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This work aims to monitor air quality in places where humans spend most of their time, such as workplaces and homes. Radon gas is a naturally occurring, colourless, odourless and tasteless gas that accumulates in enclosed spaces. It is a radioactive element produced by the decay of its natural parent elements, uranium and thorium, which is harmful to our respiratory system when inhaled. The Internet of Things (IoT) is the key to the problems of contemporary life; we are witnessing an emerging connected world, and these architectures have the potential by using sensors to take data from the physical world, transfer it over the network and store it for further decision making or action. The proposal of this work is based on a radon sensor connected to an IoT device, the Message Queuing Telemetry Transport protocol (MQTT), the Node-RED for managing data flows and a database management system on a web server. The information collected by the sensor is sent by the IoT device to be processed by Node-RED. The obtained data is stored in a database to be represented on a web server. Therefore, this work includes a case study where the technologies involved in the indoor radon gas monitoring system are presented. It is a way to perform radon gas measurements automatically. The final application would allow: displaying radon concentrations on a map with placemarks and updating the information in real-time. The database could record data from other radon sensors that any user wants to associate with this website.
Alexandra Medina-Pérez; David Sánchez-Rodríguez; Itziar Alonso-González. An Internet of Thing Architecture Based on Message Queuing Telemetry Transport Protocol and Node-RED: A Case Study for Monitoring Radon Gas. Smart Cities 2021, 4, 803 -818.
AMA StyleAlexandra Medina-Pérez, David Sánchez-Rodríguez, Itziar Alonso-González. An Internet of Thing Architecture Based on Message Queuing Telemetry Transport Protocol and Node-RED: A Case Study for Monitoring Radon Gas. Smart Cities. 2021; 4 (2):803-818.
Chicago/Turabian StyleAlexandra Medina-Pérez; David Sánchez-Rodríguez; Itziar Alonso-González. 2021. "An Internet of Thing Architecture Based on Message Queuing Telemetry Transport Protocol and Node-RED: A Case Study for Monitoring Radon Gas." Smart Cities 4, no. 2: 803-818.
Driver fatigue is one of the major causes of traffic accidents, and this need has increased the amount of driver fatigue detection systems in vehicles in order to reduce human and material losses. This work puts forward an approach based on capturing near-infrared videos from a camera mounted inside the vehicle. Then, from the captured images and using image-processing techniques the eyes are detected. Next, features are extracted from eye images using several transforms and finally, the system detects if there is fatigue or not using a SVM as classifier. Throughout the recording, eye position is tracked with a low computational time and fatigue is analysed based on the percentage of eyelid closure. This approach has been developed on two public datasets. Our experiments were able to reach an eye recognition rate of up to 96.87% and our results showed that SVM with RBF kernel were 99.66% accurate on one of the databases used for the system training. This approach shows promising results in comparison with the state of the art and deep learning approaches in order to be implemented in real conditions.
Carlos M. Travieso-Gonzalez; Jesus B. Alonso-Hernandez; Jose Miguel Canino-Rodriguez; Santiago T. Perez-Suarez; David De La Cruz Sanchez-Rodriguez; Antonio G. Ravelo-Garcia. Robust Detection of Fatigue Parameters Based on Infrared Information. IEEE Access 2021, 9, 18209 -18221.
AMA StyleCarlos M. Travieso-Gonzalez, Jesus B. Alonso-Hernandez, Jose Miguel Canino-Rodriguez, Santiago T. Perez-Suarez, David De La Cruz Sanchez-Rodriguez, Antonio G. Ravelo-Garcia. Robust Detection of Fatigue Parameters Based on Infrared Information. IEEE Access. 2021; 9 ():18209-18221.
Chicago/Turabian StyleCarlos M. Travieso-Gonzalez; Jesus B. Alonso-Hernandez; Jose Miguel Canino-Rodriguez; Santiago T. Perez-Suarez; David De La Cruz Sanchez-Rodriguez; Antonio G. Ravelo-Garcia. 2021. "Robust Detection of Fatigue Parameters Based on Infrared Information." IEEE Access 9, no. : 18209-18221.
