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Autonomous mobile robots comprise of several hardware and software components. These components interact with each other continuously in order to achieve autonomity. Due to the complexity of such a task, a monumental responsibility is bestowed upon the developer to make sure that the robot is always operable. Hence, some means of detecting faults should be readily available. In this work, the aforementioned fault-detection system is a robotic black box (RBB) attached to the robot which acquires all the relevant measurements of the system that are needed to achieve a fault-free robot. Due to limited computational and memory resources on-board the RBB, a distributed diagnosis is proposed. That is, the fault diagnosis task (detection and isolation) is shared among an on-board component (the black box) and an off-board component (an external computer). The distribution of the diagnosis task allows for a non-intrusive method of detecting and diagnosing faults, in addition to the ability of remotely diagnosing a robot and potentially issuing a repair command. In addition to decomposing the diagnosis task and allowing remote diagnosability of the robot, another key feature of this work is the addition of expert human knowledge to aid in the fault detection process.
A deployment of the Vehicle-to-Vehicle communication technology according to ETSI is in preparation in Europe. Currently, a Public Key Infrastructure policy for Intelligent Transport Systems in Europe is in discussion to enable V2V communication. This policy set aside two classes of keys and certificates for ITS vehicle stations: long term authentication keys and pseudonymous keys and certificates. We show that from our point of view the periodic sent Cooperative Awareness Messages with extensive data have technical limitations and together with the pseudonym concept cause privacy problems.
In this paper, several blocking techniques are applied to matrices that do not have a strong blocked structure. The aim is to efficiently use vectorization with current CPUs, even for matrices without an explicit block structure on nonzero elements. Different approaches are known to find fixed or variable sized blocks of nonzero elements in a matrix. We present a new matrix format for 2D rectangular blocks of variable size, allowing fill-ins per block of explicit zero values up to a user definable threshold. We give a heuristic to detect such 2D blocks in a sparse matrix. The performance of a Sparse Matrix Vector Multiplication for chosen block formats is measured and compared. Results show that the benefit of blocking formats depend – as to be expected – on the structure of the matrix and that variable sized block formats can have advantages over fixed size formats.
SDN and WMN evolved to be sophisticated technologies used in a variety of applications. However, a combined approach called wmSDN has not been widely addressed in the research community. Our idea in this field consists of WiFi-based point-to-point links managed by the OpenFlow protocol. We investigate two different issues regarding this idea. First, which WiFi operational mode is suitable in an OpenFlow managed broadcast domain? Second, does the performance decrease compared with other routing or switching principles? Therefore, we set up a real-world testbed and a suitable simulation environment. Unlike previous work, we show that it is possible to use WiFi links without conducting MAC address rewriting at each hop by utilizing the 4-address-mode.
Supported by their large size and high resolution, display walls suit well for different collaboration types. However, in order to foster instead of impede collaboration processes, interaction techniques need to be carefully designed, taking into regard the possibilities and limitations of the display size, and their effects on human perception and performance. In this paper we investigate the impact of visual distractors (which, for instance, might be caused by other collaborators' input) in peripheral vision on short-term memory and attention. The distractors occur frequently when multiple users collaborate in large wall display systems and may draw attention away from the main task, as such potentially affecting performance and cognitive load. Yet, the effect of these distractors is hardly understood. Gaining a better understanding thus may provide valuable input for designing more effective user interfaces. In this article, we report on two interrelated studies that investigated the effect of distractors. Depending on when the distractor is inserted in the task performance sequence, as well as the location of the distractor, user performance can be disturbed: we will show that distractors may not affect short term memory, but do have an effect on attention. We will closely look into the effects, and identify future directions to design more effective interfaces.
During exercise, heart rate has proven to be a good measure in planning workouts. It is not only simple to measure but also well understood and has been used for many years for workout planning. To use heart rate to control physical exercise, a model which predicts future heart rate dependent on a given strain can be utilized. In this paper, we present a mathematical model based on convolution for predicting the heart rate response to strain with four physiologically explainable parameters. This model is based on the general idea of the Fitness-Fatigue model for performance analysis, but is revised here for heart rate analysis. Comparisons show that the Convolution model can compete with other known heart rate models. Furthermore, this new model can be improved by reducing the number of parameters. The remaining parameter seems to be a promising indicator of the actual subject’s fitness.
Analyzing training performance in sport is usually based on standardized test protocols and needs laboratory equipment, e.g., for measuring blood lactate concentration or other physiological body parameters. Avoiding special equipment and standardized test protocols, we show that it is possible to reach a quality of performance simulation comparable to the results of laboratory studies using training models with nothing but training data. For this purpose, we introduce a fitting concept for a performance model that takes the peculiarities of using training data for the task of performance diagnostics into account. With a specific way of data preprocessing, accuracy of laboratory studies can be achieved for about 50% of the tested subjects, while lower correlation of the other 50% can be explained.