A novel optimized conical antenna array structure for back lobe cancellation of uniform concentric circular antenna arrays

In wireless communication systems, the existence of the antenna array back lobe represents a significant source of interference, which causes degradation of the signal-to-interference ratio (SIR), and power loss. In this paper, a novel optimized conical antenna array (O-CONAA) structure is proposed for back lobe cancellation of concentric circular antenna arrays (CCAA). Based on the CAA, It is considered to be made up Of several concentric circular antenna arrays (CCAA) which are placed in the X-Y plane. Firstly a non-optimized CONAA is constructed, by arranging these concentric CAAs with uniform vertical spacing along the Z-axis. Consequently, the CONAA seems to be treated as a combination between uniform CAAs and a linear antenna array (LAA). It has been noted that the CONAA radiation pattern has a back lobe amplitude the same as the main beam amplitude. The O-CONAA structure is suggested as a solution to this problem, which provides back lobe cancellation while maintaining the CONAA pattern characteristics like half power beamwidth (HPBW) side lobe level (SLL). The genetic algorithm(GA) approach is used in the O-CONAA structure to optimize the values of both CONAA inter-element spacing around the perimeter of each circle, and vertical spacing along the Z-axis to generate the desired radiation pattern

A novel optimized conical antenna array structure for back lobe cancellation of uniform concentric circular antenna arrays

In wireless communication systems, the existence of the antenna array back lobe represents a significant source of interference, which causes degradation of the signal-to-interference ratio (SIR), and power loss. In this paper, a novel optimized conical antenna array (O-CONAA) structure is proposed for back lobe cancellation of concentric circular antenna arrays (CCAA). Based on the CAA, It is considered to be made up Of several concentric circular antenna arrays (CCAA) which are placed in the X-Y plane. Firstly a non-optimized CONAA is constructed, by arranging these concentric CAAs with uniform vertical spacing along the Z-axis. Consequently, the CONAA seems to be treated as a combination between uniform CAAs and a linear antenna array (LAA). It has been noted that the CONAA radiation pattern has a back lobe amplitude the same as the main beam amplitude. The O-CONAA structure is suggested as a solution to this problem, which provides back lobe cancellation while maintaining the CONAA pattern characteristics like half power beamwidth (HPBW) side lobe level (SLL). The genetic algorithm(GA) approach is used in the O-CONAA structure to optimize the values of both CONAA inter-element spacing around the perimeter of each circle, and vertical spacing along the Z-axis to generate the desired radiation pattern

Characteristics of different focusing antennas in the near field region

Focusing antennas are of interest in many application including microwave wireless power transmission, remote (non-contact) sensing, and medical applications. Different kinds of antennas such as array antennas, reflector antennas and Fresnel zone plate (FZP) antennas have been used for these applications. Here, first, a new scheme in designing focused array antennas with desired sidelobe levels (SLLs) in the near field region is presented. The performance of the large focused array antennas is predicted based on the knowledge of the mutual admittances of a smaller array. The effects of various focal distances on the near field pattern of these antennas are investigated. Then, electric field pattern characteristics of the focused Fresnel zone plate lens antennas in the near-field region are presented. The FZP antenna fed by a circular horn is implemented and the effects of various focal lengths on the near field pattern of this antenna are examined. It is shown that the maximum field intensity occurs closer to the antenna aperture than to the focal point and this displacement increases as the focal point moves away from the antenna aperture. The focusing properties of ultra-wideband (UWB) array antennas are also presented. Large current radiator (LCR) antennas are modeled by replacing the antenna with a set of infinitesimal dipoles producing the same near field of the antenna. LCR antenna arrays are used to provide high concentration of microwave power into a small region. It is shown that the defocusing effect occurs in pulse radiating antennas as well. Invasive weed optimization (IWO), a new optimization algorithm, is also employed to optimize the pulsed array antenna. In the attempt of optimizing the focused arrays, a new scenario for designing thinned array antennas using this optimization method is introduced. It is shown that by using this method, the number of elements in the array can be optimized, which yields a more efficient pattern with less number of elements. By applying this new optimization method to UWB arrays, the peak power delivered to a localized region can be increased

Antenna Designs for 5G/IoT and Space Applications

This book is intended to shed some light on recent advances in antenna design for these new emerging applications and identify further research areas in this exciting field of communications technologies. Considering the specificity of the operational environment, e.g., huge distance, moving support (satellite), huge temperature drift, small dimension with respect to the distance, etc, antennas, are the fundamental device allowing to maintain a constant interoperability between ground station and satellite, or different satellites. High gain, stable (in temperature, and time) performances, long lifecycle are some of the requirements that necessitates special attention with respect to standard designs. The chapters of this book discuss various aspects of the above-mentioned list presenting the view of the authors. Some of the contributors are working strictly in the field (space), so they have a very targeted view on the subjects, while others with a more academic background, proposes futuristic solutions. We hope that interested reader, will find a fertile source of information, that combined with their interest/background will allow efficiently exploiting the combination of these two perspectives

