. The concept of cloud computing like any other concept has not only benefits but also introduces myriad of security issues, such as transparency between cloud user and provider, lack of standards, security concerns related to identity, Service Level Agreements (SLA) inadequacy etc. Providing secure, transparent, and reliable services in cloud computing environment is an important issue. This paper introduces a secured three layered architecture with an advance Intrusion Detection System (advIDS), which overcomes different vulnerabilities on cloud deployed applications. This proposed architecture can reduce the impact of different attacks by providing timely alerts, rejecting the unauthorized access over services, and recording the new threat profiles for future verification. The goal of this research is to provide more control over data and applications to the cloud user, which are now mainly controlled by Cloud Service Provider (CSP).

Hybrid Intrusion Detection Framework for Ad hoc networks

2016 ◽  
Vol 10 (4) ◽  
pp. 1-32 ◽  
Author(s):  
Abdelaziz Amara Korba ◽  
Mehdi Nafaa ◽  
Salim Ghanemi
Keyword(s):  
Intrusion Detection ◽  
Anomaly Detection ◽  
Ad Hoc Networks ◽  
Intrusion Detection System ◽  
Ad Hoc ◽  
Detection System ◽  
Security Framework ◽  
Detection Techniques ◽  
Wide Range ◽  
Hoc Networks

In this paper, a cluster-based hybrid security framework called HSFA for ad hoc networks is proposed and evaluated. The proposed security framework combines both specification and anomaly detection techniques to efficiently detect and prevent wide range of routing attacks. In the proposed hierarchical architecture, cluster nodes run a host specification-based intrusion detection system to detect specification violations attacks such as fabrication, replay, etc. While the cluster heads run an anomaly-based intrusion detection system to detect wormhole and rushing attacks. The proposed specification-based detection approach relies on a set of specifications automatically generated, while anomaly-detection uses statistical techniques. The proposed security framework provides an adaptive response against attacks to prevent damage to the network. The security framework is evaluated by simulation in presence of malicious nodes that can launch different attacks. Simulation results show that the proposed hybrid security framework performs significantly better than other existing mechanisms.

Programmed Temperature Gas Chromatography (PTGC) and Microcoulometric Detection of Chlorinated Insecticides

1963 ◽  
Vol 46 (2) ◽  
pp. 198-204
Author(s):  
Jerry Burke
Keyword(s):  
Gas Chromatography ◽  
Detection System ◽  
Operating Parameters ◽  
Gas Chromatograph ◽  
Relative Retention ◽  
Wide Range ◽  
Isothermal Gas

Abstract Commercial models of a programmed temperature gas chromatograph and microcoulometric detection system were combined to study programmed temperature gas chromatography (PTGC) of chlorinated insecticides. PTGC permitted the analysis of samples containing materials whose volatilities cover a wide range. PTGC produced better chromatograms and was faster than isothermal gas chromatography. PTGC did not achieve separations which were not possible by isothermal gas chromatography. Operating parameters for a PTGC residue screening operation were determined. Relative retention times at these conditions are given for 22 pesticides. Other advantages of the PTGC instrumentation are noted.

An Automated Detection System for Most L-α-Amino Acids

1969 ◽  
Vol 15 (2) ◽  
pp. 154-161 ◽  
Author(s):  
K Van Dyke ◽  
C Szustkiewicz
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Detection System ◽  
Automated System ◽  
Acid Oxidase ◽  
Enzymatic Reactions ◽  
Amino Acid Oxidase ◽  
Automated Method ◽  
Wide Range

Abstract An automated system for the determination of the L-α form of the majority of amino acids is presented. The method is based upon oxidative deamination of the amino acid coupled with oxidation of o-dianisidine by hydrogen peroxide. This procedure can be used comparatively for the determination of a mixture of L-α-amino acids or for the majority of separated L-α-amino acids (especially in conjunction with column separations from urine and blood which give falsely positive identification with ninhydrin detection). The stereospecific nature of the L-α-amino acid oxidase enables the investigator to quantitate the amount of L-α-amino acid in the presence of the D-α form. From an academic viewpoint, the extreme sensitivity and wide range of the detection system make it advantageous for the study of the enzyme itself. This automated method also may be employed to follow enzymatic reactions—e.g., those catalyzed by peptidases or racemases. The methodology is extremely convenient with good reagent stability and is much more sensitive than manometric technics.

scholarly journals High-Precision Noncontact Guided Wave Tomographic Imaging of Plate Structures Using a DHB Algorithm

