scholarly journals Automatic Image Annotation for Small and Ad hoc Intelligent Applications using Raspberry Pi

2019 ◽  
Vol 255 ◽  
pp. 01003 ◽  
Author(s):  
Jameel Syed Muslim ◽  
Hashmani Manzoor Ahmed ◽  
Rizvi Syed Sajjad Hussain ◽  
Uddin Vali ◽  
Rehman Mobashar
Keyword(s):  
Real Time ◽  
Ad Hoc ◽  
Image Annotation ◽  
Low Cost ◽  
Raspberry Pi ◽  
Automatic Image Annotation ◽  
Literature Study ◽  
Software Module ◽  
Single Node ◽  
Practical Applications

The cutting-edge technology Machine Learning (ML) is successfully applied for Business Intelligence. Among the various pre-processing steps of ML, Automatic Image Annotation (also known as automatic image tagging or linguistic indexing) is the process in which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image. Automatic Image Annotation (AIA) methods (which have appeared during the last several years) make a large use of many ML approaches. Clustering and classification methods are most frequently applied to annotate images. In addition, these proposed solutions require a high computational infrastructure. However, certain real-time applications (small and ad-hoc intelligent applications) for example, autonomous small robots, gadgets, drone etc. have limited computational processing capacity. These small and ad-hoc applications demand a more dynamic and portable way to automatically annotate data and then perform ML tasks (Classification, clustering etc.) in real time using limited computational power and hardware resources. Through a comprehensive literature study we found that most image pre-processing algorithms and ML tasks are computationally intensive, and it can be challenging to run them on an embedded platform with acceptable frame rates. However, Raspberry Pi is sufficient for AIA and ML tasks that are relevant to small and ad-hoc intelligent applications. In addition, few critical intelligent applications (which require high computational resources, for example, Deep Learning using huge dataset) are only feasible to run on more powerful hardware resources. In this study, we present the framework of “Automatic Image Annotation for Small and Ad-hoc Intelligent Application using Raspberry Pi” and propose the low-cost infrastructures (single node and multi node using Raspberry Pi) and software module (for Raspberry Pi) to perform AIA and ML tasks in real time for small and ad-hoc intelligent applications. The integration of both AIA and ML tasks in a single software module (with in Raspberry Pi) is challenging. This study will helpful towards the improvement in various practical applications areas relevant to small intelligent autonomous systems.

scholarly journals A Portable Fuzzy Driver Drowsiness Estimation System

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4093
Author(s):  
Alimed Celecia ◽  
Karla Figueiredo ◽  
Marley Vellasco ◽  
René González
Keyword(s):  
Real Time ◽  
Traffic Accidents ◽  
Fuzzy Inference ◽  
Low Cost ◽  
Raspberry Pi ◽  
Inference System ◽  
Visual Occlusion ◽  
Computational Performance ◽  
Estimation System ◽  
Definition Of

The adequate automatic detection of driver fatigue is a very valuable approach for the prevention of traffic accidents. Devices that can determine drowsiness conditions accurately must inherently be portable, adaptable to different vehicles and drivers, and robust to conditions such as illumination changes or visual occlusion. With the advent of a new generation of computationally powerful embedded systems such as the Raspberry Pi, a new category of real-time and low-cost portable drowsiness detection systems could become standard tools. Usually, the proposed solutions using this platform are limited to the definition of thresholds for some defined drowsiness indicator or the application of computationally expensive classification models that limits their use in real-time. In this research, we propose the development of a new portable, low-cost, accurate, and robust drowsiness recognition device. The proposed device combines complementary drowsiness measures derived from a temporal window of eyes (PERCLOS, ECD) and mouth (AOT) states through a fuzzy inference system deployed in a Raspberry Pi with the capability of real-time response. The system provides three degrees of drowsiness (Low-Normal State, Medium-Drowsy State, and High-Severe Drowsiness State), and was assessed in terms of its computational performance and efficiency, resulting in a significant accuracy of 95.5% in state recognition that demonstrates the feasibility of the approach.

scholarly journals SparkRA: Enabling Big Data Scalability for the GATK RNA-seq Pipeline with Apache Spark

Genes ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 53
Author(s):  
Zaid Al-Ars ◽  
Saiyi Wang ◽  
Hamid Mushtaq
Keyword(s):  
Low Cost ◽  
Computing Time ◽  
Scale Up ◽  
Variant Calling ◽  
Computation Time ◽  
Apache Spark ◽  
Rna Seq ◽  
Single Node ◽  
Practical Applications ◽  
Computational Resources

