A New Method Modifying Single Miller Feedforward Frequency Compensation to Drive Large Capacitive Loads: Putting an Attenuator in the Path

A new method to compensate three-stage amplifier to drive large capacitive loads is proposed in this paper. Gain Bandwidth Product is increased due to use an attenuator in the path of miller compensation capacitor. Analysis demonstrates that the gain bandwidth product will be improved significantly without using large compensation capacitor. Using a feedforward path is deployed to control a left half plane zero which is able to cancel out first non-dominant pole. A three stage amplifier is simulated in a 0.18 μm CMOS technology. The purpose of the design is to compensate three-stage amplifier loading 1000 pF capacitive load. The simulated amplifier with a 1000 pF capacitive load is performed in 3.3 MHz gain bandwidth product, and phase margin of 50. The compensation capacitor is reduced extremely compared to conventional nested miller compensation methods. Since transconductance of each stage is not distinct, and it is close to one another; as a result, this method is suitable low power design methodology.     

A 700 MHz laser radar receiver realized in 0.18 μm HV-CMOS

This study presents a CMOS receiver chip realized in 0.18 µm High-Voltage CMOS (HV-CMOS) technology and intended for high precision pulsed time-of-flight laser range finding utilizing high-energy sub-ns laser pulses. The IC chip includes a trans-impedance preamplifier, a post-amplifier and a timing comparator. Timing discrimination is based on leading edge detection and the trailing edge is also discriminated for measuring the width of the pulse. The transimpedance of the channel is 25 kΩ, the uncompensated walk error is 470 ps in the dynamic range of 1:21,000 and the input referred equivalent noise current 450 nA (rms).     

Design and Characterization of a W-Band Folded-Waveguide Slow-Wave Structure

A single-section slow-wave structure for a W-band folded-waveguide traveling-wave tube with operating bandwidth of around 4% was designed for delivering the output power of 50 W at the operating voltage of 13.5 kV and operating beam current of 80 mA. The design was carried out using analytical formulations and 3D electromagnetic simulations. The beam-wave interaction analysis was carried out using large signal Lagrangian analysis and particle-in-cell simulation. The folded-waveguide slow-wave structure along with input-output couplers and RF windows were fabricated. Cold test measurements were carried out for dispersion characteristics of the slow-wave structure and voltage standing-wave ratio and insertion loss characteristics of the RF window. The measured cold circuit parameters show close agreement with the analysis.     

TiN Nanoparticles for Enhanced THz Generation in TDS Systems

By virtue of the surface plasmon resonance effect, plasmonic nanoparticles (NPs) can localize the light field and significantly enhance the performance of some optoelectronic devices. In this work, NPs are employed for an enhanced generation of terahertz radiation from LT-GaAs-based antennas. Therefore, we have prepared plasmonic TiN NPs by direct ultrasonication (ULS) and pulsed laser ablation (PLA) techniques. The zeta potential, particle size, and absorbance were used to characterize the NPs in their colloidal forms in a comparison to commercial Au NPs. A layer of polydispersed titanium nitride (TiN) NPs prepared by PLA and deposited on the surface of an LT-GaAs device shows a significant improvement of terahertz signal generation from these devices with an enhancement of the peak to peak amplitude of 100%.     

Channel Sounding System for MM-Wave Bands and Characterization of Indoor Propagation at 28 GHz

The aim of this work is to present a vector network analyzer based channel sounding system capable of performing measurements in the range from 2 to 50 GHz. Further, this paper describes an indoor measurement campaign performed at 26–30 GHz. The sounding system is capable of receiving two channels and transmitting one. Using this feature a channel measurement has been performed using both a directional horn antenna and a virtual uniform circular array (UCA) at the same time. This allows for comparative studies of measured channels with two different antennas in a simultaneous way. The measurement has been conducted with 42 measurement positions distributed along a 10 m long path through an indoor laboratory environment. The transmitter was positioned such that measurements were conducted both in line-of-sight and non-line-of-sight scenarios. The measurements showed good agreement between the measurement data collected with the horn antenna and the data collected with the UCA. The propagation environment was found to be sparse both in delay and angular domain for the given scenario. Based on the performed measurement campaign together with validation measurements of the system stability, it is found that the system works as expected.     

