scholarly journals Secured Perimeter with Electromagnetic Detection and Tracking with Drone Embedded and Static Cameras

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7379
Author(s):  
Pedro Teixidó ◽  
Juan Antonio Gómez-Galán ◽  
Rafael Caballero ◽  
Francisco J. Pérez-Grau ◽  
José M. Hinojo-Montero ◽  
...  
Keyword(s):  
Fiber Optics ◽  
Weather Conditions ◽  
False Alarms ◽  
Motion Sensors ◽  
Detection Range ◽  
Detection Systems ◽  
Detection And Tracking ◽  
Sensing Technology ◽  
Electromagnetic Detection ◽  
Privacy Issues

Perimeter detection systems detect intruders penetrating protected areas, but modern solutions require the combination of smart detectors, information networks and controlling software to reduce false alarms and extend detection range. The current solutions available to secure a perimeter (infrared and motion sensors, fiber optics, cameras, radar, among others) have several problems, such as sensitivity to weather conditions or the high failure alarm rate that forces the need for human supervision. The system exposed in this paper overcomes these problems by combining a perimeter security system based on CEMF (control of electromagnetic fields) sensing technology, a set of video cameras that remain powered off except when an event has been detected. An autonomous drone is also informed where the event has been initially detected. Then, it flies through computer vision to follow the intruder for as long as they remain within the perimeter. This paper covers a detailed view of how all three components cooperate in harmony to protect a perimeter effectively, without having to worry about false alarms, blinding due to weather conditions, clearance areas, or privacy issues. The system also provides extra information of where the intruder is or has been, at all times, no matter whether they have become mixed up with more people or not during the attack.

scholarly journals Synthesis of algorithm of unmanned aerial vehicle detection by acoustic noise

Doklady BGUIR ◽  
2021 ◽  
Vol 19 (2) ◽  
pp. 65-73
Author(s):  
A. D. Puzanau ◽  
D. S. Nefedov
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Acoustic Noise ◽  
Signal To Noise Ratio ◽  
Weather Conditions ◽  
Wind Noise ◽  
Detection Range ◽  
Detection Systems ◽  
A Value ◽  
Coherent Accumulation ◽  
Aerial Vehicle

 The algorithm of detection of acoustic noise provided by an unmanned aerial vehicle (UAV) in the noise background due to wind is synthesized in the article. Creation of the algorithm has been carried out using the Neyman – Pearson lemma. The algorithm assumes a combination of the stages of wind noise coherent compensation and coherent accumulation of UAV’s acoustic noise sound pressure impulses. The coherent accumulation time matches doubled time of fluctuation correlation resulted by experimental research of acoustic noise of different types of  UAVs. Efficiency of the developed algorithm of UAV detection depends on flight velocity, foreshortening, amount of blades and rotor turnovers of UAV as well as weather conditions. For the probability of a false alarm value of 10–4, the probability of correct UAV detection value of 0.9 is provided wherein signal-to-noise ratio has a value of 8 dB. These indicators correspond the detection range of 200 to 300 meters. The obtained results allow discussions about perspective of acoustic UAVs detection systems adaptation. 

scholarly journals Analysis and improvement of methods for detecting shellcodes in computer systems

2021 ◽  
Vol 16 (92) ◽  
pp. 103-122
Author(s):  
Victor V. Erokhin ◽  
◽  
Larisa S. Pritchina ◽  
Keyword(s):  
Computational Complexity ◽  
Program Analysis ◽  
Code Generation ◽  
False Alarms ◽  
Detection Range ◽  
Detection Systems ◽  
Number Of False Alarms ◽  
System Service ◽  
Efficient Detection ◽  
Dynamic Program Analysis

The article discusses the problem of detecting and filtering shellcode – malicious executable code that contributes to the emergence of vulnerabilities in the operation of software applications with memory. The main such vulnerabilities are stack overflow, database overflow, and some other operating system service procedures. Currently, there are several dozen shellcode detection systems using both static and dynamic program analysis. Monitoring of existing systems has shown that methods with low computational complexity are characterized by a large percentage of false positives. Moreover, methods with a low percentage of false alarms are characterized by increased computational complexity. However, none of the currently existing solutions is able to detect all existing classes of shellcodes. This makes existing shellcode detection systems weakly applicable to real network links. Thus, the article discusses the problem of analyzing shellcode detection systems that provide complete detection of existing classes of shellcodes and are characterized by acceptable computational complexity and a small number of false alarms. This article introduces shellcode classifications and a comprehensive method of detecting them based on code emulation. This approach expands the detection range of shellcode classes that can be detected by concurrently evaluating several heuristics that correspond to low-level CPU operations during execution of various shellcode classes. The presented method allows efficient detection of simple and metamorphic shellcode. This is achieved regardless of the use of self-modifying code or dynamic code generation on which existing emulation-based polymorphic shellcode detectors are based.