The rapid proliferation of low-cost RNA-seq data has resulted in a growing interest in RNA analysis techniques for various applications, ranging from identifying genotype–phenotype relationships to validating discoveries of other analysis results. However, many practical applications in this field are limited by the available computational resources and associated long computing time needed to perform the analysis. GATK has a popular best practices pipeline specifically designed for variant calling RNA-seq analysis. Some tools in this pipeline are not optimized to scale the analysis to multiple processors or compute nodes efficiently, thereby limiting their ability to process large datasets. In this paper, we present SparkRA, an Apache Spark based pipeline to efficiently scale up the GATK RNA-seq variant calling pipeline on multiple cores in one node or in a large cluster. On a single node with 20 hyper-threaded cores, the original pipeline runs for more than 5 h to process a dataset of 32 GB. In contrast, SparkRA is able to reduce the overall computation time of the pipeline on the same single node by about 4×, reducing the computation time down to 1.3 h. On a cluster with 16 nodes (each with eight single-threaded cores), SparkRA is able to further reduce this computation time by 7.7× compared to a single node. Compared to other scalable state-of-the-art solutions, SparkRA is 1.2× faster while achieving the same accuracy of the results.

OBD-II and raspberry Pi technology to diagnose car’s machine current condition: study literature

2017 ◽  
Vol 34 (10) ◽  
pp. 15-21 ◽  
Author(s):  
Sonya Rapinta Manalu ◽  
Jurike Moniaga ◽  
Dionisius Andrian Hadipurnawan ◽  
Firda Sahidi
Keyword(s):  
Real Time ◽  
Mobile Technology ◽  
Design Methodology ◽  
Low Cost ◽  
Raspberry Pi ◽  
Data Link ◽  
Content Type ◽  
History Of ◽  
To Receive ◽  
Technology Comparison

Purpose Low-cost microcomputers such as the Raspberry Pi are common in library makerspaces. This paper aims to create an OBD-II technology to diagnose a vehicle’s condition. Design/methodology/approach An OBD-II scanner plugged into the OBD-II port or usually called the data link connector (DLC), sends diagnostics to the Raspberry Pi. Findings Compared with other microcontrollers such as Arduino, the Raspberry Pi was chosen because it sustains the application to receive real-time diagnostics, process the diagnostics and send commands to automobiles at the same time, rather than Arduino that must wait for another process finished to run another process. Originality/value This paper also represents the history of mobile technology and OBD-II technology, comparison between Arduino and Raspberry Pi and Node.

scholarly journals Artificial vision system for object classification in real time using Raspberry Pi and a Web camera

2021 ◽  
pp. 20-25
Author(s):  
Tomás Serrano-Ramírez ◽  
Ninfa del Carmen Lozano-Rincón ◽  
Arturo Mandujano-Nava ◽  
Yosafat Jetsemaní Sámano-Flores
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Vision System ◽  
Low Cost ◽  
Artificial Vision ◽  
Maintenance Cost ◽  
Raspberry Pi ◽  
Smart Cameras ◽  
Suggested Technique ◽  
Web Camera

Computer vision systems are an essential part in industrial automation tasks such as: identification, selection, measurement, defect detection and quality control in parts and components. There are smart cameras used to perform tasks, however, their high acquisition and maintenance cost is restrictive. In this work, a novel low-cost artificial vision system is proposed for classifying objects in real time, using the Raspberry Pi 3B + embedded system, a Web camera and the Open CV artificial vision library. The suggested technique comprises the training of a supervised classification system of the Haar Cascade type, with image banks of the object to be recognized, subsequently generating a predictive model which is put to the test with real-time detection, as well as the calculation for the prediction error. This seeks to build a powerful vision system, affordable and also developed using free software.

scholarly journals Real-time Vehicle Roll Angle Estimation based on IoT low-cost devices and Neural Networks

2018 ◽  
Author(s):  
Javier Garcia-Guzman ◽  
Lisardo Prieto González ◽  
Jonatan Pajares Redondo ◽  
Mat Max Montalvo Martinez ◽  
María Jesús López Boada
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Low Cost ◽  
Roll Angle ◽  
Measurement Unit ◽  
Raspberry Pi ◽  
State Variables ◽  
Angle Estimation ◽  
Experimental Environment ◽  
Time Operation

