scholarly journals Improved Performance of Trash Detection and Human Target Detection Systems using Robot Operating System (ROS)

2021 ◽  
Vol 17 (2) ◽  
Author(s):  
Kisron Kisron ◽  
Bima Sena Bayu Dewantara ◽  
Hary Oktavianto
Keyword(s):  
Operating System ◽  
Computer System ◽  
Target Detection ◽  
Detection System ◽  
Computation Time ◽  
Cascade Algorithm ◽  
Histogram Of Oriented Gradient ◽  
Detection Systems ◽  
Improved Performance ◽  
Improved Accuracy

In a visual-based real detection system using computer vision, the most important thing that must be considered is the computation time. In general, a detection system has a heavy algorithm that puts a strain on the performance of a computer system, especially if the computer has to handle two or more different detection processes. This paper presents an effort to improve the performance of the trash detection system and the target partner detection system of a trash bin robot with social interaction capabilities. The trash detection system uses a combination of the Haar Cascade algorithm, Histogram of Oriented Gradient (HOG) and Gray-Level Coocurrence Matrix (GLCM). Meanwhile, the target partner detection system uses a combination of Depth and Histogram of Oriented Gradient (HOG) algorithms. Robotic Operating System (ROS) is used to make each system in separate modules which aim to utilize all available computer system resources while reducing computation time. As a result, the performance obtained by using the ROS platform is a trash detection system capable of running at a speed of 7.003 fps. Meanwhile, the human target detection system is capable of running at a speed of 8,515 fps. In line with the increase in fps, the accuracy also increases to 77%, precision increases to 87,80%, recall increases to 82,75%, and F1-score increases to 85,20% in trash detection, and the human target detection system has also improved accuracy to 81%, %, precision increases to 91,46%, recall increases to 86,20%, and F1-score increases to 88,42%.

scholarly journals An Early Flame Detection System Based on Image Block Threshold Selection Using Knowledge of Local and Global Feature Analysis

2020 ◽  
Vol 12 (21) ◽  
pp. 8899
Author(s):  
Ting Wei Hsu ◽  
Shreya Pare ◽  
Mahendra Singh Meena ◽  
Deepak Kumar Jain ◽  
Dong Lin Li ◽  
...  
Keyword(s):  
Detection System ◽  
Global Analysis ◽  
Computation Time ◽  
Fire Detection ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Image Block ◽  
Detection Systems ◽  
Spatial Features ◽  
Flame Detection

Fire is one of the mutable hazards that damage properties and destroy forests. Many researchers are involved in early warning systems, which considerably minimize the consequences of fire damage. However, many existing image-based fire detection systems can perform well in a particular field. A general framework is proposed in this paper which works on realistic conditions. This approach filters out image blocks based on thresholds of different temporal and spatial features, starting with dividing the image into blocks and extraction of flames blocks from image foreground and background, and candidates blocks are analyzed to identify local features of color, source immobility, and flame flickering. Each local feature filter resolves different false-positive fire cases. Filtered blocks are further analyzed by global analysis to extract flame texture and flame reflection in surrounding blocks. Sequences of successful detections are buffered by a decision alarm system to reduce errors due to external camera influences. Research algorithms have low computation time. Through a sequence of experiments, the result is consistent with the empirical evidence and shows that the detection rate of the proposed system exceeds previous studies and reduces false alarm rates under various environments.

A Host-Based Intrusion Detection System Using Architectural Features to Improve Sophisticated Denial-of-Service Attack Detections

2010 ◽  
Vol 4 (1) ◽  
pp. 18-31
Author(s):  
Ran Tao ◽  
Li Yang ◽  
Lu Peng ◽  
Bin Li
Keyword(s):  
Operating System ◽  
Intrusion Detection ◽  
Detection System ◽  
Denial Of Service ◽  
Hardware Architecture ◽  
Intrusion Detection Systems ◽  
System Level ◽  
Gradient Boosting ◽  
Dos Attacks ◽  
Detection Systems

Application features like port numbers are used by Network-based Intrusion Detection Systems (NIDSs) to detect attacks coming from networks. System calls and the operating system related information are used by Host-based Intrusion Detection Systems (HIDSs) to detect intrusions toward a host. However, the relationship between hardware architecture events and Denial-of-Service (DoS) attacks has not been well revealed. When increasingly sophisticated intrusions emerge, some attacks are able to bypass both the application and the operating system level feature monitors. Therefore, a more effective solution is required to enhance existing HIDSs. In this article, the authors identify the following hardware architecture features: Instruction Count, Cache Miss, Bus Traffic and integrate them into a HIDS framework based on a modern statistical Gradient Boosting Trees model. Through the integration of application, operating system and architecture level features, the proposed HIDS demonstrates a significant improvement of the detection rate in terms of sophisticated DoS intrusions.

scholarly journals Automatic Detection of Osteosarcoma Based on Integrated Features and Feature Selection Using Binary Arithmetic Optimization Algorithm

2021 ◽  
Author(s):  
Priti Bansal ◽  
Kshitiz Gehlot ◽  
Abhishek Singhal
Keyword(s):  
Feature Selection ◽  
Deep Learning ◽  
Optimization Algorithm ◽  
New Technologies ◽  
Detection System ◽  
Computation Time ◽  
Automatic Detection ◽  
Detection Systems ◽  
Viable Tumor ◽  
Automatic Detection System

