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This work aims at proposing an affordable, non-wearable system to detect falls of people in need of care. The proposal uses artificial vision based on deep learning techniques implemented on a Raspberry Pi4 4GB RAM with a High-Definition IR-CUT camera. The CNN architecture classifies detected people into five classes: fallen, crouching, sitting, standing, and lying down. When a fall is detected, the system sends an alert notification to mobile devices through the Telegram instant messaging platform. The system was evaluated considering real daily indoor activities under different conditions: outfit, lightning, and distance from camera. Results show a good trade-off between performance and cost of the system. Obtained performance metrics are: precision of 96.4 % , specificity of 96.6 % , accuracy of 94.8 % , and sensitivity of 93.1 % . Regarding privacy concerns, even though this system uses a camera, the video is not recorded or monitored by anyone, and pictures are only sent in case of fall detection. This work can contribute to reducing the fatal consequences of falls in people in need of care by providing them with prompt attention. Such a low-cost solution would be desirable, particularly in developing countries with limited or no medical alert systems and few resources. [ABSTRACT FROM AUTHOR]

Nowadays, daily life involves the extensive use of computers, since human beings are immersed in a technological society. Therefore, it is mandatory to interact with computers, which represents a true disadvantage for people with upper limb disabilities. In this context, this work aims to develop an interface for emulating mouse and keyboard functions (EMKEY) by applying concepts of artificial vision and voice recognition to replace the use of hands. Pointer control is achieved by head movement, whereas voice recognition is used to perform interface functionalities, including speech-to-text transcription. To evaluate the interface's usability and usefulness, two studies were carried out. The first study was performed with 30 participants without physical disabilities. Throughout this study, there were significant correlations found between the emulator's usability and aspects such as adaptability, execution time, and the participant's age. In the second study, the use of the emulator was analyzed by four participants with motor disabilities. It was found that the interface was best used by the participant with cerebral palsy, followed by the participants with upper limb paralysis, spina bifida, and muscular dystrophy. In general, the results show that the proposed interface is easy to use, practical, fairly accurate, and works on a wide range of computers. [ABSTRACT FROM AUTHOR]

Multi-core and many-core processors are a promising solution to achieve high performance by maintaining a lower power consumption. However, the degree of miniaturization makes them more sensitive to soft-errors. To improve the system reliability, this work proposes a fault-tolerance approach based on redundancy and partitioning principles called N-Modular Redundancy and M-Partitions (NMR-MPar). By combining both principles, this approach allows multi-/many-core processors to perform critical functions in mixed-criticality systems. Benefiting from the capabilities of these devices, NMR-MPar creates different partitions that perform independent functions. For critical functions, it is proposed that N partitions with the same configuration participate of an N-modular redundancy system. In order to validate the approach, a case study is implemented on the KALRAY Multi-Purpose Processing Array (MPPA)-256 many-core processor running two parallel benchmark applications. The traveling salesman problem and matrix multiplication applications were selected to test different device's resources. The effectiveness of NMR-MPar is assessed by software-implemented fault-injection. For evaluation purposes, it is considered that the system is intended to be used in avionics. Results show the improvement of the application reliability by two orders of magnitude when implementing NMR-MPar on the system. Finally, this work opens the possibility to use massive parallelism for dependable applications in embedded systems. [ABSTRACT FROM AUTHOR]

Currently, there is a special interest in validating the use of Commercial-Off-The-Shelf (COTS) multi/many-core processors for critical applications thanks to their high performance, low power consumption and affordability. However, the continuous shrinking of transistor geometry and the increasing complexity of these devices dramatically affect their sensitivity to natural radiation, and thus diminish their reliability. One of the most common effects produced by natural radiation is the Single Event Upset which is the bit-flip of a memory content producing unexpected results at application-level. For this reason, manufacturers and users implement hardware and software error-mitigation techniques on multi/many-core processors. In this context, the present work aims at evaluating a new fault-tolerance approach based on N-Modular redundancy (NMR) and partitioning called NMR-MPar by means of 14 MeV neutron radiation ground testing in order to emulate the effects of high-energy neutrons present at avionics altitudes. For evaluation purposes, a case-study is implemented on the 28 nm CMOS KALRAY MPPA-256 many-core processor running two complementary benchmarks applications: a distributed Matrix Multiplication and the Travel Salesman Problem. Radiation experiments were conducted in GENEPI2 particle-accelerator. The correctness of the results of the application when an error is detected confirms the approach's effectiveness and boosts their usage on avionics applications. [ABSTRACT FROM AUTHOR]