Advanced Radio Frequency Antennas for Modern Communication and Medical Systems

The main objective of this book is to present novel radio frequency (RF) antennas for 5G, IOT, and medical applications. The book is divided into four sections that present the main topics of radio frequency antennas. The rapid growth in development of cellular wireless communication systems over the last twenty years has resulted in most of world population owning smartphones, smart watches, I-pads, and other RF communication devices. Efficient compact wideband antennas are crucial in RF communication devices. This book presents information on planar antennas, cavity antennas, Vivaldi antennas, phased arrays, MIMO antennas, beamforming phased array reconfigurable Pabry-Perot cavity antennas, and time modulated linear array

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Layoutautomatisierung im analogen IC-Entwurf mit formalisiertem und nicht-formalisiertem Expertenwissen

After more than three decades of electronic design automation, most layouts for analog integrated circuits are still handcrafted in a laborious manual fashion today. Obverse to the highly automated synthesis tools in the digital domain (coping with the quantitative difficulty of packing more and more components onto a single chip – a desire well known as More Moore), analog layout automation struggles with the many diverse and heavily correlated functional requirements that turn the analog design problem into a More than Moore challenge. Facing this qualitative complexity, seasoned layout engineers rely on their comprehensive expert knowledge to consider all design constraints that uncompromisingly need to be satisfied. This usually involves both formally specified and nonformally communicated pieces of expert knowledge, which entails an explicit and implicit consideration of design constraints, respectively. Existing automation approaches can be basically divided into optimization algorithms (where constraint consideration occurs explicitly) and procedural generators (where constraints can only be taken into account implicitly). As investigated in this thesis, these two automation strategies follow two fundamentally different paradigms denoted as top-down automation and bottom-up automation. The major trait of top-down automation is that it requires a thorough formalization of the problem to enable a self-intelligent solution finding, whereas a bottom-up automatism –controlled by parameters– merely reproduces solutions that have been preconceived by a layout expert in advance. Since the strengths of one paradigm may compensate the weaknesses of the other, it is assumed that a combination of both paradigms –called bottom-up meets top-down– has much more potential to tackle the analog design problem in its entirety than either optimization-based or generator-based approaches alone. Against this background, the thesis at hand presents Self-organized Wiring and Arrangement of Responsive Modules (SWARM), an interdisciplinary methodology addressing the design problem with a decentralized multi-agent system. Its basic principle, similar to the roundup of a sheep herd, is to let responsive mobile layout modules (implemented as context-aware procedural generators) interact with each other inside a user-defined layout zone. Each module is allowed to autonomously move, rotate and deform itself, while a supervising control organ successively tightens the layout zone to steer the interaction towards increasingly compact (and constraint compliant) layout arrangements. Considering various principles of self-organization and incorporating ideas from existing decentralized systems, SWARM is able to evoke the phenomenon of emergence: although each module only has a limited viewpoint and selfishly pursues its personal objectives, remarkable overall solutions can emerge on the global scale. Several examples exhibit this emergent behavior in SWARM, and it is particularly interesting that even optimal solutions can arise from the module interaction. Further examples demonstrate SWARM’s suitability for floorplanning purposes and its application to practical place-and-route problems. The latter illustrates how the interacting modules take care of their respective design requirements implicitly (i.e., bottom-up) while simultaneously paying respect to high level constraints (such as the layout outline imposed top-down by the supervising control organ). Experimental results show that SWARM can outperform optimization algorithms and procedural generators both in terms of layout quality and design productivity. From an academic point of view, SWARM’s grand achievement is to tap fertile virgin soil for future works on novel bottom-up meets top-down automatisms. These may one day be the key to close the automation gap in analog