Radiation-Induced Fault Simulation of SOI/SOS CMOS LSI’s Using Universal Rad-SPICE MOSFET Model

The methodology of modeling and simulation of environmentally induced faults in radiation hardened SOI/SOS CMOS IC’s is presented. It is realized at three levels: CMOS devices – typical analog or digital circuit fragments – complete IC’s. For this purpose, a universal compact SOI/SOS MOSFET model for SPICE simulation software with account for TID, dose rate and single event effects is developed. The model parameters extraction procedure is described in great depth taking into consideration radiation effects and peculiarities of novel radiation-hardened (RH) SOI/SOS MOS structures. Examples of radiation-induced fault simulation in analog and digital SOI/SOS CMOS LSI’s are presented for different types of radiation influence. The simulation results show the difference with experimental data not larger than 10–20% for all types of radiation.     

Fault Diagnosis of Smart Grid Distribution System by Using Smart Sensors and Symlet Wavelet Function

In today’s era of smart grid system scenario, the fault diagnosis is of utmost important task. Present distribution networks change drastically due to expansion and inclusion of large number of distributed generation units into power system at distribution level. To face the challenges of modernized girds, conventional fault diagnosis methodologies require drastic change by making use of advanced infrastructure and technologies. This will be helpful to achieve automation in fault diagnosis tasks, improved power quality, reliability, resilience and self healing property of the power system. This paper proposes the use of smart sensors and advanced communication technology that will be available in future smart grids to carry out automated fault diagnosis tasks using signal processing techniques. Methods of using Standard deviation features of fault transient signal and a fault location factors are proposed. Performance of various scaling levels, features and components of fault transient current signals extracted using the latest non conventional Symlet mother wavelet function are evaluated and compared. The attempt is made to select optimal features and components of fault transient currents to improve the performance of present limited types of available fault locators. The tests are taken on standard model of smart grid distribution system but can be applied for fault diagnosis of any other power equipment. Results show adequate accuracy to extend the use of proposed method for real time applications.     

On the equivalence of multivariate polynomial matrices

The equivalence of system is an important concept in multidimensional (\(n\)D) system, which is closely related to equivalence of multivariate polynomial matrices. This paper mainly investigates the equivalence of some \(n\)D polynomial matrices, several new results and conditions on the reduction by equivalence of a given \(n\)D polynomial matrix to its Smith form are obtained.     

Image modeling based on a 2-D stochastic subspace system identification algorithm

Fitting a causal dynamic model to an image is a fundamental problem in image processing, pattern recognition, and computer vision. In image restoration, for instance, the goal is to recover an estimate of the true image, preferably in the form of a parametric model, given an image that has been degraded by a combination of blur and additive white Gaussian noise. In texture analysis, on the other hand, a model of a particular texture image can serve as a tool for simulating texture patterns. Finally, in image enhancement one computes a model of the true image and the residuals between the image and the modeled image can be interpreted as the result of applying a de-noising filter. There are numerous other applications within the field of image processing that require a causal dynamic model. Such is the case in scene analysis, machined parts inspection, and biometric analysis, to name only a few. There are many types of causal dynamic models that have been proposed in the literature, among which the autoregressive moving average and state-space models (i.e., Kalman filter) are the most commonly used. In this paper we introduce a 2-D stochastic state-space system identification algorithm for fitting a quarter plane causal dynamic Roesser model to an image. The algorithm constructs a causal, recursive, and separable-in-denominator 2-D Kalman filter model. The algorithm is tested with three real images and the quality of the estimated images are assessed.     

Active queue management algorithm based on data-driven predictive control

Model predictive control (MPC) is a popular strategy for active queue management (AQM) that is able to incorporate physical and user defined constraints. However, the current MPC methods rely on explicit fluid model of TCP behavior with input time delay. In this paper, we propose a novel AQM algorithm based on data-driven predictive control, called Data-AQM. For Internet system with large delay, complex change and bad disturbance, data-driven predictive controller can be obtained directly based on the input–output data alone and does not require any explicit model of the system. According to the input–output data, the future queue length in data buffer, which is the basis of optimizing drop probability, is predicted. Furthermore, considering system constraints, the control requirement is converted to the optimal control objective, then the drop probability is obtained by solving the optimal problem online. Finally, the performances of Data-AQM are evaluated through a series of simulations.