This research work presents a detailed survey about Computational Intelligence (CI) applied to various Hotel and Travel Industry areas. Currently, the hospitality industry’s interest in data science is growing exponentially because of their expected margin of profit growth. In order to provide precise state of the art content, this survey analyzes more than 160 research works from which a detailed categorization and taxonomy have been produced. We have studied the different approaches on the various forecasting methods and subareas where CI is currently being used. This research work also shows an actual distribution of these research efforts in order to enhance the understanding of the reader about this topic and to highlight unexploited research niches. A set of guidelines and recommendations for future research areas and promising applications are also presented in a final section.
Juan Guerra-Montenegro; Javier Sanchez-Medina; Ibai Laña; David Sanchez-Rodriguez; Itziar Alonso-Gonzalez; Javier Del Ser. Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges. Applied Soft Computing 2021, 102, 107082 .
AMA StyleJuan Guerra-Montenegro, Javier Sanchez-Medina, Ibai Laña, David Sanchez-Rodriguez, Itziar Alonso-Gonzalez, Javier Del Ser. Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges. Applied Soft Computing. 2021; 102 ():107082.
Chicago/Turabian StyleJuan Guerra-Montenegro; Javier Sanchez-Medina; Ibai Laña; David Sanchez-Rodriguez; Itziar Alonso-Gonzalez; Javier Del Ser. 2021. "Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges." Applied Soft Computing 102, no. : 107082.
In recent years, indoor localization systems based on fingerprinting have had significant advances yielding high accuracies. Those approaches often use information about channel communication, such as channel state information (CSI) and received signal strength (RSS). Nevertheless, these features have always been employed separately. Although CSI provides more fine-grained physical layer information than RSS, in this manuscript, a methodology for indoor localization fusing both features from a single access point is proposed to provide a better accuracy. In addition, CSI amplitude information is processed to remove high variability information that can negatively influence location estimation. The methodology was implemented and validated in two scenarios using a single access point located in two different positions and configured in 2.4 and 5 GHz frequency bands. The experiments show that the methodology yields an average error distance of about 0.1 m using the 5 GHz band and a single access point.
David Sánchez-Rodríguez; Miguel A. Quintana-Suárez; Itziar Alonso-González; Carlos Ley-Bosch; Javier J. Sánchez-Medina. Fusion of Channel State Information and Received Signal Strength for Indoor Localization Using a Single Access Point. Remote Sensing 2020, 12, 1995 .
AMA StyleDavid Sánchez-Rodríguez, Miguel A. Quintana-Suárez, Itziar Alonso-González, Carlos Ley-Bosch, Javier J. Sánchez-Medina. Fusion of Channel State Information and Received Signal Strength for Indoor Localization Using a Single Access Point. Remote Sensing. 2020; 12 (12):1995.
Chicago/Turabian StyleDavid Sánchez-Rodríguez; Miguel A. Quintana-Suárez; Itziar Alonso-González; Carlos Ley-Bosch; Javier J. Sánchez-Medina. 2020. "Fusion of Channel State Information and Received Signal Strength for Indoor Localization Using a Single Access Point." Remote Sensing 12, no. 12: 1995.
In a fully connected world where data freely flows and people can travel anywhere a lot of research has been conducted regarding Smart Mobility, which aims to improve all sorts of traffic matters, from Vehicle Data to Traffic flow management. However, it is also surprising how this topic has been applied in such a small extent to Tourism, an area that can benefit from this kind of research. This paper summarizes the current state of the art regarding Smart Mobility, and exposes useful insights about how this topic might be applied to tourism in order to improve various kinds of tourism services by using Smart Mobility.
Juan Guerra-Montenegro; Javier Sánchez-Medina; David Sánchez-Rodríguez; Itziar Alonso-González. What Can Smart Mobility Offer to Tourism Economy? Transactions on Petri Nets and Other Models of Concurrency XV 2020, 182 -189.
AMA StyleJuan Guerra-Montenegro, Javier Sánchez-Medina, David Sánchez-Rodríguez, Itziar Alonso-González. What Can Smart Mobility Offer to Tourism Economy? Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():182-189.
