scholarly journals Review of the State-of-the-art Sewer Monitoring and Maintenance Systems Pune Municipal Corporation - A Case Study

TEM Journal ◽  
2021 ◽  
pp. 1500-1508
Author(s):  
Ravindra R. Patil ◽  
Saniya M. Ansari ◽  
Rajnish Kaur Calay ◽  
Mohamad Y. Mustafa
Keyword(s):  
Computer Vision ◽  
Real Time ◽  
Developing Country ◽  
Real World ◽  
State Of The Art ◽  
Robotic Systems ◽  
Increasing Trend ◽  
Maintenance Systems ◽  
Sewer Inspection

There is an increasing trend of using automated and robotic systems for the tasks that are hazardous or inconvenient and dirty for humans. Sewers maintenance and cleaning is such a task where robots are already being used for inspection of underground pipes for blockages and damage. This paper reviews the existing robotic systems and various platforms and algorithms along with their capabilities and limitations being discussed. A typical mid-size city in a developing country, Pune, India is selected in order to understand the concerns and identify the requirements for developing robotic systems for the same. It is found that major concern of sewers are blockages but there is not enough information on both real-time detection and removal of it with robotic systems. On-board processing with computer vision algorithms has not been efficiently utilized in terms of performance and determinations for real-world implementations of sewer robotic systems. The review highlights the available methodologies that can be utilized in developing sewer inspection and cleaning robotic systems.

Real-Time Occlusion Between Real and Digital Objects in Augmented Reality

2018 ◽  
Author(s):  
Kevin Lesniak ◽  
Conrad S. Tucker
Keyword(s):  
Augmented Reality ◽  
Coordinate System ◽  
Real Time ◽  
Real World ◽  
Virtual Object ◽  
Virtual Objects ◽  
Common Coordinate System ◽  
Realistic Representation ◽  
Lead Users

The method presented in this work reduces the frequency of virtual objects incorrectly occluding real-world objects in Augmented Reality (AR) applications. Current AR rendering methods cannot properly represent occlusion between real and virtual objects because the objects are not represented in a common coordinate system. These occlusion errors can lead users to have an incorrect perception of the environment around them when using an AR application, namely not knowing a real-world object is present due to a virtual object incorrectly occluding it and incorrect perception of depth or distance by the user due to incorrect occlusions. The authors of this paper present a method that brings both real-world and virtual objects into a common coordinate system so that distant virtual objects do not obscure nearby real-world objects in an AR application. This method captures and processes RGB-D data in real-time, allowing the method to be used in a variety of environments and scenarios. A case study shows the effectiveness and usability of the proposed method to correctly occlude real-world and virtual objects and provide a more realistic representation of the combined real and virtual environments in an AR application. The results of the case study show that the proposed method can detect at least 20 real-world objects with potential to be incorrectly occluded while processing and fixing occlusion errors at least 5 times per second.

e-Government Implementation in a Developing Country

2011 ◽  
pp. 262-289
Author(s):  
Marvine Hamner ◽  
Martin A. Negrón ◽  
Doaa Taha ◽  
Salah Brahimi
Keyword(s):  
Developing Countries ◽  
Developing Country ◽  
Real World ◽  
The Political ◽  
Economic Data ◽  
Political Turmoil

When e-Government projects fail, the costs to developing countries can be extraordinarily high. Therefore, the importance of understanding the risks, the ability to manage those risks, or when necessary, to minimize the costs, is incredibly important. One way of developing this understanding, of determining how to manage the risks present, is to study real-world examples. This case study explores one developing country’s attempts to implement e-Government. These attempts have taken place over a roughly twenty year period and four different administrations. Millions of dollars have been spent, but an interactive, inter-agency e-Government system remains elusive. The reasons for this are described in this case study along with relevant country political and economic data. The conclusion is that until the political turmoil within this country is resolved, e-Government, and likely many other government initiatives, will continue to be unsuccessful.

scholarly journals Deep Reinforcement Learning for Soft, Flexible Robots: Brief Review with Impending Challenges

