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39th SGAI International conference on Innovative Techniques and applications of Artificial Intelligence, AI 2019

39th SGAI International Conference on Innovative Techniques ...

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39th SGAI International conference on Innovative Techniques and applications of Artificial Intelligence, AI 2019

ISBN: (纸本)9783030348847

The proceedings contain 42 papers. The special focus in this conference is on Innovative Techniques and applications of Artificial Intelligence. The topics include: A Tsetlin Machine with Multigranular Clauses;Building Knowledge Intensive architectures for Heterogeneous NLP Workflows;WVD: A New Synthetic Dataset for Video-Based Violence Detection;evolving Prediction Models with Genetic Algorithm to Forecast Vehicle Volume in a Service Station (Best Application Paper);are You in Pain? Predicting Pain and Stiffness from Wearable sensor Activity Data;motif Discovery in Long Time Series: Classifying Phonocardiograms;exploring the Automatisation of Animal Health Surveillance Through Natural Language Processing;genMuse: An Evolutionary Creativity Enhancement Tool;Evolutionary Art with an EEG Fitness Function;purity Filtering: An Instance Selection Method for Support Vector Machines;a Multi-objective Design of In-Building Distributed Antenna System Using Evolutionary algorithms;investigation of Machine Learning Techniques in Forecasting of Blood Pressure Time Series Data;stock Index Forecasting Using Time Series Decomposition-Based and Machine Learning Models;effective Sub-Sequence-Based Dynamic Time Warping;developing a Catalogue of Explainability Methods to Support Expert and Non-expert Users;a Generic Model for End State Prediction of Business Processes Towards Target Compliance;an Investigation of the Accuracy of Real Time Speech Emotion Recognition;contributing Features-Based Schemes for Software Defect Prediction;induction Motor Inter-turn Short Circuit Fault Detection Using Efficient Feature Extraction for Machine Learning Based Fault Detectors;hybrid Feature Selection Method for Improving File Fragment Classification;towards Model-Based Reinforcement Learning for Industry-Near Environments;optimization of Silicon Tandem Solar Cells Using Artificial Neural Networks;analogical News Angles from Text Similarity.

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Tagging Wearable Accelerometers in Camera Frames through Information Translation between Vision sensors and Accelerometers 19

Tagging Wearable Accelerometers in Camera Frames through Inf...

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10th ACM/IEEE International conference on Cyber-Physical Systems (ICCPS)

作者： Akbari, Ali Liu, Peiming Mortazavi, Bobak J. Jafari, Roozbeh Texas A&M Univ Dept Biomed Engn College Stn TX 77843 USA Texas A&M Univ Dept Comp Sci & Engn College Stn TX USA Texas A&M Univ Dept Elect & Comp Engn College Stn TX USA

ISBN: (纸本)9781450362856

This paper presents a methodology to detect an object with an accelerometer potentially among many other moving objects in a camera scene. By matching sensor readings from a wearable accelerometer with analogous readings from a single camera or plurality of cameras, we detect instances of the same physical movement that both modalities capture. This has a wide range of potential applications in the cyber-physical systems domain such as identification, localization, and detecting context for activity recognition. We present an approach to project data from camera frames into accelerometer frames, where they share the same physical representation, allowing for comparing and determining similarities between the two modalities by using computational algorithms in the cyber world. This is challenging as depth is unknown when using a single 2D camera. We translate camera measurements into the acceleration physical domain and acquire an estimated depth, when the depth is not varying significantly during the motion. We model this translation as an optimization problem to find the optimal depth that maximizes the similarity between readings of the camera and accelerometer. Additionally, we discuss a potential solution with multiple cameras that works for arbitrary varying depth motions. Experimental results demonstrate that the system can detect matching between data stemming from physical movements observed by a wearable accelerometer and a single camera or plurality of cameras.