Chicago/Turabian StyleJuan Guerra-Montenegro; Javier Sánchez-Medina; David Sánchez-Rodríguez; Itziar Alonso-González. 2020. "What Can Smart Mobility Offer to Tourism Economy?" Transactions on Petri Nets and Other Models of Concurrency XV , no. : 182-189.
In recent years, there has been a remarkable advance in monitoring technologies in many environments, be they urban or rural. These technologies, included in the Internet of Things (IoT) domain, allow remote control and acquisition of data from sensors for their subsequence analysis. All these systems are based on the interaction between sensors and actuators. To achieve this goal, it is necessary to provide a very high level of connectivity between the devices, especially as far as wireless systems are concerned. In this sense, there is a great variety of standards in the market of communication networks oriented to this end. One of the biggest challenges today is to allow inter-operability between these different technologies in order to homogenize this field. In addition to this, it is intended to introduce new communication techniques that can provide certain additional advantages to those already existing. The main idea is the creation of a cellular network where radiofrequency and optical technologies coexist, and whose link with the rest of the world is through long-range and low-consumption wireless technologies. The center of each cell, that is the lighting system, can be powered using solar panels, as can the existing systems in the market. The objective is that these panels are capable of providing the necessary energy to the rest of the necessary systems.
Francisco Delgado-Rajo; Alexis Melian-Segura; Victor Guerra; Rafael Perez-Jimenez; David Sanchez-Rodriguez. Hybrid RF/VLC Network Architecture for the Internet of Things. Sensors 2020, 20, 478 .
AMA StyleFrancisco Delgado-Rajo, Alexis Melian-Segura, Victor Guerra, Rafael Perez-Jimenez, David Sanchez-Rodriguez. Hybrid RF/VLC Network Architecture for the Internet of Things. Sensors. 2020; 20 (2):478.
Chicago/Turabian StyleFrancisco Delgado-Rajo; Alexis Melian-Segura; Victor Guerra; Rafael Perez-Jimenez; David Sanchez-Rodriguez. 2020. "Hybrid RF/VLC Network Architecture for the Internet of Things." Sensors 20, no. 2: 478.
This work introduces a new approach for automatic identification of crickets, katydids and cicadas analyzing their acoustic signals. We propose the building of a tool to identify this biodiversity. The study proposes a sound parameterization technique designed specifically for identification and classification of acoustic signals of insects using Mel Frequency Cepstral Coefficients (MFCC) and Linear Frequency Cepstral Coefficients (LFCC). These two sets of coefficients are evaluated individually as has been done in previous studies and have been compared with the fusion proposed in this work, showing an outstanding increase in identification and classification at species level reaching a success rate of 98.07% on 343 insect species.
Juan J. Noda; Carlos M. Travieso-González; David Sánchez-Rodríguez; Jesús B. Alonso-Hernández. Acoustic Classification of Singing Insects Based on MFCC/LFCC Fusion. Applied Sciences 2019, 9, 4097 .
AMA StyleJuan J. Noda, Carlos M. Travieso-González, David Sánchez-Rodríguez, Jesús B. Alonso-Hernández. Acoustic Classification of Singing Insects Based on MFCC/LFCC Fusion. Applied Sciences. 2019; 9 (19):4097.
Chicago/Turabian StyleJuan J. Noda; Carlos M. Travieso-González; David Sánchez-Rodríguez; Jesús B. Alonso-Hernández. 2019. "Acoustic Classification of Singing Insects Based on MFCC/LFCC Fusion." Applied Sciences 9, no. 19: 4097.
The Canary Islands are a well known tourist destination with generally stable and clement weather conditions. However, occasionally extreme weather conditions occur, which although very unusual, may cause severe damage to the local economy. The ViMetRi-MAC EU funded project has among its goals, managing climate-change-associated risks. The Spanish National Meteorology Agency (AEMET) has a network of weather stations across the eight Canary Islands. Using data from those stations, we propose a novel methodology for the prediction of maximum wind speed in order to trigger an early alert for extreme weather conditions. The methodology proposed has the added value of using an innovative kind of machine learning that is based on the data stream mining paradigm. This type of machine learning system relies on two important features: models are learned incrementally and adaptively. That means the learner tunes the models gradually and endlessly as new observations are received and also modifies it when there is concept drift (statistical instability), in the modeled phenomenon. The results presented seem to prove that this data stream mining approach is a good fit for this kind of problem, clearly improving the results obtained with the accumulative non-adaptive version of the methodology.
