scholarly journals A Visual Aquaculture System Using a Cloud-Based Autonomous Drones

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 109
Author(s):  
Naomi A. Ubina ◽  
Shyi-Chyi Cheng ◽  
Hung-Yuan Chen ◽  
Chin-Chun Chang ◽  
Hsun-Yu Lan
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Cost Effective ◽  
Cloud Services ◽  
Fish Length ◽  
Surveillance Systems ◽  
Feeding Intensity ◽  
Fish Feeding ◽  
Intelligent Decision Making ◽  
Intelligent Decision

This paper presents a low-cost and cloud-based autonomous drone system to survey and monitor aquaculture sites. We incorporated artificial intelligence (AI) services using computer vision and combined various deep learning recognition models to achieve scalability and added functionality, in order to perform aquaculture surveillance tasks. The recognition model is embedded in the aquaculture cloud, to analyze images and videos captured by the autonomous drone. The recognition models detect people, cages, and ship vessels at the aquaculture site. The inclusion of AI functions for face recognition, fish counting, fish length estimation and fish feeding intensity provides intelligent decision making. For the fish feeding intensity assessment, the large amount of data in the aquaculture cloud can be an input for analysis using the AI feeding system to optimize farmer production and income. The autonomous drone and aquaculture cloud services are cost-effective and an alternative to expensive surveillance systems and multiple fixed-camera installations. The aquaculture cloud enables the drone to execute its surveillance task more efficiently with an increased navigation time. The mobile drone navigation app is capable of sending surveillance alerts and reports to users. Our multifeatured surveillance system, with the integration of deep-learning models, yielded high-accuracy results.

scholarly journals Wireless Sensor Network Synchronization for Precision Agriculture Applications

Agriculture ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 89 ◽  
Author(s):  
Alexandros Zervopoulos ◽  
Athanasios Tsipis ◽  
Aikaterini Georgia Alvanou ◽  
Konstantinos Bezas ◽  
Asterios Papamichail ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Low Cost ◽  
Time Correlation ◽  
Wireless Sensor ◽  
Network Synchronization ◽  
Transmission Delays ◽  
Intelligent Decision Making ◽  
Cost System ◽  
Intelligent Decision ◽  
Synchronized Measurements

The advent of Internet of Things has propelled the agricultural domain through the integration of sensory devices, capable of monitoring and wirelessly propagating information to producers; thus, they employ Wireless Sensor Networks (WSNs). These WSNs allow real time monitoring, enabling intelligent decision-making to maximize yields and minimize cost. Designing and deploying a WSN is a challenging and multivariate task, dependent on the considered environment. For example, a need for network synchronization arises in such networks to correlate acquired measurements. This work focuses on the design and installation of a WSN that is capable of facilitating the sensing aspects of smart and precision agriculture applications. A system is designed and implemented to address specific design requirements that are brought about by the considered environment. A simple synchronization scheme is described to provide time-correlated measurements using the sink node’s clock as reference. The proposed system was installed on an olive grove to assess its effectiveness in providing a low-cost system, capable of acquiring synchronized measurements. The obtained results indicate the system’s overall effectiveness, revealing a small but expected difference in the acquired measurements’ time correlation, caused mostly by serial transmission delays, while yielding a plethora of relevant environmental conditions.

scholarly journals Border security and surveillance System using IoT

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Low Cost ◽  
Cost Effective ◽  
Surveillance Systems ◽  
Security Assessment ◽  
Efficient System ◽  
Human Gestures ◽  
Set Up ◽  
International Border ◽  
Border Surveillance

Security along the international border is a critical process in security assessment; It must be exercised the 24x7. With the advancements in wireless IoT technology, it has become much easier to design, develop and deploy a cost-effective, automatic and efficient system for intrusion detection in the context of surveillance. This paper set up to set up the most efficient surveillance solution, we propose a Border Surveillance Systems and sensitive sites. this surveillance and security system is to detect and track intruders trespassing into the monitoring area along the border, it able which triggers off precocious alerts and valuation necessary for the catch of efficient measurements in case of a threat. Our system is based on the classification of the human gestures drawn from videos envoy by Drones equipped with cameras and sensors in real-time. All accomplished experimentation and acquired results showed the benefit diverted from the use of our system and therefore it enables our soldiers to watch the borders at each and every moment to effectively and at low cost.

scholarly journals Cloud computing survey on services, enhancements and challenges in the era of machine learning and data science

