scholarly journals Fast Object Detection Using Dimensional Based Features for Public Street Environments

Smart Cities ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. 93-111 ◽  
Author(s):  
Ivan Matveev ◽  
Kirill Karpov ◽  
Ingo Chmielewski ◽  
Eduard Siemens ◽  
Aleksey Yurchenko
Keyword(s):  
Low Power ◽  
Near Infrared ◽  
Moving Objects ◽  
Infrared Camera ◽  
Machine Learning Algorithms ◽  
Frame Rate ◽  
Raspberry Pi ◽  
Real World Data ◽  
Iot Devices

Modern object recognition algorithms have very high precision. At the same time, they require high computational power. Thus, widely used low-power IoT devices, which gather a substantial amount of data, cannot directly apply the corresponding machine learning algorithms to process it due to the lack of local computational resources. A method for fast detection and classification of moving objects for low-power single-board computers is shown in this paper. The developed algorithm uses geometric parameters of an object as well as scene-related parameters as features for classification. The extraction and classification of these features is a relatively simple process which can be executed by low-power IoT devices. The algorithm aims to recognize the most common objects in the street environment, e.g., pedestrians, cyclists, and cars. The algorithm can be applied in the dark environment by processing images from a near-infrared camera. The method has been tested on both synthetic virtual scenes and real-world data. The research showed that a low-performance computing system, such as a Raspberry Pi 3, is able to classify objects with acceptable frame rate and accuracy.

scholarly journals Employing a Machine Learning Approach to Detect Combined Internet of Things Attacks against Two Objective Functions Using a Novel Dataset

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
John Foley ◽  
Naghmeh Moradpoor ◽  
Henry Ochenyi
Keyword(s):  
Machine Learning ◽  
Low Power ◽  
Vulnerability Analysis ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Lossy Networks ◽  
Global Networks ◽  
Machine Learning Approach ◽  
Iot Devices ◽  
Network Metrics

One of the important features of routing protocol for low-power and lossy networks (RPLs) is objective function (OF). OF influences an IoT network in terms of routing strategies and network topology. On the contrary, detecting a combination of attacks against OFs is a cutting-edge technology that will become a necessity as next generation low-power wireless networks continue to be exploited as they grow rapidly. However, current literature lacks study on vulnerability analysis of OFs particularly in terms of combined attacks. Furthermore, machine learning is a promising solution for the global networks of IoT devices in terms of analysing their ever-growing generated data and predicting cyberattacks against such devices. Therefore, in this paper, we study the vulnerability analysis of two popular OFs of RPL to detect combined attacks against them using machine learning algorithms through different simulated scenarios. For this, we created a novel IoT dataset based on power and network metrics, which is deployed as part of an RPL IDS/IPS solution to enhance information security. Addressing the captured results, our machine learning approach is successful in detecting combined attacks against two popular OFs of RPL based on the power and network metrics in which MLP and RF algorithms are the most successful classifier deployment for single and ensemble models.

scholarly journals Euclid: Identification of asteroid streaks in simulated images using StreakDet software

2020 ◽  
Vol 644 ◽  
pp. A35
Author(s):  
M. Pöntinen ◽  
M. Granvik ◽  
A. A. Nucita ◽  
L. Conversi ◽  
B. Altieri ◽  
...  
Keyword(s):  
Near Infrared ◽  
Moving Objects ◽  
Infrared Camera ◽  
False Positives ◽  
Apparent Magnitude ◽  
Good Tool ◽  
Infrared Spectrometer ◽  
The Cost ◽  
Near Infrared Spectrometer ◽  
Simulated Images

Context. The ESA Euclid space telescope could observe up to 150 000 asteroids as a side product of its primary cosmological mission. Asteroids appear as trailed sources, that is streaks, in the images. Owing to the survey area of 15 000 square degrees and the number of sources, automated methods have to be used to find them. Euclid is equipped with a visible camera, VIS (VISual imager), and a near-infrared camera, NISP (Near-Infrared Spectrometer and Photometer), with three filters. Aims. We aim to develop a pipeline to detect fast-moving objects in Euclid images, with both high completeness and high purity. Methods. We tested the StreakDet software to find asteroids from simulated Euclid images. We optimized the parameters of StreakDet to maximize completeness, and developed a post-processing algorithm to improve the purity of the sample of detected sources by removing false-positive detections. Results. StreakDet finds 96.9% of the synthetic asteroid streaks with apparent magnitudes brighter than 23rd magnitude and streak lengths longer than 15 pixels (10 arcsec h−1), but this comes at the cost of finding a high number of false positives. The number of false positives can be radically reduced with multi-streak analysis, which utilizes all four dithers obtained by Euclid. Conclusions. StreakDet is a good tool for identifying asteroids in Euclid images, but there is still room for improvement, in particular, for finding short (less than 13 pixels, corresponding to 8 arcsec h−1) and/or faint streaks (fainter than the apparent magnitude of 23).

scholarly journals Design and Evaluation of a New Machine Learning Framework for IoT and Embedded Devices

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 600
Author(s):  
Gianluca Cornetta ◽  
Abdellah Touhafi
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
High Performance ◽  
Low Cost ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Smart Devices ◽  
Raspberry Pi ◽  
Embedded Devices ◽  
Iot Devices

Low-cost, high-performance embedded devices are proliferating and a plethora of new platforms are available on the market. Some of them either have embedded GPUs or the possibility to be connected to external Machine Learning (ML) algorithm hardware accelerators. These enhanced hardware features enable new applications in which AI-powered smart objects can effectively and pervasively run in real-time distributed ML algorithms, shifting part of the raw data analysis and processing from cloud or edge to the device itself. In such context, Artificial Intelligence (AI) can be considered as the backbone of the next generation of Internet of the Things (IoT) devices, which will no longer merely be data collectors and forwarders, but really “smart” devices with built-in data wrangling and data analysis features that leverage lightweight machine learning algorithms to make autonomous decisions on the field. This work thoroughly reviews and analyses the most popular ML algorithms, with particular emphasis on those that are more suitable to run on resource-constrained embedded devices. In addition, several machine learning algorithms have been built on top of a custom multi-dimensional array library. The designed framework has been evaluated and its performance stressed on Raspberry Pi III- and IV-embedded computers.

Classification of Transition Human Activities in IoT Environments via Memory-Based Neural Networks

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 409
Author(s):  
Giovanni Acampora ◽  
Gianluca Minopoli ◽  
Francesco Musella ◽  
Mariacarla Staffa
Keyword(s):  
Activity Recognition ◽  
Human Activities ◽  
Short Term Memory ◽  
Recognition Task ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Surveillance Systems ◽  
Research Activities ◽  
Iot Devices

Human activity recognition is a crucial task in several modern applications based on the Internet of Things (IoT) paradigm, from the design of intelligent video surveillance systems to the development of elderly robot assistants. Recently, machine learning algorithms have been strongly investigated to improve the recognition task of human activities. Though, in spite of these research activities, there are not so many studies focusing on the efficient recognition of complex human activities, namely transitional activities, and there is no research aimed at evaluating the effects of noise in data used to train algorithms. In this paper, we bridge this gap by introducing an innovative activity recognition system based on a neural classifier endowed with memory, able to optimize the performance of the classification of both transitional and non-transitional human activities. The system recognizes human activities from unobtrusive IoT devices (such as the accelerometer and gyroscope) integrated in commonly used smartphones. The main peculiarity provided by the proposed system is related to the exploitation of a neural network extended with short-term memory information about the previous activities’ features. The experimental study proves the reliability of the proposed system in terms of accuracy with respect to state-of-the-art classifiers and the robustness of the proposed framework with respect to noise in data.

scholarly journals Semi-automatic decision-making process in histopathological specimens from Barrett’s carcinoma patients using hyperspectral imaging (HSI)

2020 ◽  
Vol 6 (3) ◽  
pp. 261-263
Author(s):  
Marianne Maktabi ◽  
Hannes Köhler ◽  
Claire Chalopin ◽  
Thomas Neumuth ◽  
Yannis Wichmann ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Cancer Diagnostics ◽  
Machine Learning Algorithms ◽  
Infrared Range ◽  
Decision Making Process ◽  
Multi Layer Perceptron ◽  
Classification Methods ◽  
Near Infrared Range

AbstractDiscrimination of malignant and non-malignant cells of histopathologic specimens is a key step in cancer diagnostics. Hyperspectral Imaging (HSI) allows the acquisition of spectra in the visual and near-infrared range (500-1000nm). HSI can support the identification and classification of cancer cells using machine learning algorithms. In this work, we tested four classification methods on histopathological slides of esophageal adenocarcinoma. The best results were achieved with a Multi-Layer Perceptron. Sensitivity and F1-Score values of 90% were obtained.

scholarly journals Patient journey through cases of depression from claims database using machine learning algorithms

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0247059
Author(s):  
Yoshitake Kitanishi ◽  
Masakazu Fujiwara ◽  
Bruce Binkowitz
Keyword(s):  
Machine Learning ◽  
Association Analysis ◽  
Real World ◽  
Machine Learning Algorithms ◽  
Topological Data Analysis ◽  
Real World Data ◽  
Patient Journey ◽  
World Data ◽  
Medicine Research

Health insurance and acute hospital-based claims have recently become available as real-world data after marketing in Japan and, thus, classification and prediction using the machine learning approach can be applied to them. However, the methodology used for the analysis of real-world data has been hitherto under debate and research on visualizing the patient journey is still inconclusive. So far, to classify diseases based on medical histories and patient demographic background and to predict the patient prognosis for each disease, the correlation structure of real-world data has been estimated by machine learning. Therefore, we applied association analysis to real-world data to consider a combination of disease events as the patient journey for depression diagnoses. However, association analysis makes it difficult to interpret multiple outcome measures simultaneously and comprehensively. To address this issue, we applied the Topological Data Analysis (TDA) Mapper to sequentially interpret multiple indices, thus obtaining a visual classification of the diseases commonly associated with depression. Under this approach, the visual and continuous classification of related diseases may contribute to precision medicine research and can help pharmaceutical companies provide appropriate personalized medical care.

scholarly journals Characterization of the C-RED 2: a high-frame rate near-infrared camera

2019 ◽  
Vol 6 (01) ◽  
pp. 1
Author(s):  
Rose K. Gibson ◽  
Rebecca Oppenheimer ◽  
Christopher T. Matthews ◽  
Gautam Vasisht
Keyword(s):  
Near Infrared ◽  
Infrared Camera ◽  
Frame Rate ◽  
High Frame Rate

scholarly journals FUSION OF 3D POINT CLOUDS WITH TIR IMAGES FOR INDOOR SCENE RECONSTRUCTION

2018 ◽  
Vol XLII-1 ◽  
pp. 189-194 ◽  
Author(s):  
L. Hoegner ◽  
T. Abmayr ◽  
D. Tosic ◽  
S. Turzer ◽  
U. Stilla
Keyword(s):  
Point Cloud ◽  
Near Infrared ◽  
Moving Objects ◽  
Infrared Camera ◽  
Point Clouds ◽  
Thermal Infrared ◽  
Active Sensor ◽  
3D Point Clouds ◽  
3D Data ◽  
3D Scene

