Comparison of Electromagnetic Band Gap and Split-Ring Resonator Microstrip Lines as Stop Band Structures

In this paper, microstrip lines magnetically coupled to splitring resonators (SRRs) are conquved to electromagnetic bundgup (EBG) nr,rrostrip lines in terns q/ their stop-heard penjbrnmrnce and dimensions. In bath types o/ trunsmis•siou lines, signal propagation is inhibited in it certain jequency bwuL For EBG microstrip lines, the central frequency of such a forbidden band is determined by the period of the structure, whereas in SRR-hased microstrip lines the position of the frequency gap depends on the quasi-static resonant frequency of the rings. The main relevant conrributiun of this paper is to provide a tuning procedure to control the gap width in SRR microstrip lines, and to show that by using SRRs, device dimensions ale much smaller than those required by EBGs in order to obtain similar stop-banal performance. This has been demonstrated by fill-wave electromagnetic simulations and experimentally verified from the characterization ql two fabricated microstrip lines: one with rectangular SRRs etched on the upper substrate side, and the other with a periodic perturbation cf'strip width. For similar rejection and 1-(;H,. gap width centered at 4.5 Gllz, it has been found that the SRR microstrip line is•,fve times shorter. In addition, no ripple is appreciable in the allowed band for the .SRR-hared structure, whereas due to dispersion, certain mismatch is expected in the EBG prototype. Due to the high-frequency selectivity, controllable gap width, and small dimensions, it is believed that SRR coupled to planar transmission lines can have an actual impact on the design of stop-band filters compatible with planar technology, and can be an alternative to present solutions based on distributed approaches or EBG

Development of a varactor -controlled dual-frequency reconfigurable microstrip antenna

A new design for a compact electronically reconffgurable singlefeed dual frequency dual-polarized operation of a square-microstrip antenna capable of achieving tunable frequency ratios in the range 1.1 to 1.37 is proposed and experimentally studied. Varactor diodes inlegruted with the arms of the hexagonal slot and embedded in the square patch are used to tune the operating frequencies by applying reverse-bias voltage. The design has the advantage of size reduction up to 73.21% and 49.86% for the two resonant frequencies, respectively, as compared to standard rectangular patches. The antenna offers good bandwidth of 5.74% and 5.36% for the two operating frequencies. A highly simplified tuning circuitry without any transmission lines adds to the compactness of the design

Development and Analysis of a Compact Dual-Band Coplanar Antenna

The coplanar wave guide is an attractive device in microwave integrated circuits due to its uniplanar nature, ease of fabrication and low production cost. Several attempts are already done to explore the radiating modes in coplanar wave guide transmission lines. Usually coplanar wave guides are excited by an SMA connector with its centre conductor connected to the exact middle of the centre strip and the outer ground conductor to the two ground strips. The mode excited on it is purely a bound mode. The E-field distribution in the two slots are out of phase and there for cancels at the far field. This thesis addresses an attempt to excite an in phase E-field distribution in the two slots of the co planar wave guide by employing a feed asymmetry, in order to get radiation from the two large slot discontinuities of the coplanar waveguide. The omni directional distribution of the radiating energy can be achieved by widening the centre strip.The first part of the thesis deals with the investigations on the resonance phenomena of conventional coplanar waveguides at higher frequency bands. Then an offset fed open circuited coplanar waveguide supporting resonance/radiation phenomena is analyzed. Finally, a novel compact co planar antenna geometry with dual band characteristics, suitable for mobile terminal applications is designed and characterized using the inferences from the above study.

“Design and Development of Coplanar Strip Fed Planar Antennas”

With the recent progress and rapid increase in the field of communication, the designs of antennas for small mobile terminals with enhanced radiation characteristics are acquiring great importance. Compactness, efficiency, high data rate capacity etc. are the major criteria for the new generation antennas. The challenging task of the microwave scientists and engineers is to design a compact printed radiating structure having broadband behavior along with good efficiency and enhanced gain. Printed antenna technology has received popularity among antenna scientists after the introduction of planar transmission lines in mid-seventies. When we view the antenna through a transmission line concept, the mechanism behind any electromagnetic radiator is quite simple and interesting. Any electromagnetic system with a discontinuity is radiating electromagnetic energy. The size, shape and orientation of the discontinuities control the radiation characteristics of the system such as radiation pattern, gain, polarization etc. It can be either resonant or non-resonant. This thesis deals with antennas that are developed from a class of transmission lines known as coplanar strip-CPS, a planar analogy of parallel pair transmission line. The specialty of CPS is its symmetric structure compared to other transmission lines, which makes the antenna structures developed from CPS quite simple for design and fabrication. The structural modifications on either metallic strip of CPS results in different antennas. The first part of the thesis discusses a single band and dual band design derived from open ended slot lines which are very much suitable for 2.4 and 5.2 GHz WLAN applications. The second section of the study is vectored into the development of enhanced gain dipoles. A single band dipole and a wide band enhanced gain dipole suitable for 5.2/5.8 GHZ band and imaging applications are developed and discussed. Last part of the thesis discusses the development of directional UWBs. Three different types of ultra-compact UWBs are developed and almost all the frequency domain and time domain analysis of the structures are discussed.

