As data are generated more and more from multiple disparate sources, multiview data sets, where each sample has features in distinct views, have grown in recent years. However, no comprehensive package exists that enables non-specialists to use these methods easily. mvlearn is a Python library which implements the leading multiview machine learning methods. Its simple API closely follows that of scikit-learn for increased ease-of-use. The package can be installed from Python Package Index (PyPI) and the conda package manager and is released under the MIT open-source license. The documentation, detailed examples, and all releases are available at https://mvlearn.github.io/.

P4 is a domain-specific language for programming and specifying packet-processing systems. It is based on an elegant design with high-level abstractions like parsers and match-action pipelines that can be compiled to efficient implementations in software or hardware. Unfortunately, like many industrial languages, P4 has developed without a formal foundation. The P4 Language Specification is a 160-page document with a mixture of informal prose, graphical diagrams, and pseudocode, leaving many aspects of the language semantics up to individual compilation targets. The P4 reference implementation is a complex system, running to over 40KLoC of C++ code, with support for only a few targets. Clearly neither of these artifacts is suitable for formal reasoning about P4 in general. This paper presents a new framework, called Petr4, that puts P4 on a solid foundation. Petr4 consists of a clean-slate definitional interpreter and a core calculus that models a fragment of P4. Petr4 is not tied to any particular target: the interpreter is parameterized over an interface that collects features delegated to targets in one place, while the core calculus overapproximates target-specific behaviors using non-determinism. We have validated the interpreter against a suite of over 750 tests from the P4 reference implementation, exercising our target interface with tests for different targets. We validated the core calculus with a proof of type-preserving termination. While developing Petr4, we reported dozens of bugs in the language specification and the reference implementation, many of which have been fixed.

Protein structure determines biological function. Accurately conceptualizing 3D protein/ligand structures is thus vital to scientific research and education. Virtual reality (VR) enables protein visualization in stereoscopic 3D, but many VR molecular-visualization programs are expensive and challenging to use; work only on specific VR headsets; rely on complicated model-preparation software; and/or require the user to install separate programs or plugins. Here we introduce ProteinVR, a web-based application that works on various VR setups and operating systems. ProteinVR displays molecular structures within 3D environments that give useful biological context and allow users to situate themselves in 3D space. Our web-based implementation is ideal for hypothesis generation and education in research and large-classroom settings. We release ProteinVR under the open-source BSD-3-Clause license. A copy of the program is available free of charge from http://durrantlab.com/protein-vr/, and a working version can be accessed at http://durrantlab.com/pvr/.

The interests in health care have considerably increased these days as the aging population becomes larger. Health care has grown to the one of most active research areas especially in the area of wireless, mobile health monitoring systems. The wireless network technologies have advanced to the point where they can enable and help deploy a very broad gamut of systems suitable for medical applications. Several researches have proposed to replace the wired connections among medical devices with wireless connections. Wireless network technologies interwork with sensor equipped BodyLANs. Wireless personal area networks (WPANs) are well positioned to support health care applications in limited geographic areas. In particular, the characteristics of Bluetooth and its popularity make it the preferred network infrastructure for HealthNet environments. However, Bluetooth has the "bad reputation" of long connection delays, which may be disastrous in some health applications. In this paper we address the connection delay and propose a new data transfer protocol based on Bluetooth version 2.1. The 2.1 version was published very recently and offers new features that are interest to our application. Among the new features we leverage extended inquiry response (EIR) and secure simple pairing (SSP) to solve the delay problem. Extensive simulation results show that the proposed system significantly improves data delivery as well as power consumption. It solves a well known problem in Bluetooth based wireless networks. Using our proposed scheme, Bluetooth devices are now adequate to support sophisticated scenarios such as emergencies and urgent data dissemination requirements.

Remote medical monitoring with medical sensors and wireless communications has recently gained attention for the potential savings and improved quality in patient health-care. Currently, many mobile wireless medical infrastructures connect to the Internet in a point-to-point fashion, such as the cellular network. However, our research investigates new models for remote patient care that exploit P2P networking among patients and healthcare providers (nurses, doctors, emergency personnel). In this paper, we identify several medical applications based on P2P Health Networking focusing on two specific scenarios where nurses and patients are both equipped with Bluetooth devices. Nurses opportunistically collect, share and upload data in a P2P fashion. During an emergency, the nearest nurse is alerted via Bluetooth Enhanced Inquiry Response mechanism. The simulation and test-bed experimentation demonstrate that Bluetooth P2P networking is both feasible and cost-effective in remote medical monitoring.

ZigBee is popular for Wireless Sensor Network (WSN) devices because of its low power consumption, built-in security method and ratified specifications. With these features, it is also suitable to be used with medical sensor devices. Medical sensors in a human body self organize a ZigBee Personal Area Network (PAN). These PANs are interconnected and form a HealthNet. For the interconnection, a ZigBee PAN detects existence of another PAN with beacon detection or active channel scan. Different ZigBee interconnection schemes are used after detection. PAN bridge method uses special bridge node that works for different PANs with time division method. PAN merge method temporarily changes PAN coordinator's role as bridge node and merges two PANs. Peer-to-peer network temporarily share all resources when two PANs are met, but all PANs have to use the same channel and operate as an ad-hoc network. In this paper, we perform extensive evaluations using NS-2 simulations to compare PAN interconnection methods. Results show that the PAN bridge is the most useful method because it is not affected by router/node ratio and is applicable to different channel usages.

In a large scale Bluetooth network, scatternet is regarded as the only interconnection method among piconets. But, most Bluetooth devices do not support scatternet connection. Moreover, in high mobility situations, scatternet is not useful because of frequent disconnections and reconnections. We propose overlaid Bluetooth piconets (OBP) to interconnect piconets and form a virtual scatternet. Every piconet continuously changes its stages and collects metadata of piconets in the communication range. If metadata shows existence of data to transfer, an inter-piconet connection is made and data is transferred. We compared throughput and efficiency of OBP with those of scatternet. Results show the feasibility of OBP usage instead of scatternet

This paper presents an algorithm for lossy compression of hyperspectral images for implementation on field programmable gate arrays (FPGA). To greatly reduce the bit rate required to code images, linear prediction is used between the bands to exploit the large amount of inter-band correlation. The prediction residual is compressed using the set partitioning in hierarchical trees algorithm. To reduce the complexity of the predictive encoder, this paper proposes a bit plane-synchronized closed loop predictor that does not require full decompression of a previous band at the encoder. The new technique achieves almost the same compression ratio as standard closed loop predictive coding and has a simpler on-board implementation.