layout design.Nach mehr als drei Jahrzehnten Entwurfsautomatisierung werden die meisten Layouts für analoge integrierte Schaltkreise heute immer noch in aufwändiger Handarbeit entworfen. Gegenüber den hochautomatisierten Synthesewerkzeugen im Digitalbereich (die sich mit dem quantitativen Problem auseinandersetzen, mehr und mehr Komponenten auf einem einzelnen Chip unterzubringen – bestens bekannt als More Moore) kämpft die analoge Layoutautomatisierung mit den vielen verschiedenen und stark korrelierten funktionalen Anforderungen, die das analoge Entwurfsproblem zu einer More than Moore Herausforderung machen. Angesichts dieser qualitativen Komplexität bedarf es des umfassenden Expertenwissens erfahrener Layouter um sämtliche Entwurfsconstraints, die zwingend eingehalten werden müssen, zu berücksichtigen. Meist beinhaltet dies formal spezifiziertes als auch nicht-formal übermitteltes Expertenwissen, was eine explizite bzw. implizite Constraint Berücksichtigung nach sich zieht. Existierende Automatisierungsansätze können grundsätzlich unterteilt werden in Optimierungsalgorithmen (wo die Constraint Berücksichtigung explizit erfolgt) und prozedurale Generatoren (die Constraints nur implizit berücksichtigen können). Wie in dieser Arbeit eruiert wird, folgen diese beiden Automatisierungsstrategien zwei grundlegend unterschiedlichen Paradigmen, bezeichnet als top-down Automatisierung und bottom-up Automatisierung. Wesentliches Merkmal der top-down Automatisierung ist die Notwendigkeit einer umfassenden Problemformalisierung um eine eigenintelligente Lösungsfindung zu ermöglichen, während ein bottom-up Automatismus –parametergesteuert– lediglich Lösungen reproduziert, die vorab von einem Layoutexperten vorgedacht wurden. Da die Stärken des einen Paradigmas die Schwächen des anderen ausgleichen können, ist anzunehmen, dass eine Kombination beider Paradigmen –genannt bottom-up meets top down– weitaus mehr Potenzial hat, das analoge Entwurfsproblem in seiner Gesamtheit zu lösen als optimierungsbasierte oder generatorbasierte Ansätze für sich allein. Vor diesem Hintergrund stellt die vorliegende Arbeit Self-organized Wiring and Arrangement of Responsive Modules (SWARM) vor, eine interdisziplinäre Methodik, die das Entwurfsproblem mit einem dezentralisierten Multi-Agenten-System angeht. Das Grundprinzip besteht darin, ähnlich dem Zusammentreiben einer Schafherde, reaktionsfähige mobile Layoutmodule (realisiert als kontextbewusste prozedurale Generatoren) in einer benutzerdefinierten Layoutzone interagieren zu lassen. Jedes Modul darf sich selbständig bewegen, drehen und verformen, wobei ein übergeordnetes Kontrollorgan die Zone schrittweise verkleinert, um die Interaktion auf zunehmend kompakte (und constraintkonforme) Layoutanordnungen hinzulenken. Durch die Berücksichtigung diverser Selbstorganisationsgrundsätze und die Einarbeitung von Ideen bestehender dezentralisierter Systeme ist SWARM in der Lage, das Phänomen der Emergenz hervorzurufen: obwohl jedes Modul nur eine begrenzte Sichtweise hat und egoistisch seine eigenen Ziele verfolgt, können sich auf globaler Ebene bemerkenswerte Gesamtlösungen herausbilden. Mehrere Beispiele veranschaulichen dieses emergente Verhalten in SWARM, wobei besonders interessant ist, dass sogar optimale Lösungen aus der Modulinteraktion entstehen können. Weitere Beispiele demonstrieren SWARMs Eignung zwecks Floorplanning sowie die Anwendung auf praktische Place-and-Route Probleme. Letzteres verdeutlicht, wie die interagierenden Module ihre jeweiligen Entwurfsanforderungen implizit (also: bottom-up) beachten, während sie gleichzeitig High-Level-Constraints berücksichtigen (z.B. die Layoutkontur, die top-down vom übergeordneten Kontrollorgan auferlegt wird). Experimentelle Ergebnisse zeigen, dass Optimierungsalgorithmen und prozedurale Generatoren von SWARM sowohl bezüglich Layoutqualität als auch Entwurfsproduktivität übertroffen werden können. Aus akademischer Sicht besteht SWARMs große Errungenschaft in der Erschließung fruchtbaren Neulands für zukünftige Arbeiten an neuartigen bottom-up meets top-down Automatismen. Diese könnten eines Tages der Schlüssel sein, um die Automatisierungslücke im analogen Layoutentwurf zu schließen

ESSE 2017. Proceedings of the International Conference on Environmental Science and Sustainable Energy

Environmental science is an interdisciplinary academic field that integrates physical-, biological-, and information sciences to study and solve environmental problems. ESSE - The International Conference on Environmental Science and Sustainable Energy provides a platform for experts, professionals, and researchers to share updated information and stimulate the communication with each other. In 2017 it was held in Suzhou, China June 23-25, 2017

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words