9 research outputs found
Design and Analysis of Wideband Nonuniform Branch Line Coupler and Its Application in a Wideband Butler Matrix
This paper presents a novel wideband nonuniform branch line coupler. An exponential impedance taper is inserted, at the series arms of the branch line coupler, to enhance the bandwidth. The behavior of the nonuniform coupler was mathematically analyzed, and its design of scattering matrix was derived. For a return loss better than 10 dB, it achieved 61.1% bandwidth centered at 9 GHz. Measured coupling magnitudes and phase exhibit good dispersive characteristic. For the 1 dB magnitude difference and phase error within 3∘, it achieved 22.2% bandwidth centered at 9 GHz. Furthermore, the novel branch line coupler was implemented for a wideband crossover. Crossover was constructed by cascading two wideband nonuniform branch line couplers. These components were employed to design a wideband Butler Matrix working at 9.4 GHz. The measurement results show that the reflection coefficient between the output ports is better than 18 dB across 8.0 GHz–9.6 GHz, and the overall phase error is less than 7∘
Rancang Bangun Perangkat Lunak Sistem Auto Tracking Satellite Antenna Mobile Menggunakan Metode Azimut-elevasi Dan Koreksi Modem
Software Design of Mobile Antenna for Auto Satellite Tracking using Modem Correction and Elevation AzimuthMethod. Pointing accuracy is an important thing in satellite communication. Because the satellite’s distance to thesurface of the earth\u27s satellite is so huge, thus 1 degree of pointing error will make the antenna can not send data tosatellites. To overcome this, the auto-tracking satellite controller is made. This system uses a microcontroller as thecontroller, with the GPS as the indicator location of the antenna, digital compass as the beginning of antenna pointingdirection, rotary encoder as sensor azimuth and elevation, and modem to see Eb/No signal. The microcontroller useserial communication to read the input. Thus the programming should be focused on in the UART and serialcommunication software UART. This controller use 2 phase in the process of tracking satellites. Early stages is themethod Elevation-Azimuth, where at this stage with input from GPS, Digital Compass, and the position of satellites(both coordinates, and height) that are stored in microcontroller. Controller will calculate the elevation and azimuthangle, then move the antenna according to the antenna azimuth and elevation angle. Next stages is correction modem,where in this stage controller only use modem as the input, and antenna movement is set up to obtain the largest valueof Eb/No signal. From the results of the controller operation, there is a change in the value of the original input levelfrom -81.7 dB to -30.2 dB with end of Eb/No value, reaching 5.7 dB
Pengembangan Antena Mikrostrip Susun Dua Elemen Dengan Penerapan Defected Ground Structure Berbentuk Trapesium
Two Element Microstrip Antenna Array with Defected Ground Structure. This paper presents a two elementmicrostrip antenna array using trapezium shape defected ground structure (DGS). The DGS is inserted in the groundplane between two elements of antenna array. Insertion of the DGS is intended to suppress the mutual coupling effectproduced by antenna array. Simulation and measurement results were taken and compared between antenna array withand without DGS. Measurement results show that the antenna with DGS compared to antenna without DGS cansuppress mutual coupling effect to 7.9 dB, improve the return loss to 33.29% from -30.188 dB to -40.24 dB and axialratio bandwidth enhancement to 10 MHz. This bandwidth enhancement is achieved from frequency 2.63 GHz – 2.67GHz for antenna without DGS and from frequency 2.63 GHz – 2.68 GHz for antenna with DGS. In addition, the DGSantenna also improved the antenna gain to 0.6 dB. The results show that the implementation of the trapezium DGS canimprove the radiation properties of the antenna without DGS
Resolving Engineering, Industrial and Healthcare Challenges through AI-Driven Applications
The
recent technological advances have proven to be successful in facilitating
various strenuous activities and improving daily life performance. Furthermore,
the public has been amazed by the presence of Artificial Intelligence. Artificial
Intelligence, often known as AI, is a type of technology in the field of
computer science that has special abilities to solve problems. With its
intelligence, which is said to be able to compete with human cognitive
abilities, AI technology is, in fact, able to help a variety of human jobs,
from easy to complex ones.The
first work which is now recognized as AI was done by Warren McCulloch and
Walter Pitts in 1943 as they proposed a model of artificial neurons. Later from
that day, research in machine learning were florished. Therefore, Alan Turing
who was an english mathematician proposed a test to asses the machine's ability
to exhibit intelligent behavior equivalent to human intelligence. The word
artificial intelligence was first adopted by American computer scientist, John
McCarthy at the Dartmouth Conference for the first time. The finding of several
computer language such as Fortran, LISP or COBOL marked the enthusiasm for AI
at that time.The
era of AI had several idle development along the way which called as AI winter
in 1974 to 1980 and 1987-1993. This era refers to the time period where
computer scientists dealt with a severe shortage of funding from government or
companies. Until the year 1997, the IBM Deep Blue became the first computer to
beat a world chess champion, the emergence of AI never went under. Companies
like Facebook, Twitter and Netflix also started using AI deep learning, big
data and artificial general intelligence since the 2006.   The applications of AI are
vast, including in industrial automation, healthcare, transportation, finance,
entertainment, and more. AI continues to develop along with advances in
technology and research, with the ultimate goal of creating systems that have
levels of intelligence and capabilities that increasingly approach human
capabilities. Artificial intelligence also faces numerous
