Event-driven Feature Research Articles

A 62.2dB SNDR Event-Driven Level-Crossing ADC with SAR-Assisted Delay Compensation Loop for Time-Sparse Biomedical Signal Acquisition.

This paper proposed an event-driven clockless level-crossing ADC (LC-ADC) suitable for biomedical applications. Thanks to the LC loop, the sampling rate of the converter automatically adapts to the input activities. Activity-dependent power consumption and data compression can thus be realized, saving system power, especially during time-sparse signal acquisition. Meanwhile, a SAR-assisted loop is exploited to resolve the loop-delay-induced distortion in conventional LC-ADC. Therefore, the resolution and power efficiency of the LC-ADC are improved effectively while maintaining the event-driven feature. Implemented in a 55nm process, the proposed LC-ADC achieves a scalable power consumption and a peak SNDR of 62.2dB for a 20kHz input. It also achieves a Walden FoM of 29.7fJ/conv.-step and a Schreier FoM of 158.6dB, which is best in class, without using off-chip calibration. Sub μW power is realized when the input frequency is below 1.5kHz. The proposed LC-ADC is also verified by simulated electrocardiogram (ECG), neural spike, and electromyogram (EMG) signals. It provides a ~7X data compression for ECG input, providing an attractive solution for time-sparse signal acquisition in biomedical applications.

Efficient Spiking Neural Networks with Biologically Similar Lithium-Ion Memristor Neurons.

Benefiting from the brain-inspired event-driven feature and asynchronous sparse coding approach, spiking neural networks (SNNs) are becoming a potentially energy-efficient replacement for conventional artificial neural networks. However, neuromorphic devices used to construct SNNs persistently result in considerable energy consumption owing to the absence of sufficient biological parallels. Drawing inspiration from the transport nature of Na+ and K+ in synapses, here, a Li-based memristor (LixAlOy) was proposed to emulate the biological synapse, leveraging the similarity of Li as a homologous main group element to Na and K. The Li-based memristor exhibits ∼8 ns ultrafast operating speed, 1.91 and 0.72 linearity conductance modulation, and reproducible switching behavior, enabled by lithium vacancies forming a conductive filament mechanism. Moreover, these memristors are capable of simulating fundamental behaviors of a biological synapse, including long-term potentiation and long-term depression behaviors. Most importantly, a threshold-tunable leaky integrate-and-fire (TT-LIF) neuron is built using LixAlOy memristors, successfully integrating synaptic signals from both temporal and spatial levels and achieving an optimal threshold of SNNs. A computationally efficient TT-LIF-based SNN algorithm is also implemented for image recognition schemes, featuring a high recognition rate of 90.1% and an ultralow firing rate of 0.335%, which is 4 times lower than those of other memristor-based SNNs. Our studies reveal the ion dynamics mechanism of the LixAlOy memristor and confirm its potential in rapid switching and the construction of SNNs.

Development of Parametric Modeling and Finite Element Analysis System for Cylindrical Spiral Tension Spring

The survey shows that there are few researches on automatic modelling and finite element analysis by changing the relevant parameters of cylindrical spiral tension springs. This paper discusses the method of combining parametric technology with finite element analysis, using Visual Studio 2010 as the system development platform and Visual Basic (VB) as the program development language to establish a VB window operation interface. Automated modelling is carried out through the input feature size, and the VB event-driven feature is used to link automated modelling and automated finite element analysis to establish a parametric design and finite element analysis system for cylindrical spiral tension springs.

Model-based automated testing of JavaScript Web applications via longer test sequences

JavaScript has become one of the most widely used languages for Web development. Its dynamic and event-driven features make it challenging to ensure the correctness of Web applications written in JavaScript. A variety of dynamic analysis techniques have been proposed which are, however, limited in either coverage or scalability. In this paper, we propose a simple, yet effective, model-based automated testing approach to achieve a high code-coverage within the time budget via testing with longer event sequences. We implement our approach as an open-source tool LJS, and perform extensive experiments on 21 publicly available benchmarks. On average, LJS is able to achieve 86.5% line coverage in 10 minutes. Compared with JSDEP, a state-of-the-art breadth-first search based automated testing tool enriched with partial order reduction, the coverage of LJS is 11%–19% higher than that of JSDEP on real-world large Web applications. Our empirical findings support that proper longer test sequences can achieve a higher code coverage in JavaScript Web application testing.

Event-Driven Acquisition and Machine-Learning-Based Efficient Prediction of the Li-Ion Battery Capacity.

