THE OBJECTIVE OF THIS STUDY WAS TO FIND AN EFFICIENT METHOD THAT ALLOWS TIME AND YIELD INCREASE VOLUME USE AND UTILITY OF THE SAWMILLS THAT PROCESS LOGS PRUNED PINUS RADIATA, LINKING EXTERNAL INFORMATION PROVIDED BY A SCANNER INDUSTRY AND SIMULATION OF CYLINDRICAL DEFECTIVE CORE (CDC) IN THE CONSTITUTION OF A THREE-DIMENSIONAL LOG, WHERE THE OPTIMAL CUTTING PATTERN WAS ESTABLISHED BY MEANS OF A DYNAMIC PROGRAMMING ALGORITHM. SAWING WAS SIMULATED ON A SAMPLE OF 30 LOGS OBTAINED RANDOMLY INDUSTRIAL PROCESS OF SCANNING. THE RESULTS WERE COMPARED WITH THOSE OBTAINED BY A HEURISTIC DEVELOPED BY A COMPANY. DYNAMIC PROGRAMMING ALGORITHM ACHIEVED A YIELD OF THE RAW MATERIAL OF 64% AND AN AVERAGE RELATIVE NET UTILITY OF 11 US$/LOG WAS OBTAINED.

IN THIS PAPER, WE DESIGN AND EVALUATE A CONTROL SYSTEM WHICH, BY USING AS INPUT RSSI MEASURES, ALLOWS ANTICIPATORY MOVEMENTS OF ROBOTIC ARMS DECREASING IDLE TIMES AT THE CIMUBB LABORATORY. CLASSICAL LOG-NORMAL MODEL, WHICH RELATES THE STRENGTH OF A SIGNAL RECEIVED BY A NODE WITH THE DISTANCE AT WHICH THE SENDER OF THE SIGNAL IS, WAS ADOPTED. THE HIDDEN STATE OF THE SYSTEM IS DETERMINED BY THE EXTENDED KALMAN FILTER WHICH ALLOWS US TO ESTIMATE THE DISTANCE AND THE SPEED OF PALLETS MOVING OVER A CLOSED-LOOP CONVEYOR BELT. FROM THESE ESTIMATES, REMAINING TIME IN WHICH THE PALLET WILL GET TO A STOPPING POINT NEAR THE ROBOT IS DETERMINED. THIS INFORMATION IS FINALLY PROCESSED BY A CONTROLLER TO DETERMINE THE INSTANT AT WHICH THE ROBOT MUST OPERATE AND HANDLE THE PALLET. BOTH, A PROPORTIONAL-INTEGRAL AND A FUZZY CONTROLLER, WERE IMPLEMENTED AND EVALUATED. RESULTS SHOW THE FEASIBILITY OF USING WIRELESS SIGNALS TO ACCOMPLISH THE DESCRIBED GOAL, WITH SOME PRACTICAL RESTRICTIONS.

