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31 |
Genetic Algorithm based Internet of Precision Agricultural Things (IopaT) for Agriculture 4:0
اینترنت اشیاء دقیق کشاورزی مبتنی بر الگوریتم ژنتیک (IopaT) برای کشاورزی 4:0-2022 The development of IoT is increasing in our daily life. Its applications are now becoming
famous in rural areas also, such as Agriculture 4.0. Cheap sensors, climate data, soil in-
formation, and drones are now used to solve many real-time problems. One of the most
emerging topics in the IoT in the Agriculture field is IoT based precision agriculture. The
range of IoT applications can range between water spraying from drone, soil recommenda-
tion for different crops, weather prediction and recommendation for water supply, etc. In
this paper we propose a system that will recommend whether water is needed or not by
predicting the rain fall using Genetic Algorithm. In this article, we proposed a unique de-
cision making method to predict Rainfall using Genetic Algorithm (GA) to identify the ne-
cessity of manual water supply is needed or not. The sensor based system will be activated
to check wheather the GA based system completes its prediction correctly or not by sens-
ing moisture level from the soil. If the moisture level of the soil crosses the pre-defined
threshold value then plant watering is performed by quadrotor UAV. A terrace gardening
system is also implemented in this article, which uses a pump for water spraying. Various
atmospheric parameters help to develop a rainfall prediction system to enhance efficiancy
more than 80% in the proposed IopaT system to make the system more interoperable. keywords: اینترنت اشیا | تصمیم گیری | کشاورزی دقیق | الگوریتم ژنتیک | کشاورزی 4.0 | کوادکوپتر پهپاد | سنسور رطوبت خاک | Internet of Things | Decision Making | Precision Agriculture | Genetic Algorithm | Agriculture 4.0 | Quadrotor UAV | Soil Moisture Sensor |
مقاله انگلیسی |
32 |
Implementation and Calibration of an IoT Light Attenuation Turbidity Sensor
پیاده سازی و کالیبراسیون سنسور مه آلود تضعیف نور اینترنت اشیا-2022 Turbidity is an important characteristic of water quality that can indicate the presence of un-
desirable suspended particulate matter. Having access to an inexpensive and effective turbidity
sensor unlocks numerous Internet of Things (IoT) possibilities for remote environmental moni-
toring. Optical light attenuation turbidity sensors operate on the premise of detecting signal
degradation from a light source due to scattering from particles in a solution. This approach is
technologically unpretentious and only requires a handful of inexpensive electronic components
to construct. However, while this method is touted as “simple”, a significant challenge lies in
sensor calibration. That is, converting an analogue signal into a meaningful and accurate digital
reading in a known turbidity measurement standard (e.g., Nephelometric Turbidity Units (NTU)).
This paper presents an IoT light attenuation turbidity sensor design and explores the calibration
process to determine the sensor’s range and accuracy. Sensor calibration is undertaken using
Formazin turbidity standards and is cross-checked against a commercial turbidimeter. We provide
a step-by-step procedure for determining the correct signal strength to use and a functional form
for the sensor response to the Formazin standard. Finally, we specify an estimate of the accuracy
of the sensor and suggest the next steps in the proposed turbidity sensor’s development. Results
indicate that the sensor achieves within 2%-10% of accuracy at higher ranges (100–4000 NTU),
but its performance becomes significantly less reliable in low NTU ranges (< 100 NTU) where the
error rate increases to 26%. The turbidity sensor is used as part of an IoT remote aquatic envi-
ronmental monitoring platform. keywords: Internet of underwater things | Turbidity | Affordable sensors | Calibration | attenuation turbidimeter |
مقاله انگلیسی |
33 |
Digital Livestock Farming
دامداری دیجیتال-2021 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 |
مقاله انگلیسی |
34 |
Open code biometric tap pad for smartphones
باز کردن کد ضربه گیر بیومتریک برای تلفن های هوشمند-2021 Poor security practices among smartphone users, such as the use of simple, easily guessed passcodes for logins, are a result of the effort required to memorize stronger ones. In this paper, we devise a concept of ‘‘open code’’ biometric tap pad to authenticate smartphone users, which eliminates the need of memorizing secret codes. A biometric tap pad consists of a grid of buttons each labeled with a unique digit. The user attempting to log into the phone will tap these buttons in a given sequence. He/she will not memorize this tap sequence. Instead, the sequence will be displayed on the screen. The focus here is how the user types the sequence. This typing behavior is used for authentication. An open code biometric tap pad has several advantages, such as(1) users do not need to memorize passcodes, (2) manufacturers do not need to include extra sensors, and (3) onlookers have no chance to practice shoulder-surfing. We designed three tap pads and incorporated them into an Android app. We evaluated the performance of these tap pads by experimenting with three sequence styles and five different fingers: two thumbs, two index fingers, and the ‘‘usual’’ finger. We collected data from 33 participants over two weeks. We tested three machine learning algorithms: Support Vector Machine, Artificial Neural Network, and Random Forest. Experimental results show significant promise of open code biometric tap pads as a solution to the problem of weak smartphone security practices used by a large segment of the population. Keywords: Smartphone security | Behavioral biometrics | Touchscreen behavior | Open code | Biometric tap pad |