关键词： Signal fusion Camera Wearable sensors

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A Literature Study On Machine Learning fusion With IOT

A Literature Study On Machine Learning Fusion With IOT

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International conference on Trends in Electronics and Informatics (ICEI)

作者： Kirti Sharma Rainu Nandal Department of Computer Science and Engineering Maharshi Dayanand University Rohtak Haryana India

In the present era of rapidly growing mature technologies and their inter-connection with hardware devices and software applications plays a vital role for the emergence of different sensor devices that are inter-connected through internet in order to establish interaction with the physical objects in the world. The technology with the interconnection of different devices through internet is termed as internet of things (IOT). IOT generates massive amount of data with respect to various characteristics and qualities of data. Machine learning fusion with IOT ensures the pervasive development to extend the intelligence of the IOT devices and applications. The exposure of different smart IOT applications with machine learning helps in observation, systematic analysis, processing and smart usages of the large volume of data in different fields. Many industries are using the machine learning and more specifically, the Machine learning-as -a-Service (MLaaS) to exploit the IOT's potential. This present paper consists of machine learning basic introduction, machine learning algorithms, reviews of different researcher's study, various sensor devices and the various applications of machine learning algorithms with IOT. And the last section of the paper consists of discussion and conclusion.

关键词： Internet of Things Machine learning Machine learning algorithms Smart phones Security Cloud computing Support vector machines

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Single Pixel Spectral Image fusion with Side Information from a Grayscale sensor 1

Single Pixel Spectral Image Fusion with Side Information fro...

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1st IEEE Colombian conference on applications in Computational Intelligence (ColCACI)

作者： Jerez, Andres Garcia, Hans Arguello, Henry Univ Ind Santander Dept Phys Bucaramanga Colombia Univ Ind Santander Dept Elect Engn Bucaramanga Colombia Univ Ind Santander Dept Comp Sci Bucaramanga Colombia

ISBN: (纸本)9781538667408

Compressive spectral imaging (CSI) allows the acquisition of the spectral information of a three dimensional scene by using two dimensional coded projections. However, compressed sampling of information with simultaneously high spatial and high spectral resolution demands expensive high-resolution sensors. Single pixel imaging is an approach that has had a high impact in spectroscopy, due to its low-cost implementation compared to architectures with larger sensors. One of the main challenges in CSI is to obtain high quality image reconstructions using low-cost architectures. Recent works have been shown that image fusion using measurements from a CSI sensor based on side information leads to improvement in the quality of the fused image. This work proposes a methodology that combines the spectral information of a single pixel camera (SPC) and the side information of a grayscale sensor in order to improve the reconstruction quality of the spatio-spectral data cube. Simulations and experimental results for the proposed method are shown, and its performance is compared with respect to the traditional approach of upsampling the single pixel image reconstruction through bilinear interpolation.

关键词： Spectral imaging compressive sensing single pixel grayscale image data fusion side information

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Data-driven Representation Learning in Multimodal Feature fusion

Data-driven Representation Learning in Multimodal Feature Fu...

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作者： Song, Huan Arizona State University

学位级别：Ph.D.

Modern machine learning systems leverage data and features from multiple modalities to gain more predictive power. In most scenarios, the modalities are vastly different and the acquired data are heterogeneous in nature. Consequently, building highly effective fusion algorithms is at the core to achieve improved model robustness and inferencing performance. This dissertation focuses on the representation learning approaches as the fusion strategy. Specifically, the objective is to learn the shared latent representation which jointly exploit the structural information encoded in all modalities, such that a straightforward learning model can be adopted to obtain the prediction. We first consider sensor fusion, a typical multimodal fusion problem critical to building a pervasive computing platform. A systematic fusion technique is described to support both multiple sensors and descriptors for activity recognition. Targeted to learn the optimal combination of kernels, Multiple Kernel Learning (MKL) algorithms have been successfully applied to numerous fusion problems in computer vision etc. Utilizing the MKL formulation, next we describe an auto-context algorithm for learning image context via the fusion with low-level descriptors. Furthermore, a principled fusion algorithm using deep learning to optimize kernel machines is developed. By bridging deep architectures with kernel optimization, this approach leverages the benefits of both paradigms and is applied to a wide variety of fusion problems. In many real-world applications, the modalities exhibit highly specific data structures, such as time sequences and graphs, and consequently, special design of the learning architecture is needed. In order to improve the temporal modeling for multivariate sequences, we developed two architectures centered around attention models. A novel clinical time series analysis model is proposed for several critical problems in healthcare. Another model coupled with triplet ranking loss a

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New Approach to Secure Cluster Heads in Wireless sensor Networks

New Approach to Secure Cluster Heads in Wireless Sensor Netw...

