Sparsification and Separation of Deep Learning Layers for Constrained Resource Inference on Wearables

Deep learning has revolutionized the way sensor data are analyzed and interpreted. The accuracy gains these approaches o↵er make them attractive for the next generation of mobile, wearable and embedded sensory applications. However, state-of-the-art deep learning algorithms typically require a significant amount of device and processor resources, even just for the inference stages that are used to discriminate high-level classes from low-level data. The limited availability of memory, computation, and energy on mobile and embedded platforms thus pose a significant challenge to the adoption of these powerful learning techniques. In this paper, we propose SparseSep, a new approach that leverages the sparsification of fully connected layers and separation of convolutional kernels to reduce the resource requirements of popular deep learning algorithms. As a result, SparseSep allows large-scale DNNs and CNNs to run eciently on mobile and embedded hardware with only minimal impact on inference accuracy. We experiment using SparseSep across a variety of common processors such as the Qualcomm Snapdragon 400, ARM Cortex M0 and M3, and Nvidia Tegra K1, and show that it allows inference for various deep models to execute more eciently; for example, on average requiring 11.3 times less memory and running 13.3 times faster on these representative platforms

Studying patient safety culture from the viewpoint of staffs in educational hospitals in Tehran City

Introduction: Patient safety is an important issue in regard to hospital servicesand any problem can cause adverse consequences. The purpose of this study was to assess the patients’safety culture at educational hospitals in Tehran. .Material and Method: The present descriptive-cross sectional study was carried out among 312 health care workers in Baharloo, Amir Aalam, Shariati and Sina hospitals in Tehran, which were selected by cluster sampling. The participants were chosen randomly in each cluster. Safety Culture Survey questionnaire including 12 dimensions was used to assess patient safety culture. Cronbach’s alpha and test-retest coefficient were estimated 81 and 79 percent, respectively. .Result: Nurses comprised 61 percent of participants in the study. 42% of staff had less than 5 years work experience. Of the 12 dimensions of patient safety culture, the frequency of reporting and exchange of information had the minimum average of 56 and 55, respectively. Moreover, the dimensions of organizational learning and expectations-management measuresobtained the highestmean score (69)among 12 dimensions of patient safety culture. Total mean patient safety culture in understudy hospitals was 63. . Conclusion: It should be noted that paying more attention to the patient safety culture can lead to improve hospitals condition, as a whole, and to have a patient-friendly environment. Special attention should be paid to dimensions with the lowest mean score in order to strengthen them

Introduction to smart grid architecture

The smart grid that is a new concept introduced at the beginning of the 2000s intends to include bidirectional communication infrastructure to conventional grids in order to enable information and communication technologies (ICTs) at any stage of generation, transmission, distribution, and even consumption sections of utility grids. This chapter introduces essential components and novel technologies of smart grids such as sensor networks, smart metering and monitoring systems, smart management systems, wired and wireless communication technologies, security requirements, and standards and regulations for this concept. First of all, this chapter focuses on the main components of smart grids such as smart sensors and sensor networks, phasor measurement unit (PMU), smart meters (SMs), and wireless sensor networks (WSNs). Then, smart grid applications and main requirements are explained on the basis of advanced metering infrastructure (AMI), demand response (DR), station and substation automation, and demand-side management (DSM). Later, communication systems of smart grid are presented in which the communication systems are classified into two groups as wired and wireless communication systems, and they are comprehensively analyzed. Furthermore, the area networks related to smart grid concept such as home area network (HAN), building area network (BAN), industrial area network (IAN), neighborhood area network (NAN), field area network (FAN), and wide-area network (WAN) are presented in a logical way beginning from generation systems to the user side