2021 IEEE 3rd International Conference on BioInspired Processing (BIP)最新文献

{"title":"OSCAR: A Low-Cost, Open-Source Robotic Platform Design for Cognitive Research","authors":"Emanuel Fallas-Hernández, Ronald J.L. Baldares, J. L. Crespo","doi":"10.1109/BIP53678.2021.9612905","DOIUrl":"https://doi.org/10.1109/BIP53678.2021.9612905","url":null,"abstract":"Research in cognitive robotics has been traditionally carried out in high-end research robots. This poses a barrier to many research institutions that cannot handle the costs associated with purchasing and operating this type of robot. In this paper, the design of a low-cost and open-source robot called OSCAR is presented. This robot was purposely designed to adapt to manipulation tasks in the context of cognitive robotics. The morphology, sensors equipped and software architecture of the robot are explained. Moreover, an initial implementation based in the Gazebo simulator is used to carry out an experiment involving a manipulation task using the Multilevel Darwinist Brain cognitive architecture. Results show that OSCAR is capable of learning a task and to obtain general information of its environment when paired with a cognitive architecture.","PeriodicalId":155935,"journal":{"name":"2021 IEEE 3rd International Conference on BioInspired Processing (BIP)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124662301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acoustic detection of Red-capped Manakin (Ceratopipra mentalis) in Sarapiquí, Costa Rica.","authors":"Roberto Vargas-Masís, David Segura-Sequeira, Emelin Mendoza-Garro, Dania Vargas-López","doi":"10.1109/BIP53678.2021.9613055","DOIUrl":"https://doi.org/10.1109/BIP53678.2021.9613055","url":null,"abstract":"Most of the vast biodiversity of the Caribbean foothills of Costa Rica is poorly studied. Passive acoustic monitoring is useful for collecting large datasets of wildlife and apply them for conservation initiatives, but handling these large datasets can be impractical if automated detection algorithms are not applied to reduce labeling time. The use of Pattern Machine (PM) and Random Forest Models (RFM) helps to identify single species though a dataset, simplifying the work. We sampled a private reserve in the Caribbean foothills of Costa Rica with 10 Audiomoth recorders. We used PM and RFM to label the presence of the Red-capped Manakin (Ceratopipra mentalis) in the recordings based on a portion of its distinctive vocalization. The RFM scored 0.86 of accuracy and 0.82 of precision to detect the species within the recordings. Three sites showed the highestnumber of detections. The acoustic activity of the bird got three different peaks during the day. The peak of detections took place in March. Based on the results, we recommended several conservation actions to the reserve on how to reduce the impact of tourism in the trails and how to rehabilitate the landscape with assistance of the bird and its activity as seed disperser.","PeriodicalId":155935,"journal":{"name":"2021 IEEE 3rd International Conference on BioInspired Processing (BIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130531898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparative Evaluation of Modern Architectures for the Non-Local Means Filter using Performance Primitives Libraries and Compiler Directive APIs","authors":"Manuel Zumbado-Corrales, J. Castro, Esteban Meneses","doi":"10.1109/BIP53678.2021.9612827","DOIUrl":"https://doi.org/10.1109/BIP53678.2021.9612827","url":null,"abstract":"The performance achieved by an application is limited by architectural features such as program data access and processing patterns. Parallelization approaches exhibit dissimilar performance and have a direct impact in application execution time. Additionally, developing parallel code involves additional complexity and productivity for programmers to accelerate or rewrite the program. In this paper, we present a comparative performance evaluation of a CPU, GPU, and many-core (Xeon Phi KNL) architectures for the Non-Local Means filter. We asses the effect of different data access and processing patterns in two computational optimizations developed for the aforementioned filter. We follow a top-down approach in terms of the parallelization approach chosen, starting from performance primitives as a first step to give easy drop-in acceleration and then compiler directives with frameworks such as OpenMP and OpenACC as an intermediate step to map computing tasks to the underlying hardware. Results show that both libraries and directives are effective at accelerating code with a combination of both being necessary to overcome performance bottlenecks.","PeriodicalId":155935,"journal":{"name":"2021 IEEE 3rd International Conference on BioInspired Processing (BIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126058352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pandemic Search Algorithm: A Metaheuristic Inspiration of COVID-19 Outbreak","authors":"P. G. Panah, J. Guerrero","doi":"10.1109/BIP53678.2021.9612792","DOIUrl":"https://doi.org/10.1109/BIP53678.2021.9612792","url":null,"abstract":"Quick escalation of the Coronavirus crisis from epidemic to pandemic was unprecedented. A relatively longer asymptomatic period is a key feature of COVID-19 in rapid expansion. This paper suggests a search strategy inspired by the pandemic model of airborne disease transmission. The algorithm is based on straightforward principles globally experienced through the COVID-19 pandemic. Asymptomatic period, social distance, and reproduction numbers are fundaments of the Pandemic Search Algorithm (PSA). The performance assessment results compared to the Genetic Algorithm (GA) and Population Swarm optimization (PSO) indicate that PSA is a cost-effective method to establish a compromise between convergence rate and processing time. It can be privileged in computational problems exploring large feasible spaces due to lighter calculations, simpler structures, easier implementation, and tuning.","PeriodicalId":155935,"journal":{"name":"2021 IEEE 3rd International Conference on BioInspired Processing (BIP)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126619676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Detection and Localization of Genome Inversions using Principal Component Analysis","authors":"Fabian Fallas-Moya, R. J. Nowling, Scott J. Emrich, Amir Sadovnik","doi":"10.1109/BIP53678.2021.9612782","DOIUrl":"https://doi.org/10.1109/BIP53678.2021.9612782","url":null,"abstract":"Inversions occur when sections of a chromosome (DNA molecule) are completely reversed end-to-end. Large inversions (multiple megabases in length) can be detected, localized, and genotyped using principal component analysis (PCA) of single nucleotide polymorphisms (SNPs). However, detection and localization tasks are performed and interpreted manually. We propose a novel pipeline for the detection and localization tasks in an automated manner. We compare our results with manual analysis for localization and show that our algorithm can achieve a similarity score of 0.95 on average. For the classification task, we achieve an accuracy of 0.88 as compared to manual classification. Our results suggest that our proposed methods are fast and accurate for these tasks and can be used as tools for detection and localization.","PeriodicalId":155935,"journal":{"name":"2021 IEEE 3rd International Conference on BioInspired Processing (BIP)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128473301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BIP 2021 Cover Page","authors":"","doi":"10.1109/bip53678.2021.9612939","DOIUrl":"https://doi.org/10.1109/bip53678.2021.9612939","url":null,"abstract":"","PeriodicalId":155935,"journal":{"name":"2021 IEEE 3rd International Conference on BioInspired Processing (BIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130024671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forced liquid impregnation technique as validation approach of two cooling geometries tested in air and oil conditions for a humanoid robot joint actuator","authors":"Mauricio Rodriguez Calvo, Federico Ruiz Ugalde","doi":"10.1109/BIP53678.2021.9612794","DOIUrl":"https://doi.org/10.1109/BIP53678.2021.9612794","url":null,"abstract":"When a humanoid robot lifts a heavy object this demands maximum load capacity of each joint. The amount of power of these joints determines the maximum object's weight the robot is able to carry. Humanoid robots must be lightweight, small and have a load capacity similar to humans. To achieve that, first of all, each joint must be as strong as possible to lift the sum of weights of the following joints plus the weight of the object. There have been some efforts in order to extract more power from an electric motor, the most effective have integrated an external jacket to pump liquid inside it to reduce the external heat of the motor, however, this don't represent an adequate integration of the liquid in the electric motor designing process. In this work, we propose to test and validate a cooling solution for high power applications in a prototype-hollow humanoid robot joint that uses a fractional-slot concentrated-winding brushless direct current motor as an actuator. The geometry of the proposed cooling solution was built and implemented to be tested under high current conditions.","PeriodicalId":155935,"journal":{"name":"2021 IEEE 3rd International Conference on BioInspired Processing (BIP)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133377936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of Denoising Algorithms for Footsteps Sound Classification in Noisy Environments","authors":"Carlos Brenes-Jiménez, R. Caravaca-Mora, Marvin Coto-Jiménez","doi":"10.1109/BIP53678.2021.9613035","DOIUrl":"https://doi.org/10.1109/BIP53678.2021.9613035","url":null,"abstract":"Identifying a person using footsteps sounds is part of the recent research in developing biometrics, systems designed to identify an individual in a group using body measurements. The sound of footsteps has a short history in this field, and present particular challenges. One of the most important is the background noise, given that any microphone installed on the floor with the purpose of recording footstep sounds will eventually record background noise and many other sounds as well. In this paper, we evaluate the combination of several denoising and classification algorithms for a person’s identification under several noisy conditions so as to establish a baseline in the field of distant sound recognition of footsteps. The results show the convenience of applying the denoising algorithms only in cases where the signal is affected by the high-noise level, which indicates the convenience of using real-time adaptive filters or more robust algorithms for both denoising and classification.","PeriodicalId":155935,"journal":{"name":"2021 IEEE 3rd International Conference on BioInspired Processing (BIP)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114521773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OKSP: A Novel Deep Learning Automatic Event Detection Pipeline for Seismic Monitoring in Costa Rica","authors":"Leonardo van der Laat, Ronald J.L. Baldares, E. Chaves, E. Meneses","doi":"10.1109/BIP53678.2021.9612832","DOIUrl":"https://doi.org/10.1109/BIP53678.2021.9612832","url":null,"abstract":"Small magnitude earthquakes are the most abundant but the most difficult to locate robustly and well due to their low amplitudes and high frequencies usually obscured by heterogeneous noise sources. They highlight crucial information about the stress state and the spatio-temporal behavior of fault systems during the earthquake cycle, therefore, its full characterization is then crucial for improving earthquake hazard assessment. Modern deep learning algorithms along with the increasing computational power and efficiency are exploiting the continuously growing seismological databases, worldwide, allowing scientists to improve the completeness for earthquake catalogs, systematically detecting and locating smaller magnitude earthquakes and reducing the errors introduced mainly by human intervention through traditional approaches in seismological observatories. In this work, we introduce OKSP, a novel deep learning automatic earthquake detection pipeline for seismic monitoring in Costa Rica. Using Kabré supercomputer from the Costa Rica High Technology Center, we applied OKSP to the day before and the first 5 days following the Puerto Armuelles, M6.5, earthquake that occurred on 26 June, 2019, along the Costa Rica-Panama border and found 1100 more earthquakes previously unidentified by the Volcanological and Seismological Observatory of Costa Rica. From these events, a total of 23 earthquakes with magnitudes below 1.0 occurred a day to hours prior to the mainshock, shedding light about the rupture initiation and earthquake interaction leading to the occurrence of this productive seismic sequence. Our observations show that for the study period, the model was 100% exhaustive and 82% precise, resulting in an F1 score of 0.90. This effort represents the very first attempt for automatically detecting earthquakes in Costa Rica using deep learning methods and demonstrates that, in the near future, earthquake monitoring routines will be carried out entirely by AI algorithms.","PeriodicalId":155935,"journal":{"name":"2021 IEEE 3rd International Conference on BioInspired Processing (BIP)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127602017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}