Intelligent human-centric lighting for mental wellbeing improvement

In recent years, the main area of interest in the issue of influencing mental states of people is the impact of lighting on human beings, their wellbeing but also workplace productivity. This work discusses in detail the problem of positively influencing people using intelligent technologies, especially the role of the colors. We describe techniques and technologies needed to implement the case study of an intelligent lighting system. The system proposed can detect humans from an IP camera, find faces, and detect emotion. The main aim is to adjust the lights accordingly to the emotional result to improve the mood of people while taking into consideration the principles of color psychology and daytime. We have evaluated our case study solution in a real-world environment and collected the feedback from participants in the form of a questionnaire. Evaluation of participants' wellbeing was based on their subjective statements. There were several ideas on further functionality extension which needs to be explored. Among them is including wearable devices to the proposed system, validate the emotional results according to them, but also determine the impact of an increasing number of users interacting with the system at the same time.Web of Science159art. no. 155014771987587

Long‐term memory for unfamiliar voices

From a sample of young male Californians, ten speakers were selected whose voices were approximately normally distributed with respect to the "easy-to-remember" versus "hard-to-remember" judgments of a group of raters. A separate group of listeners each heard one of the voices, and, after delays of 1, 2, or 4 weeks, tried to identify the voice they had heard, using an open-set, independent-judgment task. Distributions of the results did not differ from the distributions expected under the hypothesis of independent judgments. For both "heard previously" and "not heard previously" responses, there was a trend toward increasing accuracy as a function of increasing listener certainty. Overall, heard previously responses were less accurate than not heard previously responses. For heard previously responses, there was a trend toward decreasing accuracy as a function of delay between hearing a voice and trying to identify it. Information-theoretic analysis showed loss of information as a function of delay and provided means to quantify the effects of patterns of voice confusability. Signal-detection analysis revealed the similarity of results from diverse experimental paradigms. A "prototype" model is advanced to explain the fact that certain voices are preferentially selected as having been heard previously. The model also unites several previously unconnected findings in the literature on voice recognition and makes testable predictions

Cloud-based control of industrial cyber-physical systems

This paper presents an implementation of a control algorithm to a cloud system. The motivation is that cloud implementations of low-level systems in the production industry are gradually becoming more common. Microsoft Azure platform is utilized for the cloud-based control and the case is tested using a customized laboratory model, which can be presented as an agent in a typical production system. The model offers the regulation of a ball on an inclined surface and uses two asynchronous motors connected to frequency converters to control the position of the ball. These frequency converters are controlled by a Programmable Logic Controller (PLC). Windows Communication Foundation (WCF) services and Azure IoT Hub were selected to be used with the cloud-based control system. Experimental results have shown our solution can control the system with sampling period equal or higher than 100ms. The latency of WCF service is at around 100ms and latency of Azure IoT Hub is at around 1000ms, so the prediction algorithms could be implemented in the cloud for the latter. This research also shows the feasibility of migrating machine learning algorithms that demand high computing power to the cloud to reduce the computing burden on the local control units

Norm-based coding of voice identity in human auditory cortex

Listeners exploit small interindividual variations around a generic acoustical structure to discriminate and identify individuals from their voice—a key requirement for social interactions. The human brain contains temporal voice areas (TVA) [1] involved in an acoustic-based representation of voice identity [2, 3, 4, 5 and 6], but the underlying coding mechanisms remain unknown. Indirect evidence suggests that identity representation in these areas could rely on a norm-based coding mechanism [4, 7, 8, 9, 10 and 11]. Here, we show by using fMRI that voice identity is coded in the TVA as a function of acoustical distance to two internal voice prototypes (one male, one female)—approximated here by averaging a large number of same-gender voices by using morphing [12]. Voices more distant from their prototype are perceived as more distinctive and elicit greater neuronal activity in voice-sensitive cortex than closer voices—a phenomenon not merely explained by neuronal adaptation [13 and 14]. Moreover, explicit manipulations of distance-to-mean by morphing voices toward (or away from) their prototype elicit reduced (or enhanced) neuronal activity. These results indicate that voice-sensitive cortex integrates relevant acoustical features into a complex representation referenced to idealized male and female voice prototypes. More generally, they shed light on remarkable similarities in cerebral representations of facial and vocal identity

Cloud-based control of industrial cyber-physical systems

This paper presents an implementation of a control algorithm to a cloud system. The motivation is that cloud implementations of low-level systems in the production industry are gradually becoming more common. Microsoft Azure platform is utilized for the cloud-based control and the case is tested using a customized laboratory model, which can be presented as an agent in a typical production system. The model offers the regulation of a ball on an inclined surface and uses two asynchronous motors connected to frequency converters to control the position of the ball. These frequency converters are controlled by a Programmable Logic Controller (PLC). Windows Communication Foundation (WCF) services and Azure IoT Hub were selected to be used with the cloud-based control system. Experimental results have shown our solution can control the system with sampling period equal or higher than 100ms. The latency of WCF service is at around 100ms and latency of Azure IoT Hub is at around 1000ms, so the prediction algorithms could be implemented in the cloud for the latter. This research also shows the feasibility of migrating machine learning algorithms that demand high computing power to the cloud to reduce the computing burden on the local control units

Listeners form average-based representations of individual voice identities.

Models of voice perception propose that identities are encoded relative to an abstracted average or prototype. While there is some evidence for norm-based coding when learning to discriminate different voices, little is known about how the representation of an individual's voice identity is formed through variable exposure to that voice. In two experiments, we show evidence that participants form abstracted representations of individual voice identities based on averages, despite having never been exposed to these averages during learning. We created 3 perceptually distinct voice identities, fully controlling their within-person variability. Listeners first learned to recognise these identities based on ring-shaped distributions located around the perimeter of within-person voice spaces - crucially, these distributions were missing their centres. At test, listeners' accuracy for old/new judgements was higher for stimuli located on an untrained distribution nested around the centre of each ring-shaped distribution compared to stimuli on the trained ring-shaped distribution

phi-Meson production at forward rapidity in p-Pb collisions at root s(NN)=5.02 TeV and in pp collisions at root s=2.76 TeV

The first study of phi-meson production in p-Pb collisions at forward and backward rapidity, at a nucleonnucleon centre-of-mass energy root s(NN)= 5.02 TeV, has been performed with the ALICE apparatus at the LHC. The phi-mesons have been identified in the dimuon decay channel in the transverse momentum (p(T)) range 1 <p(T) <7GeV/c, both in the p-going (2.03 <y <3.53) and the Pb-going (-4.46 <y <-2.96) directions - where ystands for the rapidity in the nucleon-nucleon centre-of-mass - the integrated luminosity amounting to 5.01 +/- 0.19nb(-1) and 5.81 +/- 0.20nb(-1), respectively, for the two data samples. Differential cross sections as a function of transverse momentum and rapidity are presented. The forward-backward ratio for f-meson production is measured for 2.96Peer reviewe