Time-of-Flight (ToF) cameras acquire 3-D images where each pixel relates to the distance in the observed scene. These images are acquired by emitting modulated light and measuring the phase of its reflection from an object. Improvements in ToF camera technology make it a promising alternative to current 3-D imaging sensors despite possessing comparatively lower resolution. Overcoming lower resolution, of single ToF camera images, is achieved using a multi-camera setup. However, the simultaneous operation of multiple ToF cameras causes interference resulting in erroneous depth measurements. An option to mitigate interference is the arrangement of multiple cameras in a consecutive order sharing the same frequency in assigned time slots, such that each camera illuminates the scene exclusively, a procedure known as time division multiple access (TDMA). The assignment of acquisition times is usually performed by a master synchronizing device implementing a time synchronization protocol that requires an additional communication channel. This paper presents a different approach requiring no additional infrastructure to utilize TDMA by adding a synchronization software procedure to the camera's microprocessor which effectively enables a camera to sense the acquisition process of other cameras and rapidly synchronize itself autonomously to operate without interference. Experimental results with two ToF cameras provide substantive evidence of the efficacy of this approach. The operation of the standalone cameras achieved the desired synchronization without illumination interference. The proposed synchronization procedure takes advantages of the zero interference of TDMA and overcomes traditional hard-wired synchronization setups, offering an autonomous and potentially mobile operation of ToF camera deployment.

Digitization of the working environment leads to tasks with high ambiguity, resulting in bad performance. The ambiguity of goals plays an important role in problem solving and could affect mental workload. In our study, we investigated whether goal ambiguity affects mental workload and task performance during planning. We hypothesized that problems with higher ambiguity require higher mental workload and degrade performance. The Tower of Hanoi (ToH) was employed and 21 participants were instructed to move disks in order to reach a particular goal state. The tower-ending goal state, where all disks had to be arranged on one peg, served as goal state with low ambiguity. The flat-ending goal state, where disks had to be arranged on different pegs, was considered as more ambiguous due to its less apparent goal structure. Mental workload was registered by means of the method of Dual Frequency Head Maps from the electroencephalogram. For evaluating subjects' performance, we registered the error rate and planning time. Analysis of the electroencephalogram yielded a significantly higher mental workload during the trials with a flat-ending goal state compared to trials with a tower-ending goal. Results of performance data were consistent with findings from the literature, indicating a higher error rate and longer planning time for the flat-ending goal state. By these, we gained proof that tasks with a higher level of goal ambiguity induce higher mental workload.

Emerging technology for brain-state monitoring offers the possibility to conduct measurements outside the laboratory. However, user-experience research is lacking. In this article, we present and test an approach for determining the development of user experience in the course of time using the so-called cross-modality matching (CMM). We conducted experiments with 24 subjects and evaluated seven mobile electroencephalography (EEG) devices. Using the CMM method, we registered the headset pressure of the EEG devices and subject's mood. We are able to identify a correlation between headset pressure and mood and to observe time trends. Subjects rated the heaviest, pin-based device as less comfortable in the course of time. The gel-based EEG cap is the most comfortable device regarding its long-time properties. The CMM approach for user-experience evaluation of new EEG technologies is direct, rapid, and easy to perform. This fact creates new opportunities for future studies in the field of user experience and human factors.

In our digitized society, advanced information and communication technology and highly interactive work environments impose high demands on cognitive capacity. Optimal workload conditions are important for assuring employee's health and safety of other persons. This is particularly relevant in safety-critical occupations, such as air traffic control. For measuring mental workload using the EEG, we have developed the method of Dual Frequency Head Maps (DFHM). The method was tested and validated already under laboratory conditions. However, validation of the method regarding reliability and reproducibility of results under realistic settings and real world scenarios was still required. In our study, we examined 21 air traffic controllers during arrival management tasks. Mental workload variations were achieved by simulation scenarios with different number of aircraft and the occurrence of a priority-flight request as an exceptional event. The workload was assessed using the EEG-based DFHM-workload index and instantaneous self-assessment questionnaire. The DFHM-workload index gave stable results with highly significant correlations between scenarios with similar traffic-load conditions (r between 0.671 and 0.809, p ≤ 0.001). For subjects reporting that they experienced workload variation between the different scenarios, the DFHM-workload index yielded significant differences between traffic-load levels and priority-flight request conditions. For subjects who did not report to experience workload variations between the scenarios, the DFHM-workload index did not yield any significant differences for any of the factors. We currently conclude that the DFHM-workload index reveals potential for applications outside the laboratory and yields stable results without retraining of the classifiers neither regarding new subjects nor new tasks.