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International conference on Advanced Computing and Communication Systems (ICACCS)

作者： R. Jyothi Nagaraj G. Cholli CSE Department Global Academy of Technology Bengaluru India ISE Department R. V. Colege of Engineering Bengaluru India

This Wireless sensor Network is a network of devices that communicates the information gathered from a monitored field through wireless links. Small size sensor nodes constitute wireless sensor networks. A sensor is a device that responds and detects some type of input from both the physical or environmental conditions, such as pressure, heat, light, etc. applications of wireless sensor networks include home automation, street lighting, military, healthcare and industrial process monitoring. As wireless sensor networks are distributed across large geographical area, these are vulnerable to various security threats. This affects the performance of the wireless sensor networks. The impact of security issues will become more critical if the network is used for mission-critical applications like tactical battlefield. In real life deployment scenarios, the probability of failure of nodes is more. As a result of resource constraints in the sensor nodes, traditional methods which involve large overhead computation and communication are not feasible in WSNs. Hence, design and deployment of secured WSNs is a challenging task. Attacks on WSNs include attack on confidentiality, integrity and availability. There are various types of architectures that are used to deploy WSNs. Some of them are data centric, hierarchical, location based, mobility based etc. This work discusses the security issue of hierarchical architecture and proposes a solution. In hierarchical architectures, sensor nodes are grouped to form clusters. Intra-cluster communication happens through cluster heads. Cluster heads also facilitate inter-cluster communication with other cluster heads. Aggregation of data generated by sensor nodes is done by cluster heads. Aggregated data also get transferred to base through multi-hop approach in most cases. Cluster heads are vulnerable to various malicious attacks and this greatly affects the performance of the wireless sensor network. The proposed solution identifies a

关键词： Wireless sensor networks Monitoring Clustering algorithms Security Genetic algorithms Head Topology

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Towards industrial assistance systems: Experiences of applying multi-sensor fusion in harsh environments 1

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3rd International conference on Physiological Computing Systems, PhyCS 2016, 4th International conference on Physiological Computing Systems, PhyCS 2017 and 5th International conference on Physiological Computing Systems, PhyCS 2018

作者： Haslgrübler, Michael Gollan, Bendikt Ferscha, Alois Institute of Pervasive Computing Johannes Kepler University Altenbergerstr. 69 Linz4040 Austria Research Studios Austria FG mbH Studio Pervasive Computing Applications Thurngasse 8 Vienna1090 Austria Pro2Future GmbH Altenbergerstr. 69 Linz4040 Austria

ISBN: (数字)9783030279509

ISBN: (纸本)9783030279493

With the availability of miniaturized low cost sensors and the general availability and easy applicability of algorithms for activity recognition, we investigate how various sensors can be deployed in a harsh environment, the industrial shop-floor. We review related work and provide an in-depth review of our own experiences were sensors wer used to enable recognition of activity, task progress and also mental and cognitive states of assembly workers. The recognition process is based on stationary (RGBD cameras, stereo vision depth sensors) and wearable devices (IMUs, GSR, ECG, mobile eye tracker). We describe in detail the used sensors, the challenges of fusing the data from these various sources together in real-time and how to interpret that data semantically. © Springer Nature Switzerland AG 2019.

关键词： Wearable sensors

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Miniaturized Low-Power Wearable System for Human Motion Tacking Incorporating Monocular Camera and Inertial sensor Data fusion for Health applications

Miniaturized Low-Power Wearable System for Human Motion Tack...