Javier J. Sánchez-Medina; Juan Antonio Guerra-Montenegro; David Sánchez-Rodríguez; Itziar G. Alonso-González; Juan L. Navarro-Mesa. Data Stream Mining Applied to Maximum Wind Forecasting in the Canary Islands. Sensors 2019, 19, 2388 .
AMA StyleJavier J. Sánchez-Medina, Juan Antonio Guerra-Montenegro, David Sánchez-Rodríguez, Itziar G. Alonso-González, Juan L. Navarro-Mesa. Data Stream Mining Applied to Maximum Wind Forecasting in the Canary Islands. Sensors. 2019; 19 (10):2388.
Chicago/Turabian StyleJavier J. Sánchez-Medina; Juan Antonio Guerra-Montenegro; David Sánchez-Rodríguez; Itziar G. Alonso-González; Juan L. Navarro-Mesa. 2019. "Data Stream Mining Applied to Maximum Wind Forecasting in the Canary Islands." Sensors 19, no. 10: 2388.
Indoor localization has received tremendous attention in the last two decades due to location-aware services being highly demanded. Wireless networks have been suggested to solve this problem in many research works, and efficient algorithms have been developed with precise location and high accuracy. Nevertheless, those approaches often have high computational and high energy consumption. Hence, in temporary environments, such as emergency situations, where a fast deployment of an indoor localization system is required, those methods are not appropriate. In this manuscript, a methodology for fast building of an indoor localization system is proposed. For that purpose, a reduction of the data dimensionality is achieved by applying data fusion and feature transformation, which allow us to reduce the computational cost of the classifier training phase. In order to validate the methodology, three different datasets were used: two of them are public datasets based mainly on Received Signal Strength (RSS) from different Wi-Fi access point, and the third is a set of RSS values gathered from the LED lamps in a Visible Light Communication (VLC) network. The simulation results show that the proposed methodology considerably amends the overall computational performance and provides an acceptable location estimation error.
David Sánchez-Rodríguez; Itziar Alonso-González; Carlos Ley-Bosch; Miguel A. Quintana-Suárez. A Simple Indoor Localization Methodology for Fast Building Classification Models Based on Fingerprints. Electronics 2019, 8, 103 .
AMA StyleDavid Sánchez-Rodríguez, Itziar Alonso-González, Carlos Ley-Bosch, Miguel A. Quintana-Suárez. A Simple Indoor Localization Methodology for Fast Building Classification Models Based on Fingerprints. Electronics. 2019; 8 (1):103.
Chicago/Turabian StyleDavid Sánchez-Rodríguez; Itziar Alonso-González; Carlos Ley-Bosch; Miguel A. Quintana-Suárez. 2019. "A Simple Indoor Localization Methodology for Fast Building Classification Models Based on Fingerprints." Electronics 8, no. 1: 103.
Juan J. Noda; David Sánchez-Rodríguez; Carlos M. Travieso-González. A Methodology Based on Bioacoustic Information for Automatic Identification of Reptiles and Anurans. Reptiles and Amphibians 2018, 1 .
AMA StyleJuan J. Noda, David Sánchez-Rodríguez, Carlos M. Travieso-González. A Methodology Based on Bioacoustic Information for Automatic Identification of Reptiles and Anurans. Reptiles and Amphibians. 2018; ():1.
Chicago/Turabian StyleJuan J. Noda; David Sánchez-Rodríguez; Carlos M. Travieso-González. 2018. "A Methodology Based on Bioacoustic Information for Automatic Identification of Reptiles and Anurans." Reptiles and Amphibians , no. : 1.