In almost all contemporary power systems, the battery is an elementary component, and it is routinely used in a variety of critical applications such as drones, avionics, and cell phones. Due to their superior characteristics compared to the concurrent technologies, Li-ion batteries are widely utilized. Since batteries are costly, their usage is closely monitored by battery management systems (BMSs). It ensures that batteries survive and serve longer. Modern BMSs’ are complex and sophisticated and can deal with hundreds of cells in a battery pack. It results in an increased processing resources requirement and can cause an overhead power consumption. The aim of this work is to improve current BMSs by redesigning their associative processing chain. It focuses on improving data collection, processing and prediction processes for Li-ion battery cell capacities. To prevent the processing of a large amount of unnecessary data, the classical sensing approach that is fix-rate is avoided and replaced by event-driven sensing (EDS) mechanism to digitize battery cell parameters such as voltages, currents, and temperatures in a way that allows for real-time data compressing. A new approach is proposed for event-driven feature extraction. The robust machine-learning algorithms are employed for processing the extracted features and to predict the capacity of considered battery cell. Results show a considerable compression gain with a correlation coefficient of 0.999 and the relative absolute error (RAE) and root relative squared error (RRSE) of 1.88% and 2.08%, respectively.

Efficient Epileptic Seizure Detection Based on the Event Driven Processing

Big number of people all over the world is affected by epilepsy. Electroencephalography (EEG) is a crucial component in the evaluation of epilepsy. Onset seizure detection is essential to prevent the seizure activity and improve patients’ life quality. Presurgical treatment, precise assessment, seizure prevention, and emergency alerts all depend on the quick detection of seizure onset. Moreover, manual examination of EEG signals is boring and time-consuming task. Numerous automated epileptic seizure detection schemes have been developed to help neurologists. The aim of this paper is to develop an intelligent event-driven EEG signal acquisition module to achieve an efficient solution for epileptic seizure detection. The suggested method acquires the signal with an event-driven A/D converter (EDADC). The output of EDADC is passed through the activity selection and interpolation blocks. It allows focusing only on the important signal parts and resampling it uniformly. The autoregressive (AR) Burg modelling is employed to extract the features. Afterwards, the extracted features are utilized by C4.5 and NBTree classification techniques. The event-driven feature enables to adapt the system processing load according to the signal temporal variations. This interesting feature of the devised system aptitudes a drastic reduction in its processing activity and therefore in the power consumption as compared to the traditional ones. A comparison of the performance of different classifiers is also made in terms of accuracy. Results show that the proposed system is a potential candidate for an automated epileptic seizure detection.

JSTrace: Fast reproducing web application errors

JavaScript has become the most popular language for client-side web applications. Due to JavaScript's highly-dynamic and event-driven features, it is challenging to diagnose web application errors. Record-replay techniques are used to reproduce errors in web applications. After a long run, these techniques will record a long event trace that triggers an error. Although the error-related events are few, they are interleaved with other massive error-irrelevant events. It is time-consuming to diagnose errors with long event traces.In this article, we present JSTrace, which effectively removes error-irrelevant events from the long event trace, and further facilitates error diagnosis. Based on fine-grained dependences of JavaScript and DOM instructions, we develop a novel dynamic slicing technique that can remove events irrelevant to the error. We further present rules to remove irrelevant events, which cannot be removed by dynamic slicing. In this process, many events and related instructions are removed without losing the error reproducing accuracy. Our evaluation on 13 real-world web application errors shows that the reduced event traces can faithfully reproduce errors with an average reduction rate of 97%. We further performed case studies on 4 real-world errors, and the result shows that JSTrace is useful to diagnose web application errors.

Non-linear behaviour of charge-pump phase-locked loops

Abstract. The analysis of the mixed analogue and digital structure of charge-pump phase-locked loops (CP-PLL) is a challenge in modelling and simulation. In most cases the system is designed and characterized using its continuous linear model or its discrete linear model neglecting its non-linear switching behaviour. I.e., the time-varying model is approximated by a time-invariant representation using its average dynamics. Depending on what kind of phase detector is used, the scopes of validity of these approximations are different. Here, a preeminent characterization and simulation technique based on the systems event-driven feature is presented, merging the logical and analogue inherent characteristics of the system. In particular, the high-grade non-linear locking process and the dead-zone are analyzed.

Testing web applications

Traditional testing techniques are not adequate for web-based applications, since they miss their additional features such as their multi-tier nature, hyperlink-based structure, and event-driven feature. Limited work has been done on testing web applications. In this paper, we propose new techniques for white box testing of web applications developed in the .NET environment with emphasis on their event-driven feature. We extend recent work on modeling of web applications by enhancing previous dependence graphs and proposing an event-based dependence graph model. We apply data flow testing techniques to these dependence graphs and propose an event flow testing technique. Also, we present a few coverage testing approaches for web applications. Further, we propose mutation testing operators for evaluating the adequacy of web application tests.