THE APPLICATION OF DEEP LEARNING MODELS IN FRUIT ANALYSIS HAS GARNERED SIGNIFICANT ATTENTION DUE TO ITS POTENTIAL TO REVOLUTIONIZE THE AGRICULTURAL SECTOR AND ENHANCE CROP MONITORING. THIS PAPER PRESENTS A COMPREHENSIVE REVIEW OF RECENT RESEARCH EFFORTS IN FRUIT ANALYSIS USING DEEP LEARNING TECHNIQUES. THE STUDY DELVES INTO MODEL SELECTION, DATASET CHARACTERISTICS, EVALUATION METRICS, CHALLENGES, AND FUTURE DIRECTIONS IN THIS DOMAIN. VARIOUS MODEL ARCHITECTURES, INCLUDING CLASSICAL CONVOLUTIONAL NEURAL NETWORKS (CNNS) AND ADVANCED DETECTION MODELS LIKE R-CNN AND YOLO, HAVE BEEN EXPLORED FOR TASKS RANGING FROM FRUIT CLASSIFICATION TO DETECTION. EVALUATION METRICS SUCH AS PRECISION, RECALL, F1-SCORE, AND MEAN AVERAGE PRECISION (MAP) HAVE BEEN COMMONLY USED TO ASSESS MODEL PERFORMANCE. CHALLENGES, INCLUDING DATA SCARCITY, LABELING COMPLEXITIES, VARIATIONS IN FRUIT CHARACTERISTICS, AND COMPUTATIONAL EFFICIENCY, HAVE BEEN IDENTIFIED AND DISCUSSED. THE PAPER ALSO PRESENTS AN OVERVIEW OF AVAILABLE DATASETS, ENCOMPASSING BOTH PROPRIETARY AND PUBLICLY ACCESSIBLE SOURCES. FUTURE RESEARCH DIRECTIONS INVOLVE EXPLORING ENHANCED DATA AUGMENTATION, MULTI-MODAL INTEGRATION, KNOWLEDGE TRANSFER ACROSS SPECIES, ROBUSTNESS IN DYNAMIC ENVIRONMENTS, IMPROVED COMPUTATIONAL EFFICIENCY, AND PRACTICAL INTEGRATION OF MODELS INTO REAL-WORLD AGRICULTURAL SYSTEMS. THIS REVIEW PROVIDES VALUABLE INSIGHTS FOR RESEARCHERS AND PRACTITIONERS AIMING TO LEVERAGE DEEP LEARNING FOR FRUIT ANALYSIS IN THE PURSUIT OF SUSTAINABLE AGRICULTURE AND FOOD PRODUCTION.

ENERGY CONSUMPTION REPRESENTS A HIGH OPERATIONAL COST IN MINING OPERATIONS. ORE SIZE REDUCTION STAGE IS THE MAIN CONSUMER IN THAT PROCESS, WHERE THE SEMIAUTOGENOUS MILL (SAG) IS ONE OF THE MAIN COMPONENTS. THE IMPLEMENTATION OF CONTROL AND AUTOMATION STRATEGIES THAT CAN ACHIEVE PRODUCTION GOALS ALONG WITH ENERGY EFFICIENCY ARE A COMMON GOAL IN CONCENTRATOR PLANTS; HOWEVER, DESIGNING SUCH CONTROLS REQUIRES A PROPER UNDERSTANDING OF PROCESS DYNAMICS WHICH ARE HIGHLY COMPLEX. THIS WORK STUDIES MACHINE LEARNING AND DEEP LEARNING STRATEGIES THAT CAN BE USED TO GENERATE MODELS FOR PREDICTING ENERGY CONSUMPTION, USING KEY PROCESS VARIABLES. IN PARTICULAR, THE APPLICATION OF K-NEAREST NEIGHBORS REGRESSOR (KNN-REG), POLYNOMIAL REGRESSION (PR), SUPPORT VECTOR REGRESSION (SVR) AND LONG-SHORT TERM MEMORY (LSTM) STRATEGIES FOR ENERGY PREDICTION OVER SAG MILL PROCESS DATA IS DEVELOPED IN ORDER TO IDENTIFY CONFIGURATIONS SUITABLE TO BE IMPLEMENTED FOR REAL-TIME PREDICTION INTEGRATED OVER INDUSTRIAL DATA INFRASTRUCTURES. ALL TECHNIQUES ARE COMPARED IN TERMS OF ROOT MEAN SQUARE ERROR (RMSE) WHERE, ALTHOUGH ALL THE MODELS ACHIEVED ACCEPTABLE PERFORMANCE, BEST RESULTS WERE OBTAINED BY A LSTM IMPLEMENTATION WHICH YIELDED AN ERROR OF LESS THAN 4% ASSOCIATED TO ENERGY PREDICTION.