Indoor localization estimation has become an attractive research topic due to growing interest in location-aware services. Many research works have proposed solving this problem by using wireless communication systems based on radiofrequency. Nevertheless, those approaches usually deliver an accuracy of up to two metres, since they are hindered by multipath propagation. On the other hand, in the last few years, the increasing use of light-emitting diodes in illumination systems has provided the emergence of Visible Light Communication technologies, in which data communication is performed by transmitting through the visible band of the electromagnetic spectrum. This brings a brand new approach to high accuracy indoor positioning because this kind of network is not affected by electromagnetic interferences and the received optical power is more stable than radio signals. Our research focus on to propose a fingerprinting indoor positioning estimation system based on neural networks to predict the device position in a 3D environment. Neural networks are an effective classification and predictive method. The localization system is built using a dataset of received signal strength coming from a grid of different points. From the these values, the position in Cartesian coordinates (x,y,z) is estimated. The use of three neural networks is proposed in this work, where each network is responsible for estimating the position by each axis. Experimental results indicate that the proposed system leads to substantial improvements to accuracy over the widely-used traditional fingerprinting methods, yielding an accuracy above 99% and an average error distance of 0.4 mm.
Itziar Alonso-González; David Sánchez-Rodríguez; Carlos Ley-Bosch; Miguel Angel Quintana-Suarez. Discrete Indoor Three-Dimensional Localization System Based on Neural Networks Using Visible Light Communication. Sensors 2018, 18, 1040 .
AMA StyleItziar Alonso-González, David Sánchez-Rodríguez, Carlos Ley-Bosch, Miguel Angel Quintana-Suarez. Discrete Indoor Three-Dimensional Localization System Based on Neural Networks Using Visible Light Communication. Sensors. 2018; 18 (4):1040.
Chicago/Turabian StyleItziar Alonso-González; David Sánchez-Rodríguez; Carlos Ley-Bosch; Miguel Angel Quintana-Suarez. 2018. "Discrete Indoor Three-Dimensional Localization System Based on Neural Networks Using Visible Light Communication." Sensors 18, no. 4: 1040.
Indoor localization has gained considerable attention over the past decade because of the emergence of numerous location-aware services. Research works have been proposed on solving this problem by using wireless networks. Nevertheless, there is still much room for improvement in the quality of the proposed classification models. In the last years, the emergence of Visible Light Communication (VLC) brings a brand new approach to high quality indoor positioning. Among its advantages, this new technology is immune to electromagnetic interference and has the advantage of having a smaller variance of received signal power compared to RF based technologies. In this paper, a performance analysis of seventeen machine leaning classifiers for indoor localization in VLC networks is carried out. The analysis is accomplished in terms of accuracy, average distance error, computational cost, training size, precision and recall measurements. Results show that most of classifiers harvest an accuracy above 90 %. The best tested classifier yielded a 99.0 % accuracy, with an average error distance of 0.3 centimetres.
David De La Cruz Sánchez Rodríguez; I. Alonso-González; J. Sánchez-Medina; C. Ley-Bosch; L. Díaz-Vilariño. PERFORMANCE ANALYSIS OF CLASSIFICATION METHODS FOR INDOOR LOCALIZATION IN VLC NETWORKS. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2017, IV-2/W4, 385 -391.
AMA StyleDavid De La Cruz Sánchez Rodríguez, I. Alonso-González, J. Sánchez-Medina, C. Ley-Bosch, L. Díaz-Vilariño. PERFORMANCE ANALYSIS OF CLASSIFICATION METHODS FOR INDOOR LOCALIZATION IN VLC NETWORKS. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2017; IV-2/W4 ():385-391.
Chicago/Turabian StyleDavid De La Cruz Sánchez Rodríguez; I. Alonso-González; J. Sánchez-Medina; C. Ley-Bosch; L. Díaz-Vilariño. 2017. "PERFORMANCE ANALYSIS OF CLASSIFICATION METHODS FOR INDOOR LOCALIZATION IN VLC NETWORKS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W4, no. : 385-391.