<p><strong>Abstract.</strong> Obtaining accurate 3D descriptions in the thermal infrared (TIR) is a quite challenging task due to the low geometric resolutions of TIR cameras and the low number of strong features in TIR images. Combining the radiometric information of the thermal infrared with 3D data from another sensor is able to overcome most of the limitations in the 3D geometric accuracy. In case of dynamic scenes with moving objects or a moving sensor system, a combination with RGB cameras and profile laserscanners is suitable. As a laserscanner is an active sensor in the visible red or near infrared (NIR) and the thermal infrared camera captures the radiation emitted by the objects in the observed scene, the combination of these two sensors for close range applications are independent from external illumination or textures in the scene. This contribution focusses on the fusion of point clouds from terrestrial laserscanners and RGB cameras with images from thermal infrared mounted together on a robot for indoor 3D reconstruction. The system is geometrical calibrated including the lever arm between the different sensors. As the field of view is different for the sensors, the different sensors record the same scene points not exactly at the same time. Thus, the 3D scene points of the laserscanner and the photogrammetric point cloud from the RGB camera have to be synchronized before point cloud fusion and adding the thermal channel to the 3D points.</p>

scholarly journals Remote Diagnosis of the Patient through IOT and Virtual Reality, Classification of the Cloud Data Using ANN

2021 ◽  
Vol 11 (1) ◽  
pp. 6025-6034
Author(s):  
V. Alagammai Nachiappan ◽  
Raj esh ◽  
Rajalakshmi Devaraj
Keyword(s):  
Virtual Reality ◽  
Current Trend ◽  
Wearable Devices ◽  
Raspberry Pi ◽  
Cloud Data ◽  
Future Technology ◽  
Advanced Technologies ◽  
Iot Devices ◽  
Self Learning

Telemedicine was an existing field, but the current situation its becoming a more important necessity in the health care industry.my major aim is To increase the reliability of the online diagnosis using IoT and virtual Reality for the future with help of advanced technologies. Bridge between the patients and doctors. Patients may have wearable devices with AR glass, the measured data will be send to the raspberry pi based router device which is having the Node Red Software for connecting N- no of patients easily and also control the devices remotely based on the self-learning algorithms. All the information can be classified based on type of diseases and classified based on artificial neural network-based algorithms, the information is passed to doctors. Doctor may also have device with wearable Glass, with patient information and details will be displayed on the AR glass. So, we can connect N- of Patients and N- doctors with this technology also sharing the information through the cloud and IOT devices, which will help for the current trend and future technology for the society.

Portable multispectral imaging system based on Raspberry Pi

Sensor Review ◽  
2017 ◽  
Vol 37 (3) ◽  
pp. 322-329 ◽  
Author(s):  
Nuria Lopez-Ruiz ◽  
Fernando Granados-Ortega ◽  
Miguel Angel Carvajal ◽  
Antonio Martinez-Olmos
Keyword(s):  
Near Infrared ◽  
Multispectral Imaging ◽  
Imaging System ◽  
Light Emitting Diode ◽  
Low Cost ◽  
Image Sensor ◽  
Raspberry Pi ◽  
Image Processing Algorithm ◽  
Content Type

Purpose In this work, the authors aim to present a compact low-cost and portable spectral imaging system for general purposes. The developed system provides information that can be used for a fast in situ identification and classification of samples based on the analysis of captured images. The connectivity of the instrument allows a deeper analysis of the images in an external computer. Design/methodology/approach The wavelength selection of the system is carried out by light multiplexing through a light-emitting diode panel where eight wavelengths covering the spectrum from ultraviolet (UV) to near-infrared region (NIR) have been included. The image sensor used is a red green blue – infrared (RGB-IR) micro-camera controlled by a Raspberry Pi board where a basic image processing algorithm has been programmed. It allows the visualization in an integrated display of the reflectance and the histogram of the images at each wavelength, including UV and NIRs. Findings The prototype has been tested by analyzing several samples in a variety of applications such as detection of damaged, over-ripe and sprayed fruit, classification of different type of plastic materials and determination of properties of water. Originality/value The designed system presents some advantages as being non-expensive and portable in comparison to other multispectral imaging systems. The low-cost and size of the camera module connected to the Raspberry Pi provides a compact instrument for general purposes.

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