The Enigma of Digitized Property A Tribute to John Perry Barlow

Compressive Sensing has attracted a lot of attention over the last decade within the areas of applied mathematics, computer science and electrical engineering because of it suggesting that we can sample a signal under the limit that traditional sampling theory provides. By then using dierent recovery algorithms we are able to, theoretically, recover the complete original signal even though we have taken very few samples to begin with. It has been proven that these recovery algorithms work best on signals that are highly compressible, meaning that the signals can have a sparse representation where the majority of the signal elements are close to zero. In this thesis we implement some of these recovery algorithms and investigate how these perform practically on a real video signal consisting of 300 sequential image frames. The video signal will be under sampled, using compressive sensing, and then recovered using two types of strategies, - One where no time correlation between successive frames is assumed, using the classical greedy algorithm Orthogonal Matching Pursuit (OMP) and a more robust, modied OMP called Predictive Orthogonal Matching Pursuit (PrOMP). - One newly developed algorithm, Dynamic Iterative Pursuit (DIP), which assumes and utilizes time correlation between successive frames. We then performance evaluate and compare these two strategies using the Peak Signal to Noise Ratio (PSNR) as a metric. We also provide visual results. Based on investigation of the data in the video signal, using a simple model for the time correlation and transition probabilities between dierent signal coecients in time, the DIP algorithm showed good recovery performance. The main results showed that DIP performed better and better over time and outperformed the PrOMP up to a maximum of 6 dB gain at half of the original sampling rate but performed slightly below the PrOMP in a smaller part of the video sequence where the correlation in time between successive frames in the original video sequence suddenly became weaker.Compressive sensing har blivit mer och mer uppmarksammat under det senaste decenniet inom forskningsomraden sasom tillampad matematik, datavetenskap och elektroteknik. En stor anledning till detta ar att dess teori innebar att det blir mojligt att sampla en signal under gransen som traditionell samplingsteori innebar. Genom att sen anvanda olika aterskapningsalgoritmer ar det anda teoretiskt mojligt att aterskapa den ursprungliga signalen. Det har visats sig att dessaaterskapningsalgoritmer funkar bast pa signaler som ar mycket kompressiva, vilket innebar att dessa signaler kan representeras glest i nagon doman dar merparten av signalens koecienter ar nara 0 i varde. I denna uppsats implementeras vissa av dessaaterskapningsalgoritmer och vi undersoker sedan hur dessa presterar i praktiken pa en riktig videosignal bestaende av 300 sekventiella bilder. Videosignalen kommer att undersamplas med compressive sensing och sen aterskapas genom att anvanda 2 typer av strategier, - En dar ingen tidskorrelation mellan successiva bilder i videosignalen antas genom att anvanda klassiska algoritmer sasom Orthogonal Matching Pursuit (OMP) och en mer robust, modierad OMP : Predictive Orthogonal Matching Pursuit (PrOMP). - En nyligen utvecklad algoritm, Dynamic Iterative Pursuit (DIP), som antar och nyttjar en tidskorrelation mellan successiva bilder i videosignalen. Vi utvarderar och jamfor prestandan i dessa tva olika typer av strategier genom att anvanda Peak Signal to Noise Ratio (PSNR) som jamforelseparameter. Vi ger ocksa visuella resultat fran videosekvensen. Baserat pa undersokning av data i videosignalen visade det sig, genom att anvanda enkla modeller, bade for tidskorrelationen och sannolikhetsfunktioner for vilka koecienter som ar aktiva vid varje tidpunkt, att DIP algoritmen visade battre prestanda an de tva andra tidsoberoende algoritmerna under visa tidsekvenser. Framforallt de sekvenser dar videosignalen inneholl starkare korrelation i tid. Som mest presterade DIP upp till 6 dB battre an OMP och PrOMP

Open Innovation via Open Source: Collaboration of Companies to Infuse Automobiles with Digital Technologies

Open innovation is a process through which companies open their borders and collaborate with external stakeholders like open source communities to bring new ideas and develop novel digital technologies to gain a competitive position. In this paper, we studied an open source project, i.e., Automotive Grade Linux (AGL) – a Linux Foundation project started by automobile manufacturers and technology companies to innovate technologies for automobiles. By analyzing the code contribution of AGL, we show that much of the code contribution is made by external companies supplying technology to automotive companies and later using the open innovation process to benefit from it. We find evidence that automobile manufacturers engage in open source communities for outside-in, inside-out, and coupled open innovation. As such, this paper shows to managers in larger companies the importance of open source as a way to do open innovation