scholarly journals A Feasibility Study of the Use of Smartwatches in Wearable Fall Detection Systems

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2254
Author(s):  
Francisco Javier González-Cañete ◽  
Eduardo Casilari
Keyword(s):  
Low Cost ◽  
Fall Detection ◽  
The Elderly ◽  
Point Of View ◽  
False Alarms ◽  
Battery Life ◽  
Area Network ◽  
Battery Lifetime ◽  
Detection Systems ◽  
Operational Aspects

Over the last few years, the use of smartwatches in automatic Fall Detection Systems (FDSs) has aroused great interest in the research of new wearable telemonitoring systems for the elderly. In contrast with other approaches to the problem of fall detection, smartwatch-based FDSs can benefit from the widespread acceptance, ergonomics, low cost, networking interfaces, and sensors that these devices provide. However, the scientific literature has shown that, due to the freedom of movement of the arms, the wrist is usually not the most appropriate position to unambiguously characterize the dynamics of the human body during falls, as many conventional activities of daily living that involve a vigorous motion of the hands may be easily misinterpreted as falls. As also stated by the literature, sensor-fusion and multi-point measurements are required to define a robust and reliable method for a wearable FDS. Thus, to avoid false alarms, it may be necessary to combine the analysis of the signals captured by the smartwatch with those collected by some other low-power sensor placed at a point closer to the body’s center of gravity (e.g., on the waist). Under this architecture of Body Area Network (BAN), these external sensing nodes must be wirelessly connected to the smartwatch to transmit their measurements. Nonetheless, the deployment of this networking solution, in which the smartwatch is in charge of processing the sensed data and generating the alarm in case of detecting a fall, may severely impact on the performance of the wearable. Unlike many other works (which often neglect the operational aspects of real fall detectors), this paper analyzes the actual feasibility of putting into effect a BAN intended for fall detection on present commercial smartwatches. In particular, the study is focused on evaluating the reduction of the battery life may cause in the watch that works as the core of the BAN. To this end, we thoroughly assess the energy drain in a prototype of an FDS consisting of a smartwatch and several external Bluetooth-enabled sensing units. In order to identify those scenarios in which the use of the smartwatch could be viable from a practical point of view, the testbed is studied with diverse commercial devices and under different configurations of those elements that may significantly hamper the battery lifetime.

scholarly journals Design of a Microwave Power Detection System in the 5G-Communication Frequency Band

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2674
Author(s):  
Qingying Ren ◽  
Wen Zuo ◽  
Jie Xu ◽  
Leisheng Jin ◽  
Wei Li ◽  
...  
Keyword(s):  
Frequency Band ◽  
Microwave Power ◽  
Communication Systems ◽  
Detection System ◽  
Measurement Sensitivity ◽  
Detection Range ◽  
Detection Systems ◽  
Detection Frequency ◽  
Communication Frequency ◽  
5G Communication

At present, the proposed microwave power detection systems cannot provide a high dynamic detection range and measurement sensitivity at the same time. Additionally, the frequency band of these detection systems cannot cover the 5G-communication frequency band. In this work, a novel microwave power detection system is proposed to measure the power of the 5G-communication frequency band. The detection system is composed of a signal receiving module, a power detection module and a data processing module. Experiments show that the detection frequency band of this system ranges from 1.4 GHz to 5.3 GHz, the dynamic measurement range is 70 dB, the minimum detection power is −68 dBm, and the sensitivity is 22.3 mV/dBm. Compared with other detection systems, the performance of this detection system in the 5G-communication frequency band is significantly improved. Therefore, this microwave power detection system has certain reference significance and application value in the microwave signal detection of 5G communication systems.

A New Approach to Pipeline Leak Detection Using Electromagnetic Sensing

2016 ◽  
Author(s):  
Chris Dawson ◽  
Stuart Inkpen ◽  
Chris Nolan ◽  
David Bonnell
Keyword(s):  
Leak Detection ◽  
False Alarms ◽  
Detection Accuracy ◽  
Minimal Risk ◽  
Detection Systems ◽  
Acoustic Signatures ◽  
Cost System ◽  
Trade Offs ◽  
In Situ Sensors ◽  
Operational Range