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Smart Systems Integration;13th International conference and Exhibition on Integration Issues of Miniaturized Systems

作者： Mariusz P. Wilk Brendan O'Flynn Tyndall National Institute UCC

ISBN: (纸本)9783800749195

Human motion tracking systems play an important role in many application spaces where the 3D pose of the body, or its parts, need to be tracked over time, e.g. fitness, rehabilitation, etc. While it is considered a solved problem in expensive infrastructure-intensive setups, it remains an active research area in low-cost and low-power applications. In this work, we propose our wearable motion tracking system that leverages the advances in the state-of-the-art in system miniaturization and integration. Our proposed system integrates a monocular camera and an inertial motion sensor (IMU) in a single wearable unit. Moreover, it can perform a six degree-of-freedom (DOF) pose detection and tracking with our novel data fusion algorithms and a smart coupling with the ambient environment.

关键词： monocular camera Human motion Wearable LOW POWER sensor data fusion ambient environment Data fusion TRACKING SYSTEM Problem solving

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Kinect 3D Reconstruction fusion Mapping with C# Algorithm by RGB-D sensor

Kinect 3D Reconstruction Fusion Mapping with C# Algorithm by...

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International conference on Advances in Science, Engineering and Robotics Technology

作者： Ashikur Rahman Shoumik Rahman Mehedy Rashedur M. Rahman Dept. of Computer Science and Engineering Brac University Dept. of Electrical and Computer Engineering North South University

ISBN: (纸本)9781728134468

The fundamental objective of this paper is to describe the applications of Microsoft Kinect sensor with robust algorithm and solution to present 3-D surface reconstruction mapping with different measurements. In the line of sensor operations, there could be two possible categories into 3-D reconstructions such as exorbitant laser scanner and other is by taking photos from various angles. In this paper, we have used indoor environmental scenes by depth sensor camera Microsoft Kinect. Data Pre-operations, sensor posture evaluations, computational fusion of the depth sensor data and 3-D surface extractions are the four major part of this experiment. The algorithm of this paper has been built uniquely with C# programming.

关键词： Microsoft kinect sensor 3D reconstruction Depth-sensor camera Image processing algorithms three-dimensional displays algorithms Image processing game player sensors laser scanners three-dimensional surface paper Microsoft

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Polarimetric Imaging: System architectures and Trade-Offs 15

Polarimetric Imaging: System Architectures and Trade-Offs

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conference on Electro-Optical and Infrared Systems - Technology and applications XV

作者： Hickman, Duncan L. Smith, Moira I. Kim, Kyung Su Choi, Hyun-Jin Tektonex Ltd Long View Argyll Rd Kilcreggan Scotland Agcy Def Dev Daejeon South Korea

ISBN: (数字)9781510621749

ISBN: (纸本)9781510621749

Polarimetric sensing provides additional scene information which can be used to enhance the target detection and recognition performance of an imaging system. Such information is particularly valuable in the discrimination of weak target signatures from their surroundings and, as such, is attracting a growing interest for future military and surveillance applications. However, the extraction of polarisation information adds complexity in terms of the physical sensor design and the required data processing. Imaging polarimeters typically require four images to be captured of the same scene in different polarisation orientations through either time or spatial division techniques. These sensor architectures introduce system performance constraints in terms of temporal and spatial resolution as well as the attendant degradations associated with the use of additional optical components. Issues associated with the physical envelope, operational robustness, and cost must also be considered. In terms of the processing of the polarimetric data, accurate registration and calibration is required to extract small polarisation signatures which are typically found in natural scenes. The polarimetric image data must then be processed using an image fusion or data fusion method which introduces further demands on the software design and system processor. For some applications, these limitations are acceptable relative to the polarimetric gain, whilst in others a conventional imaging sensor may offer a better overall solution. Consequently, it is important that a trade-off analysis is undertaken which evaluates the realistic performance gains with respect to the implementation and cost issues.

关键词： Polarimetric imaging systems design trade-off studies polarimetric image processing image fusion weak target detection concealed and cameoflaged targets

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