Design and Analysis of Microstrip Lines with EBG-Backed Ground Planes of Different Geometrical Shapes

Propagation of electromagnetic waves through a microstrip line with 2D electromagnetic baud gap (EBG) structures of different geometrical shapes in the ground plane is investigated in this paper. Using transmission-line theory, the design equations for EBG structures are calculated. The measured, numerical. and simulated results are in gone) agreement

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Synthesis of Bio-Compatible SPION–based Aqueous Ferrofluids and Evaluation of RadioFrequency Power Loss for Magnetic Hyperthermia

Bio-compatible magnetic fluids having high saturation magnetization find immense applications in various biomedical fields. Aqueous ferrofluids of superparamagnetic iron oxide nanoparticles with narrow size distribution, high shelf life and good stability is realized by controlled chemical co-precipitation process. The crystal structure is verified by X-ray diffraction technique. Particle sizes are evaluated by employing Transmission electron microscopy. Room temperature and low-temperature magnetic measurements were carried out with Superconducting Quantum Interference Device. The fluid exhibits good magnetic response even at very high dilution (6.28 mg/cc). This is an advantage for biomedical applications, since only a small amount of iron is to be metabolised by body organs. Magnetic field induced transmission measurements carried out at photon energy of diode laser (670 nm) exhibited excellent linear dichroism. Based on the structural and magnetic measurements, the power loss for the magnetic nanoparticles under study is evaluated over a range of radiofrequencies.

Channel Adaptive MAC Protocol with Traffic-Aware Distributed Power Management in Wireless Sensor Networks-Some Performance Issues

In Wireless Sensor Networks (WSN), neglecting the effects of varying channel quality can lead to an unnecessary wastage of precious battery resources and in turn can result in the rapid depletion of sensor energy and the partitioning of the network. Fairness is a critical issue when accessing a shared wireless channel and fair scheduling must be employed to provide the proper flow of information in a WSN. In this paper, we develop a channel adaptive MAC protocol with a traffic-aware dynamic power management algorithm for efficient packet scheduling and queuing in a sensor network, with time varying characteristics of the wireless channel also taken into consideration. The proposed protocol calculates a combined weight value based on the channel state and link quality. Then transmission is allowed only for those nodes with weights greater than a minimum quality threshold and nodes attempting to access the wireless medium with a low weight will be allowed to transmit only when their weight becomes high. This results in many poor quality nodes being deprived of transmission for a considerable amount of time. To avoid the buffer overflow and to achieve fairness for the poor quality nodes, we design a Load prediction algorithm. We also design a traffic aware dynamic power management scheme to minimize the energy consumption by continuously turning off the radio interface of all the unnecessary nodes that are not included in the routing path. By Simulation results, we show that our proposed protocol achieves a higher throughput and fairness besides reducing the delay

A Numerically efficient power optimization scheme for coded OFDM systems in achieving minimum frame error rate

Coded OFDM is a transmission technique that is used in many practical communication systems. In a coded OFDM system, source data are coded, interleaved and multiplexed for transmission over many frequency sub-channels. In a conventional coded OFDM system, the transmission power of each subcarrier is the same regardless of the channel condition. However, some subcarrier can suffer deep fading with multi-paths and the power allocated to the faded subcarrier is likely to be wasted. In this paper, we compute the FER and BER bounds of a coded OFDM system given as convex functions for a given channel coder, inter-leaver and channel response. The power optimization is shown to be a convex optimization problem that can be solved numerically with great efficiency. With the proposed power optimization scheme, near-optimum power allocation for a given coded OFDM system and channel response to minimize FER or BER under a constant transmission power constraint is obtained

A Reinforcement Learning Algorithm to Economic Dispatch Considering Transmission Losses

Reinforcement Learning (RL) refers to a class of learning algorithms in which learning system learns which action to take in different situations by using a scalar evaluation received from the environment on performing an action. RL has been successfully applied to many multi stage decision making problem (MDP) where in each stage the learning systems decides which action has to be taken. Economic Dispatch (ED) problem is an important scheduling problem in power systems, which decides the amount of generation to be allocated to each generating unit so that the total cost of generation is minimized without violating system constraints. In this paper we formulate economic dispatch problem as a multi stage decision making problem. In this paper, we also develop RL based algorithm to solve the ED problem. The performance of our algorithm is compared with other recent methods. The main advantage of our method is it can learn the schedule for all possible demands simultaneously.