2020 ◽  
Vol 10 (12) ◽  
pp. 4360
Author(s):  
Junpil Park ◽  
Jaesun Lee ◽  
Zong Le ◽  
Younho Cho
Keyword(s):  
Nuclear Power ◽  
Power Plants ◽  
Detection Efficiency ◽  
Detection System ◽  
Automatic Detection ◽  
Guided Wave ◽  
Direct Access ◽  
Experimental Conditions ◽  
Plate Structures ◽  
Wide Range

The safety diagnostic inspection of large plate structures, such as nuclear power plant containment liner plates and aircraft wings, is an important issue directly related to the safety of life. This research intends to present a more quantitative defect imaging in the structural health monitoring (SHM) technique by using a wide range of diagnostic techniques using guided ultrasound. A noncontact detection system was applied to compensate for such difficulties because direct access inspection is not possible for high-temperature and massive areas such as nuclear power plants and aircraft. Noncontact systems use unstable pulse laser and air-coupled transducers. Automatic detection systems were built to increase inspection speed and precision and the signal was measured. In addition, a new Difference Hilbert Back Projection (DHB) algorithm that can replace the reconstruction algorithm for the probabilistic inspection of damage (RAPID) algorithm used for imaging defects has been successfully applied to quantitative imaging of plate structure defects. Using an automatic detection system, the precision and detection efficiency of data collection has been greatly improved, and the same results can be obtained by reducing errors in experimental conditions that can occur in repeated experiments. Defects were made in two specimens, and comparative analysis was performed to see if each algorithm can quantitatively represent defects in multiple defects. The new DHB algorithm presented the possibility of observing and predicting the growth direction of defects through the continuous monitoring system.

scholarly journals An Anomaly-Based Intrusion Detection System for Internet of Medical Things Networks

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2562
Author(s):  
Georgios Zachos ◽  
Ismael Essop ◽  
Georgios Mantas ◽  
Kyriakos Porfyrakis ◽  
José C. Ribeiro ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Computational Cost ◽  
Healthcare Sector ◽  
The Internet ◽  
Full Potential ◽  
Log Files ◽  
Wide Range ◽  
Internet Of Medical Things

Over the past few years, the healthcare sector is being transformed due to the rise of the Internet of Things (IoT) and the introduction of the Internet of Medical Things (IoMT) technology, whose purpose is the improvement of the patient’s quality of life. Nevertheless, the heterogenous and resource-constrained characteristics of IoMT networks make them vulnerable to a wide range of threats. Thus, novel security mechanisms, such as accurate and efficient anomaly-based intrusion detection systems (AIDSs), considering the inherent limitations of the IoMT networks, need to be developed before IoMT networks reach their full potential in the market. Towards this direction, in this paper, we propose an efficient and effective anomaly-based intrusion detection system (AIDS) for IoMT networks. The proposed AIDS aims to leverage host-based and network-based techniques to reliably collect log files from the IoMT devices and the gateway, as well as traffic from the IoMT edge network, while taking into consideration the computational cost. The proposed AIDS is to rely on machine learning (ML) techniques, considering the computation overhead, in order to detect abnormalities in the collected data and thus identify malicious incidents in the IoMT network. A set of six popular ML algorithms was tested and evaluated for anomaly detection in the proposed AIDS, and the evaluation results showed which of them are the most suitable.

scholarly journals Reduct ECOC Framework for Network Intrusion Detection System

2020 ◽  
Vol 9 (3) ◽  
pp. 258-266
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Class Imbalance ◽  
Classification Problem ◽  
Network Intrusion ◽  
Wide Range ◽  
Multi Class Classification ◽  
Network Technologies ◽  
Better Than

Now a day’s network security is major concern for e-government and e-commerce applications. A wide range of malicious activities are increasing with the usage of internet and network technologies. Identifying novel threats and finding modern solutions for network to prevent from these threats are important. Designing an effective intrusion detection system is significant to continuously look out the network activities to efficiently thwart malicious attacks or to identify the intruders. To tackle multi class imbalance classification problem in networks, a reduct based ECOC ensemble framework for NIDS is proposed to efficiently identify attacks in a multi class scenario. The Reduct-ECOC classifier is validated on highly imbalanced benchmark NSL-KDD intrusion datasets as well as other UCI-ML datasets. The experimental results on eight highly imbalanced datasets show that Reduct-ECOC classifier performs better than many other state-of-art multi-class classification ECOC learning methods.

scholarly journals A Performance Comparison and Enhancement of Animal Species Detection in Images with Various R-CNN Models

AI ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 552-577
Author(s):  
Mai Ibraheam ◽  
Kin Fun Li ◽  
Fayez Gebali ◽  
Leonard E. Sielecki
Keyword(s):  
Object Detection ◽  
Network Architecture ◽  
Animal Species ◽  
Detection System ◽  
Real Life ◽  
Medical Diagnostics ◽  
Performance Comparison ◽  
Species Detection ◽  
Detection Techniques ◽  
Wide Range

