Low-frequency noise modeling and estimation is a critical issue in the design of analog and digital circuits, especially for short-channel CMOS technologies. The conventional noise models do not fit the experimental results adequately at frequencies close to DC for amplifiers and close to the carrier frequency for oscillators. Furthermore, measuring this noise necessitates the use of specialized and expensive equipment. In this paper, the bound of carrier trapping and releasing are extracted while examining the characteristics of low-frequency noise using the EMD method, and an innovative model based on carrier number fluctuations is proposed. The method introduces a low-cost measurement system for low-frequency noise. The performance accuracy of this measurement system and the proposed model is evaluated and compared with conventional noise modeling methods. The evaluation results demonstrate the success of the proposed measurement setup and model in estimating near-DC noise.

This paper presents a new framework based on modified EMD method for detection of single and multiple PQ issues. In modified EMD, DWT precedes traditional EMD process. This scheme makes EMD better by eliminating the mode mixing problem. This is a two step algorithm; in the first step, input PQ signal is decomposed in low and high frequency components using DWT. In the second stage, the low frequency component is further processed with EMD technique to get IMFs. Eight features are extracted from IMFs of low frequency component. Unlike low frequency component, features are directly extracted from the high frequency component. All these features form feature vector which is fed to PNN classifier for classification of PQ issues. For comparative analysis of performance of PNN, results are compared with SVM classifier. Moreover, performance of proposed methodology is also validated with noisy PQ signals. PNN has outperformed SVM for both noiseless and noisy PQ signals.

Nowadays, brain-computer interface system based on steady-state visual evoked potentials is increased due to advantages such as accepted accuracy and minimal need for user training. Despite these benefits, the unwanted noise that affects SSVEP is one of the issues that can reduce the efficiency of such systems. This paper uses the EMD algorithm in the initial phase and CCA or LASSO for the recognition of the stimulation frequency. In the first step, the EMD algorithm is applied so that non-stationary SSVEP signal breaks into oscillating functions and meaningful information are extracted. Among the IMFs obtained from the EMD method, only IMFs whose amplitude of the frequency spectrum in the frequency ranges corresponding to the excitation is higher were selected. With this selection, noisy signals and unprofitable information can be omitted. In the proposed method, two CCA and LASSO diagnostic methods were performed on the sum of selected signals to identify the frequency of stimulation. The simulation results show the recognition accuracy of 81.76% and 82.26% for the proposed method EMD-CCA and EMD-LASSO, respectively. While detection accuracy is 78.10% and 78.72% for conventional methods of CCA and LASSO.

Dear Editor-in-ChiefElectromechanical delay (EMD) means a lag time between the onset of electrical activity and force output (1). In case of the EMD phenomenon, the time when force starts to be produced is called “ development EMD (d-EMD)” , while the time when force ends is called “ relaxation EMD (r-EMD). This phenomenon indicates that electrical activation occurs before force is produced in case of muscle contraction.....

In this paper, a novel method is proposed to detect and to track pupil of one eye using modified circular Hough transform and a pair of goggles equipped with an infra -red (IR-EMD) camera. The IR-EMD, which is used in this research, is designed and built after studying several different schemes and despite its simplicity, it has very good performance and could be thought as one the practical novelties of the project. In the proposed method for pupil detection, circular Hough transform is used and its speed and accuracy is enhanced performing several modifications. Applying the modified circular Hough transform to the image captured with an average quality camera of the IR-EMD, which is also equipped with an IR-LED ring, the accurate position of the pupil would be detected. In addition to high speed and precision, the algorithm does not require high quality camera and is robust to the input image noise and some other environmental parameters such as rotation of the head, eye make-up and background light of the image, as well. Practical experiments on several people indicate precision and performance of the proposed algorithm. Besides, in order to confirm the performance of the algorithm for almost all possible situations, the method is applied to more than 700 eye images of the CASIA-Iris V.3 database. Results show outstanding performance of the proposed method. For instance, in presence of pepper and salt noise with 50 percent intensity, the algorithm could detect the correct position of the pupil of the database images with almost 90 percent accuracy.