Time-of-Flight (ToF) cameras are a promising alternative to current 3-D imaging systems. A setup of multiple ToF cameras is used to overcome their comparatively lower resolution, increase the field of view, and to reduce occlusion. However, the simultaneous operation introduces the possibility of interference resulting in erroneous depth measurements. One option to mitigate interference is the consecutive arrangement of multiple cameras in time slots, such that each camera illuminates the scene exclusively, a procedure known as time division multiple access (TDMA). Instead of requiring a master synchronizing device, an approach is presented requiring no additional hardware or infrastructure to utilize TDMA by adding a synchronization software procedure to the camera's firmware. It effectively enables a camera to sense the acquisition process of other cameras and rapidly synchronize its acquisition times to operate without interference. During the experimental verification with three ToF cameras no interference occurred. The cameras each acquired 29 depth images per second while synchronizing themselves every 10 s. The proposed synchronization procedure implements an interference-free TDMA operation and overcomes traditional hard-wired synchronization setups, offering an easy setup and a methodology to upgrade existing systems without hardware modifications.

One central topic in ergonomics and human-factors research is the assessment of mental workload. Heart rate and heart rate variability are common for registering mental workload. However, a major problem of workload assessment is the dissociation among different workload measures. One potential reason could be the disregard of their inherent timescales and the interrelation between participants’ individual differences and timescales. The aim of our study was to determine if different cardiovascular biomarkers exhibit different timescales. We focused on air traffic controller and investigated biomarkers’ ability to distinguish between conditions with different load levels connected to prior work experience and different time slots. During an interactive real-time simulation, we varied the load situations with two independent variables: the traffic volume and the occurrence of a priority-flight request. Dependent variables for registering mental workload were the heart rate and heart rate variability from two time slots. Our results show that all cardiovascular biomarkers were sensitive to workload differences with different inherent timescales. The heart rate responded sooner than the heart rate variability features from the frequency domain and it was most indicative during the time slot immediately after the priority-flight request. The heart rate variability parameters from the frequency domain responded with latency and were most indicative during the subsequent time slot. Furthermore, by consideration of biomarkers’ inherent timescales, we were able to assess a significant effect of work experience on heart rate and mid/high frequency-band ratio of the heart rate variability. Results indicated that different cardiovascular biomarkers reveal different inherent timescales.

Time-of-flight (ToF) cameras are an evolving technology, finding applications in a wide range of fields, including machine vision, automotive applications, face or hand recognition, and human-robot collaboration. A setup consisting of multiple ToF cameras can provide a 3-D model of the recorded scene with a larger field of view and avoid occlusion. The operating principle of ToF cameras' requires the emission of light to measure its round-trip time to the objects in the field of view and the reflection back to the camera. If multiple ToF cameras operate simultaneously, their light waves may interfere, resulting in erroneous depth measurements. In this paper, we propose a multi-camera time-of-flight system based on dynamic time-division multiple access (dynamic TDMA) to avoid any interference. The time synchronization is implemented optically, so that no further time synchronization, external to the cameras, is required. To achieve a high frame rate, every camera is extended with a photodiode, signaling a free channel. A synchronization protocol is implemented to allow for an autonomous operation of the ToF cameras.

In our study, we focused on air traffic controller’s working position for arrival management. Our aim was to evaluate cardiovascular parameters regarding their ability to distinguish between conditions with different traffic volumes and between conditions with and without the occurrence of an extraordinary event. Our sample consisted of 21 subjects. During an interactive simulation, we varied the load situations with two independent variables: the traffic volume and the occurrence of a priority-flight request. Dependent variables for registering mental workload were cardiovascular parameters, i.e., the heart rate, relative low-frequency and high-frequency band powers, and band-power ratio of the low- and high-frequency bands. Heart rate was the only parameter able to differentiate significantly between simulations with minimal and high air-traffic volume, while the effect of the priority-flight request remained doubtful. No significant interaction between traffic volume and priority request could be identified for any of the cardiovascular parameters.

The last decade bore witness to increased development of time-of-flight (ToF) camera hardware, algorithms, and applications, particularly 3-D measurement in the fields of human-robot collaboration. Their advantage is in faster digital 3-D image target representation from efficient signal computations. ToF cameras emit modulated light, measuring the round-trip time from its illumination source to its sensor, deriving a distance image. A disadvantage of using multiple ToF cameras is light interference with other cameras' 3-D measurements. Commonly, adopting an access protocol called frequency division multiple access (FDMA) minimizes interference-related errors but does not eliminate them. The quantification of this distance error has remained a challenging research problem. This paper presents an interference model for two time-of-flight cameras, quantitative analysis, and mathematical modeling to determine the interference-related distance error when using FDMA.

