DESAIN DAN SIMULASI PENGATURAN SUHU VENDING MACHINE MAKANAN MENGGUNAKAN ADAPTIVE NEURO FUZZY INFERENCE SYSTEM(ANFIS)

The current temperature control system has developed rapidly and has been used in various fields, one of which is the food industry. As we know in the presentation of food, temperature is a very influential thing in the taste of the food. With the development of technology, the way humans sell is now more modern, one of which is selling with vending machines. It is very important to pay attention to the selection of the method in setting the right vending machine temperature to avoid reduced enjoyment in consuming the products sold due to changes in the temperature that has been determined. Therefore, in this study, the Anfis method is proposed for temperature regulation in vending machines. From the simulation results from the simulation results, the Anfis control can accelerate the temperature in reaching the set point and can also maintain the temperature to the set poin

Comparison of FLC and ANFIS Methods to Keep Constant Power Based on Zeta Converter

The rapid development of technology encourages humans to always create various types of renewable innovations, which are useful for facilitating work and fulfill user’s order as desired. Especially in household appliances that use renewable energy sources in the form of solar cell, this implementation produces a fluctuating output power according to the properties of solar cell. So, it needs to be stabilized by zeta converter with the help of technology in the engineering sector, it is carried out by means of an interface as a liaison between the software and the controlled hardware. Therefore, a fuzzy set theory emerged to solve the problem in control design. However, there are other controls that can improve fuzzy deficiencies, called ANFIS. ANFIS has advantages in the learning process from the plant and the rules that will be made by the Neural Network have the main ability in terms of learning and adaptation, then decision making is done by FLC. This paper aims to compare the performance of the FLC and ANFIS as a control to keep stability of the output power of the zeta converter, where the converter work like a buck-boost converter that can increase or decrease the output power to be consumed in order to stabilize. The use of these two controllers can also compare the time at steady state and the constant power before learning occurs and after  learning process. The simulation results show that the accuracy of ANFIS is 99.82% higher than accuracy of FLC which is 98.08%

Dynamic evolving neural fuzzy inference system equalization scheme in mode division multiplexing for optical fiber transmission

The performance of optical mode division multiplexing (MDM) is affected by intersymbol interference (ISI) from nonlinear channel impairments arising from higherorder mode coupling and modal dispersion in multimode fiber. However, the existing MDM equalization algorithms can only mitigate the linear distortion, but they cannot address nonlinear distortion in the signal accurately. Therefore, there is a need to explore how ISI can be mitigated to recover the transmitted signal. This research aims to control the broadening of the MDM signal and minimize the undesirable distortion among channels in MMF by signal reshaping at the receiver. A dynamic evolving neural fuzzy inference system (DENFIS) equalization scheme has been used to achieve this objective. This research was conducted through a few steps commencing with modelling the MDM system in Optsim and collecting the data. Then, the signal reshaping parameters were determined. After that, DENFIS equalization, least mean square (LMS) and recursive least squares (RLS) equalizations were implemented and evaluated. Results illustrated that nonlinear DENFIS equalization scheme can improve MDM signal at a higher accuracy than previous linear equalization schemes. DENFIS equalization demonstrates better signal reshaping accuracy with an average root mean square error (RMSE) of 0.0338 and outperformed linear LMS and RLS equalization schemes with high average RMSE values of 0.101 and 0.1914 respectively. The reduced RMSE implies that DENFIS equalization scheme mitigates ISI more effectively in a nonlinear channel. This effect can hasten data transmission rates in MDM. Moreover, the successful offline implementation of DENFIS equalization in MDM encourages future online implementation of DENFIS equalization in embedded optical systems

Analog Filtering of EEG signals in the Presence of Artifact Signals

Recovery of bio-electric signals, such as EEG, ECG, have been routinely done in the past via sophisticated numerical algorithms and extensive computing resources. Current works on filtering of EEG signals does not clearly reveal the electronic circuit operations by which the artifact signals can be obtained to serve as a reference signal for filtering operation. A technique to isolate the artifact signals mixed with intended bio-electric signals (i.e., EEG), by using analog circuit components has been proposed in the thesis. The artifact signal band is assumed to be separated from the intended signal bands. The novelty of the approach lies in recovering the artifact signal from the mixture of contaminated biomedical signals and then re-using it to recover the intended signals. Prior knowledge about the artifact signal is not needed, except for demonstration of the principles through simulation work. Data base of CMOSP 18 technology available in the VLSI laboratory of Concordia University has been used for all simulations. The use of recovered artifact signal as a reference signal for the filtering process by elimination of the intended signal, is presented in the thesis. All these operations were easily implemented with analog circuit components. Further work along this direction will be very useful in developing wearable electronic devices for communication of point-of-care health data from a human body, wirelessly to distant medical center locations for further processing. This is expected to provide relatively inexpensive solutions toward current day trend of wireless point-of-care electronic circuits and systems for public health monitoring

Optimization of Mobility Parameters using Fuzzy Logic and Reinforcement Learning in Self-Organizing Networks

In this thesis, several optimization techniques for next-generation wireless networks are proposed to solve different problems in the field of Self-Organizing Networks and heterogeneous networks. The common basis of these problems is that network parameters are automatically tuned to deal with the specific problem. As the set of network parameters is extremely large, this work mainly focuses on parameters involved in mobility management. In addition, the proposed self-tuning schemes are based on Fuzzy Logic Controllers (FLC), whose potential lies in the capability to express the knowledge in a similar way to the human perception and reasoning. In addition, in those cases in which a mathematical approach has been required to optimize the behavior of the FLC, the selected solution has been Reinforcement Learning, since this methodology is especially appropriate for learning from interaction, which becomes essential in complex systems such as wireless networks. Taking this into account, firstly, a new Mobility Load Balancing (MLB) scheme is proposed to solve persistent congestion problems in next-generation wireless networks, in particular, due to an uneven spatial traffic distribution, which typically leads to an inefficient usage of resources. A key feature of the proposed algorithm is that not only the parameters are optimized, but also the parameter tuning strategy. Secondly, a novel MLB algorithm for enterprise femtocells scenarios is proposed. Such scenarios are characterized by the lack of a thorough deployment of these low-cost nodes, meaning that a more efficient use of radio resources can be achieved by applying effective MLB schemes. As in the previous problem, the optimization of the self-tuning process is also studied in this case. Thirdly, a new self-tuning algorithm for Mobility Robustness Optimization (MRO) is proposed. This study includes the impact of context factors such as the system load and user speed, as well as a proposal for coordination between the designed MLB and MRO functions. Fourthly, a novel self-tuning algorithm for Traffic Steering (TS) in heterogeneous networks is proposed. The main features of the proposed algorithm are the flexibility to support different operator policies and the adaptation capability to network variations. Finally, with the aim of validating the proposed techniques, a dynamic system-level simulator for Long-Term Evolution (LTE) networks has been designed

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering