Digital Livestock Farming
As the global human population increases, livestock agriculture must adapt to provide more livestock products and with improved efficiency while also addressing concerns about animal welfare, environmental sustainability, and public health. The purpose of this paper is to critically review the current state of the art in digitalizing animal agriculture with Precision Livestock Farming (PLF) technologies, specifically biometric sensors, big data, and blockchain technology. Biometric sensors include either noninvasive or invasive sensors that monitor an individual animal’s health and behavior in real time, allowing farmers to integrate this data for population-level analyses. Real-time information from biometric sensors is processed and integrated using big data analytics systems that rely on statistical algorithms to sort through large, complex data sets to provide farmers with relevant trending patterns and decision-making tools. Sensors enabled blockchain technology affords secure and guaranteed traceability of animal products from farm to table, a key advantage in monitoring disease outbreaks and preventing related economic losses and food-related health pandemics. Thanks to PLF technologies, livestock agriculture has the potential to address the abovementioned pressing concerns by becoming more transparent and fostering increased consumer trust. However, new PLF technologies are still evolving and core component technologies (such as blockchain) are still in their infancy and insufficiently validated at scale. The next generation of PLF technologies calls for preventive and predictive analytics platforms that can sort through massive amounts of data while accounting for specific variables accurately and accessibly. Issues with data privacy, security, and integration need to be addressed before the deployment of multi-farm shared PLF solutions be- comes commercially feasible. Implications Advanced digitalization technologies can help modern farms optimize economic contribution per animal, reduce the drudgery of repetitive farming tasks, and overcome less effective isolated solutions. There is now a strong cultural emphasis on reducing animal experiments and physical contact with animals in-order-to enhance animal welfare and avoid disease outbreaks. This trend has the potential to fuel more research on the use of novel biometric sensors, big data, and blockchain technology for the mutual benefit of livestock producers, consumers, and the farm animals themselves. Farmers’ autonomy and data-driven farming approaches compared to experience-driven animal manage- ment practices are just several of the multiple barriers that digitalization must overcome before it can become widely implemented.
Keywords: Precision Livestock Farming | digitalization | Digital Technologies in Livestock Systems | sensor technology | big data | blockchain | data models | livestock agriculture
Zynq-based acceleration of robust high density myoelectric signal processing
شتاب مبتنی بر Zynq از پردازش سیگنال میو الکتریک با چگالی بالا-2019
Advances in electromyographic (EMG) sensor technology and machine learning algorithms have led to an increased research effort into high density EMG-based pattern recognition methods for prosthesis control. With the goal set on an autonomous multi-movement prosthesis capable of performing training and classification of an amputee’s EMG signals, the focus of this paper lies in the acceleration of the embedded signal processing chain. We present two Xilinx Zynq-based architectures for accelerating two inherently different high density EMG-based control algorithms. The first hardware accelerated design achieves speed-ups of up to 4.8 over the software-only solution, allowing for a processing delay lower than the sample period of 1 ms. The second system achieved a speed-up of 5.5 over the software-only version and operates at a still satisfactory low processing delay of up to 15 ms wh Keywords: High density electromyography | FPGA acceleration | Medical signal processing | Pattern recognition | Prosthetics
بازاریابی موبایل، شبکه اجتماعی و دیجیتال (DSMM) در صنعت خرید: نیاز به دسته بندی مشتریان وجود دارد؟ شواهد تجربی از لهستان و آلمان
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 14 - تعداد صفحات فایل doc فارسی: 44
این مطالعه نیاز به طبقه بندی مشتریان را در صنعت خرید براساس بازاریابی موبایل، شبکه اجتماعی و دیجیتال از دیدگاه یک تامین کننده آلمانی مورد بررسی قرار می دهد. در ابتدا، ما یک سیستم بازبینی کارهای قبلی را ایجاد کردیم، 37 مقاله توسط تیم ما با همراهی تیم فروشی که توسط تامین کننده معرفی شده بود استخراج شد. این تیم 5 تغییر را در رفتار اطلاعات مربوط به طبقه بندی مصرف کننده ها معرفی شد: افزایش نیاز برای اطلاعات، افزایش تعداد منابع، افزایش نیازهای مربوط به امنیت داده و استفاده از دستگاه های موبایل به موازات شبکه های اجتماعی در صنعت خرید. به این ترتیب، ما به سوالات تحقیق با یک مطالعه تجربی پاسخ دادیم. نمونه ما شامل 139 شرکت صنعتی از لهستان و آلمان بودند که تکنولوژی سنسور را از یک تامین کننده مطرح آلمانی خریداری کرده اند. ما میزان تاثیر فرکانس فروش، عملکرد شخص خریدار، بخش صنعتی و مبدا در درک پنج تحول معرفی شده در بررسی کارهای گذشته مربوط به DSMM که توسط ما انجام شده بود مشخص شده است. بر اساس این یافته ها، استراتژی هایی را برای تقسیم بندی مشتری در ارتباط با DSMM در خرید صنعتی استخراج می کنیم.
کلید واژه ها: دیجیتال | رسانه های اجتماعی و بازاریابی موبایل (DSMM) | خرید صنعتی | بازاریابی B2B | بررسی ادبیات سیستماتیک | مطالعه کمی
|مقاله ترجمه شده|
The IoT for smart sustainable cities of the future: An analytical framework for sensor-based big data applications for environmental sustainability
اینترنت اشیا برای شهرهای پایدار هوشمند از آینده: یک چارچوب تحلیلی برای کاربردهای داده های بزرگ مبتنی بر حسگر برای سازگاری با محیط زیست-2018
The Internet of Things (IoT) is one of the key components of the ICT infrastructure of smart sustainable cities as an emerging urban development approach due to its great potential to advance environmental sustainability. As one of the prevalent ICT visions or computing paradigms, the IoT is associated with big data analytics, which is clearly on a penetrative path across many urban domains for optimizing energy efficiency and mitigating en vironmental effects. This pertains mainly to the effective utilization of natural resources, the intelligent man agement of infrastructures and facilities, and the enhanced delivery of services in support of the environment. As such, the IoT and related big data applications can play a key role in catalyzing and improving the process of environmentally sustainable development. However, topical studies tend to deal largely with the IoT and related big data applications in connection with economic growth and the quality of life in the realm of smart cities, and largely ignore their role in improving environmental sustainability in the context of smart sustainable cities of the future. In addition, several advanced technologies are being used in smart cities without making any con tribution to environmental sustainability, and the strategies through which sustainable cities can be achieved fall short in considering advanced technologies. Therefore, the aim of this paper is to review and synthesize the relevant literature with the objective of identifying and discussing the state-of-the-art sensor-based big data applications enabled by the IoT for environmental sustainability and related data processing platforms and computing models in the context of smart sustainable cities of the future. Also, this paper identifies the key challenges pertaining to the IoT and big data analytics, as well as discusses some of the associated open issues. Furthermore, it explores the opportunity of augmenting the informational landscape of smart sustainable cities with big data applications to achieve the required level of environmental sustainability. In doing so, it proposes a framework which brings together a large number of previous studies on smart cities and sustainable cities, including research directed at a more conceptual, analytical, and overarching level, as well as research on specific technologies and their novel applications. The goal of this study suits a mix of two research approaches: topical literature review and thematic analysis. In terms of originality, no study has been conducted on the IoT and related big data applications in the context of smart sustainable cities, and this paper provides a basis for urban researchers to draw on this analytical framework in future research. The proposed framework, which can be replicated, tested, and evaluated in empirical research, will add additional depth to studies in the field of smart sustainable cities. This paper serves to inform urban planners, scholars, ICT experts, and other city sta keholders about the environmental benefits that can be gained from implementing smart sustainable city in itiatives and projects on the basis of the IoT and related big data applications.
Keywords: Smart sustainable cities , The IoT , Big data analytics , Sensor technology , Data processing platforms , Environmental sustainability , Big data applications , Cloud computing , Fog/edge computing
On the effect of human mobility to the design of metropolitan mobile opportunistic networks of sensors
در اثر تحرک انسان به طراحی شبکه های فرصت طلب شهری از سنسورها-2017
We live in a world where demand for monitoring natural and artificial phenomena is growing. The practical importance of Sensor Networks is continuously increasing in our society due to their broad applicability to tasks such as traffic and air-pollution monitoring, forest-fire detection, agriculture, and battlefield communication. Furthermore, we have seen the emergence of sensor technology being integrated in everyday objects such as cars, traffic lights, bicycles, phones, and even being attached to living beings such as dolphins, trees, and humans. The consequence of this widespread use of sensors is that new sensor network infrastructures may be built out of static (e.g., traffic lights) and mobile nodes (e.g., mobile phones, cars). The use of smart devices carried by people in sensor network infrastructures creates a new paradigm we refer to as Social Networks of Sensors (SNoS). This kind of opportunistic network may be fruitful and economically advantageous where the connectivity, the performance, of the scalability provided by cellular networks fail to provide an adequate quality of service. This paper delves into the issue of understanding the impact of human mobility patterns to the performance of sensor network infrastructures with respect to four different metrics, namely: detection time, report time, data delivery rate, and network coverage area ratio. Moreover, we evaluate the impact of several other mobility patterns (in addition to human mobility) to the performance of these sensor networks on the four metrics above. Finally, we propose possible improvements to the design of sensor network infrastructures.
Keywords: Wireless Sensor Networks (WSNs) | Human mobility | Opportunistic networks | Social Networks of Sensors (SNoS) | Mobile Ad-Hoc Networks (MANETs)
Toward automatic evaluation of defect detectability in infrared images of composites and honeycomb structures
به سوی ارزیابی خودکار کشف نقص در تصاویر مادون قرمز از مواد مرکب و سازه های لانه زنبوری-2015
Non-destructive testing (NDT) refers to inspection methods employed to assess a material specimen without impairing its future usefulness. An important type of these methods is infrared (IR) for NDT (IRNDT), which employs the heat emitted by bodies/objects to rapidly and noninvasively inspect wide surfaces and to find specific defects such as delaminations, cracks, voids, and discontinuities in materials. Current advancements in sensor technology for IRNDT generate great amounts of image sequences. These data require further processing to determine the integrity of objects. Processing techniques for IRNDT data implicitly looks for defect visibility enhancement. Commonly, IRNDT community employs signal to noise ratio (SNR) to measure defect visibility. Nonetheless, current applications of SNR are local, thereby overseeing spatial information, and depend on a-priori knowledge of defect’s location. In this paper, we present a general framework to assess defect detectability based on SNR maps derived from processed IR images. The joint use of image segmentation procedures along with algorithms for filling regions of interest (ROI) estimates a reference background to compute SNR maps. Our main contributions are: (i) a method to compute SNR maps that takes into account spatial variation and are independent of apriori knowledge of defect location in the sample, (ii) spatial background analysis in processed images, and (iii) semi-automatic calculation of segmentation algorithm parameters. We test our approach in carbon fiber and honeycomb samples with complex geometries and defects with different sizes and depths. Keywords: Infrared inspection | Infrared image processing | Defect detectability | Signal to noise ratio | Mean shift segmentation
System of Systems and Big Data analytics – Bridging the gap
سیستمی از سیستم ها و تجزیه و تحلیل ترافیک داده های بزرگ - حذف فاصله-2014
Large data has been accumulating in all aspects of our lives for quite some time. Advances in sensor technology, the Internet, wireless communication, and inexpensive memory have all contributed to an explosion of ‘‘Big Data’’. System of Systems (SoS) integrate independently operating, non-homogeneous systems to achieve a higher goal than the sum of the parts. Today’s SoS are also contributing to the existence of unmanageable ‘‘Big Data’’. Recent efforts have developed a promising approach, called ‘‘Data Analytics’’, which uses statistical and computational intelligence (CI) tools such as principal component analysis (PCA), clustering, fuzzy logic, neuro-computing, evolutionary computation (such as genetic algorithms), Bayesian networks, etc. to reduce the size of ‘‘Big Data’’ to a manageable size and apply these tools to (a) extract information, (b) build a knowledge base using the derived data, and (c) eventually develop a non-parametric model for the ‘‘Big Data’’. This paper demonstrates how to construct a bridge between SoS and Data Analytics to develop reliable models for such systems. The subject material for this demonstration is using data analytics to generate a model to forecast produced photovoltaic energy to assist in the optimization of a micro grid SoS. Tools like fuzzy interference, neural networks, PCA, and genetic algorithms are used.