Object detection is one of the vital and challenging tasks of computer vision. It supports a wide range of applications in real life, such as surveillance, shipping, and medical diagnostics. Object detection techniques aim to detect objects of certain target classes in a given image and assign each object to a corresponding class label. These techniques proceed differently in network architecture, training strategy and optimization function. In this paper, we focus on animal species detection as an initial step to mitigate the negative impacts of wildlife–human and wildlife–vehicle encounters in remote wilderness regions and on highways. Our goal is to provide a summary of object detection techniques based on R-CNN models, and to enhance the performance of detecting animal species in accuracy and speed, by using four different R-CNN models and a deformable convolutional neural network. Each model is applied on three wildlife datasets, results are compared and analyzed by using four evaluation metrics. Based on the evaluation, an animal species detection system is proposed.

scholarly journals Analysis of an open source, closed-loop, realtime system for hippocampal sharp-wave ripple disruption

2018 ◽  
Author(s):  
Shayok Dutta ◽  
Etienne Ackermann ◽  
Caleb Kemere
Keyword(s):  
Open Source ◽  
Neural Activity ◽  
Closed Loop ◽  
Detection System ◽  
Detection Accuracy ◽  
Sharp Wave ◽  
Trade Offs ◽  
Wide Range ◽  
Realtime System

AbstractTransient neural activity pervades hippocampal electrophysiological activity. During more quiescent states, brief ≈100 ms periods comprising large ≈150–250 Hz oscillations known as sharp-wave ripples (SWR) which co-occur with ensemble bursts of spiking activity, are regularly found in local field potentials recorded from area CA1. SWRs and their concomitant neural activity are thought to be important for memory consolidation, recall, and memory-guided decision making. Temporally-selective manipulations of hippocampal neural activity upon online hippocampal SWR detection have been used as causal evidence of the importance of SWR for mnemonic process as evinced by behavioral and/or physiological changes. However, though this approach is becoming more wide spread, the performance trade-offs involved in building a SWR detection and disruption system have not been explored, limiting the design and interpretation of SWR detection experiments. We present an open source, plug-and-play, online ripple detection system with a detailed performance characterization. Our system has been constructed to interface with an open source software platform, Trodes, and two hardware acquisition platforms, Open Ephys and SpikeGadgets. We show that our in vivo results — approximately 80% detection latencies falling in between ≈20–66 ms with ≈2 ms closed-loop latencies while maintaining <10 false detections per minute — are dependent upon both algorithmic trade-offs and acquisition hardware. We discuss strategies to improve detection accuracy and potential limitations of online ripple disruptions. By characterizing this system in detail, we present a template for analyzing other closed-loop neural detection and perturbation systems. Thus, we anticipate our modular, open source, realtime system will facilitate a wide range of carefully-designed causal closed-loop neuroscience experiments.

scholarly journals Development of a high-throughput real-time PCR system for detection of enzootic pathogens in pigs

2019 ◽  
Vol 32 (1) ◽  
pp. 51-64 ◽  
Author(s):  
Nicole B. Goecke ◽  
Charlotte K. Hjulsager ◽  
Jesper S. Krog ◽  
Kerstin Skovgaard ◽  
Lars E. Larsen
Keyword(s):  
Real Time ◽  
High Throughput ◽  
Real Time Pcr ◽  
Pathogen Detection ◽  
Detection System ◽  
Working Hours ◽  
Negative Influence ◽  
Optimal Choice ◽  
National Veterinary Institute ◽  
Wide Range

Respiratory and intestinal diseases in pigs can have significant negative influence on productivity and animal welfare. A wide range of real-time PCR (rtPCR) assays are used in our laboratory (National Veterinary Institute, Technical University of Denmark) for pathogen detection, and PCR analyses are performed on traditional rtPCR platforms in which a limited number of samples can be analyzed per day given limitations in equipment and personnel. To mitigate these restrictions, rtPCR assays have been optimized for the high-throughput rtPCR BioMark platform (Fluidigm). Using this platform, we developed a high-throughput detection system that can be used for simultaneous examination of 48 samples with detection specificity for 18 selected respiratory and enteric viral and bacterial pathogens of high importance to Danish pig production. The rtPCR assays were validated and optimized to run under the same reaction conditions using a BioMark 48.48 dynamic array (DA) integrated fluidic circuit chip, and the sensitivity and specificity were assessed by testing known positive samples. Performance of the 48.48DA was similar to traditional rtPCR analysis, and the specificity of the 48.48DA was high. Application of the high-throughput platform has resulted in a significant reduction in cost and working hours and has provided production herds with a new innovative service with the potential to facilitate the optimal choice of disease control strategies such as vaccination and treatment.

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