Robotics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 4 ◽  
Author(s):  
Sarthak Bhagat ◽  
Hritwick Banerjee ◽  
Zion Ho Tse ◽  
Hongliang Ren
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Degrees Of Freedom ◽  
Review Article ◽  
Imitation Learning ◽  
Robotic Systems ◽  
Soft Robotics ◽  
Flexible Robots ◽  
Increasing Trend ◽  
Embodied Intelligence

The increasing trend of studying the innate softness of robotic structures and amalgamating it with the benefits of the extensive developments in the field of embodied intelligence has led to the sprouting of a relatively new yet rewarding sphere of technology in intelligent soft robotics. The fusion of deep reinforcement algorithms with soft bio-inspired structures positively directs to a fruitful prospect of designing completely self-sufficient agents that are capable of learning from observations collected from their environment. For soft robotic structures possessing countless degrees of freedom, it is at times not convenient to formulate mathematical models necessary for training a deep reinforcement learning (DRL) agent. Deploying current imitation learning algorithms on soft robotic systems has provided competent results. This review article posits an overview of various such algorithms along with instances of being applied to real-world scenarios, yielding frontier results. Brief descriptions highlight the various pristine branches of DRL research in soft robotics.

scholarly journals Waste generation prediction under uncertainty in smart cities through deep neuroevolution

2019 ◽  
pp. 128-138 ◽  
Author(s):  
Andrés Camero ◽  
Jamal Toutouh ◽  
Javier Ferrer ◽  
Enrique Alba
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Smart Cities ◽  
Uncertain Data ◽  
Recurrent Network ◽  
The State ◽  
Waste Generation ◽  
Waste Collection ◽  
The Way

The unsustainable development of countries has created a problem due to the unstoppable waste generation. Moreover, waste collection is carried out following a pre-defined route that does not take into account the actual level of the containers collected. Therefore, optimizing the way the waste is collected presents an interesting opportunity. In this study, we tackle the problem of predicting the waste generation ratio in real-world conditions, i.e., under uncertainty. Particularly, we use a deep neuroevolutionary technique to automatically design a recurrent network that captures the filling level of all waste containers in a city at once, and we study the suitability of our proposal when faced to noisy and faulty data. We validate our proposal using a real-world case study, consisting of more than two hundred waste containers located in a city in Spain, and we compare our results to the state-of-the-art. The results show that our approach exceeds all its competitors and that its accuracy in a real-world scenario, i.e., under uncertain data, is good enough for optimizing the waste collection planning.

scholarly journals Quantification of the suitable rooftop area for solar panel installation from overhead imagery using Convolutional Neural Networks

2021 ◽  
Vol 2042 (1) ◽  
pp. 012002
Author(s):  
Roberto Castello ◽  
Alina Walch ◽  
Raphaël Attias ◽  
Riccardo Cadei ◽  
Shasha Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Computer Vision ◽  
State Of The Art ◽  
Solar Panel ◽  
Post Processing ◽  
Processing Step ◽  
Recent Method

Abstract The integration of solar technology in the built environment is realized mainly through rooftop-installed panels. In this paper, we leverage state-of-the-art Machine Learning and computer vision techniques applied on overhead images to provide a geo-localization of the available rooftop surfaces for solar panel installation. We further exploit a 3D building database to associate them to the corresponding roof geometries by means of a geospatial post-processing approach. The stand-alone Convolutional Neural Network used to segment suitable rooftop areas reaches an intersection over union of 64% and an accuracy of 93%, while a post-processing step using building database improves the rejection of false positives. The model is applied to a case study area in the canton of Geneva and the results are compared with another recent method used in the literature to derive the realistic available area.

scholarly journals MG-DVD: A Real-time Framework for Malware Variant Detection Based on Dynamic Heterogeneous Graph Learning

2021 ◽  
Author(s):  
Chen Liu ◽  
Bo Li ◽  
Jun Zhao ◽  
Ming Su ◽  
Xu-Dong Liu
Keyword(s):  
Real Time ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
Evolutionary Patterns ◽  
Graph Learning ◽  
Fine Grained ◽  
Variant Detection ◽  
Effectiveness And Efficiency ◽  
The Cost