2020 ◽  
Vol 9 (2) ◽  
pp. 117
Author(s):  
Wajid Hassan ◽  
Te-Shun Chou ◽  
Omar Tamer ◽  
John Pickard ◽  
Patrick Appiah-Kubi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Cloud Computing ◽  
Data Science ◽  
Service Level Agreement ◽  
Resource Scarcity ◽  
Cloud Services ◽  
Future Research ◽  
Intelligent Decision Making ◽  
Intelligent Decision

<p>Cloud computing has sweeping impact on the human productivity. Today it’s used for Computing, Storage, Predictions and Intelligent Decision Making, among others. Intelligent Decision Making using Machine Learning has pushed for the Cloud Services to be even more fast, robust and accurate. Security remains one of the major concerns which affect the cloud computing growth however there exist various research challenges in cloud computing adoption such as lack of well managed service level agreement (SLA), frequent disconnections, resource scarcity, interoperability, privacy, and reliability. Tremendous amount of work still needs to be done to explore the security challenges arising due to widespread usage of cloud deployment using Containers. We also discuss Impact of Cloud Computing and Cloud Standards. Hence in this research paper, a detailed survey of cloud computing, concepts, architectural principles, key services, and implementation, design and deployment challenges of cloud computing are discussed in detail and important future research directions in the era of Machine Learning and Data Science have been identified.</p>

scholarly journals SecureDL: A privacy preserving deep learning model for image recognition over cloud

2021 ◽  
Author(s):  
Vishesh Kumar Tanwar ◽  
Balasubramanian Raman ◽  
Amitesh Singh Rajput ◽  
Rama Bhargava
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Visual Information ◽  
Low Cost ◽  
Number System ◽  
Privacy Preserving ◽  
Cloud Services ◽  
Binary Number ◽  
Encryption Scheme ◽  
Encrypted Data

<div>The key benefits of cloud services, such as low cost, access flexibility, and mobility, have attracted users worldwide to utilize the deep learning algorithms for developing computer vision tasks. Untrusted third parties maintain these cloud servers, and users are always concerned about sharing their confidential data with them. In this paper, we addressed these concerns for by developing SecureDL, a privacy-preserving image recognition model for encrypted data over cloud. Additionally, we proposed a block-based image encryption scheme to protect images’ visual information. The scheme constitutes an order-preserving permutation ordered binary number system and pseudo-random matrices. The encryption scheme is proved to be secure in a probabilistic viewpoint and through various cryptographic attacks. Experiments are performed for several image recognition datasets, and the achieved recognition accuracy for encrypted data is close with non-encrypted data. SecureDL overcomes the storage, and computational overheads occurred in fully-homomorphic and multi-party computations based secure recognition schemes. </div>

scholarly journals Deep Learning-Enabled Point-of-Care Sensing Using Multiplexed Paper-Based Sensors

2019 ◽  
Author(s):  
Zachary Ballard ◽  
Hyou-Arm Joung ◽  
Artem Goncharov ◽  
Jesse Liang ◽  
Karina Nugroho ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Point Of Care ◽  
Low Cost ◽  
High Sensitivity ◽  
Cost Effective ◽  
Serum Samples ◽  
Cvd Risk ◽  
Medical Test ◽  
Sensing Membrane

ABSTRACTWe present a deep learning-based framework to design and quantify point-of-care sensors. As its proof-of-concept and use-case, we demonstrated a low-cost and rapid paper-based vertical flow assay (VFA) for high sensitivity C-Reactive Protein (hsCRP) testing, a common medical test used for quantifying the degree of inflammation in patients at risk of cardio-vascular disease (CVD). A machine learning-based sensor design framework was developed for two key tasks: (1) to determine an optimal configuration of immunoreaction spots and conditions, spatially-multiplexed on a paper-based sensing membrane, and (2) to accurately infer the target analyte concentration based on the signals of the optimal VFA configuration. Using a custom-designed mobile-phone based VFA reader, a clinical study was performed with 85 human serum samples to characterize the quantification accuracy around the clinically defined cutoffs for CVD risk stratification. Results from blindly-tested VFAs indicate a competitive coefficient of variation of 11.2% with a linearity of R2 = 0.95; in addition to the success in the high-sensitivity CRP range (i.e., 0-10 mg/L), our results further demonstrate a mitigation of the hook-effect at higher CRP concentrations due to the incorporation of antigen capture spots within the multiplexed sensing membrane of the VFA. This paper-based computational VFA that is powered by deep learning could expand access to CVD health screening, and the presented machine learning-enabled sensing framework can be broadly used to design cost-effective and mobile sensors for various point-of-care diagnostics applications.