The Role of Open Source in New Business Formation: Innovations for Development

Innovative uses of ICTs can bring about development. The open source software movement offers new opportunities for innovation. In particular, the use of such platforms can enable entrepreneurs in low resource environments to access and use needed software to support their new businesses. This paper investigates the role of open source software for development. This research shows that participation in open source communities is significantly correlated with new business formation. Through an analysis of datasets from the World Bank and GitHub, the largest open source platform, this paper finds a relationship between open source participation, new business formation and their effects on development, through unemployment rates. There is a strong, positive correlation between new business registrations and active GitHub users, which was statistically significant. The implications for development are in the effect of a positive relationship in job creation based on business formation and open source participation

Is it Egalitarianism or Enterprise Strategy? Exploring a New Method of Innovation in Open Source

This research article explores a new way of innovation found in open source communities. No longer is innovation closed where research and development is kept internal to the firm. Instead, it is becoming more open, where ideas, inventions, and intellectual property are readily traded in a global marketplace. Our research observed open source communities as something different from the received view on open source. We observed open source communities as highly organized platforms for strategic innovation where profit-seeking firms are actively involved in governance, strategic direction, and technology development. We explore the evolving relationship between firms and communities and provide insight into how these communities are organized. Our research depicts Open Innovation and open source in a new light – Federated Innovation – where open source communities are now acting as platforms to drive for strategic innovation._x000D_ This work has been funded through the National Science Foundation VOSS-IOS Grant: 1122642

Towards understanding open-coopetition – Lessons from the automotive industry

Products are often co-developed in networks that embed multiple organizations. Paradoxically, such product development networks can tie rival and competing firms that cooperate with each other in an open-source way. The management of such modus operandi, where firms give up some intellectual property rights granted by law and work with competitors in an open-source way, can be very challenging as it can lead to commoditization, free-riding, and unintended spillover effects. Building upon extant knowledge in coopetition, open-source software, product development, and innovation, we conducted an exploratory case study aimed at understanding open-coopetition (i.e., cooperation among competitors in an open-source way) in the automotive industry. To do so, we leveraged publicly available naturally occurring digital data and qualitative interviews pertaining to four coopetitive open-source projects. Out preliminary results highlight the increasing complexity of the software that powers modern cars and consequent convergence of the automotive industry with the software industry

Understanding Open Source Communities as Complex Adaptive Systems: A Case of the R Project Community

Open source communities evolve. This evolution is, at times, driven by corporate engagement with those communities. In these corporate-communal contexts, open source foundations often serve as facilitators in the evolution process and make these arrangements more stable over time. This paper expands the application of complex adaptive systems (CAS) theory to understand the role of open source foundations as facilitators in the evolution of corporate-communal arrangements. We present the case of the R Project community and how we can leverage complex adaptive systems as a way to understand the evolution of the community as driven by corporate engagement and facilitated by open source foundations. We develop the theory of CAS by enhancing the understanding of attractors in the evolution of CAS

The Role of Open Source Communities in Development: Policy Implications for Governments

Open Source Software (OSS) communities engage in a shared design of software that meets the needs of community members. This dynamic may have a positive influence on development by enabling the growth of micro-enterprises thus offering opportunities for governments to stimulate their growth. This paper explores the connection between OSS communities and development outcomes to arrive at a theoretical framework that enables the investigation of the role of OSS communities in development. By examining existing government policies, we find that policymakers recognize the potential for OSS communities to create shared value through private-collective innovation. In understanding the transformative role of OSS, this research investigates (1) how OSS communities contribute to development efforts and (2) how government policy can stimulate development efforts through OSS. The contribution of this paper is in the policy implications for governments on how they may use OSS to drive development

Knowing and Designing: Understanding Information Use in Open Source Design Through the Lens of Information Archetypes

The early phases of the product design process are crucial to the success of design outcomes. While information utilized during idea development has tremendous potential to impact the final design, there is a lack of understanding about the types of information utilized in industry, making it challenging to develop and teach methodologies that support the design of competitive products. As a first step in understanding this process, this study focuses on developing a framework of Information Archetypes utilized by designers in industry. This was accomplished through in-depth analysis of qualitative interviews with large software engineering companies. The results reveal two archetypes of information utilized by decision-makers within these companies during the development of new products and services. The findings of this study allow for future research that investigates the role of information during the product design process