Una eficiente implementación en FPGA del reconocimiento de gestos de mano basado en redes neurales

Los diferentes gestos de la mano que es un poderoso canal de comunicación entre hombre a hombre y / o hombre a máquina, transfieren gran cantidad de información en nuestra vida diaria. Por ejemplo, los lenguajes de señas son ampliamente utilizados por personas con discapacidad del habla. El reconocimiento de los gestos de las manos en la imagen puede considerarse como un parámetro poderoso en la comunicación hombre-máquina. Aunque los investigadores han intentado implementar diferentes gestos con las manos en varias plataformas de hardware durante los últimos años, sus intentos se han enfrentado a muchos desafíos, incluidos los recursos restringidos de las plataformas de hardware, factores de ruido en el entorno o una precisión insuficiente de la salida en un gran número de muestras experimentales. En este trabajo se desarrolla un método óptimo y paralelizado para implementar el reconocimiento de diferentes gestos con las manos en imagen en FPGA. El método introducido utiliza una red MLP con un gran número de capas ocultas sin desperdiciar recursos de la plataforma de hardware. Los resultados que comparan el método optimizado propuesto con los métodos de última generación muestran que el método sugerido se puede implementar en la plataforma FPGA con una alta precisión de salida y menos recurso

Stress and its related factors in families of patients with cancer

BACKGROUND: Cancer is one of the most common kinds of chronic diseases. In addition, it is a cause of stress in the family members of the patient. Therefore, the aim of this study was to determine the amount of stress and its related factors in families of patients with cancer. METHODS: In this descriptive study, 96 family members of cancer patients admitted to 3 hospitals in Ahvaz, Iran, were recruited in the study. Data gathering tools consisted of the Perceived Stress Scale (PSS-14), and a researcher-made questionnaire for demographic data and factors associated with caregiver stress. Collected data were analyzed using SPSS software. RESULTS: A total of 55 (57.3%) subjects showed moderate stress levels and 20 subjects (20.8%) showed severe stress levels. There was a significant relationship between the levels of stress and age of less than 30 years and female gender. Moreover, a significant relationship was observed between the level of stress and factors such as uncomfortable treatment environment, feeling dissatisfied with staff, fear of recurrence, difficulties in everyday life, no spiritual practice, negative attitudes toward treatment outcome, refusing to participate in favorite activities, changes in interactions with others, lack of leisure time, imbalance between daily responsibilities and care, inadequate income, and lack of appropriate facilities (P < 0.05). CONCLUSION: There were several factors causing stress in patients’ families. It is recommended that nurses and the medical team be informed of these factors in order to manage stress in patients and their families

The use of reversible logic gates in the design of residue number systems

Reversible computing is an emerging technique to achieve ultra-low-power circuits. Reversible arithmetic circuits allow for achieving energy-efficient high-performance computational systems. Residue number systems (RNS) provide parallel and fault-tolerant additions and multiplications without carry propagation between residue digits. The parallelism and fault-tolerance features of RNS can be leveraged to achieve high-performance reversible computing. This paper proposed RNS full reversible circuits, including forward converters, modular adders and multipliers, and reverse converters used for a class of RNS moduli sets with the composite form {2k, 2p-1}. Modulo 2n-1, 2n, and 2n+1 adders and multipliers were designed using reversible gates. Besides, reversible forward and reverse converters for the 3-moduli set {2n-1, 2n+k, 2n+1} have been designed. The proposed RNS-based reversible computing approach has been applied for consecutive multiplications with an improvement of above 15% in quantum cost after the twelfth iteration, and above 27% in quantum depth after the ninth iteration. The findings show that the use of the proposed RNS-based reversible computing in convolution results in a significant improvement in quantum depth in comparison to conventional methods based on weighted binary adders and multipliers

Stress and its related factors in families of patients with cancer

BACKGROUND: Cancer is one of the most common kinds of chronic diseases. In addition, it is a cause of stress in the family members of the patient. Therefore, the aim of this study was to determine the amount of stress and its related factors in families of patients with cancer. METHODS: In this descriptive study, 96 family members of cancer patients admitted to 3 hospitals in Ahvaz, Iran, were recruited in the study. Data gathering tools consisted of the Perceived Stress Scale (PSS-14), and a researcher-made questionnaire for demographic data and factors associated with caregiver stress. Collected data were analyzed using SPSS software. RESULTS: A total of 55 (57.3%) subjects showed moderate stress levels and 20 subjects (20.8%) showed severe stress levels. There was a significant relationship between the levels of stress and age of less than 30 years and female gender. Moreover, a significant relationship was observed between the level of stress and factors such as uncomfortable treatment environment, feeling dissatisfied with staff, fear of recurrence, difficulties in everyday life, no spiritual practice, negative attitudes toward treatment outcome, refusing to participate in favorite activities, changes in interactions with others, lack of leisure time, imbalance between daily responsibilities and care, inadequate income, and lack of appropriate facilities (P < 0.05). CONCLUSION: There were several factors causing stress in patients’ families. It is recommended that nurses and the medical team be informed of these factors in order to manage stress in patients and their families

Zero-Aware Low-Precision RNS Scaling Scheme

Scaling is one of the complex operations in the Residue Number System (RNS). This operation is necessary for RNS-based implementations of deep neural networks (DNNs) to prevent overflow. However, the state-of-the-art RNS scalers for special moduli sets consider the 2k modulo as the scaling factor, which results in a high-precision output with a high area and delay. Therefore, low-precision scaling based on multi-moduli scaling factors should be used to improve performance. However, low-precision scaling for numbers less than the scale factor results in zero output, which makes the subsequent operation result faulty. This paper first presents the formulation and hardware architecture of low-precision RNS scaling for four-moduli sets using new Chinese remainder theorem 2 (New CRT-II) based on a two-moduli scaling factor. Next, the low-precision scaler circuits are reused to achieve a high-precision scaler with the minimum overhead. Therefore, the proposed scaler can detect the zero output after low-precision scaling and then transform low-precision scaled residues to high precision to prevent zero output when the input number is not zero

Zero-Aware Low-Precision RNS Scaling Scheme

Scaling is one of the complex operations in the Residue Number System (RNS). This operation is necessary for RNS-based implementations of deep neural networks (DNNs) to prevent overflow. However, the state-of-the-art RNS scalers for special moduli sets consider the 2k modulo as the scaling factor, which results in a high-precision output with a high area and delay. Therefore, low-precision scaling based on multi-moduli scaling factors should be used to improve performance. However, low-precision scaling for numbers less than the scale factor results in zero output, which makes the subsequent operation result faulty. This paper first presents the formulation and hardware architecture of low-precision RNS scaling for four-moduli sets using new Chinese remainder theorem 2 (New CRT-II) based on a two-moduli scaling factor. Next, the low-precision scaler circuits are reused to achieve a high-precision scaler with the minimum overhead. Therefore, the proposed scaler can detect the zero output after low-precision scaling and then transform low-precision scaled residues to high precision to prevent zero output when the input number is not zero