Current conveyors and related current-mode circuits have begun to emerge as an important class of circuits with properties that enable them to rival their voltage-mode counterparts in a wide range of applications. The use of current rather than voltage as the active parameter can result in higher usable gain, accuracy and bandwidth due to reduced voltage excursion at sensitive nodes. A current-mode approach is not just restricted to current processing, but also offers certain important advantages when interfaced to voltage-mode circuits. The author sets out to survey developments in conveyors and current-mode circuits from both an historical and technical viewpoint and to act as a useful source of reference material.

Recent developments in current conveyors and current-mode circuits

This article provides an overview of current applications of machine learning (ML) in seismology. ML techniques are becoming increasingly widespread in seismology, with applications ranging from identifying unseen signals and patterns to extracting features that might improve our physical understanding. The survey of the applications in seismology presented here serves as a catalyst for further use of ML. Five research areas in seismology are surveyed in which ML classification, regression, clustering algorithms show promise: earthquake detection and phase picking, earthquake early warning (EEW), ground‐motion prediction, seismic tomography, and earthquake geodesy. We conclude by discussing the need for a hybrid approach combining data‐driven ML with traditional physical modeling.

Machine Learning in Seismology: Turning Data into Insights

In this paper, a current-mode (CM) Kerwin–Huelsman–Newcomb (KHN) biquad is proposed. The circuit employs three differential-voltage current conveyors (DVCCs) as active elements together with two capacitors and four resistors as passive elements, which all are grounded. The circuit simultaneously provides the three basic filter functions, namely bandpass (BP), highpass (HP) and lowpass (LP) functions. The notch and allpass (AP) functions can be obtained by connecting appropriate output currents directly without using additional active elements. The output signals are obtained at high output impedance ports, which is important for easy cascading in CM operation. SPICE simulation results are given to verify the theoretical analyses.

A 22.5 MHz current-mode KHN-biquad using differential voltage current conveyor and grounded passive elements

Change detection based on remote sensing (RS) data is an important method of detecting changes on the Earth’s surface and has a wide range of applications in urban planning, environmental monitoring, agriculture investigation, disaster assessment, and map revision. In recent years, integrated artificial intelligence (AI) technology has become a research focus in developing new change detection methods. Although some researchers claim that AI-based change detection approaches outperform traditional change detection approaches, it is not immediately obvious how and to what extent AI can improve the performance of change detection. This review focuses on the state-of-the-art methods, applications, and challenges of AI for change detection. Specifically, the implementation process of AI-based change detection is first introduced. Then, the data from different sensors used for change detection, including optical RS data, synthetic aperture radar (SAR) data, street view images, and combined heterogeneous data, are presented, and the available open datasets are also listed. The general frameworks of AI-based change detection methods are reviewed and analyzed systematically, and the unsupervised schemes used in AI-based change detection are further analyzed. Subsequently, the commonly used networks in AI for change detection are described. From a practical point of view, the application domains of AI-based change detection methods are classified based on their applicability. Finally, the major challenges and prospects of AI for change detection are discussed and delineated, including (a) heterogeneous big data processing, (b) unsupervised AI, and (c) the reliability of AI. This review will be beneficial for researchers in understanding this field.

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Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

Machine learning (ML) has evolved rapidly over recent years with the promise to substantially alter and enhance the role of data science in a variety of disciplines. Compared with traditional appro...

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

An inductorless allpass filter using three second generation current conveyors (CCIIs) has been reported. The circuit offers high input impedance, controllable voltage gain and uses grounded capacitors.

Inductorless current conveyor allpass filter using grounded capacitors

Modified current conveyors and their applications

The letter reports two circuits simulating ideal floating inductances and a capacitor floatation scheme using grounded copacitors. Both the inductances and capacitors are tunable through a single resistance and it has been shown that it is possible to simulate these realisations with unmatched passive components.

New inductance and capacitor floatation schemes using current conveyors

The letter presents a new ideal floating inductance using second generation current conveyors (CCIIs). The novel feature of this scheme is its realisation with all grounded passive components.

Novel floating inductance using current conveyors

Three circuits each realizing a, first-order all-pass transfer function are described in this communication, These circuits offer high input impedance and controllable voltage gain.

Kirat Pal

Papers

Inductorless current conveyor allpass filter using grounded capacitors

Modified current conveyors and their applications

New inductance and capacitor floatation schemes using current conveyors

Novel floating inductance using current conveyors

Realization of current conveyor all-pass networks