Effect of optical pulse shaping and adaptive equalization on the performance of 100G DP-QPSK WDM system

Dual polarization quadrature phase shifting keying (DP-QPSK) modulation format along with coherent receiver helps in increasing the data carrying capability of existing optical networks without any major change in existing transmission infrastructure. The various linear and nonlinear fiber effects, frequency and phase errors are corrected in electrical domain at the receiver end with digital back propagation algorithms (DBP) instead of in-line compensation. In such a case the selection of optimum values of system parameters make the task easier for DBP algorithms. This paper highlights the importance of finding optimum operating point of continuous modulus algorithm (CMA) for better adaptive equalization (AE). The paper also discusses the optical pulse shaping using Gaussian optical band-pass filter to improve the spectral characteristics of DP-QPSK signal.

A simple faulted phase-based fault distance estimation algorithm for a loop distribution system

This paper presents a single ended faulted phase-based traveling wave fault localization algorithm for loop distribution grids which is that the sensor can get many reflected signals from the fault point to face the complexity of localization. This localization algorithm uses a band pass filter to remove noise from the corrupted signal. The arriving times of the faulted phase-based filtered signals can be obtained by using phase-modal and discrete wavelet transformations. The estimated fault distance can be calculated using the traveling wave method. The proposed algorithm presents detail level analysis using three detail levels coefficients. The proposed algorithm is tested with MATLAB simulation single line to ground fault in a 10 kV grounded loop distribution system. The simulation result shows that the faulted phase time delay can give better accuracy than using conventional time delays. The proposed algorithm can give fault distance estimation accuracy up to 99.7% with 30 dB contaminated signal-to-noise ratio (SNR) for the nearest lines from the measured terminal.

High-Performance RF Balanced Microstrip Mixer Configuration for Cryogenic and Room Temperatures

A high-performance S-band down-conversion microstrip mixer, for operation from 77 K to 300 K, is described. The balanced mixer combines a 90 degree hybrid coupler, two Schottky diodes, a band pass filter, and a low pass filter. The coupler phase shift drastically improves noise rejection. The circuit was implemented according to the configuration obtained from extensive simulation results based on electromagnetic analysis. The experimental results agreed well with the simulation results, showing a maximum measured insertion loss of 0.4 dB at 2 GHz. The microstrip mixer can be easily adjusted to different frequency ranges, up to about 50 GHz, through the proper choice of microstrip configuration. This novel S-band cryogenic mixer, implemented without resorting to special components, shows a very high performance at liquid nitrogen temperatures, making this mixer very suitable for high-temperature superconductive applications, such as front-ends.

DEVELOPMENT OF A REPEATER FOR A DEVICE FOR MONITORING CHANGES IN THE COMPOSITION OF THE AIR ENVIRONMENT

This work is part of a project to create a device for monitoring changes in the composition of the air environment. The article describes the main elements of the repeater unit included in the measuring device. The results of modeling the developed band-pass filter, which is part of the waveguide path of the repeater, are presented. Experimental studies of the filter and circulator parameters are also carried out. The technical capabilities of the electronic part of the repeater unit, namely microwave amplifiers and a controlled phase shifter, are described.

Development of a Microwave Sensor for Solid and Liquid Substances Based on Closed Loop Resonator

In this work, a compact dielectric sensor for the detection of adulteration in solid and liquid samples using planar resonators is presented. Six types of filter prototypes operating at 2.4 GHz are presented, optimized, numerically assessed, fabricated and experimentally validated. The obtained experimental results provided an error less than 6% with respect to the simulated results. Moreover, a size reduction of about 69% was achieved for the band stop filter and a 75% reduction for band pass filter compared to standard sensors realized using open/short circuited stub microstrip lines. From the designed filters, the miniaturised filter with Q of 95 at 2.4 GHz and size of 35 mm × 35 mm is formulated as a sensor and is validated theoretically and experimentally. The designed sensor shows better sensitivity, and it depends upon the dielectric property of the sample to be tested. Simulation and experimental validation of the designed sensor is carried out by loading different samples onto the sensor. The adulteration detection of various food samples using the designed sensor is experimentally validated and shows excellent sensing on adding adulterants to the original sample. The sensitivity of the sensor is analyzed by studying the variations in resonant frequency, scattering parameters, phase and Q factor with variation in the dielectric property of the sample loaded onto the sensor.

Intracellular Recording of Cardiomyocytes by Integrated Electrical Signal Recording and Electrical Pulse Regulating System

The electrophysiological signal can reflect the basic activity of cardiomyocytes, which is often used to study the working mechanism of heart. Intracellular recording is a powerful technique for studying transmembrane potential, proving a favorable strategy for electrophysiological research. To obtain high-quality and high-throughput intracellular electrical signals, an integrated electrical signal recording and electrical pulse regulating system based on nanopatterned microelectrode array (NPMEA) is developed in this work. Due to the large impedance of the electrode, a high-input impedance preamplifier is required. The high-frequency noise of the circuit and the baseline drift of the sensor are suppressed by a band-pass filter. After amplifying the signal, the data acquisition card (DAQ) is used to collect the signal. Meanwhile, the DAQ is utilized to generate pulses, achieving the electroporation of cells by NPMEA. Each channel uses a voltage follower to improve the pulse driving ability and isolates each electrode. The corresponding recording control software based on LabVIEW is developed to control the DAQ to collect, display and record electrical signals, and generate pulses. This integrated system can achieve high-throughput detection of intracellular electrical signals and provide a reliable recording tool for cell electro-physiological investigation.

Automatic Diagnosis of Epileptic Seizures in EEG Signals Using Fractal Dimension Features and Convolutional Autoencoder Method

This paper proposes a new method for epileptic seizure detection in electroencephalography (EEG) signals using nonlinear features based on fractal dimension (FD) and a deep learning (DL) model. Firstly, Bonn and Freiburg datasets were used to perform experiments. The Bonn dataset consists of binary and multi-class classification problems, and the Freiburg dataset consists of two-class EEG classification problems. In the preprocessing step, all datasets were prepossessed using a Butterworth band pass filter with 0.5–60 Hz cut-off frequency. Then, the EEG signals of the datasets were segmented into different time windows. In this section, dual-tree complex wavelet transform (DT-CWT) was used to decompose the EEG signals into the different sub-bands. In the following section, in order to feature extraction, various FD techniques were used, including Higuchi (HFD), Katz (KFD), Petrosian (PFD), Hurst exponent (HE), detrended fluctuation analysis (DFA), Sevcik, box counting (BC), multiresolution box-counting (MBC), Margaos-Sun (MSFD), multifractal DFA (MF-DFA), and recurrence quantification analysis (RQA). In the next step, the minimum redundancy maximum relevance (mRMR) technique was used for feature selection. Finally, the k-nearest neighbors (KNN), support vector machine (SVM), and convolutional autoencoder (CNN-AE) were used for the classification step. In the classification step, the K-fold cross-validation with k = 10 was employed to demonstrate the effectiveness of the classifier methods. The experiment results show that the proposed CNN-AE method achieved an accuracy of 99.736% and 99.176% for the Bonn and Freiburg datasets, respectively.