debates regarding potential impacts on individuals. Although it could be risky,
it's also offering a fantastic opportunity. It is estimated that the global Artificial
Intelligence market will reach 267 billion dollars by 2027
Digital Innovation: Creating Competitive Advantages
The diffusion of innovations during the fourth
industrial revolution reshaped economic systems and caused structural changes
in different economic sectors. These innovations have become the basis of the
new digital infrastructure of society. Digital technology is used to manage integrated
product whole-life cycles and enhance efficient, reliable, and sustainable
business operations. Intelligent production processes and supply chains can be
used to optimize entire end-to-end workflows and create business competitive
advantages. Artificial intelligence, internet of things, machine learning,
blockchain, big data and other digital technologies have been used to create
business agility and resilience and further transform societal behavior.Digitalization creates new ways for companies to
create business added value. Modernizing business enterprises by combining
digital technologies, physical resources, and the creativity of individuals, is
an essential step in innovative business transformation that may constitute a
competitive advantage. Companies need to transform their business
processes and enhance the satisfaction of their customers by using digital
technologies that connect people, systems, and products or render their
services more effective and efficient. Digital technologies create new ways for
companies to integrate customers’ requirements into product development or
service delivery across entire process chains.
Digital
technologies are becoming increasingly important due to strong market
competition. Many studies have shown that there is a strong correlation between
business growth and the use of digital technologies to create innovative
business models. Technological innovations create new products, processes, and
services that generate more added value for companies. 
Accelerating Sustainable Energy Development through Industry 4.0 Technologies
Utilizing
Industry 4.0 technologies to create a sustainable energy industry enables a decentralized
energy system in which energy can be effectively produced, managed, and
controlled from local resources. Furthermore, the technologies also enable data
capture and analysis to improve energy performance. As digital energy is being
developed and increasingly decentralized, renewable energy is now a more attractive
option for creating sustainable development. The technologies are capable of
integrating different energy sources to respond to an increasingly demanding
and distributed market by providing sustainable and efficient resources.
The technologies
of the fourth industrial revolution (Industry 4.0) are already being used in
the energy sector to transform the business processes of the industry. Energy
management systems based on emerging technologies, including artificial
intelligence (AI), internet of things (IoT), big data, blockchain, and machine
learning (ML), have been used to support industry players in analyzing the
energy market, improving the supply–demand chain, real-time monitoring, and
generating more options for using alternative sources of energy, such as
storage devices, fuel cells, and intelligent energy performance.
The
optimization of the energy industry can be achieved through energy production
and distribution efficiency by the digitization of manufacturing processes and
service delivery. Optimized energy pricing and capital resources, predictive
operation and maintenance plans, efficiency of energy usage, and further
maximizing asset lifetime and usage are among the solutions produced from the technologies
of Industry 4.0.
These
technologies are set to transform the energy industry to being more
sustainable. This transformation has happened through the provision of
integrated information in both planning and operational processes. Industry 4.0
technologies contribute to the efficiency and effectiveness of energy product
life-cycles and value chains, therefore impacting business strategies to
produce better energy management systems.
Smart
energy ecosystems that employ cyber-physical systems enhance all production and consumption energy chain processes. Smart applications in energy
production and usage consumption processes can be used efficiently in managing
and optimizing energy, such as by storing energy on demand or reducing
consumption. Utilizing
Industry 4.0 technologies to create a sustainable energy industry enables a decentralized
energy system in which energy can be effectively produced, managed, and
controlled from local resources. Furthermore, the technologies also enable data
capture and analysis to improve energy performance. As digital energy is being
developed and increasingly decentralized, renewable energy is now a more attractive
option for creating sustainable development. The technologies are capable of
integrating different energy sources to respond to an increasingly demanding
and distributed market by providing sustainable and efficient resources
Green Touchable Nanorobotic Sensor Networks
Recent advancements in biological nanomachines have motivated the research on nanorobotic sensor networks (NSNs), where the nanorobots are green (i.e., biocompatible and biodegradable) and touchable (i.e., externally controllable and continuously trackable). In the former aspect, NSNs will dissolve in an aqueous environment after finishing designated tasks and are harmless to the environment. In the latter aspect, NSNs employ cross-scale interfaces to interconnect the in vivo environment and its external environment. Specifically, the in-messaging and out-messaging interfaces for nanorobots to interact with a macro-unit are defined. The propagation and transient characteristics of nanorobots are described based on the existing experimental results. Furthermore, planning of nanorobot paths is discussed by taking into account the effectiveness of region-of-interest detection and the period of surveillance. Finally, a case study on how NSNs may be applied to microwave breast cancer detection is presented