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We demonstrate accurate spatio-temporal gait data classification from raw tomography sensor data without the need to reconstruct images. This is based on a simple yet efficient machine learning methodology based on a convolutional neural network architecture for learning spatio-temporal features, automatically end-to-end from raw sensor data. In a case study on a floor pressure tomography sensor, experimental results show an effective gait pattern classification F-score performance of 97.88 $\pm$ 1.70%. It is shown that the automatic extraction of classification features from raw data leads to a substantially better performance, compared to features derived by shallow machine learning models that use the reconstructed images as input, implying that for the purpose of automatic decision-making it is possible to eliminate the image reconstruction step. This approach is portable across a range of industrial tasks that involve tomography sensors. The proposed learning architecture is computationally efficient, has a low number of parameters and is able to achieve reliable classification F-score performance from a limited set of experimental samples. We also introduce a floor sensor dataset of 892 samples, encompassing experiments of 10 manners of walking and 3 cognitive-oriented tasks to yield a total of 13 types of gait patterns. [ABSTRACT FROM PUBLISHER]

Analog balanced Photodetection has found extensive usage for sensing of a weak absorption signal buried in laser intensity noise. This paper proposes schemes for compact, affordable, and flexible digital implementation of the already established analog balanced detection, as part of a multichannel digital tomography system. Variants of digitally balanced detection (DBD) schemes, suitable for weak signals on a largely varying background or weakly varying envelopes of high frequency carrier waves, are introduced analytically and elaborated in terms of algorithmic and hardware flow. The DBD algorithms are implemented on a low-cost general purpose reconfigurable hardware (field-programmable gate array), utilizing less than half of its resources. The performance of the DBD schemes compare favorably with their analog counterpart: A common mode rejection ratio of 50 dB was observed over a bandwidth of 300 kHz, limited mainly by the host digital hardware. The close relationship between the DBD outputs and those of known analog balancing circuits is discussed in principle and shown experimentally in the example case of propane gas detection. [ABSTRACT FROM AUTHOR]

This article introduces the design and implementation of a hierarchical multi digital signal processor system aimed to perform parallel multichannel measurements and data processing of the type widely used in hard-field tomography. Details are presented of a complete tomography system with modular and expandable architecture, capable of accommodating a variety of data processing modalities, configured by software. The configuration of the acquisition and processing circuits and the management of the data flow allow a data frame rate of up to 250 kHz. Results of a case study, guided path tomography for temperature mapping, are shown as a direct demonstration of the system’s capabilities. Digital lock-in detection is employed for data processing to extract the information from ac measurements of the temperature-induced resistance changes in an array of 32 noninteracting transducers, which is further exported for visualization. [ABSTRACT FROM AUTHOR]

This article introduces the design and implementation of an affordable high-performance set of identical data acquisition channels with digital processing capabilities. Each channel incorporates a versatile 16-bit sigma-delta analog-to-digital converter (ADC) with reconfigurable filter characteristics. The main component of each channel, a low-cost field-programmable gate array (FPGA), controls the ADC, serves as a random access memory to store the ADCs user-defined filters, and performs digital processing. A special case is illustrated, with the FPGA software configured to perform lock-in detection, which is widely applied in a number of tomography modalities. The detection scheme, based on a quadrature demodulator, utilizes only a fraction of the FPGA resources and introduces errors orders of magnitude less than the quantization error of the ADC. Implementations other than a lock-in amplifier can be realized without additional hardware intervention. [ABSTRACT FROM AUTHOR]

Human footsteps can provide a unique behavioural pattern for robust biometric systems. We propose spatio-temporal footstep representations from floor-only sensor data in advanced computational models for automatic biometric verification. Our models deliver an artificial intelligence capable of effectively differentiating the fine-grained variability of footsteps between legitimate users (clients) and impostor users of the biometric system. The methodology is validated in the largest to date footstep database, containing nearly 20,000 footstep signals from more than 120 users. The database is organized by considering a large cohort of impostors and a small set of clients to verify the reliability of biometric systems. We provide experimental results in 3 critical data-driven security scenarios, according to the amount of footstep data made available for model training: at airports security checkpoints (smallest training set), workspace environments (medium training set) and home environments (largest training set). We report state-of-the-art footstep recognition rates with an optimal equal false acceptance and false rejection rate (equal error rate) of 0.7 percent an improvement ratio of 371 percent compared to previous state-of-the-art. We perform a feature analysis of deep residual neural networks showing effective clustering of client's footstep data and to provide insights of the feature learning process. [ABSTRACT FROM AUTHOR]

Abstract: We report here the first application of chemical species tomography (CST) in a multi-cylinder automotive engine. This technique offers high-speed continuous imaging of hydrocarbon fuel distribution and mixing within the combustion chamber and is therefore of interest to both engine designers and combustion scientists. Many of the methods described are equally applicable to chemically selective imaging of other highly dynamic mixing and reaction processes. A measurement grid consisting of 27 dual-wavelength optical paths has been implemented in one cylinder of an otherwise standard four-cylinder port-injected gasoline engine, using a unique OPtical Access Layer (OPAL) carrying embedded optical fibres and collimators. The OPAL provided adequate performance on many beams for more than 2h of fired engine operation. To improve sensitivity and to cope with fuel spray injection directly into the cylinder (in other engine types), a low-noise opto-electronic system has been developed, offering laser intensity modulation at frequencies up to 1MHz. Dual-wavelength measurements are recorded on each channel at 100kSPS, prior to off-line processing that typically reduces the effective frame rate to 3000–4000 frames/s, dependent upon engine speed. The performance of the system is assessed, using running conditions chosen to provide a qualitatively known (homogeneous) fuel distribution for validation purposes. Examples of measured data and processing schemes are discussed. Sample tomographic images, obtained using a novel quality-based approach to data selection, are presented. [Copyright &y& Elsevier]