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In this paper, we define an auction protocol and implementation of a decentralized and distributed auctioning platform for demand-supply matching of components and materials subject to recycling. Auctioning is the process of buying and selling goods or services by offering them up for bid, taking bids, and then selling the item to a winner according to preset rules. Some auction types relies on sealed-bids while others implement an open-bid procedure that allows for multiple bids before a known or unknown deadline. The English auction and its variants is most common type. It relies on that the current highest bid is always available to potential bidders. Online auction platforms are nowadays used to trade various services and goods. We address the iterative negotiations in form of auction bids aiming at signed contractual agreements stated in legal prose and captured by Ricardian contracts. We evaluate our model towards requirements including privacy, transparency and fairness in terms of acknowledged publishing of highest bids and the ordered arrival of individual bids. In addition, we demonstrate the mechanisms for distributed storage of cryptographically signed Ricardian contracts, omitting the need for trusting the auctioneer or relying on a trusted third party for this storage.

Synthetic aperture sonar (SAS) is emerging as the reference technique for high resolution imaging of the seabed. Given the insufficient positioning accuracy, the harsh ocean environment and lack of stability on relatively small platforms carrying the sonar, an optimal SAS image quality is not always guaranteed. In this paper, we present a technique to quantify the image quality based on the actual SAS images through-the-sensor. We have previously presented approaches to predict the sharpness in SAS images from meta-data, but in practice those have not always been precise given their predictive nature. We now introduce an image-based sharpness estimate that can be combined with the meta-data prediction. The image-based estimate is derived by locating candidate point scatterers and estimating key parameters from their point-spread functions. These provide information about defocus and unwanted grating lobes, the two most common types of image degradation in SAS. This technique requires texture in the image. For areas where no point scatterers can be detected, we rely only on meta-data to predict sharpness.

The use of smartphones for activity recognition is becoming common practice. Most approaches use a single pretrained classifier to recognize activities for all users. Research studies, however, have highlighted how a personalized trained classifier could provide better accuracy. Data labeling for ground truth generation, however, is a time-consuming process. The challenge is further exacerbated when opting for a personalized approach that requires user specific datasets to be labeled, making conventional supervised approaches unfeasible. In this work, we present early results on the investigation into a weakly supervised approach for online personalized activity recognition. This paper describes: (i) a heuristic to generate weak labels used for personalized training, (ii) a comparison of accuracy obtained using a weakly supervised classifier against a conventional ground truth trained classifier. Preliminary results show an overall accuracy of 87% of a fully supervised approach against a 74% with the proposed weakly supervised approach.

How can a marine company best combine people, process and technology to optimize its Product Innovation system for advanced, complex products produced at low volumes? This paper discusses the possibility of using the Lean concept to improve the company's product development system. The operational context of the case company is analyzed up against the framework of Morgan and Liker's 13 Lean Product Development (LPD) principles. Our hypothesis is that although the business context of the case company is radically different from Toyota, several principles and practices will still be applicable once ‘contextualized’. A workshop was held with a multidisciplinary product team to assess the practices of the company relative to LPD. The team evaluated current company practices and desired future practices. The results are summarized and discussed herein. It is concluded that the original LPD principles have a varying degree of applicability to the context of the case company.

The adoption of autonomous underwater vehicles equipped with high-resolution side-looking sonars in mine countermeasures (MCM) operations has resulted in a requirement to quantify the effectiveness of these platforms; e. g., by the probability of correctly detecting and classifying mines along with the associated false alarm rate. Recently, the Norwegian Defence Research Establishment (FFI) proposed a performance model based on a combination of parameters given a priori and measured in situ, resulting in a through-the-sensor approach to performance evaluation for the detection and classification part of MCM operations. In this paper we investigate whether these parameters are consistent across different sonar systems and frequencies. This is done on basis of data from the MANEX'14 sea trial off the coast of Levanto, Italy, which was organized by the Centre for Maritime Research and Experimentation (CMRE). This unique data set spans five different frequency bands with carrier frequencies at 25 kHz, 72.5 kHz, 100 kHz, 300 kHz and 900 kHz, gathered from two synthetic aperture sonar systems (CMRE's MUSCLE and FFI's HISAS) as well as a real aperture sidescan sonar system.

Indoor positioning is recognized as one of the upcoming major applications which can be used in wide variety of applications such as indoor navigation and enterprise asset tracking. The significance of localization in indoor environments have made the use of Wi-Fi based indoor positioning so that it can utilize available current wireless infrastructure and perform positioning very easily. In this paper we introduced a user friendly prototype for Wi-Fi based indoor positioning system where a user can identify its own position in indoor. Wi-Fi received signal strength (RSS) fluctuations over time introduce incorrect positioning. To minimize the fluctuation of RSS, we developed Particle Filters with the prototype. A comparison between with and without Particle Filter for error performance is presented and at the same time it is also noticed that variation in number of particles could change the positioning accuracy. Moreover comparison between calibration data in all directions and in one direction while constructing a radio map is presented. Godkänd; 2014; 20140129 (karand)