THE USE OF MACHINING SYSTEMS BY COMPUTER NUMERICAL CONTROL (CNC) HAS NOTABLE ADVANTAGES IN THE AREA OF INDUSTRIAL PRODUCTION COMPARED WITH TRADITIONAL TECHNIQUES. THIS FACILITATES A SIGNIFICANT DECREASE OF TIME, HIGHER PRECISION AND OPTIMIZATION OF OPERATION PARAMETERS. THE CONTROL OF SEQUENCES IN THESE SYSTEMS IS BASED ON CODES THAT DEFINE THE PARAMETERS TO PRODUCE THE MACHINING OF A DETERMINED PIECE. HOWEVER, THE GENERATION OF THESE CODES PRESENTS TWO MAJOR CHALLENGES, FIRST, KNOW THE TRIDIMENSIONAL INFORMATION OF THE PIECE TO PRODUCE, AND SECOND, DEFINE THE SEQUENCE BY CNC MACHINING. IN THIS WORK, THE FRINGE PROJECTION TECHNIQUE IS USED TO OBTAIN THREE-DIMENSIONAL INFORMATION FROM AN OBJECT AND BASED ON THIS INFORMATION, AUTOMATICALLY GENERATE PROGRAMMING CODES FOR THE MACHINING ROUTINE OF A THREE-AXIAL CNC MILLING MACHINE. THE RESULTS ARE COMPARED TO APPLY THE FRINGE PROJECTION TECHNIC TO RECOVER THREE-DIMENSIONAL SHAPE OF AN OBJECT BASED ON LEAST SQUARES ALGORITHM, USING INFORMATION FROM THREE TO EIGHT IMAGES.

THE USE OF ROBOTIC MANIPULATORS IS A SUBJECT THAT HAS BECOME RELEVANT IN THE PROCESS OF AUTOMATION IN DIFFERENT BRANCHES OF INDUSTRIAL MANUFACTURING. TODAY IS POSSIBLE TO MAKE ROUTINES WORKS WITH ROBOTIC MANIPULATOR IN DANGEROUS CONDITIONS FOR HUMAN OPERATORS, PROVIDING FLEXIBILITY IN PRODUCTION LINES, DOING MULTIPLE TYPES OF TASKS AND EXECUTING ACTIONS WITH PRECISION AND QUICKLY. ALL OPERATIONS OF A ROBOTIC ARM IS CONTROLLED BY A COMPUTER SYSTEM THAT CONTROLS THE MECHANISM POSITIONS. SINCE THE WORK OF THESE MACHINES IS TO MANIPULATE TOOLS OR PIECES, IT IS NECESSARY TO HAVE TRIDIMENSIONAL INFORMATION OF THE ENVIRONMENT OR MANIPULATED ELEMENTS. IN THIS WORK, THE FRINGE PROJECTION TECHNIQUE IS USED TO OBTAIN THREE-DIMENSIONAL SHAPE OF AN OBJECT, AND BASED ON THIS INFORMATION TO GENERATE THE TRAJECTORIES OF THE MANIPULATOR FOR THE PAINTING OF COMPLEX OBJECTS THROUGH PAINT SPRAYING. THE OBTAINED RESULTS HAVE BEEN SUCCESSFUL, GENERATING SIMULATED TRAJECTORIES FOR THE PAINTING OF PIECES WITH GOOD QUALITY AND SHORT TIMES COMPARED WITH TIMES USING TRADITIONAL METHODS TO PROGRAM SEQUENCES.

IN BLUEBERRY FARMING, ACCURATELY ASSESSING MATURITY IS CRITICAL TO EFFICIENT HARVESTING. DEEP LEARNING SOLUTIONS, WHICH ARE INCREASINGLY POPULAR IN THIS AREA, OFTEN UNDERGO EVALUATION THROUGH METRICS LIKE MEAN AVERAGE PRECISION (MAP). HOWEVER, THESE METRICS MAY ONLY PARTIALLY CAPTURE THE ACTUAL PERFORMANCE OF THE MODELS, ESPECIALLY IN SETTINGS WITH LIMITED RESOURCES LIKE THOSE IN AGRICULTURAL DRONES OR ROBOTS. TO ADDRESS THIS, OUR STUDY EVALUATES DEEP LEARNING MODELS, SUCH AS YOLOV7, RT-DETR, AND MASK-RCNN, FOR DETECTING AND CLASSIFYING BLUEBERRIES. WE PERFORM THESE EVALUATIONS ON BOTH POWERFUL COMPUTERS AND EMBEDDED SYSTEMS. USING TYPE-INFLUENCE DETECTOR ERROR (TIDE) ANALYSIS, WE CLOSELY EXAMINE THE ACCURACY OF THESE MODELS. OUR RESEARCH REVEALS THAT PARTIAL OCCLUSIONS COMMONLY CAUSE ERRORS, AND OPTIMIZING THESE MODELS FOR EMBEDDED DEVICES CAN INCREASE THEIR SPEED WITHOUT LOSING PRECISION. THIS WORK IMPROVES THE UNDERSTANDING OF OBJECT DETECTION MODELS FOR BLUEBERRY DETECTION AND MATURITY ESTIMATION.

