3D-LIDAR Based Object Detection and Tracking on the Edge of IoT for Railway Level Crossing

Object detection is an essential technology for surveillance systems, particularly in areas with a high risk of accidents such as railway level crossings. To prevent future collisions, the system must detect and track any object that passes through the monitored area with high accuracy, and this process must be performed fulfilling real-time specifications. In this work, an edge IoT HW platform implementation capable of detecting and tracking objects in a railway level crossing scenario is proposed. The response of the system has to be calculated and sent from the proposed IoT platform to the train, so as to trigger a warning action to avoid a possible collision. The system uses a low-resolution 3D 16-channel LIDAR as a sensor that provides an accurate point cloud map with a large amount of data. The element used to process the information is a custom embedded edge platform with low computing resources and low-power consumption. This processing element is located as close as possible to the sensor, where data is generated to improve latency, privacy, and avoid bandwidth limitations, compared to performing processing in the cloud. Additionally, lightweight object detection and tracking algorithm is proposed in this work to process a large amount of information provided by the LIDAR, allowing to reach real-time specifications. The proposed method is validated quantitatively by carrying out implementation on a car road, emulating a railway level crossing

Fear recognition for women using a reduced set of physiological signals

This article belongs to the Section Biomedical Sensors.Emotion recognition is benefitting from the latest research into physiological monitoring and wireless communications, among other remarkable achievements. These technologies can indeed provide solutions to protect vulnerable people in scenarios such as personal assaults, the abuse of children or the elderly, gender violence or sexual aggression. Cyberphysical systems using smart sensors, artificial intelligence and wearable and inconspicuous devices can serve as bodyguards to detect these risky situations (through fear-related emotion detection) and automatically trigger a protection protocol. As expected, these systems should be trained and customized for each user to ensure the best possible performance, which undoubtedly requires a gender perspective. This paper presents a specialized fear recognition system for women based on a reduced set of physiological signals. The architecture proposed is characterized by the usage of three physiological sensors, lightweight binary classification and the conjunction of linear (temporal and frequency) and non-linear features. Moreover, a binary fear mapping strategy between dimensional and discrete emotional information based on emotional self-report data is implemented to avoid emotional bias. The architecture is evaluated using a public multi-modal physiological dataset with two approaches (subject-dependent and subject-independent models) focusing on the female participants. As a result, the proposal outperforms the state-of-the-art in fear recognition, achieving a recognition rate of up to 96.33% for the subject-dependent model.This activity is partially supported by Community of Madrid in the pluri-annual agreement with Universidad Carlos III de Madrid, in the line of action "Excelence with the University Faculty", V Regional Plan of Scientific Research and Technology Innovation 2016-2020, and by the Community of Madrid Region Government under the Synergic Program: EMPATIA-CM, Y2018/TCS-5046

A Machine Learning-Based Methodology for in-Process Fluid Characterization With Photonic Sensors

This paper proposes a novel methodology for run-time fluid characterization through the application of machine learning techniques. It aims to integrate sophisticated multi-dimensional photonic sensors inside the chemical processes, following the Industry 4.0 paradigm. Currently, this analysis is done offline in laboratory environments, which increases the decision-making times. As an alternative, the proposed method tunes the spectralbased machine learning solutions to the requirements of each case enabling the integration of compound detection systems at the computing edge. It includes a novel feature selection strategy that combines filters and wrappers, namely Wavelength-based Hybrid Feature Selection, to select the relevant information of the spectrum (i.e., the relevant wavelengths). This technique allows providing different trade-offs involving the spectrum dimensionality, complexity, and detection quality. In terms of execution time, the provided solutions outperform the state-of-the-art up to 61.78 times using less than 99% of the wavelengths while maintaining the same detection accuracy. Also, these solutions were tested in a real-world edge platform, decreasing up to 68.57 times the energy consumption for an ethanol detection use case

Towards an Machine Learning-based Edge Computing Oriented Monitoring System for the Desert Border Surveillance Use Case

