Acoustical properties in inhaling singing : a case-study

A highly experienced versatile female professional singer displaying no apparent vocal complaint, developed inhaling singing, an innovative approach to reverse phonation. Although there are some reports in literature that describe the characteristics of ingressive phonation and sounds, to the best of our knowledge, no reports on actual inhaling singing are available in literature. This paper reports a case study on the acoustical analysis of inhaling singing, comparing this innovative technique with traditional exhaling singing. As this is rather undiscovered territory, we have decided to address several questions: is it possible to match the same pitches using inhaling singing compared to exhaling singing? Is the harmonic structure and energy distribution similar? Is it possible to maintain the same phonation duration in both techniques? Are there differences in volume and tessitura (vocal range)? This paper, reporting on the experience of one individual, demonstrates that a tessitura can be mastered in inhaling singing. Spectral analysis reveals a similar frequency distribution in both conditions. However, in inhaling singing the energy of the harmonics is significantly lower for the first 3 overtones, while the maximum phonation time is larger, than in exhaling singing. The singer reports that less effort is required for inhaling singing in the high register. As such, inhaling singing offers new possibilities for vocal performance

Assessment of the influence of photodiode size on RSS-based visible light positioning precision

This work discusses the influence of a photodiodes effective area size on the precision of received signal strength-based visible light positioning. It analyzes how two silicon-based photodiodes with different effective area perform as a receiver under varying illumination conditions. The two main findings are that it is not particularly needed to select a photodiode with a large surface area, despite the higher received signal strength, due to a higher noise contribution. On the other hand, the spread on the distance estimation is much smaller than 1 mm under standard illumination levels for the two photodiodes with a significantly different surface area, so that both photodiodes deliver enough precision

Experimental evaluation of the precision of received signal strength based visible light positioning

In this work, the experimental evaluation of the distance estimation variance is executed for received signal strength based visible light positioning. It is shown that based on the signal to noise ratio at the matched filter output, an accurate determination of the precision is achieved. In order to suppress dc ambient light which contains no information regarding the distance between the LED and the receiver, matched filtering with the dc-balanced part of the transmitted signal is required. As a consequence, the theoretical lower bound for the precision can not be achieved

New photodiode responsivity model for RSS-based VLP

Visible Light Positioning (VLP) might enable auspicious tracking systems, well-suited for low-cost and route-configurable autonomous guided vehicles. Yielding the high accuracy required, necessitates a detailed modelling of a photodiode (PD) receiver's angular characteristics. Still lacking, current RSS-based VLP systems implicitly cope by measuring and (arbitrarily) fitting the received power - distance relation. Upon PD changeover, a recalibration is needed. In this paper, it is shown that adequately modelling the receiver's angular dependencies (i.e. the responsivity) obsoletes the calibrating fit. Hereto, a new responsivity model is proposed, which is a function of the square of the incidence angle rather than its cosine. An extensive measurement set highlights that this model better matches the measured angular characteristics. In terms of the coefficient of determination R2, the new model outscores the baseline Lambertian and generalised Lambertian responsivity models by 1.64% and 0.17% for a Lambertian-like receiver, and by 133% and 1.24% for a non-Lambertian-resembling receiver

Experimental evaluation of machine learning methods for robust received signal strength-based visible light positioning

In this work, the use of Machine Learning methods for robust Received Signal Strength (RSS)-based Visible Light Positioning (VLP) is experimentally evaluated. The performance of Multilayer Perceptron (MLP) models and Gaussian processes (GP) is investigated when using relative RSS input features. The experimental set-up for the RSS-based VLP technology uses light-emitting diodes (LEDs) transmitting intensity modulated light and a single photodiode (PD) as a receiver. The experiments focus on achieving robustness to cope with unknown received signal strength modifications over time. Therefore, several datasets were collected, where per dataset either the LEDs transmitting power is modified or the PD aperture is partly obfuscated by dust particles. Two relative RSS schemes are investigated. The first scheme uses the maximum received light intensity to normalize the received RSS vector, while the second approach obtains RSS ratios by combining all possible unique pairs of received intensities. The Machine Learning (ML) methods are compared to a relative multilateration implementation. It is demonstrated that the adopted MLP and GP models exhibit superior performance and higher robustness when compared to the multilateration strategies. Furthermore, when comparing the investigated ML models, the GP model is proven to be more robust than the MLP for the considered scenarios

Data-efficient Gaussian process regression for accurate visible light positioning

In the field of indoor localization systems, Received Signal Strength (RSS) based Visible Light Positioning (VLP) has gained increased attention due to the dual functionality of lighting and localization. Previously geometrical models have been used to determine the position of a mobile entity, however these are unsuited when dealing with tilted surfaces and non-Lambertian sources. For this reason, machine learning techniques like Multi Layer Perceptrons (MLPs) have been considered recently. In this work, Gaussian Processes (GPs) are introduced in the context of RSS-based VLP, since they have proven to work well when using small, noisy datasets for different applications. Their performance is evaluated using both simulated data with a small transmitter tilt tolerance and measurements. It is demonstrated that the GP model outperforms both the multilateration approach and the MLP approach for the simulations and measurements data

Metagenomics meets time series analysis : unraveling microbial community dynamics

The recent increase in the number of microbial time series studies offers new insights into the stability and dynamics of microbial communities, from the world's oceans to human microbiota. Dedicated time series analysis tools allow taking full advantage of these data. Such tools can reveal periodic patterns, help to build predictive models or, on the contrary, quantify irregularities that make community behavior unpredictable. Microbial communities can change abruptly in response to small perturbations, linked to changing conditions or the presence of multiple stable states. With sufficient samples or time points, such alternative states can be detected. In addition, temporal variation of microbial interactions can be captured with time-varying networks. Here, we apply these techniques on multiple longitudinal datasets to illustrate their potential for microbiome research.Peer reviewe

Impact of a photodiode's angular characteristics on RSS-based VLP accuracy

Photodiode (PD)-based Visible Light Positioning (VLP)-based localisation systems seem propitious for the low-cost tracking and route-configurable navigation of automated guided vehicles, found in warehouse settings. Delivering the required high accuracy, currently necessitates measuring and fitting the received power - distance relation. This paper shows that accurately modelling the PD receiver & x2019;s angular characteristics obsoletes this calibrating fit, while still providing accurate positioning estimates. A new responsivity model Square (SQ) is proposed, which is a function of the square of the incidence angle rather than its cosine. Both its aptitude in matching real-life propagation and its associated localisation accuracy are verified using two extensive measurement sets, each detailing the propagation of a PD moving across a 2D plane 3 m below a 4-LED plane. SQ is compared to the responsivity and calibration fit models available in the literature. In conjunction with model-based fingerprinting positioning, SQ outscores the Lambertian and generalised Lambertian model in terms of the 90(th) percentile root-mean-square error (rMSE) $p_{90}$ by 45.36 cm (83.1 & x0025;) and 0.84 cm (8.4 & x0025;) respectively for the non-Lambertian-like receiver. $SQ$ exhibits an equivalent performance as the generalised Lambertian model for the Lambertian-like photodiode. Accounting for the appropriate receiver model can also boost trilateration & x2019;s rMSE. A 50(th) percentile rMSE reduction of respectively 1.87 cm and 2.66 cm is found in the setup