مقاله انگلیسی |
35 |
3D-printable conductive materials for tissue engineering and biomedical applications
مواد رسانای قابل چاپ سه بعدی برای مهندسی بافت و کاربردهای زیست پزشکی-2021 Many patients that undergo autografting suffer from donor site morbidity and risk of immune rejection. Tissue
engineering is receiving considerable attention as engineered tissues could help overcome the drawbacks of
autografts and achieve better performance on tissue repair, replacement and regeneration. Conductivity is one of
the desired properties of engineered scaffolds and tissue constructs as bioelectricity plays an important role in the
native physiological environment. Hence, conductive materials have been extensively used in the making of
biosensors, tissue engineering scaffolds and drug delivery systems to elicit electrically-mediated signals, thus
mimicking the natural cellular environment. Conductive polymers, carbon-based materials, and metal nanoparticles are the main categories of conductive materials used. Ionic liquids, especially biocompatible ionic
liquids, is currently being explored as a competitive filler composite to greatly improve the conductivity of
polymers with little to zero cytotoxicity. The effects of electrical stimulation on cell alignment, migration,
proliferation, and differentiation as well as detailed properties of different types of conductive materials are
briefly yet succinctly reviewed. Furthermore, 3D printing of conductive scaffolds and hydrogels, and their corresponding biomedical applications are also discussed.
Keywords: Conductive biomaterials | Bioprinting | Tissue engineering | Ionic liquids | Electrical stimulation |
مقاله انگلیسی |
36 |
Distinct classes of potassium channels fused to GPCRs as electrical signaling biosensors
کلاس های مجزا از کانال های پتاسیم به عنوان حسگرهای زیستی سیگنالینگ الکتریکی با GPCR ترکیب شده اند:-2021 Ligand-gated ion channels (LGICs) are natural biosensors generating electrical signals in response to the
binding of specific ligands. Creating de novo LGICs for biosensing applications is technically challenging.
We have previously designed modified LGICs by linking G protein-coupled receptors (GPCRs) to the
Kir6.2 channel. In this article, we extrapolate these design concepts to other channels with different structures and oligomeric states, namely a tetrameric viral Kcv channel and the dimeric mouse TREK-1 channel.
After precise engineering of the linker regions, the two ion channels were successfully regulated by a GPCR
fused to their N-terminal domain. Two-electrode voltage-clamp recordings showed that Kcv and mTREK-1
fusions were inhibited and activated by GPCR agonists, respectively, and antagonists abolished both effects.
Thus, dissimilar ion channels can be allosterically regulated through their N-terminal domains, suggesting
that this is a generalizable approach for ion channel engineering. |
مقاله انگلیسی |
37 |
The importance of accounting-integrated information systems for realising productivity and sustainability in the agricultural sector
اهمیت سیستم های اطلاعاتی حسابداری یکپارچه برای تحقق بهره وری و پایداری در بخش کشاورزی-2021 Agricultural information systems are an integral part of modern farming and are helping to
make a significant contribution to improved farm productivity and profitability. To date,
however, there has been a failure to integrate accounting information systems with onfarm data, despite today’s farmers facing unprecedented and interconnected economic
and resource pressures. This study explores this problem in more detail, defines the objectives of the solution and develops a model of integrated accounting and agricultural information systems, drawing on a ‘fads and fashions’ framework and advancing our
understanding of bundled innovations. Using data from a participatory case study in
Australian potato farming, the study integrates accounting data with soil moisture and climate data to track, alert and inform irrigation decisions. Development of preliminary digital software based on the model demonstrates how cost-informed tracking, alerts and
forecasting can be supported by bundling accounting information systems and sensing
technology. In doing so, the model extends the fads and fashions framework for agricultural information systems and demonstrates how accounting information can be the key
for improved water productivity, profitability and agricultural sustainability.