The design of border surveillance systems is critical for most countries in the world, having each border specific needs. This paper focuses on an Internet of Things oriented surveillance system to be deployed in the Sahara Desert, which is composed of many unattended fixed platforms, where the nodes in the edge have a Forward Looking InfraRed (FLIR) camera for field monitoring. To reduce communications and decentralise the processing, IR images should be fully computed on the edge by an Automated Target Recognition (ATR) algorithm, tracking and identifying targets of interest. As edge nodes are constrained in energy and computing capacity, this work proposes two ATR systems to be executed in low-power microprocessors. Both proposals are based on using Bag-of-Features for feature extraction and a supervised algorithm for classification, both differing in segmenting the InfraRed image in regions of interest or working directly with the whole image. Both proposals are successfully applied to infer about a dataset generated to this end, getting a trade-off between computing cost and detection capacity. As a result, the authors obtained a detection capacity of up to 97% and a frame rate of up to 5.71 and 59.17, running locally on the edge device and the workstation, respectively.

PENGARUH MEDIA PEMBELAJARAN MIND MAPPING TERHADAP MINAT BELAJAR PESERTA DIDIK PADA PELAJARAN EKONOMI KELAS XI IPS 1 di SMA NEGERI 27 BANDUNG

Judul penelitian ini adalah pengaruh media pembelajaran mind mapping terhadap minat belajar peserta didik pada pelajaran ekonomi kelas XI IPS 1 di SMA Negeri 27 Bandung tahun ajaran 2016-2017. Berdasarkan tinjauan langsung di SMA Negeri 27 Bandung kelas XI IPS 1, Pembelajaran konvensional (ceramah) hanya membuat peserta didik kurang aktif dan malas dalam menerima materi yang ada, karena pembelajaran hanya terpusat pada apa yang disampaikan sehingga tidak dapat dimengerti secara maksimal. Tujuan dari penelitian ini adalah: (1) Mengetahui penggunaan media mind mapping pada mata pelajaran ekonomi kelas XI IPS 1 di SMA Negeri 27 Bandung. (2) Mengetahui minat belajar peserta didik pada mata pelajaran ekonomi kelas XI IPS 1 di SMA Negeri 27 Bandung. (3) Mengetahui seberapa besar pengaruh media mind mapping terhadap minat belajar peserta didik pada mata pelajaran ekonomi kelas XI IPS 1 di SMA Negeri 27 Bandung. Metode yang digunakan dalam penelitian ini adalah survei. Teknik pengumpulan data yang digunakan yaitu observasi dan angket dengan teknik pengolahan data uji validitas, uji reliabilitas, uji normalitas data, analisis regresi linier sederhana dan koefisien determinasi, Hipotesis penelitian berbunyi “Terdapat pengaruh antara media pembelajaran mind mapping (X) terhadap minat belajar peserta didik (Y) pada mata pelajaran ekonomi. Hasil penelitian ini adalah terdapat pengaruh antara variabel X dan Variabel Y sebesar 0,336 atau 33,60%. Untuk mengetahui hubungan fungsional antara variabel X dan variabel Y maka digunakan analisis regresi linier sederhana dengan hasil perhitungan sebagai berikut : Y = 9.915 + 0.537 X artinya bahwa setiap media pembelajaran mind mapping bertambah 9,915 maka minat belajar meningkat sebesar 0,537. Untuk mengetahui seberapa besar pengaruh variabel X (media pembelajaran mind mapping) terhadap variabel Y (minat belajar), maka digunakan koefisien determinasi dengan hasil perhitungan sebesar 33,60% maka sebagian lainnya ditentukan oleh faktor lain yang tidak diteliti. Kesimpulan hipotesis penelitian dapat diterima, sebagai akhir penelitian penulis menyampaikan saran kepada guru agar sebaiknya guru menggunakan variasi media pembelajaran yang menarik berkaitan dengan materi pelajaran yang akan disampaikan, karena cara ini dapat membuat peserta didik aktif dan kreatif untuk belajar. Kepada para praktisi atau peneliti lain di bidang pendidikan dapat melakukan penelitian serupa dengan media pembelajaran dan metode yang berbeda agar diperoleh berbagai alternatif untuk peningkatan pemahaman pembelajaran pada pokok bahasan selanjutnya dan kepada pihak sekolah sebagai bahan kajian bagi sekolah dan menyarankan guru untuk menggunakan media pembelajaran yang mampu meningkatkan prestasi belajar peserta didik untuk mendukung pendekatan saintifik dan media pembelajaran yang baik. Kata Kunci : media pembelajaran mind mapping, minat Belajar