Ambient Assisted Living (AAL) has become an attractive research topic due to growing interest in remote monitoring of older people. Development in sensor technologies and advances in wireless communications allows to remotely offer smart assistance and monitor those people at their own home, increasing their quality of life. In this context, Wireless Acoustic Sensor Networks (WASN) provide a suitable way for implementing AAL systems which can be used to infer hazardous situations via environmental sounds identification. Nevertheless, satisfying sensor solutions have not been found with the considerations of both low cost and high performance. In this paper, we report the design and implementation of a wireless acoustic sensor to be located at the edge of a WASN for recording and processing environmental sounds which can be applied to AAL systems for personal healthcare because it has the following significant advantages: low cost, small size, audio sampling and computation capabilities for audio processing. The proposed wireless acoustic sensor is able to record audio samples at least to 10 kHz sampling frequency and 12-bit resolution. Also, it is capable of doing audio signal processing without compromising the sample rate and the energy consumption by using a new microcontroller released at the last quarter of 2016. The proposed low cost wireless acoustic sensor has been verified using four randomness tests for doing statistical analysis and a classification system of the recorded sounds based on audio fingerprints.
Miguel A. Quintana-Suárez; David Sánchez-Rodríguez; Itziar Alonso-González; Jesús B. Alonso-Hernández. A Low Cost Wireless Acoustic Sensor for Ambient Assisted Living Systems. Applied Sciences 2017, 7, 877 .
AMA StyleMiguel A. Quintana-Suárez, David Sánchez-Rodríguez, Itziar Alonso-González, Jesús B. Alonso-Hernández. A Low Cost Wireless Acoustic Sensor for Ambient Assisted Living Systems. Applied Sciences. 2017; 7 (9):877.
Chicago/Turabian StyleMiguel A. Quintana-Suárez; David Sánchez-Rodríguez; Itziar Alonso-González; Jesús B. Alonso-Hernández. 2017. "A Low Cost Wireless Acoustic Sensor for Ambient Assisted Living Systems." Applied Sciences 7, no. 9: 877.
Bioacoustic research of reptile calls and vocalizations has been limited due to the general consideration that they are voiceless. However, several species of geckos, turtles, and crocodiles are abletoproducesimpleandevencomplexvocalizationswhicharespecies-specific.Thisworkpresents a novel approach for the automatic taxonomic identification of reptiles through their bioacoustics by applying pattern recognition techniques. The sound signals are automatically segmented, extracting each call from the background noise. Then, their calls are parametrized using Linear and Mel Frequency Cepstral Coefficients (LFCC and MFCC) to serve as features in the classification stage. In this study, 27 reptile species have been successfully identified using two machine learning algorithms: K-Nearest Neighbors (kNN) and Support Vector Machine (SVM). Experimental results show an average classification accuracy of 97.78% and 98.51%, respectively.
Juan J. Noda; Carlos M. Travieso; David Sánchez-Rodríguez. Fusion of Linear and Mel Frequency Cepstral Coefficients for Automatic Classification of Reptiles. Applied Sciences 2017, 7, 178 .
AMA StyleJuan J. Noda, Carlos M. Travieso, David Sánchez-Rodríguez. Fusion of Linear and Mel Frequency Cepstral Coefficients for Automatic Classification of Reptiles. Applied Sciences. 2017; 7 (2):178.
Chicago/Turabian StyleJuan J. Noda; Carlos M. Travieso; David Sánchez-Rodríguez. 2017. "Fusion of Linear and Mel Frequency Cepstral Coefficients for Automatic Classification of Reptiles." Applied Sciences 7, no. 2: 178.
Fish as well as birds, mammals, insects and other animals are capable of emitting sounds for diverse purposes, which can be recorded through microphone sensors. Although fish vocalizations have been known for a long time, they have been poorly studied and applied in their taxonomic classification. This work presents a novel approach for automatic remote acoustic identification of fish through their acoustic signals by applying pattern recognition techniques. The sound signals are preprocessed and automatically segmented to extract each call from the background noise. Then, the calls are parameterized using Linear and Mel Frequency Cepstral Coefficients (LFCC and MFCC), Shannon Entropy (SE) and Syllable Length (SL), yielding useful information for the classification phase. In our experiments, 102 different fish species have been successfully identified with three widely used machine learning algorithms: K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Machine (SVM). Experimental results show an average classification accuracy of 95.24%, 93.56% and 95.58%, respectively.
Juan J. Noda; Carlos M. Travieso; David Sánchez-Rodríguez. Automatic Taxonomic Classification of Fish Based on Their Acoustic Signals. Applied Sciences 2016, 6, 443 .