The rise of pervasive computing presents unique opportunities due to increasing availability of smart devices such as mobile phones and tablets equipped with various sensors enabling Near Field Communication (NFC) technologies. The growth of mobile computing has led to an increase in access to digital music. With the growth of digital music, the development of music information sharing services for users becomes important. The existing sharing methods are based on the users' social network and preferences in music. However, sometimes, sharing music according to location and time is needed. This paper presents work on smart spaces equipped with NFC tags, deployed at different locations in hallways for discovering and sharing new music experiences. This concept provides a new way of interaction between passers-by for discovering music in relation to location. For example, the hallway locations use sensing devices to provide an automatic means of exchanging music information among the passers-by. We utilized NFC tags as Music-Relay hot spots. The hot spot retrieves information about the music a user is playing on her/his device while s/he is passing by the hot spot. The work contributes to a pervasive service that equips an environment with music context intelligence about a passer-bys choice of music and allows users to feel the musical presence of other users who have been in the same location at previous point in time. In general, this paper proposes a new music information sharing service using the music information captured from users at a specific location in time.

This paper proposes a social distribution mechanism for finding, connecting and propagating contents in social networks using context and communication history of users together with meta information about the content. The proposed mechanism operates by propagating invitation messages to a prioritized group of users to build up an ad-hoc social network from the original social network while at the same time preventing spamming the users with unwanted invitation messages. The proposed mechanism makes it possible to implement and deploy a wide variety of services targeting the specific needs of a user. As a proof-of-concept, the paper shows how the proposed social distribution mechanism can be used to invite users to a shared space, which can contain various kinds of collaboration tools allowing a group of users to communicate and solve problems together. In order to investigate the efficiency of the proposed mechanism, the paper also presents result from a simulation study. The result shows that the social distribution mechanism should consider both social strength and context for propagating social media contents as some of the recipients may have equal interest of the social media but different level of trust about the originator.

This paper presents a framework for Aggregated Social Graphs, the ASG Framework, for developing applications that harness information available on social networking sites and telecom platforms to make communication simpler and richer. The framework aggregates users social graph by harnessing contact and social information from multiple communication environments and then calculating the social strength between the users, based on the users communication history. The framework discovers communication patterns in different level which is eventually used to simplify and enhance communication services by inviting appropriate users for collaboration, selecting suitable tools for communication and prioritizing information flows over the cloud. The framework also enables new types of applications such as social search clients, smart dialers or contact applications.

Mobile Semantic Web provides a new way of developing context-aware social networking applications. Social networking applications are changing the way of communication by using user’s context-information. For example, micro-blogging has become a smart way of conveying the current situation and activity by using user context. There is currently a significant difference between using social networking applications on a static computer compared to a mobile device, even if current mobile devices are powerful and have good connectivity. The difference is primarily related to the mobility aspect since the user contexts may change more frequently and the user may not be able to interact with the mobile device. In this paper we identify common characteristics of current social networking applications and how they attract users. Finally, we propose an agent-based system architecture that is based on a distributed platform for developing semantic and semi-automated mobile social networking applications.

The potential of smart living environments to provide a form of independent living for the ageing population is becoming more recognised. These environments are comprised of sensors which are used to assess the state of the environment, some form of information management to process the sensor data and finally a suite of actuators which can be used to change the state of the environment. When providing a form of support which may impinge upon the well being of the end user it is essential that a high degree of reliability can be maintained. Within this paper we present an information management framework to process sensor based data within smart environments. Based on this framework we assess the impact of sensor reliability on the classification of activities of daily living. From this assessment we show how it is possible to identify which sensors within a given set of experiments can be considered to be the most critical and as such consider how this information may be used for managing sensor reliability from a practical point of view.

As users of wearable computers are often involved in real-world tasks of critical nature, the management and handling of interruptions is crucial for efficient interaction and task performance. We present a study about the impact that different methods for interruption have on those users, to determine how interruptions should be handled. The study is performed using an apparatus called "HotWire" for simulating primary tasks in a laboratory experiment, while retaining the properties of wearable computers being used in mobile, physical, and practical tasks. Godkänd; 2006; 20061228 (ysko)

This paper describes a system where Internet-enabled sensor technology was integrated into a context-aware platform to give viewers of sport events an enriched media experience. The system was developed as a proof of concept and was evaluated during real-life use at the Vasaloppet cross-country ski event. Using Bluetooth wireless ad-hoc networking and GPRS technology, sensor data was transmitted from contestants to the context-aware platform Alipes, which in turn presented the sport event viewer with a personalized, context-aware view. In this paper we discuss the system architecture and integration of components. The system was evaluated both from technical and user perspectives, where the evaluation results confirm our approach to be technically feasible and that the system provide an enriched media-experience for the majority of viewers. Godkänd; 2004; 20060916 (ysko)

An important part of making a wearable computer unobtrusive is the user interface and the way it interrupts the user. The amount of interruption that have to be made on the users primary task should be minimized. Usage without interruption is of course best for the performance of the primary task but is often not possible as many tasks that the wearable computer is involved in do need interaction with the user. It is therefore important to understand what type of interruption that will benefit the user most. The question about which interruption that causes the least amount of stress for the user is as important as which interruption that gives the best performance. This paper continues on previous work where performance of the users was measured for different types of interruption. In this paper subjective data is analyzed to understand how to build user interfaces for wearable computers and head mounted displays that considers stress and other subjective variables