THIS PAPER PRESENTS A NOVEL CNN-BASED ARCHITECTURE, REFERRED TO AS Q-NET, TO LEARN LOCAL FEATURE DESCRIPTORS THAT ARE USEFUL FOR MATCHING IMAGE PATCHES FROM TWO DIFFERENT SPECTRAL BANDS. GIVEN CORRECTLY MATCHED AND NON-MATCHING CROSS-SPECTRAL IMAGE PAIRS, A QUADRUPLET NETWORK IS TRAINED TO MAP INPUT IMAGE PATCHES TO A COMMON EUCLIDEAN SPACE, REGARDLESS OF THE INPUT SPECTRAL BAND. OUR APPROACH IS INSPIRED BY THE RECENT SUCCESS OF TRIPLET NETWORKS IN THE VISIBLE SPECTRUM, BUT ADAPTED FOR CROSS-SPECTRAL SCENARIOS, WHERE, FOR EACH MATCHING PAIR, THERE ARE ALWAYS TWO POSSIBLE NON-MATCHING PATCHES: ONE FOR EACH SPECTRUM. EXPERIMENTAL EVALUATIONS ON A PUBLIC CROSS-SPECTRAL VIS-NIR DATASET SHOWS THAT THE PROPOSED APPROACH IMPROVES THE STATE-OF-THE-ART. MOREOVER, THE PROPOSED TECHNIQUE CAN ALSO BE USED IN MONO-SPECTRAL SETTINGS, OBTAINING A SIMILAR PERFORMANCE TO TRIPLET NETWORK DESCRIPTORS, BUT REQUIRING LESS TRAINING DATA.

CURRENT CNN-BASED STEREO DEPTH ESTIMATION MODELS CAN BARELY RUN UNDER REAL-TIME CONSTRAINTS ON EMBEDDED GRAPHIC PROCESSING UNIT (GPU) DEVICES. MOREOVER, STATE-OF-THE-ART EVALUATIONS USUALLY DO NOT CONSIDER MODEL OPTIMIZATION TECHNIQUES, BEING THAT IT IS UNKNOWN WHAT IS THE CURRENT POTENTIAL ON EMBEDDED GPU DEVICES. IN THIS WORK, WE EVALUATE TWO STATE-OF-THE-ART MODELS ON THREE DIFFERENT EMBEDDED GPU DEVICES, WITH AND WITHOUT OPTIMIZATION METHODS, PRESENTING PERFORMANCE RESULTS THAT ILLUSTRATE THE ACTUAL CAPABILITIES OF EMBEDDED GPU DEVICES FOR STEREO DEPTH ESTIMATION. MORE IMPORTANTLY, BASED ON OUR EVALUATION, WE PROPOSE THE USE OF A U-NET LIKE ARCHITECTURE FOR POSTPROCESSING THE COST-VOLUME, INSTEAD OF A TYPICAL SEQUENCE OF 3D CONVOLUTIONS, DRASTICALLY AUGMENTING THE RUNTIME SPEED OF CURRENT MODELS. IN OUR EXPERIMENTS, WE ACHIEVE REAL-TIME INFERENCE SPEED, IN THE RANGE OF 5-32 MS, FOR 1216 × 368 INPUT STEREO IMAGES ON THE JETSON TX2, JETSON XAVIER, AND JETSON NANO EMBEDDED DEVICES.