AMA StyleJuan J. Noda, Carlos M. Travieso, David Sánchez-Rodríguez. Automatic Taxonomic Classification of Fish Based on Their Acoustic Signals. Applied Sciences. 2016; 6 (12):443.
Chicago/Turabian StyleJuan J. Noda; Carlos M. Travieso; David Sánchez-Rodríguez. 2016. "Automatic Taxonomic Classification of Fish Based on Their Acoustic Signals." Applied Sciences 6, no. 12: 443.
Acoustic vocalizations are common in marine mammals which can be used for classification purposes. Pinnipeds are a group of carnivore mammals composed by seals, sea lions, and walruses. But although, there is a great interest in research literature about acoustic monitoring of marine mammals, the identification of pinnipeds trough experts systems has been poorly studied. This paper brings a novel method for the automatic taxonomic classification of pinnipeds using a fusion of Mel and Linear Frequency Cepstral Coefficients (MFCCs and LFCCs), representing the acoustic signal in both low and high frequencies. In our experiment, we have used kNN for classification, achieving an average identification of 96.48% ± 9.17 over 18 pinniped species.
Juan J. Noda; Carlos M. Travieso; David Sanchez-Rodriguez; Malay Kishore Dutta; Anushikha Singh. Automatic classification of pinnipeds based on their vocalizations and fusion of cepstral features. 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN) 2016, 215 -219.
AMA StyleJuan J. Noda, Carlos M. Travieso, David Sanchez-Rodriguez, Malay Kishore Dutta, Anushikha Singh. Automatic classification of pinnipeds based on their vocalizations and fusion of cepstral features. 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN). 2016; ():215-219.
Chicago/Turabian StyleJuan J. Noda; Carlos M. Travieso; David Sanchez-Rodriguez; Malay Kishore Dutta; Anushikha Singh. 2016. "Automatic classification of pinnipeds based on their vocalizations and fusion of cepstral features." 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN) , no. : 215-219.
This work presents a new approach for automatic recognition of insects through intelligent systems. Insect species employ a set of sound signals for communication purposes which are specie-specific. Based on this fact, an acoustic signal recognition method has been designed to allow an efficient taxonomic classification of this animal group. In this paper, the sound signals have been characterized by Mel and Linear Frequency Cepstral Coefficients (MFCCs and LFCCs) to compare their efficacy. Then, a Support Vector Machine algorithm has been used for classification achieving an average success rate of 99.08% over 88 insect species.
Juan J. Noda; Carlos M. Travieso; David Sanchez-Rodriguez; Malay Dutta; Anushikha Singh. Using bioacoustic signals and Support Vector Machine for automatic classification of insects. 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN) 2016, 656 -659.
AMA StyleJuan J. Noda, Carlos M. Travieso, David Sanchez-Rodriguez, Malay Dutta, Anushikha Singh. Using bioacoustic signals and Support Vector Machine for automatic classification of insects. 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN). 2016; ():656-659.
Chicago/Turabian StyleJuan J. Noda; Carlos M. Travieso; David Sanchez-Rodriguez; Malay Dutta; Anushikha Singh. 2016. "Using bioacoustic signals and Support Vector Machine for automatic classification of insects." 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN) , no. : 656-659.
In the last few years, the increasing use of LEDs in illumination systems has been conducted due to the emergence of Visible Light Communication (VLC) technologies, in which data communication is performed by transmitting through the visible band of the electromagnetic spectrum. In 2011, the Institute of Electrical and Electronics Engineers (IEEE) published the IEEE 802.15.7 standard for Wireless Personal Area Networks based on VLC. Due to limitations in the coverage of the transmitted signal, wireless networks can suffer from the hidden node problems, when there are nodes in the network whose transmissions are not detected by other nodes. This problem can cause an important degradation in communications when they are made by means of the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) access control method, which is used in IEEE 802.15.7 This research work evaluates the effects of the hidden node problem in the performance of the IEEE 802.15.7 standard We implement a simulator and analyze VLC performance in terms of parameters like end-to-end goodput and message loss rate. As part of this research work, a solution to the hidden node problem is proposed, based on the use of idle patterns defined in the standard. Idle patterns are sent by the network coordinator node to communicate to the other nodes that there is an ongoing transmission. The validity of the proposed solution is demonstrated with simulation results.