Background Registration of brain activity has become increasingly popular and offers a way to identify the mental state of the user, prevent inappropriate workload, and control other devices by means of brain-computer interfaces. However, electroencephalography (EEG) is often related to user acceptance issues regarding the measuring technique. Meanwhile, emerging mobile EEG technology offers the possibility of gel-free signal acquisition and wireless signal transmission. Nonetheless, user experience research about the new devices is lacking. Objective This study aimed to evaluate user experience aspects of emerging mobile EEG devices and, in particular, to investigate wearing comfort and issues related to emotional design. Methods We considered 7 mobile EEG devices and compared them for their wearing comfort, type of electrodes, visual appearance, and subjects’ preference for daily use. A total of 24 subjects participated in our study and tested every device independently of the others. The devices were selected in a randomized order and worn on consecutive day sessions of 60-min duration. At the end of each session, subjects rated the devices by means of questionnaires. Results Results indicated a highly significant change in maximal possible wearing duration among the EEG devices (χ26=40.2, n=24; P<.001). Regarding the visual perception of devices’ headset design, results indicated a significant change in the subjects’ ratings (χ26=78.7, n=24; P<.001). Results of the subjects’ ratings regarding the practicability of the devices indicated highly significant differences among the EEG devices (χ26=83.2, n=24; P<.001). Ranking order and posthoc tests offered more insight and indicated that pin electrodes had the lowest wearing comfort, in particular, when coupled with a rigid, heavy headset. Finally, multiple linear regression for each device separately revealed that users were not willing to accept less comfort for a more attractive headset design. Conclusions The study offers a differentiated look at emerging mobile and gel-free EEG technology and the relation between user experience aspects and device preference. Our research could be seen as a precondition for the development of usable applications with wearables and contributes to consumer health informatics and health-enabling technologies. Furthermore, our results provided guidance for the technological development direction of new EEG devices related to the aspects of emotional design.

There is a growing consensus concerning the negative consequences of inappropriate workload on employee’s health and the safety of persons. In a simulator study, we focused on air traffic controllers during arrival management tasks. Our aim was to find out if the number of aircraft or the occurrence of an exceptional event added load to the subjectively experienced workload. The workload was assessed using the NASA-TLX, instantaneous self-assessment (ISA) questionnaire, and expert ratings. Our sample consisted of 21 subjects. According to standard ANOVA procedures, controllers’ subjective ratings showed a high-significant discrimination between the different air traffic demands but only a weak-significant discrimination between sessions with and without event. In particular, we were not able to obtain a significant interaction effect between traffic volume and event. However, the examination of between-subject factors could reveal additional information about controller’s rating behavior. We currently conclude that while the effect of the number of aircraft was evident, the impact of an exceptional event remained doubtful.

V dannoi knige predstavleny naibolee vazhnye metody obrabotki signalov. Avtory podrobno razyasnyayut matematicheskie formulirovki i effekty, dostigaemye pri ispolzovanii razlichnykh metodov. Krome togo obrashchaetsya vnimanie na to, kakie usloviya dolzhny soblyudatsya pri ikh primenenii. Chitatel uchitsya ocenivatj metody kriticheski i raspoznavatj alternativy. Avtory v ravnoi stepeni obsuzhdayut kak zavisyashchie ot vremeni signaly, tak i signaly izobrazhenii. Primery iz aktualnykh issledovatelskikh proektov demonstriruyut vozmozhnosti primeneniya i uglublyayut ponimanie. Otdelnye glavy soderzhat zadachi, kotorye reshayutsya s pomoshchyu prostykh vspomogatelnykh sredstv. Resheniya k zadacham ukazany v knige. Avtory polagayut, chto dannoe uchebnoe posobie okazhetsya poleznym kak dlya studentov vuzov, tak i dlya specialistov v oblasti cifrovoi obrabotki signalov.

In distributed systems, exact synchronization is essential for many applications. Wireless localization and communication systems, as well as high-precision sensor networks, demand time synchronization accuracies in the order of magnitude of nanoseconds or picoseconds. In this paper, we present a novel method of phase-based high-precision frequency and time synchronization for wireless devices. By observing the network traffic, the slave devices detect and compensate the drift of their local oscillator in relation to a master device. Once the frequency is synchronized, our new algorithm adjusts the system clock of the slaves. We realize our approach with the help of a software-defined radio consisting of a Field Programmable Gate Array and a radio frequency transceiver. By using a 5 GHz experimental setup based on IEEE 802.11a, the clock of a slave device is phase aligned to the clock of a master device with a precision of less than 5 ps and an accuracy of ±50 ps.