Fine-grained parallelization of fitness functions in bioinformatics optimization problems: gene selection for cancer classification and biclustering of gene expression data

ANTECEDENTES: las metaheurísticas se utilizan ampliamente para resolver grandes problemas de optimización combinatoria en bioinformática debido al enorme conjunto de posibles soluciones. Dos problemas representativos son la selección de genes para la clasificación del cáncer y el agrupamiento de los datos de expresión génica. En la mayoría de los casos, estas metaheurísticas, así como otras técnicas no lineales, aplican una función de adecuación a cada solución posible con una población de tamaño limitado, y ese paso involucra latencias más altas que otras partes de los algoritmos, lo cual es la razón por la cual el tiempo de ejecución de las aplicaciones dependerá principalmente del tiempo de ejecución de la función de aptitud. Además, es habitual encontrar formulaciones aritméticas de punto flotante para las funciones de fitness. De esta manera, una paralelización cuidadosa de estas funciones utilizando la tecnología de hardware reconfigurable acelerará el cálculo, especialmente si se aplican en paralelo a varias soluciones de la población. RESULTADOS: una paralelización de grano fino de dos funciones de aptitud de punto flotante de diferentes complejidades y características involucradas en el biclustering de los datos de expresión génica y la selección de genes para la clasificación del cáncer permitió obtener mayores aceleraciones y cómputos de potencia reducida con respecto a los microprocesadores habituales. CONCLUSIONES: Los resultados muestran mejores rendimientos utilizando tecnología de hardware reconfigurable en lugar de los microprocesadores habituales, en términos de tiempo de consumo y consumo de energía, no solo debido a la paralelización de las operaciones aritméticas, sino también gracias a la evaluación de aptitud concurrente para varios individuos de la población en La metaheurística. Esta es una buena base para crear soluciones aceleradas y de bajo consumo de energía para escenarios informáticos intensivos.BACKGROUND: Metaheuristics are widely used to solve large combinatorial optimization problems in bioinformatics because of the huge set of possible solutions. Two representative problems are gene selection for cancer classification and biclustering of gene expression data. In most cases, these metaheuristics, as well as other non-linear techniques, apply a fitness function to each possible solution with a size-limited population, and that step involves higher latencies than other parts of the algorithms, which is the reason why the execution time of the applications will mainly depend on the execution time of the fitness function. In addition, it is usual to find floating-point arithmetic formulations for the fitness functions. This way, a careful parallelization of these functions using the reconfigurable hardware technology will accelerate the computation, specially if they are applied in parallel to several solutions of the population. RESULTS: A fine-grained parallelization of two floating-point fitness functions of different complexities and features involved in biclustering of gene expression data and gene selection for cancer classification allowed for obtaining higher speedups and power-reduced computation with regard to usual microprocessors. CONCLUSIONS: The results show better performances using reconfigurable hardware technology instead of usual microprocessors, in computing time and power consumption terms, not only because of the parallelization of the arithmetic operations, but also thanks to the concurrent fitness evaluation for several individuals of the population in the metaheuristic. This is a good basis for building accelerated and low-energy solutions for intensive computing scenarios.• Ministerio de Economía y Competitividad y Fondos FEDER. Contrato TIN2012-30685 (I+D+i) • Gobierno de Extremadura. Ayuda GR15011 para grupos TIC015 • CONICYT/FONDECYT/REGULAR/1160455. Beca para Ricardo Soto Guzmán • CONICYT/FONDECYT/REGULAR/1140897. Beca para Broderick CrawfordpeerReviewe

A Machine Learning-based Distributed System for Fault Diagnosis with Scalable Detection Quality in Industrial IoT