Many different approaches have been adopted for identifying leaks in pipelines. Leak detection systems, however, generally suffer from a number of difficulties and limitations. For existing and new pipelines, these inevitably force significant trade-offs to be made between detection accuracy, operational range, responsiveness, deployment cost, system reliability, and overall effectiveness. Existing leak detection systems frequently rely on the measurement of secondary effects such as temperature changes, acoustic signatures or flow differences to infer the existence of a leak. This paper presents an alternative approach to leak detection employing electromagnetic measurements of the material in the vicinity of the pipeline that can potentially overcome some of the difficulties encountered with existing approaches. This sensing technique makes direct measurements of the material near the pipeline resulting in reliable detection and minimal risk of false alarms. The technology has been used successfully in other industries to make critical measurements of materials under challenging circumstances. A number of prototype sensors were constructed using this technology and they were tested by an independent research laboratory. The test results show that sensors based on this technique exhibit a strong capability to detect oil, and to distinguish oil from water (a key challenge with in-situ sensors).

scholarly journals Sensing Technology Survey for Obstacle Detection in Vegetation

2021 ◽  
Vol 1 (3) ◽  
pp. 672-685
Author(s):  
Shreya Lohar ◽  
Lei Zhu ◽  
Stanley Young ◽  
Peter Graf ◽  
Michael Blanton
Keyword(s):  
Autonomous Vehicles ◽  
Weather Conditions ◽  
Climatic Conditions ◽  
Obstacle Detection ◽  
Agricultural Field ◽  
Thermal Camera ◽  
Agricultural Vehicles ◽  
Sensing Systems ◽  
Sensing Technology ◽  
Hyperspectral Sensing

This study reviews obstacle detection technologies in vegetation for autonomous vehicles or robots. Autonomous vehicles used in agriculture and as lawn mowers face many environmental obstacles that are difficult to recognize for the vehicle sensor. This review provides information on choosing appropriate sensors to detect obstacles through vegetation, based on experiments carried out in different agricultural fields. The experimental setup from the literature consists of sensors placed in front of obstacles, including a thermal camera; red, green, blue (RGB) camera; 360° camera; light detection and ranging (LiDAR); and radar. These sensors were used either in combination or single-handedly on agricultural vehicles to detect objects hidden inside the agricultural field. The thermal camera successfully detected hidden objects, such as barrels, human mannequins, and humans, as did LiDAR in one experiment. The RGB camera and stereo camera were less efficient at detecting hidden objects compared with protruding objects. Radar detects hidden objects easily but lacks resolution. Hyperspectral sensing systems can identify and classify objects, but they consume a lot of storage. To obtain clearer and more robust data of hidden objects in vegetation and extreme weather conditions, further experiments should be performed for various climatic conditions combining active and passive sensors.

scholarly journals Using a qualitative and quantitative validation methodology to evaluate a drone detection system

ACTA IMEKO ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 20
Author(s):  
Daniela Doroftei ◽  
Geert De Cubber
Keyword(s):  
Detection System ◽  
Weather Conditions ◽  
Careful Consideration ◽  
Technical Aspects ◽  
Operational Conditions ◽  
Qualitative And Quantitative ◽  
Detection Systems ◽  
Quantitative Score ◽  
Validation Procedure ◽  
Quantitative Validation

Now that the use of drones is becoming more common, the need to regulate the access to airspace for these systems is becoming more pressing. A necessary tool in order to do this is a means of detecting drones. Numerous parties have started the development of such drone detection systems. A big problem with these systems is that the evaluation of the performance of drone detection systems is a difficult operation that requires the careful consideration of all technical and non-technical aspects of the system under test. Indeed, weather conditions and small variations in the appearance of the targets can have a huge difference on the performance of the systems. In order to provide a fair evaluation, it is therefore paramount that a validation procedure that finds a compromise between the requirements of end users (who want tests to be performed in operational conditions) and platform developers (who want statistically relevant tests) is followed. Therefore, we propose in this article a qualitative and quantitative validation methodology for drone detection systems. The proposed validation methodology seeks to find this compromise between operationally relevant benchmarking (by providing qualitative benchmarking under varying environmental conditions) and statistically relevant evaluation (by providing quantitative score sheets under strictly described conditions).

Microscopic Traffic Data Collection by Remote Sensing

2003 ◽  
Vol 1855 (1) ◽  
pp. 121-128 ◽  
Author(s):  
S. P. Hoogendoorn ◽  
H. J. Van Zuylen ◽  
M. Schreuder ◽  
B. Gorte ◽  
G. Vosselman
Keyword(s):  
Data Collection ◽  
Digital Camera ◽  
Vehicle Detection ◽  
Weather Conditions ◽  
Lateral Position ◽  
Aerial Images ◽  
Detection And Tracking ◽  
Vehicle Trajectories ◽  
Vehicle Detection And Tracking ◽  
Traffic Data Collection

