1,720,978 research outputs found
MushR-Project-Raw-Image-Dataset (Oyster Mushrooms)
No full textMushR General Summary:
MushR is a modular and scalable gourmet mushroom growing and harvesting system that goes beyond the state of the art, which merely monitors and controls the growing environment, by introducing an image recognition system that determines when and which mushrooms are ready to be harvested in conjunction with a proof-of-concept of an automated mushroom harvesting mechanism for harvesting the mushrooms without human interaction. The image recognition setup monitors the growing status of the mushrooms and guides the harvesting process. We present a Mask R-CNN model for the detection of oyster mushroom maturity as well as a semi-automated harvesting system, integrating a Raspberry Pi for control, an electrical switch, an air compressor, and a pneumatic cylinder with a cutting knife to facilitate timely mushroom harvesting. The modularity and scalability of the system allow for industry-level usage and can be scaled according to the required mushroom-growing systems within the facility.
MushR Dataset:
The dataset created for this project focuses on capturing images of the mushroom-growing environment from three different perspectives within each of our two growth tents for mushroom production. Instead of providing images of every individual bucket and mushroom, we capture the overall scene and its variations. The images from each perspective are captured simultaneously and automatically hourly. This approach allows for monitoring the development and maturity of the oyster mushrooms over time. We captured and accumulated 34,400 images over ten months to ensure a comprehensive dataset
MushR-Project-Annotated-Images-Dataset (Oyster Mushrooms)
No full textMushR is a modular and scalable gourmet mushroom growing and harvesting system that goes beyond the state of the art, which merely monitors and controls the growing environment, by introducing an image recognition system that determines when and which mushrooms are ready to be harvested in conjunction with a proof-of-concept of an automated mushroom harvesting mechanism for harvesting the mushrooms without human interaction. The image recognition setup monitors the growing status of the mushrooms and guides the harvesting process. We present a Mask R-CNN model for the detection of oyster mushroom maturity as well as a semi-automated harvesting system, integrating a Raspberry Pi for control, an electrical switch, an air compressor, and a pneumatic cylinder with a cutting knife to facilitate timely mushroom harvesting. The modularity and scalability of the system allow for industry-level usage and can be scaled according to the required mushroom-growing systems within the facility.
The dataset created for this project focuses on capturing images of the mushroom-growing environment from three different perspectives within each of our two growth tents for mushroom production. Instead of providing images of every individual bucket and mushroom, we capture the overall scene and its variations. The images from each perspective are captured simultaneously and automatically hourly. This approach allows for monitoring the development and maturity of the oyster mushrooms over time. We captured and accumulated 34,400 images over ten months to ensure a comprehensive dataset. This special repository contains the annotated images that we used to train our AI model
Blogchain – Disruptives Publizieren auf der Blockchain
No full textWir stellen ein neues Konzept als Metamodell für das wissenschaftliche Publikationswesen vor. Unser Konzept ist im Kontext eines dreistufigen Phasenmodells digitaler Disruption von Geschäftsprozessen angesiedelt. Die erste Phase besteht dabei aus Technologie ohne Prozessanpassung. Die zweite Phase umfasst eine Prozessanpassung unter der Kontrolle von Intermediären und führt zu unerwünschter aber schwer vermeidbarer Zentralisierung. Die dritte Phase durchbricht schließlich die Vormachtstellung intermediärer Institutionen und nutzt dazu die disruptiven Möglichkeiten der Blockchain-Technologie
Rechained: Sybil-Resistant Distributed Identities for the Internet of Things and Mobile Ad Hoc Networks
No full textToday, increasing Internet of Things devices are deployed, and the field of applications for decentralized, self-organizing networks keeps growing. The growth also makes these systems more attractive to attackers. Sybil attacks are a common issue, especially in decentralized networks and networks that are deployed in scenarios with irregular or unreliable Internet connectivity. The lack of a central authority that can be contacted at any time allows attackers to introduce arbitrary amounts of nodes into the network and manipulate its behavior according to the attacker’s goals, by posing as a majority participant. Depending on the structure of the network, employing Sybil node detection schemes may be difficult, and low powered Internet of Things devices are usually unable to perform impactful amounts of work for proof-of-work based schemes. In this paper, we present Rechained, a scheme that monetarily disincentivizes the creation of Sybil identities for networks that can operate with intermittent or no Internet connectivity. We introduce a new revocation mechanism for identities, tie them into the concepts of self-sovereign identities, and decentralized identifiers. Case-studies are used to discuss upper- and lower-bounds for the costs of Sybil identities and, therefore, the provided security level. Furthermore, we formalize the protocol using Colored Petri Nets to analyze its correctness and suitability. Proof-of-concept implementations are used to evaluate the performance of our scheme on low powered hardware as it might be found in Internet of Things applications
Can Distributed Ledgers Help to Overcome the Need of Labeled Data for Agricultural Machine Learning Tasks?
Full text linkSafeguarding a formalized Blockchain-enabled identity-authentication protocol by applying security risk-oriented patterns
No full text
Formalizing the Blockchain-Based BlockVoke Protocol for Fast Certificate Revocation Using Colored Petri Nets
No full textProtocol flaws such as the well-known Heartbleed bug, security and privacy issues or incomplete specifications, in general, pose risks to the direct users of a protocol and further stakeholders. Formal methods, such as Colored Petri Nets (CPNs), facilitate the design, development, analysis and verification of new protocols; the detection of flaws; and the mitigation of identified security risks. BlockVoke is a blockchain-based scheme that decentralizes certificate revocations, allows certificate owners and certificate authorities to revoke certificates and rapidly distributes revocation information. CPNs in particular are well-suited to formalize blockchain-based protocols—thus, in this work, we formalize the BlockVoke protocol using CPNs, resulting in a verifiable CPN model and a formal specification of the protocol. We utilize an agent-oriented modeling (AOM) methodology to create goal models and corresponding behavior interface models of BlockVoke. Subsequently, protocols semantics are defined, and the CPN models are derived and implemented using CPN Tools. Moreover, a full state-space analysis of the resulting CPN model is performed to derive relevant model properties of the protocol. The result is a complete and correct formal BlockVoke specification used to guide future implementations and security assessments
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