IN THIS WORK, WE EXPLORE THE USE OF IMAGES FROM DIFFERENT SPECTRAL BANDS TO CLASSIFY DEFECTS IN MELAMINE FACED PANELS, WHICH COULD APPEAR THROUGH THE PRODUCTION PROCESS. THROUGH EXPERIMENTAL EVALUATION, WE EVALUATE THE USE OF IMAGES FROM THE VISIBLE (VS), NEAR-INFRARED (NIR), AND LONG WAVELENGTH INFRARED (LWIR), TO CLASSIFY THE DEFECTS USING A FEATURE DESCRIPTOR LEARNING APPROACH TOGETHER WITH A SUPPORT VECTOR MACHINE CLASSIFIER. TWO DESCRIPTORS WERE EVALUATED, EXTENDED LOCAL BINARY PATTERNS (E-LBP) AND SURF USING A BAG OF WORDS (BOW) REPRESENTATION. THE EVALUATION WAS CARRIED ON WITH AN IMAGE SET OBTAINED DURING THIS WORK, WHICH CONTAINED FIVE DIFFERENT DEFECT CATEGORIES THAT CURRENTLY OCCURS IN THE INDUSTRY. RESULTS SHOW THAT USING IMAGES FROM BEYOND THE VISUAL SPECTRUM HELPS TO IMPROVE CLASSIFICATION PERFORMANCE IN CONTRAST WITH A SINGLE VISIBLE SPECTRUM SOLUTION.

THIS PAPER PRESENTS A NOVEL FEATURE POINT DESCRIPTOR FOR THE MULTISPECTRAL IMAGE CASE FAR-INFRARED AND VISIBLE SPECTRUM IMAGES. IT ALLOWS MATCHING INTEREST POINTS ON IMAGES OF THE SAME SCENE BUT ACQUIRED IN DIFFERENT SPECTRAL BANDS. INITIALLY, POINTS OF INTEREST ARE DETECTED ON BOTH IMAGES THROUGH A SIFT-LIKE BASED SCALE SPACE REPRESENTATION. THEN, THESE POINTS ARE CHARACTERIZED USING AN EDGE ORIENTED HISTOGRAM (EOH) DESCRIPTOR. FINALLY, POINTS OF INTEREST FROM MULTISPECTRAL IMAGES ARE MATCHED BY FINDING NEAREST COUPLES USING THE INFORMATION FROM THE DESCRIPTOR. THE PROVIDED EXPERIMENTAL RESULTS AND COMPARISONS WITH SIMILAR METHODS SHOW BOTH THE VALIDITY OF THE PROPOSED APPROACH AS WELL AS THE IMPROVEMENTS IT OFFERS WITH RESPECT TO THE CURRENT STATE-OF-THE-ART.

WE REPORT A NOVEL PROPOSAL FOR REDUCING THE DIGITAL DIVIDE IN RURAL MULTIGRADE SCHOOLS, INCORPORATING KNOWLEDGE OF ROBOTICS WITH A STEM APPROACH TO SIMULTANEOUSLY PROMOTE CURRICULAR LEARNING IN MATHEMATICS AND SCIENCE IN SEVERAL SCHOOL GRADES. WE USED AN EXPLORATORY QUALITATIVE METHODOLOGY TO IMPLEMENT THE PROPOSAL WITH 12 MULTIGRADE RURAL STUDENTS. WE EXPLORED THE CONTRIBUTION OF THE APPROACHES TO THE PROMOTION OF CURRICULAR LEARNING IN MATHEMATICS AND SCIENCE AND THE PERCEPTIONS OF USING ROBOTICS TO LEARN MATHEMATICS AND SCIENCE. AS DATA COLLECTION TECHNIQUES, WE CONDUCTED FOCUS GROUPS AND SEMI-STRUCTURED INTERVIEWS WITH THE PARTICIPANTS AND ANALYZED THEIR RESPONSES THEMATICALLY. WE CONCLUDED THAT THE PROPOSAL COULD CONTRIBUTE TO MEETING THE CHALLENGES OF MULTIGRADE TEACHING. OUR FINDINGS SUGGEST THAT THE PROPOSAL WOULD SIMULTANEOUSLY PROMOTE THE DEVELOPMENT OF CURRICULAR LEARNING IN MATHEMATICS AND SCIENCE IN SEVERAL SCHOOL GRADES, OFFERING AN ALTERNATIVE FOR ADDRESSING VARIOUS TOPICS WITH DIFFERENT DEGREES OF DEPTH.