To gain insight into the behavior of drivers during congestion, and to develop and test theories and models that describe congested driving behavior, very detailed data are needed. A new data-collection system prototype is described for determining individual vehicle trajectories from sequences of digital aerial images. Software was developed to detect and track vehicles from image sequences. In addition to longitudinal and lateral position as a function of time, the system can determine vehicle length and width. Before vehicle detection and tracking can be achieved, the software handles correction for lens distortion, radiometric correction, and orthorectification of the image. The software was tested on data collected from a helicopter by a digital camera that gathered high-resolution monochrome images, covering 280 m of a Dutch motorway. From the test, it was concluded that the techniques for analyzing the digital images can be applied automatically without much problem. However, given the limited stability of the helicopter, only 210 m of the motorway could be used for vehicle detection and tracking. The resolution of the data collection was 22 cm. Weather conditions appear to have a significant influence on the reliability of the data: 98% of the vehicles could be detected and tracked automatically when conditions were good; this number dropped to 90% when the weather conditions worsened. Equipment for stabilizing the camera—gyroscopic mounting—and the use of color images can be applied to further improve the system.

Method for oil balance control under quasi-stationary modes of operation of an oil pipeline section with free flow

2021 ◽  
pp. 40-45
Author(s):  
Рустам Зайтунович Сунагатуллин ◽  
Антон Михайлович Чионов ◽  
Семен Васильевич Петренко
Keyword(s):  
Balance Control ◽  
Operation Mode ◽  
Rate Of Change ◽  
Free Flow ◽  
False Alarms ◽  
Oil Pipeline ◽  
Detection Systems ◽  
Pipeline Section ◽  
Number Of False Alarms ◽  
Oil Flow

Автоматизированные системы управления используются в нефтепроводном транспорте с целью автоматизации технологических процессов транспортировки нефти и нефтепродуктов, при этом основной задачей является обеспечение надежности и безопасности перекачки, что невозможно без контроля целостности трубопровода. В связи с этим актуальной остается тема обнаружения утечек, требуют продолжения исследования в области повышения надежности автоматизированных систем обнаружения утечек (СОУ). При эксплуатации СОУ особую важность представляет описание процессов заполнения и опорожнения участков трубопровода с безнапорным течением. Скорость установления стационарного режима работы таких участков и участков с полным сечением существенно отличается. Слабые возмущения давления могут приводить к значительному дебалансу расхода нефти и, как следствие, вызывать ложные срабатывания СОУ. Авторами представлен алгоритм вычисления скорости изменения запаса нефти на участке трубопровода при медленном изменении размера самотечной полости, на основании которого предложен способ корректировки уравнения баланса вещества. Показано использование разработанного алгоритма для повышения чувствительности СОУ и уменьшения количества ложных срабатываний. During the operation of leak detection systems (LDS), it is of great importance to describe the processes of filling and emptying pipeline free flow sections. The speed of establishing a stationary operation mode of such sections and full sections is significantly different. Weak pressure perturbations can lead to significant imbalance in the oil flow rate and, as a consequence, cause false LDS positives. The authors present an algorithm for calculating rate of change in oil reserve in the pipeline section with a slow change in the size of gravity cavity, on the basis of which a method for adjusting the substance balance equation is proposed. The use of a developed algorithm is shown to increase the sensitivity of LDS and reduce the number of false alarms.

Intrusion Detection of Cyber-Physical Attacks in Manufacturing Systems: A Review

2019 ◽  
Author(s):  
Mingtao Wu ◽  
Young B. Moon
Keyword(s):  
Intrusion Detection ◽  
Manufacturing Systems ◽  
High Volume ◽  
Cyber Attacks ◽  
Intrusion Detection Systems ◽  
Detection Methods ◽  
False Alarms ◽  
Alert Correlation ◽  
Acoustic Image ◽  
Detection Systems

Abstract Cyber-physical manufacturing system is the vision of future manufacturing systems where physical components are fully integrated through various networks and the Internet. The integration enables the access to computation resources that can improve efficiency, sustainability and cost-effectiveness. However, its openness and connectivity also enlarge the attack surface for cyber-attacks and cyber-physical attacks. A critical challenge in defending those attacks is that current intrusion detection methods cannot timely detect cyber-physical attacks. Studies showed that the physical detection provides a higher accuracy and a shorter respond time compared to network-based or host-based intrusion detection systems. Moreover, alert correlation and management methods help reducing the number of alerts and identifying the root cause of the attack. In this paper, the intrusion detection research relevant to cyber-physical manufacturing security is reviewed. The physical detection methods — using side-channel data, including acoustic, image, acceleration, and power consumption data to disclose attacks during the manufacturing process — are analyzed. Finally, the alert correlation methods — that manage the high volume of alerts generated from intrusion detection systems via logical relationships to reduce the data redundancy and false alarms — are reviewed. The study show that the cyber-physical attacks are existing and rising concerns in industry. Also, the increasing efforts in cyber-physical intrusion detection and correlation research can be utilized to secure the future manufacturing systems.

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