In this paper, a methodology based on machine learning for fault detection in continuous processes is presented. It aims to monitor fully distributed scenarios, such as the Tennessee Eastman Process, selected as the use case of this work, where sensors are distributed throughout an industrial plant. A hybrid feature selection approach based on filters and wrappers, called Hybrid Fisher Wrapper method, is proposed to select the most representative sensors to get the highest detection quality for fault identification. The proposed methodology provides a complete design space of solutions differing in the sensing effort, the processing complexity, and the obtained detection quality. It constitutes an alternative to the typical scheme in Industry 4.0, where multiple distributed sensor systems collect and send data to a centralised cloud. Differently, the proposed technique follows a distributed approach, in which processing can be done eventually close to the sensors where data is generated, i.e., at the edge of the Internet of Things. This approach overcomes the bandwidth, privacy, and latency limitations that centralised approaches may suffer. The experimental results show that the proposed methodology provides Tennessee Eastman Process fault detection solutions with state-of-the-art detection quality figures. In terms of latency, solutions obtained outperform in 37.5 times the implementation with the highest detection quality, using 1.99 times fewer features, on average. Also, the scalability of the framework provides a design space where the optimal implementation can be chosen according to the application needs

Q-Learnheuristics: towards data-driven balanced metaheuristics

One of the central issues that must be resolved for a metaheuristic optimization process to work well is the dilemma of the balance between exploration and exploitation. The metaheuristics (MH) that achieved this balance can be called balanced MH, where a Q-Learning (QL) integration framework was proposed for the selection of metaheuristic operators conducive to this balance, particularly the selection of binarization schemes when a continuous metaheuristic solves binary combinatorial problems. In this work the use of this framework is extended to other recent metaheuristics, demonstrating that the integration of QL in the selection of operators improves the exploration-exploitation balance. Specifically, the Whale Optimization Algorithm and the Sine-Cosine Algorithm are tested by solving the Set Covering Problem, showing statistical improvements in this balance and in the quality of the solutions

Multiobjective metaheuristics for solving the relay node placement problem in wireless sensor networks

Tesis doctoral con la Mención de "Doctor Internacional"Una Red de Sensores Inalámbricos (WSN) se compone de un conjunto de sensores, que capturan información sobre el entorno, y un nodo central, que recolecta toda la información proporcionada por la red. Estas redes son sensibles al consumo energético, sobre todo al considerar protocolos de enrutado multi-salto, donde todos los dispositivos pueden comunicarse entre sí. Recientemente, un nuevo dispositivo especializado en tareas de comunicación y denominado Nodo Repetidor (RN), fue añadido a las WSNs tradicionales como una posible vía de abordar esta cuestión, dando lugar al Problema del Posicionamiento de Nodos Repetidores (RNPP), que es un problema de optimización NP-completo. En esta tesis abordamos tres diferentes versiones del RNPP, divididas en torno a dos grupos: WSNs exteriores y WSNs interiores. En la primera versión estudiamos cómo desplegar RNs en WSNs exteriores estáticas previamente establecidas, con el objetivo de optimizar el consumo energético medio y la cobertura media. La segunda versión aporta un enfoque más realista sobre la primera, donde además optimizamos la robustez de la red. Ambas versiones se resuelven mediante múltiples metaheurísticas multiobjetivo: NSGA-II, SPEA2, MO-VNS, MO-ABC, MO-FA, MO-GSA y MOEA/D. En la tercera versión y basándonos en el conocimiento adquirido, proponemos una novedosa línea de investigación: el despliegue de WSNs interiores estáticas de bajo coste, tratando de aprovechar la infraestructura existente. Este nuevo problema de optimización se deriva de la necesidad de desplegar redes interiores de bajo coste para proporcionar servicios de localización, ej. para robótica doméstica y del hogar.A Wireless Sensor Network (WSN) is composed of a set of sensors, capturing information about the environment, and a sink node, which collects all the information provided by the network. WSNs are particularly sensitive to energy cost, especially with multi-hop routing protocols, where the devices send data to each other’s. In recent years, a new device specialised in communication tasks and called Relay Node (RN) is added to traditional WSNs as a possible way to address this issue, resulting in the NP-hard optimisation Relay Node Placement Problem (RNPP). In this thesis, we tackle three different approaches of the RNPP, divided into two groups: outdoor and indoor networks. In the first approach, we study how to efficiently deploy energy-harvesting RNs in previously-established static outdoor WSNs for optimising average energy consumption and average coverage. The second approach is a more realistic version of the previous deployment problem, where we also optimise network reliability. Both approaches are solved by applying a wide range of Multi-Objective (MO) metaheuristics. Specifically, we implement NSGA-II, SPEA2, MO-VNS, MO-ABC, MOFA, MO-GSA, and MOEA/D algorithms. In the third approach and based on the knowledge obtained from the outdoor problem, we propose a new line of research not considered before in the literature: the deployment of low-cost static indoor WSNs, trying to leverage existing infrastructure. This new MO problem derives from the need to deploy low-cost networks for providing indoor localisation services, e.g. for domestic and industrial robots