EN ESTE TRABAJO, SE DESCRIBE LA IMPLEMENTACIÓN DE UNA PLATAFORMA DE PRUEBAS PARA UN SISTEMA DE INSPECCIÓN DE PLANTAS DE ARÁNDANOS BASADO EN UN DRON CON CÁMARAS MULTIESPECTRALES. EL OBJETIVO PRINCIPAL ES IMPLEMENTAR, EN UN ROBOT UR3E, EL SEGUIMIENTO DINÁMICO DE TRAYECTORIAS COMANDADAS POR UN SISTEMA DE VISIÓN INCORPORADO EN SU EFECTOR FINAL, QUE PERMITA PROBAR Y EVALUAR DIVERSAS ALTERNATIVAS DE ESCANEO, DETECCIÓN E INSPECCIÓN DE LOS RACIMOS DE ARÁNDANOS EN LAS PLANTAS, QUE SERÍAN IMPLEMENTADAS POSTERIORMENTE EN EL DRON. PARA REALIZAR EXPERIMENTOS, SE COLOCARON IMÁGENES DE RACIMOS DE ARÁNDANOS, EN PEQUEÑNOS POSTES DE MADERA, QUE SE UBICARON EN EL ESPACIO ACCESIBLE POR EL UR3E. CUANDO EL ROBOT SE ACCIONA, COMIENZA A MOVER LA CÁMARA HASTA DETECTAR LOS FRUTOS, GUARDA UN VIDEO DE LA INSPECCIÓN DEL RACIMO, Y CONTINÚA SU TRAYECTORIA EN BUSCA DE MÁS FRUTOS. LOS RESULTADOS DE ESTAS PRUEBAS PERMITIERON ESTABLECER EL RANGO DE MOVIMIENTOS NECESARIOS PARA CAPTURAR IMÁGENES REPRESENTATIVAS DE LAS PLANTAS DE ARÁNDANOS, ESTABLECER Y VALIDAR LA COMUNICACIÓN ROBUSTA ENTRE LOS DISPOSITIVOS INVOLUCRADOS EN EL SISTEMA DE INSPECCIÓN Y CORROBORAR LA EFECTIVIDAD DEL MODELO DE DETECCIÓN DE ARÁNDANOS.

THIS ARTICLE INTRODUCES A VOICE-CONTROLLED ROBOTIC SYSTEM FOR EARLY EDUCATION, ENABLING CHILDREN AS YOUNG AS FOUR TO INTERACT WITH ROBOTS USING NATURAL VOICE COMMANDS. RECOGNIZING THE CHALLENGES POSED BY PROGRAMMING LANGUAGES AND ROBOT THEORY FOR YOUNG LEARNERS, THIS STUDY LEVERAGES RECENT ADVANCEMENTS IN ARTIFICIAL INTELLIGENCE, SUCH AS LARGE LANGUAGE MODELS, TO MAKE ROBOTS MORE INTELLIGENT AND EASIER TO USE. THIS INNOVATIVE APPROACH FOSTERS A NATURAL AND INTUITIVE INTERACTION BETWEEN THE CHILD AND THE ROBOT, EFFECTIVELY REMOVING BARRIERS TO ACCESS AND EXPANDING THE EDUCATIONAL POSSIBILITIES OF ROBOTICS IN THE CLASSROOM. IN THIS CONTEXT, A SOFTWARE PIPELINE IS PROPOSED THAT TRANSLATES VOICE COMMANDS INTO ROBOT ACTIONS. EACH COMPONENT IS TESTED USING DIFFERENT DEEP LEARNING MODELS AND CLOUD SERVICES TO DETERMINE THEIR SUITABILITY, WITH THE BEST ONES BEING SELECTED. FINALLY, THE CHOSEN SETUP IS VALIDATED THROUGH AN INTEGRATION TEST INVOLVING CHILDREN AGED 4 TO 6 YEARS. PRELIMINARY RESULTS DEMONSTRATE THE SYSTEM