Multi-Sensor Context-Awareness in Mobile Devices and Smart Artefacts

The use of context in mobile devices is receiving increasing attention in mobile and ubiquitous computing research. In this article we consider how to augment mobile devices with awareness of their environment and situation as context. Most work to date has been based on integration of generic context sensors, in particular for location and visual context. We propose a different approach based on integration of multiple diverse sensors for awareness of situational context that can not be inferred from location, and targeted at mobile device platforms that typically do not permit processing of visual context. We have investigated multi-sensor context-awareness in a series of projects, and report experience from development of a number of device prototypes. These include development of an awareness module for augmentation of a mobile phone, of the Mediacup exemplifying context-enabled everyday artifacts, and of the Smart-Its platform for aware mobile devices. The prototypes have been explored in various applications to validate the multi-sensor approach to awareness, and to develop new perspectives of how embedded context-awareness can be applied in mobile and ubiquitous computing

An adaptive protocol for distributed beamforming

We study distributed adaptive beamforming in networks of wireless nodes. In particular, we observe that for the synchronisation of carrier phases, distinct algorithmic configurations are optimal in various environmental settings and propose a protocol that utilises organic computing principles to find optimum parameters. Furthermore, we study the impact of different modulation schemes on the bit error rate of a signal sequence transmitted collaboratively by distributed devices via adaptive beamforming

A miniaturized display network for situated glyphs

We demonstrate a novel approach for building situated information systems using wirelessly connected miniaturized displays. These displays are spatially distributed in a physical work environment and present situated glyphs - human-readable abstract graphical signs - to provide activity centric notification and feedback. The demo will showcase how such miniaturized display networks can be used in dynamic workplaces, e.g., a hospital to support complex activities

Predicting B2B Customer Churn for Software Maintenance Contracts

Customer churn prediction is a well-known application of machine learning and data mining in Customer Relationship Management, which allows a company to predict the probability of its customer churning. In this study, we extended the application of customer churn prediction to the context of software maintenance contract. In addition, we examined the predictive power of economic factors. Random forest, gradient boosting machine, stacking of random forest and gradient boosting machine, XGBoost, and long short-term memory networks were applied. While an ensemble model and XGBoost performed best, macroeconomic variables did not yield statistically significant improvement in any prediction

ARMart – AR-Based Shopping Assistant to Choose and Find Store Items

Supermarkets offer a wide range of products which makes it challenging for consumers to choose between the different options and find the items they are looking for. Augmented Reality (AR) applications, however, have a high potential to enrich real-world objects with information which can be leveraged to improve this process. We developed an application that runs on a regular smartphone and helps users to choose between packaged groceries based on factors such as calories or sugar, rated on a scale from red (bad) to green (good). Compared to previous work, there is no need for a priori knowledge about product locations making the system suitable for many use cases. Moreover, information maps precisely onto the outline of the product\u27s and not on the approximate shelf. To do so, no modifications of the objects, such as specialized tags, are necessary. Additionally, users can find items just by entering the name. Highlighting the packaging virtually helps to find the desired product. It is also possible to make a binary distinction between groceries that contain specific ingredients

Design Space and Usability of Earable Prototyping

Earable computing gains growing attention within research and becomes ubiquitous in society. However, there is an emerging need for prototyping devices as critical drivers of innovation. In our work, we reviewed the features of existing earable platforms. Based on 24 publications, we characterized the design space of earable prototyping. We used the open eSense platform (6-axis IMU, auditory I/O) to evaluate the problem-based learning usability of non-experts. We collected data from 79 undergraduate students who developed 39 projects. Our questionnaire-based results suggest that the platform creates interest in the subject matter and supports self-directed learning. The projects align with the research space, indicating ease of use, but lack contributions for more challenging topics. Additionally, many projects included games not present in current research. The average SUS score of the platform was 67.0. The majority of problems are technical issues (e.g., connecting, playing music)

Split Additive Manufacturing for Printed Neuromorphic Circuits

Printed and flexible electronics promises smart devices for application domains, such as smart fast moving consumer goods and medical wearables, which are generally untouchable by conventional rigid silicon technologies. This is due to their remarkable properties such as flexibility, non-toxic materials, and having low-cost per area. Combined with neuromorphic computing, printed neuromorphic circuits pose an attractive solution for these application domains. Particularly, the additive printing technologies can reduce large amount of fabrication complexities and costs. On the one hand, high-throughput additive printing processes, such as roll-to-roll printing, can reduce the per-device fabrication time and cost. On the other hand, jet-printing can provide point-of-use customization at the expense of lower fabrication throughput. In this work, we propose a machine learning based design framework, that respects the objective and physical constraints of split additive manufacturing for printed neuromorphic circuits. With the proposed framework, multiple printed neural networks are trained jointly with the aim to sensibly combine multiple fabrication techniques (e.g., roll-to-roll and jet-printing). This should lead to a cost-effective fabrication of multiple different printed neuromorphic circuits and achieve high fabrication throughput, lower cost, and point-of-use customization

Aging-Aware Training for Printed Neuromorphic Circuits

Printed electronics allow for ultra-low-cost circuit fabrication with unique properties such as flexibility, non-toxicity, and stretchability. Because of these advanced properties, there is a growing interest in adapting printed electronics for emerging areas such as fast-moving consumer goods and wearable technologies. In such domains, analog signal processing in or near the sensor is favorable. Printed neuromorphic circuits have been recently proposed as a solution to perform such analog processing natively. Additionally, their learning-based design process allows high efficiency of their optimization and enables them to mitigate the high process variations associated with low-cost printed processes. In this work, we propose a learning-based approach to address another major challenge of printed electronics, namely the aging of the printed components. This effect can significantly degrade the accuracy of printed neuromorphic circuits over time. For this, we develop a stochastic aging-model to describe the behavior of aged printed resistors and modify the training objective by considering the expected loss over the lifetime of the device. This approach ensures to provide acceptable accuracy over the device lifetime. Our experiments show that an overall 35.8\% improvement in terms of expected accuracy over the device lifetime can be achieved using the proposed learning approach