A Ghorbani-Nejad, A Jannesari,
Volume 9, Issue 4 (December 2013)
Abstract
A two stage sub-µW Inverter-based switched-capacitor amplifier-filter is presented which is capable of amplifying both spikes and local field potentials (LFP) signals. Here we employ a switched capacitor technique for frequency tuning and reducing of 1/f noise of two stages. The reduction of power consumption is very necessary for neural recording devices however, in switched capacitor (SC) circuits OTA is a major building block that consumes most of the power. Therefore an OTA-less technique utilizing a class-C inverter is employed that significantly reduces the power consumption. A detailed analysis of noise performance for the inverter-based SC circuits is presented. A mathematical model useful for analysis of such SC integrators is derived and a good comparison is obtained between simulation and analytical technique. With a supply voltage of 0.7V and using 0.18 µm CMOS technology, this design can achieves a power consumption of about 538 nW. The designed amplifier-filter has the gains 18.6 dB and 28.2 dB for low pass only and cascaded filter, respectively. By applying different sampling frequencies, the filter attains a reconfigurable bandwidth.
Zahra Mobini-Serajy, Mehdi Radmehr, Alireza Ghorbani,
Volume 20, Issue 0 (In Press 2024)
Abstract
Micro-Grids harness the benefits of non-inverter and inverter based Distributed Energy Resources (DER) in grid-connected and island environments. Adoption them with the various type of electric loads in modern MGs has led to stability and power quality issues. In this paper, two-level control approach is proposed to overcome these problems. A state-space dynamic model is performed for Micro-Grids, for this goal, the state-space equations for generation, network and load components are separately developed in a local DQ reference frame, and after linearization around the set point, then combining them into a common DQ reference frame. In the first level, the control of inverter-based DERs and some types of loads with fast response are activated, and in the second level, the control of synchronous diesel generator resources with slower response are used. In order to validate and effectiveness evaluation of proposed control approach, numerical studies have been stablished on a standard test MG under normal and a symmetrical three-phase fault conditions. Finally, the simulations results are summarized.
Pedram Yamini, Fatemeh Daneshfar, Abuzar Ghorbani,
Volume 20, Issue 4 (December (Special Issue on ADLEEE) 2024)
Abstract
With the exponential growth of unstructured data on the Web and social networks, extracting relevant information from multiple sources; has become increasingly challenging, necessitating the need for automated summarization systems. However, developing machine learning-based summarization systems largely depends on datasets, which must be evaluated to determine their usefulness in retrieving data. In most cases, these datasets are summarized with humans’ involvement. Nevertheless, this approach is inadequate for some low-resource languages, making summarization a daunting task. To address this, this paper proposes a method for developing the first abstractive text summarization corpus with human evaluation and automated summarization model for the Sorani Kurdish language. The researchers compiled various documents from information available on the Web (rudaw), and the resulting corpus was released publicly. A customized and simplified version of the mT5-base transformer was then developed to evaluate the corpus. The model's performance was assessed using criteria such as Rouge-1, Rouge-2, Rouge-L, N-gram novelty, manual evaluation and the results are close to reference summaries in terms of all the criteria. This unique Sorani Kurdish corpus and automated summarization model have the potential to pave the way for future studies, facilitating the development of improved summarization systems in low-resource languages.