Given the high number of vehicle-crash victims, it has been established as a priority to reduce this figure in the transportation sector. For this reason, many of the recent researches are focused on including control systems in existing vehicles, to improve their stability, comfort and handling. These systems need to know in every moment the behavior of the vehicle (state variables), among others, when the different maneuvers are performed, to actuate by means of the systems in the vehicle (brakes, steering, suspension) and, in this way, to achieve a good behavior. The main problem arises from the lack of ability to directly capture several required dynamic vehicle variables, such as roll angle, from low-cost sensors. Previous studies demonstrate that low-cost sensors can provide data in real-time with the required precision and reliability. Even more, other research works indicate that neural networks are efficient mechanisms to estimate roll angle. Nevertheless, it is necessary to assess that the fusion of data coming from low-cost devices and estimations provided by neural networks can fulfill the reliability and appropriateness requirements for using these technologies to improve overall safety in production vehicles. Because of the increasing of computing power, the reduction of consumption and electric devices size, along with the high variety of communication technologies and networking protocols using Internet have yield to Internet of Things (IoT) development. In order to address this issue, this study has two main goals: 1) Determine the appropriateness and performance of neural networks embedded in low-cost sensors kits to estimate roll angle required to evaluate rollover risk situations. 2) Compare the low-cost control unit devices (Intel Edison and Raspberry Pi 3 Model B), to provide the roll angle estimation with this artificial neural network-based approach. To fulfil these objectives an experimental environment has been set up composed of a van with two set of low-cost kits, one including a Raspberry Pi 3 Model B, low cost Inertial Measurement Unit (BNO055 - 37€) and GPS (Mtk3339 - 53€) and the other having an Intel Edison System on Chip linked to a SparkFun 9 Degrees of Freedom module. This experimental environment will be tested in different maneuvers for comparison purposes. Neural networks embedded in low-cost sensor kits provide roll angle estimations very approximated to real values. Even more, Intel Edison and Raspberry Pi 3 Model B have enough computing capabilities to successfully run roll angle estimation based on neural networks to determine rollover risks situation fulfilling real-time operation restrictions stated for this problem.

scholarly journals Raspberry Pi for Commercial Applications

2013 ◽  
Vol 11 (2) ◽  
pp. 2250-2255 ◽  
Author(s):  
Chaitanya Bysani ◽  
T. S. Rama Krishna Prasad ◽  
Sridhar Chundi
Keyword(s):  
User Interface ◽  
Real Time ◽  
Low Cost ◽  
Raspberry Pi ◽  
Video Playback ◽  
Automotive Safety ◽  
Internet Connectivity ◽  
Connected Network ◽  
Commercial Off The Shelf ◽  
Commercial Applications

The objective of this paper is to create a low cost commercial off the shelf data analyzer for improving automotive safety and design a user interface infotainment system by using Raspberry Pi.  In this paper we propose Raspberry pi based application that monitor the vehicle ECUs through an OBD-II(On Board Diagnostics) interface, perform Diagnostics with DTCs (Diagnostics trouble codes). Infotainment system having functions such as audio and video playback, games, internet connectivity through either USB Wi-Fi dongles or USB Modems and dashboard camera operation. Raspberry Pi will transmit the data over Wi-Fi in real-time in xml format over Wi-Fi on a DHCP connected network.

scholarly journals Deep Learning-Based Portable Device for Audio Distress Signal Recognition in Urban Areas

2020 ◽  
Vol 10 (21) ◽  
pp. 7448
Author(s):  
Jorge Felipe Gaviria ◽  
Alejandra Escalante-Perez ◽  
Juan Camilo Castiblanco ◽  
Nicolas Vergara ◽  
Valentina Parra-Garces ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Urban Areas ◽  
Low Cost ◽  
Raspberry Pi ◽  
Automatic Identification ◽  
Signal Recognition ◽  
Main Challenge

Real-time automatic identification of audio distress signals in urban areas is a task that in a smart city can improve response times in emergency alert systems. The main challenge in this problem lies in finding a model that is able to accurately recognize these type of signals in the presence of background noise and allows for real-time processing. In this paper, we present the design of a portable and low-cost device for accurate audio distress signal recognition in real urban scenarios based on deep learning models. As real audio distress recordings in urban areas have not been collected and made publicly available so far, we first constructed a database where audios were recorded in urban areas using a low-cost microphone. Using this database, we trained a deep multi-headed 2D convolutional neural network that processed temporal and frequency features to accurately recognize audio distress signals in noisy environments with a significant performance improvement to other methods from the literature. Then, we deployed and assessed the trained convolutional neural network model on a Raspberry Pi that, along with the low-cost microphone, constituted a device for accurate real-time audio recognition. Source code and database are publicly available.

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