Abstract Osteosarcoma is one of the most common malignant bone tumor mostly found in children and teenagers. Manual detection of osteosarcoma requires expertise and is a labour-intensive process. If detected on time, the mortality rate can be reduced. With the advent of new technologies, automatic detection systems are used to analyse and classify images obtained from different sources. Here, we propose an automatic detection system Integrated Features-Feature Selection Model for Classification (IF-FSM-C) that detect osteosarcoma from the high-resolution whole slide images (WSIs). The novelty of the proposed approach is the use of integrated features obtained by fusion of features extracted using traditional handcrafted feature extraction techniques and deep learning models. It is quite possible that the integrated features may contain some redundant and irrelevant features which may unnecessarily increases the computation time and leads to wastage of resources. To avoid this, we perform feature selection (FS) before giving the integrated features to the classifier. To perform feature selection, we propose two binary variants of recently proposed Arithmetic Optimization Algorithm (AOA) known as BAOA-S and BAOA-V. The selected features are given to a classifier that classifies the WSIs into Viable tumor (VT), Non-viable tumor (NVT) and non-tumor (NT). Experiments are performed and the results prove the superiority of the proposed IF-FSM-C that uses integrated features and feature selection in classifying WSIs as compared to the classifiers which use handcrafted or deep learning features alone as well as state-of-the-art methods for osteosarcoma detection.

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.

Mini-ATX Computer System in Vehicle

2015 ◽  
Vol 1115 ◽  
pp. 484-487 ◽  
Author(s):  
Muhammad Sami ◽  
Akram M. Zeki
Keyword(s):  
Operating System ◽  
Computer System ◽  
Linux Kernel

The aim of this study is to create and assemble the system with customizing/building Linux kernel and environments to be compatible and efficient on mini-ITX computer. The objective of the study is to create/customizing lightweight operating system using GNU/Linux to be used on computer to be used on vehicle. The system would also optimize the size and functionalities most probably would be implemented on car computer system.Keywords: mini-ATX, CarPC, Linux, Ubuntu, Qt, QML

FPGA-Based Configurable Frequency-Diverse Ultrasonic Target-Detection System

2011 ◽  
Vol 58 (3) ◽  
pp. 871-879 ◽  
Author(s):  
Joshua Weber ◽  
Erdal Oruklu ◽  
Jafar Saniie
Keyword(s):  
Target Detection ◽  
Detection System

Fast Automatic Target Detection System Based on a Visible Image Sensor and Ripple Algorithm

2013 ◽  
Vol 13 (7) ◽  
pp. 2720-2728 ◽  
Author(s):  
Jinhui Lan ◽  
Jian Li ◽  
Yong Xiang ◽  
Tonghuan Huang ◽  
Yixin Yin ◽  
...  
Keyword(s):  
Target Detection ◽  
Detection System ◽  
Image Sensor ◽  
Visible Image ◽  
Automatic Target Detection

Pipeline Diagnostics With Ultrasonic Meters

2016 ◽  
Author(s):  
Nicole Gailey ◽  
Noman Rasool
Keyword(s):  
Real Time ◽  
Measurement Errors ◽  
Flow Measurement ◽  
Detection System ◽  
Leak Detection ◽  
The United States ◽  
Time Data ◽  
Critical System ◽  
Detection Systems ◽  
Transient Models

Canada and the United States have vast energy resources, supported by thousands of kilometers (miles) of pipeline infrastructure built and maintained each year. Whether the pipeline runs through remote territory or passing through local city centers, keeping commodities flowing safely is a critical part of day-to-day operation for any pipeline. Real-time leak detection systems have become a critical system that companies require in order to provide safe operations, protection of the environment and compliance with regulations. The function of a leak detection system is the ability to identify and confirm a leak event in a timely and precise manner. Flow measurement devices are a critical input into many leak detection systems and in order to ensure flow measurement accuracy, custody transfer grade liquid ultrasonic meters (as defined in API MPMS chapter 5.8) can be utilized to provide superior accuracy, performance and diagnostics. This paper presents a sample of real-time data collected from a field install base of over 245 custody transfer grade liquid ultrasonic meters currently being utilized in pipeline leak detection applications. The data helps to identify upstream instrumentation anomalies and illustrate the abilities of the utilization of diagnostics within the liquid ultrasonic meters to further improve current leak detection real time transient models (RTTM) and pipeline operational procedures. The paper discusses considerations addressed while evaluating data and understanding the importance of accuracy within the metering equipment utilized. It also elaborates on significant benefits associated with the utilization of the ultrasonic meter’s capabilities and the importance of diagnosing other pipeline issues and uncertainties outside of measurement errors.

scholarly journals Target Detection System (Tds) for Enhancing Security in Ad Hoc Network

2013 ◽  
Vol 4 (5) ◽  
pp. 15-28
Author(s):  
Hoshiyar Singh Kanyal ◽  
Rahamatkar S ◽  
Sharma B.K ◽  
Bhasker Sharma
Keyword(s):  
Target Detection ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Detection System
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