This paper introduced a “PID-NN” based on Particle Swarm Optimization control that was applied to a boost converter operating in large-signal domains. Simulation results have shown that the proposed “PID-NN controller” could enhance the (boost converter) startup response with the use of fewer on-off switch operations compared to the Conventional “PID controllers”. This result is of high importance in practice since reducing the number of on-off switches can effectively decrease the transient disturbances and losses due to switching. Simulations also prove that the proposed “PID-NN controller” is capable of efficiently improving rejecting potential disturbances that could happen in the input voltage. Moreover, it has been noticed that the output voltage is more efficiently controlled when applying “PID-NN controller”. The results of the simulation show the efficiency of the suggested algorithm compared with other well-known learning methods.
The futuristic age requires progress in handwork or even sub-machine dependency and Brain-Computer Interface (BCI) provides the necessary BCI procession. As the article suggests, it is a pathway between the signals created by a human brain thinking and the computer, which can translate the signal transmitted into action. BCI-processed brain activity is typically measured using EEG. Throughout this article, further intend to provide an available and up-to-date review of EEG-based BCI, concentrating on its technical aspects. In specific, we present several essential neuroscience backgrounds that describe well how to build an EEG-based BCI, including evaluating which signal processing, software, and hardware techniques to use. Individuals discuss Brain-Computer Interface programs, demonstrate some existing device shortcomings, and propose some eld’s viewpoints.
The ability of the human brain to communicate with its environment has become a reality through the use of a Brain-Computer Interface (BCI)-based mechanism. Electroencephalography (EEG) has gained popularity as a non-invasive way of brain connection. Traditionally, the devices were used in clinical settings to detect various brain diseases. However, as technology advances, companies such as Emotiv and NeuroSky are developing low-cost, easily portable EEG-based consumer-grade devices that can be used in various application domains such as gaming, education. This article discusses the parts in which the EEG has been applied and how it has proven beneficial for those with severe motor disorders, rehabilitation, and as a form of communicating with the outside world. This article examines the use of the SVM, k-NN, and decision tree algorithms to classify EEG signals. To minimize the complexity of the data, maximum overlap discrete wavelet transform (MODWT) is used to extract EEG features. The mean inside each window sample is calculated using the Sliding Window Technique. The vector machine (SVM), k-Nearest Neighbor, and optimize decision tree load the feature vectors.
<p>In general, the noise shaping responses, a cyclic second order response is delivered by the method of data weighted averaging (DWA) in which the output of the digital-to-analog convertor (DAC) is restricted to one of two states. DWA works efficiently for rather low levels of quantizing; it begins presenting considerable difficulties when internal levels of quantizing are extended further. Though, each added bit of internal quantizing causes an exponentially increasing in power dissipation, complexity and size of the DWA logic and the DAC. This gives a controlled seconnd order response accounting for the mismatch of the elements of DAC. The multi-bit DAC is made up of numerous single-bit DACs having values thereof chosen via a digital encoder. This research presents a discussion of the influence of mismatching between unit elements of the Delta-Sigma DAC. This results in a constrained second order response accounting for mismatch of DAC elements. The results of the simulation showed how the effectiveness of DWA method is in reducing band tones. Furthermore, DWA method has proved its efficiency in solving the mismatching of DAC unit elements. The noise of the mismatching elements is enhanced 11 dB at 0.01 with the proposed DWA, thereby enhancing the efficiency of the DAC in comparison to the efficiency of the DAC with no use of the circuit of DWA</p>
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