keywords: تصمیم گیری کشاورزی | سیستم های حسابداری یکپارچه | نوآوری های همراه | سنسور | اطلاعات دیجیتال | ایستگاه های آب و هوا | تصویربرداری ماهواره ای | Agricultural decision-making | Integrated accounting systems | Bundled innovations | Sensors | Digital information | Weather stations | Satellite imagery |
مقاله انگلیسی |
38 |
GaitCode: Gait-based continuous authentication using multimodal learning and wearable sensors
GaitCode: احراز هویت پیوسته مبتنی بر راه رفتن با استفاده از یادگیری چند حالته و حسگرهای پوشیدنی-2021 The ever-growing threats of security and privacy loss from unauthorized access to mobile devices have led to the development of various biometric authentication methods for easier and safer data access. Gait-based authentication is a popular biometric authentication as it utilizes the unique patterns of human locomotion and it requires little cooperation from the user. Existing gait-based biometric authentication methods however suffer from degraded performance when using mobile devices such as smart phones as the sensing device, due to multiple reasons, such as increased accelerometer noise, sensor orientation and positioning, and noise from body movements not related to gait. To address these drawbacks, some researchers have adopted methods that fuse information from multiple accelerometer sensors mounted on the human body at different lo- cations. In this work we present a novel gait-based continuous authentication method by applying multimodal learning on jointly recorded accelerometer and ground contact force data from smart wearable devices. Gait cycles are extracted as a basic authentication element, that can continuously authenticate a user. We use a network of auto-encoders with early or late sensor fusion for feature extraction and SVM and soft max for classification. The effectiveness of the proposed approach has been demonstrated through extensive experiments on datasets collected from two case studies, one with commercial off-the-shelf smart socks and the other with a medical-grade research prototype of smart shoes. The evaluation shows that the proposed approach can achieve a very low Equal Error Rate of 0.01% and 0.16% for identification with smart socks and smart shoes respectively, and a False Acceptance Rate of 0.54%–1.96% for leave-one-out authentication. Keywords: Biometric authentication | Gait authentication | Autoencoders | Sensor fusion | Multimodal learning | Wearable sensors |
مقاله انگلیسی |
39 |
Smart sensors network for accurate indirect heat accounting in apartment buildings
سنسورهای هوشمند شبکه برای حسابداری دقیق غیر مستقیم در ساختمان های آپارتمان-2021 A new method for accurate indirect heat accounting in apartment buildings has been recently
developed by the Centre Suisse d’Electronique et de Microtechnique (CSEM). It is based on a data
driven approach aimed to the smart networking of any type of indirect heat allocation devices,
which can provide, for each heat delivery point of an apartment building, measurements or es-
timations of the temperature difference between the heat transfer fluid and the indoor environ-
ment. The analysis of the data gathered from the devices installed on the heating bodies, together
with the measurements of the overall building heat consumption provided by direct heat
metering, allows the evaluation of the characteristic thermal model parameters of heating bodies
at actual installation and working conditions. Thus overcoming the negative impact on accuracy
of conventional indirect heat accounting due to off-design operation, in which these measurement
systems normally operate. The method has been tested on conventional heat cost allocators
(HCA), and on innovative smart radiator thermostatic valves developed by CSEM. The evaluations
were carried out at the centralized heating system mock-up of the Istituto Nazionale di Ricerca
Metrologica (INRIM), and also in a real building in Neuchatel, Switzerland. The method has
proven to be an effective tool to improve the accuracy of indirect heat metering systems;
compared to conventional HCA systems, the error on the individual heating bill is reduced by
20%–50%. keywords: شبکه سنسورهای هوشمند | حسابداری غیر مستقیم | اندازه گیری حرارت | مخزن هزینه های حرارتی | سیستم های گرمایش متمرکز | Smart sensors network | Indirect heat accounting | Heat metering | Heat cost allocators | Centralized heating systems |
مقاله انگلیسی |
40 |
Real-time plant phenomics under robotic farming setup: A vision-based platform for complex plant phenotyping tasks
پدیده های گیاهی در زمان واقعی تحت راه اندازی رباتیک کشاورزی: یک پلت فرم مبتنی بر دید برای کارهای پیچیده فنوتیپ سازی گیاهان-2021 Plant phenotyping in general refers to quantitative estimation of the plant’s anatomical, ontogenetical, physiological and biochemical properties. Analyzing big data is challenging, and non-trivial given the different complexities involved. Efficient processing and analysis pipelines are the need of the hour with the increasing popularity of phenotyping technologies and sensors. Through this work, we largely address the overlapping object segmentation & localization problem. Further, we dwell upon multi-plant pipelines that pose challenges as detection and multi-object tracking becomes critical for single frame/set of frames aimed towards uniform tagging & visual features extraction. A plant phenotyping tool named RTPP (Real-Time Plant Phenotyping) is presented that can aid in the detection of single/multi plant traits, modeling, and visualization for agricultural settings. We compare our system with the plantCV platform. The relationship of the digital estimations, and the measured plant traits are discussed that plays a vital roadmap towards precision farming and/or plant breeding. Keywords: Phenotype | Image processing | Spectral | Robotics | Object localization | Precision agriculture | Plant science | Pattern recognition | Computer vision | Automation | Perception |
مقاله انگلیسی |