Summary Medical records contain vital, critical, and confidential information of any person. Paper‐based medical records are being used by many hospitals that suffer from various issues like large physical storage requirements, limited security, and life. Electronic health records (EHRs) can transform this entire system but faces various security threats like unintentional disclosure, tampering, data leaks, lack of security mechanisms, and data access policies. Thus, there arises a need for a decentralized, tamper‐proof, and transparent healthcare network. In this article, a permissioned blockchain‐based framework using Hyperledger Fabric (HLF) is proposed, which provides secure storage, sharing, and querying of EHRs. For better data accessibility and security, algorithms have been designed to define the functionality of patients, doctors, and third‐party administrator using smart‐contracts. In the existing approaches, LevelDB (key store database) is used as a state database that is not suitable for handling complex queries. To overcome this issue, we have used CouchDB (document store database) as a state database where the search is based on keys and data values both rather than only keys. To measure the various performance indicators for this framework, a blockchain benchmark tool “Hyperledger Caliper” is used. The experimental results show the effectiveness and efficiency of the proposed framework.

Deep pushdown transducers (DPDT) are deterministic with respect to depth, but they lack strict determinism. The aim of this paper is to address the open problem of determinism in DPDT, as discussed in [1], by proposing a deterministic DPDT (DDPDT). Furthermore, we also explore a parallel variant of a DDPDT. The proposed deterministic models are strictly deterministic as well as deterministic with respect to depth. We support this discussion of these transducers with numerical examples. Furthermore, relationships among deterministic transducers, non-strict deterministic transducers and their parallel variants will be discussed.

In this paper, we present the generalized version of the Hoplites coordination framework designed to efficiently solve complex, tightly-coupled multirobot planning problems. Our extensions greatly increase the flexibility with which teammates can both plan and coordinate with each other; consequently, we can apply Hoplites to a wider range of domains and plan coordination between robots more efficiently. We apply our framework to the constrained exploration domain and compare Hoplites in simulation to competing distributed and centralized approaches. Our results demonstrate that Hoplites significantly outperforms both approaches in terms of the quality of solutions produced while remaining computationally competitive with much simpler approaches. We further demonstrate features such as scalability and validate our approach with field results from a team of large autonomous vehicles performing constrained exploration in an outdoor environment

Less than 3% of the 145 million blind people living in developing countries are literate. This low literacy rate is partly due to the lack of trained teachers and the challenges associated with learning to write Braille on a traditional slate and stylus. These challenges include writing from right to left, writing mirrored images of letters, and receiving significantly delayed feedback. Extensive conversations with the Mathru School for the Blind near Bangalore, India, revealed the need for a robust, low-power, low-cost Braille writing tutor. We present an iterative and participatory design process resulting in the creation and refinement of a prototype Braille writing tutor system. This system uses a novel input device to capture a student's activity on a slate using a stylus and uses a range of techniques to teach Braille writing skills to both beginner and advanced students. We report on lessons learned from the implementation of this project and from a six-week pilot study at the Mathru school, and outline future directions for improvement.

We present a replanning algorithm for repairing rapidly-exploring random trees when changes are made to the configuration space. Instead of abandoning the current RRT, our algorithm efficiently removes just the newly-invalid parts and maintains the rest. It then grows the resulting tree until a new solution is found. We use this algorithm to create a probabilistic analog to the widely-used D* family of deterministic algorithms, and demonstrate its effectiveness in a multirobot planning domain

Market-based multirobot coordination approaches have received significant attention and are growing in popularity within the robotics research community. They have been successfully implemented in a variety of domains ranging from mapping and exploration to robot soccer. The research literature on market-based approaches to coordination has now reached a critical mass that warrants a survey and analysis. This paper addresses this need for a survey of the relevant literature by providing an introduction to market-based multirobot coordination, a review and analysis of the state of the art in the field, and a discussion of remaining research challenges

In this paper we address tasks for multirobot teams that require solving a distributed multi-agent planning problem in which the actions of robots are tightly coupled. The uncertainty inherent in these tasks also necessitates persistent tight coordination between teammates throughout execution. Existing approaches to coordination cannot adequately meet the technical demands of such tasks. In response, we have developed a market-based framework, Hoplites, that consists of two novel coordination mechanisms. Passive coordination quickly produces locally-developed solutions while active coordination produces complex team solutions via negotiation between teammates. Robots use the market to efficiently vet candidate solutions and to choose the coordination mechanism that best matches the current demands of the task. In experiments, Hoplites significantly outperforms even its nearest competitors, particularly in the most complex instances of a domain. We also present implementation results on a team of mobile robots.