A Software-based Low-Jitter Servo Clock for Inexpensive Phasor Measurement Units

This paper presents the design and the implementation of a servo-clock (SC) for low-cost Phasor Measurement Units (PMUs). The SC relies on a classic Proportional Integral (PI) controller, which has been properly tuned to minimize the synchronization error due to the local oscillator triggering the on-board timer. The SC has been implemented into a PMU prototype developed within the OpenPMU project using a BeagleBone Black (BBB) board. The distinctive feature of the proposed solution is its ability to track an input Pulse-Per-Second (PPS) reference with good long-term stability and with no need for specific on-board synchronization circuitry. Indeed, the SC implementation relies only on one co-processor for real-time application and requires just an input PPS signal that could be distributed from a single substation clock

A Servo-Clock Model for Chains of Transparent Clocks Affected by Synchronization Period Jitter

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A methodology for the design of dynamic accuracy operators by runtime back bias

Mobile and IoT applications must balance increasing processing demands with limited power and cost budgets. Approximate computing achieves this goal leveraging the error tolerance features common in many emerging applications to reduce power consumption. In particular, adequate (i.e., energy/quality-configurable) hardware operators are key components in an error tolerant system. Existing implementations of these operators require significant architectural modifications, hence they are often design-specific and tend to have large overheads compared to accurate units. In this paper, we propose a methodology to design adequate data-path operators in an automatic way, which uses threshold voltage scaling as a knob to dynamically control the power/accuracy tradeoff. The method overcomes the limitations of previous solutions based on supply voltage scaling, in that it introduces lower overheads and it allows fine-grain regulation of this tradeoff. We demonstrate our approach on a state-of-the-art 28nm FDSOI technology, exploiting the strong effect of back biasing on threshold voltage. Results show a power consumption reduction of as much as 39% compared to solutions based only on supply voltage scaling, at iso-accuracy

Adaptive Task Migration Policies for Thermal control in MPSoCs

In deep submicron circuits, high temperatures have created critical issues in reliability, timing, performance, coolings costs and leakage power. Task migration techniques have been proposed to manage efficiently the thermal distribution in multi-processor systems but at the cost of important performance penalties. While traditional techniques have focused on reducing the average temperature of the chip, they have not considered the effect that temperature gradients have in system reliability. In this work, we explore the benefits of thermal-aware task migration techniques for embedded multi-processor systems. We propose several policies that are able to reduce the average temperature of the chip and the thermal gradients with a negligible performance overhead. With our techniques, hot spots and temperature gradients are decreased up to 30% with respect to state-of-the-art thermal management approache

A Cloud-to-Edge Approach to Support Predictive Analytics in Robotics Industry

Data management and processing to enable predictive analytics in cyber physical systems holds the promise of creating insight over underlying processes, discovering anomalous behaviours and predicting imminent failures threatening a normal and smooth production process. In this context, proactive strategies can be adopted, as enabled by predictive analytics. Predictive analytics in turn can make a shift in traditional maintenance approaches to more effective optimising their cost and transforming maintenance from a necessary evil to a strategic business factor. Empowered by the aforementioned points, this paper discusses a novel methodology for remaining useful life (RUL) estimation enabling predictive maintenance of industrial equipment using partial knowledge over its degradation function and the parameters that are affecting it. Moreover, the design and prototype implementation of a plug-n-play end-to-end cloud architecture, supporting predictive maintenance of industrial equipment is presented integrating the aforementioned concept as a service. This is achieved by integrating edge gateways, data stores at both the edge and the cloud, and various applications, such as predictive analytics, visualization and scheduling, integrated as services in the cloud system. The proposed approach has been implemented into a prototype and tested in an industrial use case related to the maintenance of a robotic arm. Obtained results show the effectiveness and the efficiency of the proposed methodology in supporting predictive analytics in the era of Industry 4.0

Management of Measurement Uncertainty for Effective Statistical Process Control

In the context of quality assurance strategies, statistical process control techniques and conformance testing are necessary to perform a correct quality auditing of process outcomes. However, data collection is based on measurements and every measurement is intrinsically affected by uncertainty. Even if adopted instruments are in a condition of metrological confirmation, random and systematic measurement errors can not be completely eliminated. Moreover, the consequence of wrong measurement–based decisions can seriously decrease company profits because of larger repairing and shipping costs, as well as for the loss of reputation due to customers’ dissatisfaction. This paper deals with a theoretical analysis aimed at estimating the growth in decisional risks due to both random and systematic errors. Also, it provides some useful guidelines about how to choose the Test Uncertainty Ratio (TUR) of industry–rated measurement instruments in order to bound the risk of making wrong decisions below a preset maximum value

Management of Distributed Measurement Systems Based on Abstract Client-Server Paradigms

This paper describes in detail a Java-based, clientserver architecture specifically conceived to allow a flexible control of remote devices. The main attributes of the proposed solution are portability and flexibility. The former feature is assured by the employment of the TCP/IP protocol suite and by the Java language properties. The latter is due to the high level of abstraction of the system implementation, that addresses multi-user issues and a wide range of possible applications with a high code reusability. In particular, the proposed architecture can be easily upgraded so as to fit different kinds of devices, by simply adding a limited amount of code on the server-side of the overall system

Measurement Uncertainty and Metrological Confirmation in Quality-Oriented Organizations

The effect of measurement uncertainty on estimates and decisions performed under a regime of quality control and improvement, is considered in this paper. Standard statistical quality tools are analyzed such as control charts and instrument calibration procedures. Their performance is characterized under the assumption of both normally and uniformly distributed measurement uncertainty. Exact and approximate expressions are derived that allow the design of suitable procedures including the contribution of measurement uncertainty

FFT Benchmarking for Digital Signal Processing Technologies

An appropriate choice of the computing devices employed in digital signal processing applications requires to characterize and to compare various technologies, so that the best component in terms of cost and performance can be used in a given system design. In this paper, a benchmark strategy is presented to measure the performances of various types of digital signal processing devices. Although different metrics can be used as performance indexes, Fast Fourier Transform (FFT) computation time and Real-Time Bandwidth (RTBW) have proved to be excellent and complete performance parameters. Moreover, a new index, measuring the architectural efficiency in computing FFT, is introduced and explained. Both parameters can be used to compare several digital signal processing technologies, thus guiding designers in optimal component selection

Fast Estimation of A/D Converter Nonlinearities

This paper deals with an innovative strategy to shorten the record size required to estimate the Integral Non-Linearity (INL) of Analog-to-Digital Converters (ADC’s) through the so-called Sinewave Histogram Test (SHT). Such a size reduction is achieved by low-pass filtering the collected sequences of test samples using a simple moving average filter. After some preliminary simulations, the validity of the proposed approach have been confirmed by some experimental results