Carlos Ley-Bosch; Itziar Alonso-González; David Sánchez-Rodríguez; Carlos Ramírez-Casañas. Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance. Sensors 2016, 16, 216 .
AMA StyleCarlos Ley-Bosch, Itziar Alonso-González, David Sánchez-Rodríguez, Carlos Ramírez-Casañas. Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance. Sensors. 2016; 16 (2):216.
Chicago/Turabian StyleCarlos Ley-Bosch; Itziar Alonso-González; David Sánchez-Rodríguez; Carlos Ramírez-Casañas. 2016. "Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance." Sensors 16, no. 2: 216.
Indoor position estimation has become an attractive research topic due to growing interest in location-aware services. Nevertheless, satisfying solutions have not been found with the considerations of both accuracy and system complexity. From the perspective of lightweight mobile devices, they are extremely important characteristics, because both the processor power and energy availability are limited. Hence, an indoor localization system with high computational complexity can cause complete battery drain within a few hours. In our research, we use a data mining technique named boosting to develop a localization system based on multiple weighted decision trees to predict the device location, since it has high accuracy and low computational complexity. The localization system is built using a dataset from sensor fusion, which combines the strength of radio signals from different wireless local area network access points and device orientation information from a digital compass built-in mobile device, so that extra sensors are unnecessary. Experimental results indicate that the proposed system leads to substantial improvements on computational complexity over the widely-used traditional fingerprinting methods, and it has a better accuracy than they have.
David Sánchez-Rodríguez; Pablo Hernández-Morera; José Ma. Quinteiro; Itziar Alonso-González. A Low Complexity System Based on Multiple Weighted Decision Trees for Indoor Localization. Sensors 2015, 15, 14809 -14829.
AMA StyleDavid Sánchez-Rodríguez, Pablo Hernández-Morera, José Ma. Quinteiro, Itziar Alonso-González. A Low Complexity System Based on Multiple Weighted Decision Trees for Indoor Localization. Sensors. 2015; 15 (6):14809-14829.
Chicago/Turabian StyleDavid Sánchez-Rodríguez; Pablo Hernández-Morera; José Ma. Quinteiro; Itziar Alonso-González. 2015. "A Low Complexity System Based on Multiple Weighted Decision Trees for Indoor Localization." Sensors 15, no. 6: 14809-14829.
Visible Light Communications (VLC) uses visible light spectrum as transmission medium for communications. VLC has gained recent interest as a favorable complement to radio frequency (RF) wireless communications systems due to the ubiquity and wide variety of applications. In 2011 the Institute of Electrical and Electronic Engineers published the standard IEEE 802.15.7 [1]. Nowadays, simulation tools are widely used to study, understand and achieve better network performance. This paper describes the design and implementation of a physical layer model based in IEEE802.15.7 standard using OMNET++ simulation tool [2]. This software is a popular tool for building networks’ and modeling their behavior. The main goal of this paper is to introduce the developing and implementing of a software module to simulate the Physical Layer (PHY) based on IEEE802.15.7. The developed module, called simVLC will let researchers and students to study and simulate different scenarios in this standard.
Carlos Ley-Bosch; Roberto Medina-Sosa; Itziar Alonso-González; David Sánchez-Rodríguez. Implementing an IEEE802.15.7 Physical Layer Simulation Model with OMNET++. Advances in Intelligent Systems and Computing 2015, 251 -258.
AMA StyleCarlos Ley-Bosch, Roberto Medina-Sosa, Itziar Alonso-González, David Sánchez-Rodríguez. Implementing an IEEE802.15.7 Physical Layer Simulation Model with OMNET++. Advances in Intelligent Systems and Computing. 2015; ():251-258.
Chicago/Turabian StyleCarlos Ley-Bosch; Roberto Medina-Sosa; Itziar Alonso-González; David Sánchez-Rodríguez. 2015. "Implementing an IEEE802.15.7 Physical Layer Simulation Model with OMNET++." Advances in Intelligent Systems and Computing , no. : 251-258.