Multiobjective metaheuristics for solving the relay node placement problem in wireless sensor networks

Tesis doctoral con la Mención de "Doctor Internacional"Una Red de Sensores Inalámbricos (WSN) se compone de un conjunto de sensores, que capturan información sobre el entorno, y un nodo central, que recolecta toda la información proporcionada por la red. Estas redes son sensibles al consumo energético, sobre todo al considerar protocolos de enrutado multi-salto, donde todos los dispositivos pueden comunicarse entre sí. Recientemente, un nuevo dispositivo especializado en tareas de comunicación y denominado Nodo Repetidor (RN), fue añadido a las WSNs tradicionales como una posible vía de abordar esta cuestión, dando lugar al Problema del Posicionamiento de Nodos Repetidores (RNPP), que es un problema de optimización NP-completo. En esta tesis abordamos tres diferentes versiones del RNPP, divididas en torno a dos grupos: WSNs exteriores y WSNs interiores. En la primera versión estudiamos cómo desplegar RNs en WSNs exteriores estáticas previamente establecidas, con el objetivo de optimizar el consumo energético medio y la cobertura media. La segunda versión aporta un enfoque más realista sobre la primera, donde además optimizamos la robustez de la red. Ambas versiones se resuelven mediante múltiples metaheurísticas multiobjetivo: NSGA-II, SPEA2, MO-VNS, MO-ABC, MO-FA, MO-GSA y MOEA/D. En la tercera versión y basándonos en el conocimiento adquirido, proponemos una novedosa línea de investigación: el despliegue de WSNs interiores estáticas de bajo coste, tratando de aprovechar la infraestructura existente. Este nuevo problema de optimización se deriva de la necesidad de desplegar redes interiores de bajo coste para proporcionar servicios de localización, ej. para robótica doméstica y del hogar.A Wireless Sensor Network (WSN) is composed of a set of sensors, capturing information about the environment, and a sink node, which collects all the information provided by the network. WSNs are particularly sensitive to energy cost, especially with multi-hop routing protocols, where the devices send data to each other’s. In recent years, a new device specialised in communication tasks and called Relay Node (RN) is added to traditional WSNs as a possible way to address this issue, resulting in the NP-hard optimisation Relay Node Placement Problem (RNPP). In this thesis, we tackle three different approaches of the RNPP, divided into two groups: outdoor and indoor networks. In the first approach, we study how to efficiently deploy energy-harvesting RNs in previously-established static outdoor WSNs for optimising average energy consumption and average coverage. The second approach is a more realistic version of the previous deployment problem, where we also optimise network reliability. Both approaches are solved by applying a wide range of Multi-Objective (MO) metaheuristics. Specifically, we implement NSGA-II, SPEA2, MO-VNS, MO-ABC, MOFA, MO-GSA, and MOEA/D algorithms. In the third approach and based on the knowledge obtained from the outdoor problem, we propose a new line of research not considered before in the literature: the deployment of low-cost static indoor WSNs, trying to leverage existing infrastructure. This new MO problem derives from the need to deploy low-cost networks for providing indoor localisation services, e.g. for domestic and industrial robots