Analysis On Drone Detection and Classification in LTE-Based Passive Forward Scattering Radar System

Long-Term Evolution (LTE) is most commonly used in connection with 4G networks with high spectral efficiency, high peak data rates, flexible in frequency and bandwidth. By utilizing LTE signal in passive forward scattering radar as transmitter, this system is able to create a microwave domain at the radar's receiver part which generated a moving object's Doppler signature. The emergence of guided missiles, humans, airplanes, and drones that travel through between the forward scatter radar systems can really be spotted with this passive radar system. This study's primary goal is to employ passive forward scattering radar and an LTE signal to detect drones, which are commonly used by individuals to violate or invade private and secure places. In detail, a drone was detected at two distinct heights of two meters (lower) and three meters (higher) from the ground by utilizing passive forward scattering radar to generate Doppler signature of the flying drone. This experimental work is conducted at two locations which are Taman Suria (UiTM, Shah Alam) and Teluk Kemang (Port Dickson), due to the telecommunication transmitter antenna transmits Long-Term Evolution (LTE) signal with frequency of 1.8 GHz and 2.6 GHz. The results of drone detection at various heights were evaluated using Principal Component Analysis (PCA) on all the experimental data obtained. According to the evaluation, the lower height of the drone performed better in classification and confusion matrices analysis than the upper height due to a larger cross-sectional area for the lower height of the drone that travelled through the forward scatter zone. In summary, the overall study clearly demonstrates the effective categorization of flying drone detection at upper and lower positions in Principle Component Analysis (PCA). For future contribution of this research, it can be used at the airport to detect any unwanted drones trespassing the flight departure area, and important areas such as the Federal Administrative Centre of Malaysia, Putrajaya for spying purposes

Analysis On Drone Detection and Classification in LTE-Based Passive Forward Scattering Radar System

Long-Term Evolution (LTE) is most commonly used in connection with 4G networks with high spectral efficiency, high peak data rates, flexible in frequency and bandwidth. By utilizing LTE signal in passive forward scattering radar as transmitter, this system is able to create a microwave domain at the radar's receiver part which generated a moving object's Doppler signature. The emergence of guided missiles, humans, airplanes, and drones that travel through between the forward scatter radar systems can really be spotted with this passive radar system. This study's primary goal is to employ passive forward scattering radar and an LTE signal to detect drones, which are commonly used by individuals to violate or invade private and secure places. In detail, a drone was detected at two distinct heights of two meters (lower) and three meters (higher) from the ground by utilizing passive forward scattering radar to generate Doppler signature of the flying drone. This experimental work is conducted at two locations which are Taman Suria (UiTM, Shah Alam) and Teluk Kemang (Port Dickson), due to the telecommunication transmitter antenna transmits Long-Term Evolution (LTE) signal with frequency of 1.8 GHz and 2.6 GHz. The results of drone detection at various heights were evaluated using Principal Component Analysis (PCA) on all the experimental data obtained. According to the evaluation, the lower height of the drone performed better in classification and confusion matrices analysis than the upper height due to a larger cross-sectional area for the lower height of the drone that travelled through the forward scatter zone. In summary, the overall study clearly demonstrates the effective categorization of flying drone detection at upper and lower positions in Principle Component Analysis (PCA). For future contribution of this research, it can be used at the airport to detect any unwanted drones trespassing the flight departure area, and important areas such as the Federal Administrative Centre of Malaysia, Putrajaya for spying purposes

Potential of data minimization techniques approach in improving DNA sequences alignment and comparison / Syed Abdul Mutalib Al Junid … [et al.]

Nowadays, the demand for advanced and high performance computational method for comparing and searching biological sequences have increased according to exponential growth rate of biological sequences database. Besides this demand, the requirement for high performance and sensitive comparison and alignment tools have also increased after the advantage of the system for defining the solution relates to Deoxyribonucleic Acid (DNA), human genomes and molecular biology figured out through bioinformatics study. Thereby, this research is an attempt to study and investigate the potential of data minimization technique approach in improving the process of aligning and comparing DNA sequences for determine the region of similarity between two or more DNA sequences. Specifically, the research objectives are i) to study various DNA sequences alignment algorithms characteristics, architectures and techniques such as scoring functions, optimal alignment and gap; and ii) to investigate the potential of data minimization approach in minimizing the size of data towards discovering the region of similarity in DNA sequences alignment and comparison and iii) to analyze the optimal sequences alignment properties for molecular biology and bioinformatics application. This study will initially involve algorithm modification, mathematical formulation, logic design and simulation for data minimization and existing DNA sequences alignment algorithm. At the end of the study, it is expecting that we will discover the potential of data minimization technique in minimizing the size of data towards aligning DNA sequences using basic dynamic programming concept

Performance study of the coexistence of wireless sensor networks (WSN) and wireless local area networks (WLAN)

This paper presents a special state machine of interference temperature multiple access (ITMA) that characterizes both interference and noise with single parameter in wireless sensor networks (WSN) application. By measuring current interference temperature before data transmission, cognitive node is able to determine transmit power and data rate that should be used to achieve desired capacity without violating interference temperature limit at other co-channel transceiver. Simulations of NS-2 are shown to simultaneously satisfy the three main objectives of extending battery life of cognitive node through optimum transmit power provision, maximizing the traffic carrying of networks capacity and limit interference to other coexist node. Hence, cognitive node is feasible to support QoS provisioning, and fairness among WSN and WLAN users

Building data driven culture for digital competitiveness in construction industry: a theoretical exploration

Despite studies related to big data in construction is growing, most of them have focused more on data application and less on the social element of the big data technology (e.g., data culture). This study aims to explore the key elements of data culture practices, towards providing a deeper insight into how they could drive digital competitiveness in the industry. A total of 136 papers related to big data in construction published in peer-reviewed journals were reviewed and analysed using the well-established systematic literature review (SLR) methodology. The findings indicate that four key drivers of data: data analytics, data literacy, data democratization and data leadership are critical for organisation’s competitiveness in digital environment. A framework for data driven culture is proposed consisting of five elements: culture of production, culture of use, cultivation of data, datafication and data infrastructure. As cultural shifts are complicated endeavors, exploring the key elements on what it takes to drive the data culture in construction is necessary for the development of an effective digital ecosystem of the organisation. This study extends the digital literature in the construction context by providing deeper insights into the conceptualisation of data driven culture

Building data driven culture for digital competitiveness in construction industry: a theoretical exploration

Despite studies related to big data in construction is growing, most of them have focused more on data application and less on the social element of the big data technology (e.g. data culture). This study aims to explore the key elements of data culture practices, towards providing a deeper insight into how they could drive digital competitiveness in the industry. A total of 136 papers related to big data in construction published in peer-reviewed journals were reviewed and analysed using the well-established systematic literature review (SLR) methodology. The findings indicate that four key drivers of data: data analytics, data literacy, data democratization and data leadership are critical for an organization’s competitiveness in a digital environment. A framework for data driven culture is proposed consisting of five elements: culture of production, culture of use, cultivation of data, datafication and data infrastructure. As cultural shifts are complicated endeavors, exploring the key elements on what it takes to drive the data culture in construction is necessary for the development of an effective digital ecosystem of the organization. This study extends the digital literature in the construction context by providing deeper insights into the conceptualization of data driven culture.</p