OBJECTIVE:Biological and non-biological artifacts cause severe problems when dealing with electroencephalogram (EEG) recordings. Independent component analysis (ICA) is a widely used method for eliminating various artifacts from recordings. However, evaluating and classifying the calculated independent components (IC) as artifact or EEG is not fully automated at present.APPROACH:In this study, we propose a new approach for automated artifact elimination, which applies machine learning algorithms to ICA-based features.MAIN RESULTS:We compared the performance of our classifiers with the visual classification results given by experts. The best result with an accuracy rate of 95% was achieved using features obtained by range filtering of the topoplots and IC power spectra combined with an artificial neural network.SIGNIFICANCE:Compared with the existing automated solutions, our proposed method is not limited to specific types of artifacts, electrode configurations, or number of EEG channels. The main advantages of the proposed method is that it provides an automatic, reliable, real-time capable, and practical tool, which avoids the need for the time-consuming manual selection of ICs during artifact removal.

Artifact elimination is a central issue in neurosciences. A method that has established itself as an important part of EEG analysis is the application of independent component analysis (ICA). It decomposes the multi-channel EEG into linearly independent components (ICs) that then can be classified as artifact or EEG signal component. However, classification of the ICs still requires visual, time-intensive inspection by experts.In order to develop an automated artifact elimination method, we apply several classification algorithms on feature vectors extracted from ICA components via image processing algorithms. We compare their performance with the ratings of experts and identify range filtering as a feature extraction method with great potential. Range images classified with artificial neuronal networks yield accuracy rates of 95.5%. The results are very promising regarding automated IC artifact recognition.Compared to existing automated solutions the proposed method has the main advantage that it is not limited to a specific number or type of artifact. Furthermore, it is an automatic, real-time capable, and practical tool that reduces the time-intensive manual selection of ICs for artifact removal.

High-precision sensor networks and localization systems require precise time and frequency synchronization. In this paper, we present a novel high-precision frequency synchronization approach for wireless network devices. It adapts the local oscillator frequency of a receiver to the frequency of a transmitter and can be integrated into existing wireless communication systems. The measurement of frequency differences as well as the frequency adjustment is realized in Field Programmable Gate Arrays (FPGAs). Using a 60 GHz wireless experimental setup, the receiver clock is aligned to the transmitter clock with a precision of 37 picoseconds.

References [1] Thakur, R. K., Bienefeld, K.; Keller, R.: Varroa defence behavior in Apis mellifera carnica. American Bee Journal 137, pp. 143-148, 1997. [2] Viola, P., Jones, M.: Rapid Object Detection using a Boosted Cascade of Simple Features. CVPR 2001. [3] Knauer, U. et al.: Application of an Adaptive Background Model for Monitoring Honeybees. VIIP 2005 . [4] Knauer, U. et al.: Evaluation based combining of classifiers. WACV 2009. Methods TPR FPR Rank 1 Template Matching 93,01% 4,98% 3 2 Haarlike Features 91,26% 4,01% 5 3 Contour Detection 95,97% 1,74% 1 4 Interest Point Density 95,87% 4% 2 5 Hough Transform 92,59% 5,23% 4 Methods Precision Recall Rank 1 Template Matching 69,40% 82,50% 5 2 Haarlike Features 91,17% 90,50% 2 3 Contour Detection 94,66% 91,20% 1 4 Interest Point Density 86,70% 88% 3 5 Hough Transform 79,41% 84,37% 4

Next generation postal sorting machines reuse once extracted mail piece addresses in different sorting steps by means of the mail piece image. Based on the mail piece uniqueness, characteristics derived from the image guarantee the assignment of stored addresses. During the first sorting step mail piece characteristics are extracted and stored together with the target address in a database. In subsequent sorting steps the address is accessed by determining the corresponding mail piece characteristics in the database. Appropriate mail piece image characteristics and procedures for their distance measurement were presented in a previous work. Image based mail piece identification poses a challenge by a constantly changing and non-deterministic mail spectrum and the differentiation of nearly identical bulk mail. In particular, the rejection of unknown mail pieces requires the definition of carefully chosen rejection classes depending on the current mail spectrum. In this paper we present an approach for distance based mail piece identification using a two-stage classification process. Bulk and private mail are handled individually by an unsupervised learning process which clusters similar mail piece characteristics. Based on these clusters specific rejection classes can be estimated within each cluster. The first step in the identification process is the determination of the corresponding cluster for a given mail piece. Using the cluster specific rejection classes a mail piece is either identified or rejected. Experimental results obtained on real-world data sets prove the applicability of the proposed method.