scholarly journals Real-Time Surveillance Using Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Muhammad Javed Iqbal ◽  
Muhammad Munwar Iqbal ◽  
Iftikhar Ahmad ◽  
Madini O. Alassafi ◽  
Ahmed S. Alfakeeh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cost Effective ◽  
Feature Reduction ◽  
Surveillance Systems ◽  
Threat Detection ◽  
Average Precision ◽  
Aerial Surveillance ◽  
Fast Learning ◽  
Extraction Step ◽  
Current Systems

It is crucial to ensure proper surveillance for the safety and security of people and their assets. The development of an aerial surveillance system might be very effective in catering to the challenges in surveillance systems. Current systems are expensive and complex. A cost-effective and efficient solution is required, which is easily accessible to anyone with a moderate budget. In aerial surveillance, quadcopters are equipped with state-of-the-art image processing technology that captures detailed photographs of every object underneath. A quadcopter-based solution is proposed to monitor desired premises for any unusual activities, like the movement of persons with weapons and face detection to achieve the desired surveillance. After detection of any unusual activity, the proposed system generates an alert for security personals. The proposed solution is based on quadcopter surveillance and video streaming for anomaly detection in the received video streams through deep learning models. A well-known FasterRCNN algorithm is modified for fast learning with feature reduction in the initial feature extraction step. Five different kinds of CNNs were evaluated for their ability to identify objects of interest in surveillance images. ResNet-50–based FasterRCNN with the highest average precision performed as an excellent solution for threat detection. The average precision of the system is 79% across all categories achieved.

scholarly journals SecureDL: A privacy preserving deep learning model for image recognition over cloud

2021 ◽  
Author(s):  
Vishesh Kumar Tanwar ◽  
Balasubramanian Raman ◽  
Amitesh Singh Rajput ◽  
Rama Bhargava
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Visual Information ◽  
Low Cost ◽  
Number System ◽  
Privacy Preserving ◽  
Cloud Services ◽  
Binary Number ◽  
Encryption Scheme ◽  
Encrypted Data

<div>The key benefits of cloud services, such as low cost, access flexibility, and mobility, have attracted users worldwide to utilize the deep learning algorithms for developing computer vision tasks. Untrusted third parties maintain these cloud servers, and users are always concerned about sharing their confidential data with them. In this paper, we addressed these concerns for by developing SecureDL, a privacy-preserving image recognition model for encrypted data over cloud. Additionally, we proposed a block-based image encryption scheme to protect images’ visual information. The scheme constitutes an order-preserving permutation ordered binary number system and pseudo-random matrices. The encryption scheme is proved to be secure in a probabilistic viewpoint and through various cryptographic attacks. Experiments are performed for several image recognition datasets, and the achieved recognition accuracy for encrypted data is close with non-encrypted data. SecureDL overcomes the storage, and computational overheads occurred in fully-homomorphic and multi-party computations based secure recognition schemes. </div>

scholarly journals A cost-effective maize ear phenotyping platform enables rapid categorization and quantification of kernels

2020 ◽  
Author(s):  
Cedar Warman ◽  
Christopher M. Sullivan ◽  
Justin Preece ◽  
Michaela E. Buchanan ◽  
Zuzana Vejlupkova ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Low Cost ◽  
Digital Camera ◽  
Cost Effective ◽  
Detection Model ◽  
Biological Phenomena ◽  
Effective Combination ◽  
Wide Range ◽  
Maize Ear

AbstractHigh-throughput phenotyping systems are powerful, dramatically changing our ability to document, measure, and detect biological phenomena. Here, we describe a cost-effective combination of a custom-built imaging platform and deep-learning-based computer vision pipeline. A minimal version of the maize ear scanner was built with low-cost and readily available parts. The scanner rotates a maize ear while a cellphone or digital camera captures a video of the surface of the ear. Videos are then digitally flattened into two-dimensional ear projections. Segregating GFP and anthocyanin kernel phenotype are clearly distinguishable in ear projections, and can be manually annotated using image analysis software. Increased throughput was attained by designing and implementing an automated kernel counting system using transfer learning and a deep learning object detection model. The computer vision model was able to rapidly assess over 390,000 kernels, identifying male-specific transmission defects across a wide range of GFP-marked mutant alleles. This includes a previously undescribed defect putatively associated with mutation of Zm00001d002824, a gene predicted to encode a vacuolar processing enzyme (VPE). We show that by using this system, the quantification of transmission data and other ear phenotypes can be accelerated and scaled to generate large datasets for robust analyses.One sentence summaryA maize ear phenotyping system built from commonly available parts creates images of the surface of ears and identifies kernel phenotypes with a deep-learning-based computer vision pipeline.

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