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.&nbsp

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