The application of a supervised endurance exercise training programme in a home setting offering convenience and prolonged effects is a challenge.In total, 48 patients were initially assessed by the incremental shuttle walk test (ISWT), spirometry and the Short Form-12 (SF-12) quality-of-life questionnaire, and then every 4 weeks for 3 months thereafter and again after 1 yr. During the first 3 months, 24 patients in the cell phone group were asked to perform daily endurance walking at 80% of their maximal capacity by following the tempo of music from a program installed on a cell phone. The level of endurance walking at home was readjusted monthly according to the result of ISWT. In the control group, 24 patients received the same protocol and were verbally asked to take daily walking exercise at home.Patients in the cell phone group significantly improved their ISWT distance and duration of endurance walking after 8 weeks. The improvements in ISWT distance, inspiratory capacity and SF-12 scoring at 12 weeks persisted until the end of the study, with less acute exacerbations and hospitalisations.In the present pilot study, the cell phone-based system provides an efficient, home endurance exercise training programme with good compliance and clinical outcomes in patients with moderate-to-severe chronic obstructive pulmonary disease.

A real-time wireless electroencephalogram (EEG)-based brain-computer interface (BCI) system for drowsiness detection has been proposed. Drowsy driving has been implicated as a causal factor in many accidents. Therefore, real-time drowsiness monitoring can prevent traffic accidents effectively. However, current BCI systems are usually large and have to transmit an EEG signal to a back-end personal computer to process the EEG signal. In this study, a novel BCI system was developed to monitor the human cognitive state and provide biofeedback to the driver when drowsy state occurs. The proposed system consists of a wireless physiological signal-acquisition module and an embedded signal-processing module. Here, the physiological signal-acquisition module and embedded signal-processing module were designed for long-term EEG monitoring and real-time drowsiness detection, respectively. The advantages of low owner consumption and small volume of the proposed system are suitable for car applications. Moreover, a real-time drowsiness detection algorithm was also developed and implemented in this system. The experiment results demonstrated the feasibility of our proposed BCI system in a practical driving application.

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We present VOQC, the first fully verified optimizer for quantum circuits, written using the Coq proof assistant. Quantum circuits are expressed as programs in a simple, low-level language called SQIR, a simple quantum intermediate representation, which is deeply embedded in Coq. Optimizations and other transformations are expressed as Coq functions, which are proved correct with respect to a semantics of SQIR programs. SQIR uses a semantics of matrices of complex numbers, which is the standard for quantum computation, but treats matrices symbolically in order to reason about programs that use an arbitrary number of quantum bits. SQIR's careful design and our provided automation make it possible to write and verify a broad range of optimizations in VOQC, including full-circuit transformations from cutting-edge optimizers.