Detecting the newly emerging malware variants in real time is crucial for mitigating cyber risks and proactively blocking intrusions. In this paper, we propose MG-DVD, a novel detection framework based on dynamic heterogeneous graph learning, to detect malware variants in real time. Particularly, MG-DVD first models the fine-grained execution event streams of malware variants into dynamic heterogeneous graphs and investigates real-world meta-graphs between malware objects, which can effectively characterize more discriminative malicious evolutionary patterns between malware and their variants. Then, MG-DVD presents two dynamic walk-based heterogeneous graph learning methods to learn more comprehensive representations of malware variants, which significantly reduces the cost of the entire graph retraining. As a result, MG-DVD is equipped with the ability to detect malware variants in real time, and it presents better interpretability by introducing meaningful meta-graphs. Comprehensive experiments on large-scale samples prove that our proposed MG-DVD outperforms state-of-the-art methods in detecting malware variants in terms of effectiveness and efficiency.

scholarly journals Understanding Generalization in Neural Networks for Robustness against Adversarial Vulnerabilities

2020 ◽  
Vol 34 (10) ◽  
pp. 13714-13715
Author(s):  
Subhajit Chaudhury
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Real World ◽  
Speech Processing ◽  
State Of The Art ◽  
Feature Learning ◽  
Equivalent Problem ◽  
Tremendous Progress ◽  
Real World Applications ◽  
Out Of Sample

Neural networks have contributed to tremendous progress in the domains of computer vision, speech processing, and other real-world applications. However, recent studies have shown that these state-of-the-art models can be easily compromised by adding small imperceptible perturbations. My thesis summary frames the problem of adversarial robustness as an equivalent problem of learning suitable features that leads to good generalization in neural networks. This is motivated from learning in humans which is not trivially fooled by such perturbations due to robust feature learning which shows good out-of-sample generalization.

scholarly journals Advances in Agriculture Robotics: A State-of-the-Art Review and Challenges Ahead

Robotics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 52
Author(s):  
Luiz F. P. Oliveira ◽  
António P. Moreira ◽  
Manuel F. Silva
Keyword(s):  
Computer Vision ◽  
State Of The Art ◽  
Research Work ◽  
Development Stage ◽  
Robotic Systems ◽  
Future Research ◽  
Yield Estimation ◽  
Commercial Development ◽  
Land Preparation ◽  
Smart Agriculture

The constant advances in agricultural robotics aim to overcome the challenges imposed by population growth, accelerated urbanization, high competitiveness of high-quality products, environmental preservation and a lack of qualified labor. In this sense, this review paper surveys the main existing applications of agricultural robotic systems for the execution of land preparation before planting, sowing, planting, plant treatment, harvesting, yield estimation and phenotyping. In general, all robots were evaluated according to the following criteria: its locomotion system, what is the final application, if it has sensors, robotic arm and/or computer vision algorithm, what is its development stage and which country and continent they belong. After evaluating all similar characteristics, to expose the research trends, common pitfalls and the characteristics that hinder commercial development, and discover which countries are investing into Research and Development (R&D) in these technologies for the future, four major areas that need future research work for enhancing the state of the art in smart agriculture were highlighted: locomotion systems, sensors, computer vision algorithms and communication technologies. The results of this research suggest that the investment in agricultural robotic systems allows to achieve short—harvest monitoring—and long-term objectives—yield estimation.

scholarly journals Real-Time Object Detection Model

2020 ◽  
Vol 6 (12) ◽  
pp. 360-364
Author(s):  
Akash Kumar, Dr. Amita Goel Prof. Vasudha Bahl and Prof. Nidhi Sengar
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Object Detection ◽  
Real Time ◽  
Real World ◽  
The Real ◽  
Detection Model ◽  
Bounding Boxes ◽  
The Given ◽  
Single Neural Network

Object Detection is a study in the field of computer vision. An object detection model recognizes objects of the real world present either in a captured image or in real-time video where the object can belong to any class of objects namely humans, animals, objects, etc. This project is an implementation of an algorithm based on object detection called You Only Look Once (YOLO v3). The architecture of yolo model is extremely fast compared to all previous methods. Yolov3 model executes a single neural network to the given image and then divides the image into predetermined bounding boxes. These boxes are weighted by the predicted probabilities. After non max-suppression it gives the result of recognized objects together with bounding boxes. Yolo trains and directly executes object detection on full images.

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