Electronic instrumentation and sensors are playing a relevant role in preventing and detecting road accidents, and improving the overall driving experience. Consequently fault detection on vehicle instrumentation and sensors also emerged as a main topic for its direct impact on cost and road safety. As solution, the employment of the analytical redundancy of measurement information is particularly suitable and/or necessary. This paper is about the estimation of suspension stroke exploiting the analytical redundancy among the measurement signals provided by a typically adopted instrumentation set. A software sensor for the rear suspension is designed according to a systematic approach, which is focused on recurrent Artificial Neural Networks able to predict the dynamic behavior of the vehicle. Experimental results show the rear suspension elongation can be correctly estimated. They disclose the possibility of setting-up an effective Instrument Fault Detection and Isolation scheme based on the real-time adoption of the proposed software sensor in order to improve the system reliability.

The paper deals with the problem of measuring harmonic and interharmonic pollution in electrical power delivery systems. The attention is specifically focused on the possibility of exploiting the compressed sampling in order to implement cost-effective nodes for distributed acquisition of the voltage signals. Differently from the traditional distributed measurement systems, the proposed approach should allow a dramatic cost reduction of the monitoring network. According to the compressed sampling protocol, the operations of distributed nodes will, in fact, be limited to random digitization and transmission of few samples for each voltage line; no high performance architectures should, thus, be necessary, with a consequent money saving. Assessing the compliance of the achieved measurements with the current standards turns out to be mandatory, thus verifying the absence of artifacts introduced by the adopted compressed sampling approach. Results obtained in numerical and experimental tests have highlight the promising performance of the proposed approach, thus suggesting its implementation in an actual measurement instrument.

Noise signals are needed for test and validation of electronic systems and communication channels. Available noise sources are however limited to wide band white noise sources while the arbitrary waveform generators are poorly suited for the generation of long random or pseudo random noise sequences. The paper explores the possibility of using a single bit pseudo random sequence, filtered with a FIR filter, for the generation of an analogue noise signal with programmable spectral features. The results show that the technique is suitable for real time applications. When implemented on a Field Programmable Gate Array device, the technique uses less resources compared with the state of the art.

The functionalities of signal sources have kept the pace with the mounting complexity of the available systems. It could appear, that the types of signal sources nowadays available on the market allow the user to satisfy every need in test and measurement applications. On the contrary, despite the wide range of functionalities provided by the very last generation sources, there is still room to imagine challenges and propose hypothetical architectures of generators suitable to face them. The speculative case of architectures made up of multiple direct digital synthesizers (DDS) circuits that operate synchronously to produce non periodic signals and emulate interesting scenarios is discussed.

A procedure for reducing both deterministic and uncertainty effects of parasitic capacitance in designing standard inductors is presented. Metrological performance is enhanced while minimizing both deterministic and uncertainty effects simultaneously. In particular, statistical parameter design is exploited, by choosing optimum levels of design variables of the standard inductor. At this aim, initially, a first model is identified in order to define the impact of design parameters on parasitic capacitances considered as deterministic and the configuration for their minimization. Then, in the optimum configuration, a second model defines the impact of the design parameter uncertainty on the parasitic capacitance considered as random in order to assess the corresponding uncertainty budget. The method effectiveness is highlighted by a case study related to the design of an Ironless Inductive Position Sensor (I2PS).