The role of big data analytics in industrial Internet of Things

Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational- and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT.We identify and discuss the indispensable challenges that remain to be addressed as future research directions as well

The role of big data analytics in industrial internet of things

Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational- and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT. We identify and discuss the indispensable challenges that remain to be addressed, serving as future research directions. © 2019 Elsevier B.V

Neural simulation pipeline: Enabling container-based simulations on-premise and in public clouds

In this study, we explore the simulation setup in computational neuroscience. We use GENESIS, a general purpose simulation engine for sub-cellular components and biochemical reactions, realistic neuron models, large neural networks, and system-level models. GENESIS supports developing and running computer simulations but leaves a gap for setting up today's larger and more complex models. The field of realistic models of brain networks has overgrown the simplicity of earliest models. The challenges include managing the complexity of software dependencies and various models, setting up model parameter values, storing the input parameters alongside the results, and providing execution statistics. Moreover, in the high performance computing (HPC) context, public cloud resources are becoming an alternative to the expensive on-premises clusters. We present Neural Simulation Pipeline (NSP), which facilitates the large-scale computer simulations and their deployment to multiple computing infrastructures using the infrastructure as the code (IaC) containerization approach. The authors demonstrate the effectiveness of NSP in a pattern recognition task programmed with GENESIS, through a custom-built visual system, called RetNet(8 × 5,1) that uses biologically plausible Hodgkin–Huxley spiking neurons. We evaluate the pipeline by performing 54 simulations executed on-premise, at the Hasso Plattner Institute's (HPI) Future Service-Oriented Computing (SOC) Lab, and through the Amazon Web Services (AWS), the biggest public cloud service provider in the world. We report on the non-containerized and containerized execution with Docker, as well as present the cost per simulation in AWS. The results show that our neural simulation pipeline can reduce entry barriers to neural simulations, making them more practical and cost-effective

ARACHNE: A neural-neuroglial network builder with remotely controlled parallel computing

Creating and running realistic models of neural networks has hitherto been a task for computing professionals rather than experimental neuroscientists. This is mainly because such networks usually engage substantial computational resources, the handling of which requires specific programing skills. Here we put forward a newly developed simulation environment ARACHNE: it enables an investigator to build and explore cellular networks of arbitrary biophysical and architectural complexity using the logic of NEURON and a simple interface on a local computer or a mobile device. The interface can control, through the internet, an optimized computational kernel installed on a remote computer cluster. ARACHNE can combine neuronal (wired) and astroglial (extracellular volume-transmission driven) network types and adopt realistic cell models from the NEURON library. The program and documentation (current version) are available at GitHub repository https://github.com/LeonidSavtchenko/Arachne under the MIT License (MIT)

NetPyNE, a tool for data-driven multiscale modeling of brain circuits

Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, for example connectivity rules, to create millions of cell-to-cell connections. NetPyNE then enables users to generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis – connectivity matrices, voltage traces, spike raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing standardized formats (NeuroML and SONATA). NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate brain regions and phenomena

NetPyNE, a tool for data-driven multiscale modeling of brain circuits.

Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, for example connectivity rules, to create millions of cell-to-cell connections. NetPyNE then enables users to generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis - connectivity matrices, voltage traces, spike raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing standardized formats (NeuroML and SONATA). NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate brain regions and phenomena

Inimeste tuvastamine ning kauguse hindamine kasutades kaamerat ning YOLOv3 tehisnärvivõrku

Inimestega vähemalt samal tasemel keskkonnast aru saamine masinate poolt oleks kasulik paljudes domeenides. Mitmed erinevad sensored aitavad selle ülesande juures, enim on kasutatud kaameraid. Objektide tuvastamine on tähtis osa keskkonnast aru saamisel. Selle täpsus on viimasel ajal palju paranenud tänu arenenud masinõppe meetoditele nimega konvolutsioonilised närvivõrgud (CNN), mida treenitakse kasutades märgendatud kaamerapilte. Monokulaarkaamerapilt sisaldab 2D infot, kuid ei sisalda sügavusinfot. Teisalt, sügavusinfo on tähtis näiteks isesõitvate autode domeenis. Inimeste ohutus tuleb tagada näiteks töötades autonoomsete masinate läheduses või kui jalakäija ületab teed autonoomse sõiduki eest. Antud töös uuritakse võimalust, kuidas tuvastada inimesi ning hinnata nende kaugusi samaaegselt, kasutades RGB kaamerat, eesmärgiga kasutada seda autonoomseks sõitmiseks maastikul. Selleks täiustatakse hetkel parimat objektide tuvastamise konvolutsioonilist närvivõrku YOLOv3 (ingl k. You Only Look Once). Selle töö väliselt on simulatsioonitarkvaradega AirSim ning Unreal Engine loodud lumine metsamaastik koos inimestega erinevates kehapoosides. YOLOv3 närvivõrgu treenimiseks võeti simulatsioonist välja vajalikud andmed, kasutades skripte. Lisaks muudeti närvivõrku, et lisaks inimese asukohta tuvastavale piirikastile väljastataks ka inimese kauguse ennustus. Antud töö tulemuseks on mudel, mille ruutkesmine viga RMSE (ingl k. Root Mean Square Error) on 2.99m objektidele kuni 50m kaugusel, säilitades samaaegselt originaalse närvivõrgu inimeste tuvastamise täpsuse. Võrreldavate meetodite RMSE veaks leiti 4.26m (teist andmestikku kasutades) ja 4.79m (selles töös kasutatud andmestikul), mis vastavalt kasutavad kahte eraldiseisvat närvivõrku ning LASSO meetodit. See näitab suurt parenemist võrreldes teiste meetoditega. Edasisteks eesmärkideks on meetodi treenimine ning testimine päris maailmast kogutud andmetega, et näha, kas see üldistub ka sellistele keskkondadele.Making machines perceive environment better or at least as well as humans would be beneficial in lots of domains. Different sensors aid in this, most widely used of which is monocular camera. Object detection is a major part of environment perception and its accuracy has greatly improved in the last few years thanks to advanced machine learning methods called convolutional neural networks (CNN) that are trained on many labelled images. Monocular camera image contains two dimensional information, but contains no depth information of the scene. On the other hand, depth information of objects is important in a lot of areas related to autonomous driving, e.g. working next to an automated machine, pedestrian crossing a road in front of an autonomous vehicle, etc. This thesis presents an approach to detect humans and to predict their distance from RGB camera for off-road autonomous driving. This is done by improving YOLO (You Only Look Once) v3[1], a state-of-the-art object detection CNN. Outside of this thesis, an off-road scene depicting a snowy forest with humans in different body poses was simulated using AirSim and Unreal Engine. Data for training YOLOv3 neural network was extracted from there using custom scripts. Also, network was modified to not only predict humans and their bounding boxes, but also their distance from camera. RMSE of 2.99m for objects with distances up to 50m was achieved, while maintaining similar detection accuracy to the original network. Comparable methods using two neural networks and a LASSO model gave 4.26m (in an alternative dataset) and 4.79m (with dataset used is this work) RMSE respectively, showing a huge improvement over the baselines. Future work includes experiments with real-world data to see if the proposed approach generalizes to other environments