Faculty Profiles

Chowdhury, B. U., Valles Molina, D., & Shougat, M. R. E. U. (n.d.). Low-Cost Sensor Fusion Framework for Organic Substance Classification and Quality Control Using Classification Methods. IEEE.

Bowler, D. W., Deinhardt, A. M., Menezes De Carvalho, M., Liu, T., & Valles Molina, D. (n.d.). Simulation-Based Smart Home Architecture for Autism Support Using CSI-Based Movement Detection. In 11th National Workshop for REU Research in Networking and Systems (REUNS 2025) in the 22nd IEEE International Conference on Mobile Ad-Hoc and Smart Systems (MASS 2025). IEEE.

Deinhardt, A. M., Bowler, D. W., Menezes De Carvalho, M., Liu, T., & Valles Molina, D. (n.d.). IoT for Autism: Analyzing Motor Behaviors in Virtual Environments Using Visual Data. In 11th National Workshop for REU Research in Networking and Systems (REUNS 2025) in the 22nd IEEE International Conference on Mobile Ad-Hoc and Smart Systems (MASS 2025). IEEE.

Kaya, E. B., Aslan, S., Valles Molina, D., Dutta, A. K., & Iscan, S. (2025). Motor Fault Diagnosis Across Variable Power Using Deep Learning. IEEE. https://doi.org/10.1109/CITS65975.2025.11099471

Deb, P., Valles Molina, D., & Aslan, S. (2025). FPGA based Matrix Multiplication Accelerator. IEEE. https://doi.org/10.1109/CITS65975.2025.11099187

Schulze, M., Aslan, S., Valles Molina, D., & Stern, H. P. (2025). Image Restoration with Variational Dynamic Memory Net, U-Net and LNNS. IEEE. https://doi.org/10.1109/CITS65975.2025.11099192

Talley, K. G., Muci-Kuchler, K. H., Valles Molina, D., Gutierrez, C. F., & Tate, J. S. (2025). Sowing the SEEDs (Scholars of Excellence in Engineering Design): Starting the SEED NSF S-STEM Program at Texas State University. In Proceedings of the 2025 ASEE Annual Conference and Exposition, Montreal, Quebec, Canada, June 22 to 25, 2025. ASEE Paper ID # 46128. ASEE. https://doi.org/10.18260/1-2--55827

Yang, A., Smith, E. R., Valles Molina, D., & Aslan, S. (2025). HoloLens AR-Based Handwashing Tool for Children with Autism Spectrum Disorder. IEEE. https://doi.org/10.1109/FNWF63303.2024.11028805

Lopez, A., Valles Molina, D., & Drewery, M. L. (2025). Developing and Validating Artificial Intelligence Models to Predict Cattle Behavior, Movement, and Emotion. Journal of Animal Science. https://doi.org/https://doi.org/10.1093/jas/skaf170.094

Shrestha, D., & Valles Molina, D. (2025). Reinforced NEAT Algorithms for Autonomous Rover Navigation in Multi-Room Dynamic Scenario. Fire, 8. https://doi.org/https://doi.org/10.3390/fire8020041

Li, J. B., Farrell, J. W., & Valles Molina, D. (2025). Data Augmentation for Classifying Multiple Sclerosis Severity through Inertial Measurement Unit-Based Gait Analysis (pp. 6443–6450). IEEE. https://doi.org/10.1109/BIBM62325.2024.10822219

Smith, E., Koldenhoven, R. M., Farrell, J. W., Li, Y., Aslan, S., Resendiz, M. D., … Valles Molina, D. (2024). Development of an Augmented Reality Handwashing Tool for Children With Autism Spectrum Disorder. Las Vegas, NV, USA: IEEE.

Gonzalez, B., Grubenhoff, J., Guerra, K., Chen, H., & Valles Molina, D. (2024). Passive Self-Righting Robot Design for Cattle Farming.

Nachega, M., Yahaya, S., Wallace, I., Liu, T., Valles Molina, D., Koldenhoven, R. M., & Li, Y. (2024). Enhancing Early Diagnosis of Autism With Machine Learning Algorithms Using Postural Control Features (pp. 593–598). IEEE. https://doi.org/10.1109/UEMCON62879.2024.10754700

Khan, S. A., & Valles Molina, D. (2024). Deepfake Detection Using Transfer Learning (pp. 556–562). IEEE. https://doi.org/10.1109/UEMCON62879.2024.10754706

Nonaka, H., & Valles Molina, D. (2024). Fully Auto-Regressive Multi-modal Large Language Model for Contextual Emotion Recognition (pp. 0291–0299). IEEE. https://doi.org/10.1109/UEMCON62879.2024.10754673

Malik, A. A., Zaki, A. M., Tran, N. C., Liang, I. X., Liu, T., & Valles Molina, D. (2024). Virtual Reality on Assessing the Motor Skills of Individuals with Autism Spectrum Disorder (pp. 548–555). IEEE. https://doi.org/10.1109/UEMCON62879.2024.10754723

Shrestha, D., & Valles Molina, D. (2024). Evolving Autonomous Navigation: A NEAT Approach for Firefighting Rover Operations in Dynamic Environments (pp. 247–255). IEEE. https://doi.org/10.1109/eIT60633.2024.10609942

Smith, E. R., Grahm, A. T., McCawley, J. E., & Valles Molina, D. (2024). Ultrasonic Frequency Anomaly Localization with Machine and Deep Learning (pp. 29–37). IEEE. https://doi.org/10.1109/AIIoT61789.2024.10579011

Farzana, F., & Valles Molina, D. (2024). Enhancing Pedestrian Safety: Predicting Movements with Deep Learning Models (pp. 8–15). IEEE. https://doi.org/10.1109/AIIoT61789.2024.10579012

Rafiq, S., Ellsworth, E. S., Resendiz, O., Varanasi, S. H., Ishola, A. A., Rolfe, R. M., … Valles Molina, D. (2024). Design of Autonomous Rover for Firefighter Rescue: Integrating Deep Learning with ROS2 (pp. 421–428). IEEE. https://doi.org/10.1109/AIIoT61789.2024.10578950

Hossain, S., & Valles Molina, D. (2024). Traffic Safety through Machine Learning: A Study of Crash Severity Factors (pp. 16–23). IEEE. https://doi.org/10.1109/AIIoT61789.2024.10579015

Jackson, G., & Valles Molina, D. (2024). Dataset Enlargement with Generative Adversarial Neural Networks (pp. 45–51). IEEE. https://doi.org/10.1109/AIIoT61789.2024.10578969

Tran, N. C., Liang, I. X., Liu, T., & Valles Molina, D. (2024). Board 300: Impact of Virtual Reality on Motor-Skill Performance in Children with Autism Spectrum Disorder. ASEE. https://doi.org/10.18260/1-2--46878

Liu, T., Wallace, I., Cressman, H., Rolfe, R. M., Li, Y., & Valles Molina, D. (2024). Enhancing early diagnosis of autism spectrum disorder with machine learning algorithms using postural control features. Journal of Motor Learning and Development, 12(S1). https://doi.org/10.1123/jmld.2024-0049

Little, A., Xiang, Y., Yang, H., Dong, S., & Valles Molina, D. (2024). Pandemic Wave-Based Influence Analysis of Social Media Information. IISE Annual Conference and Expo. Retrieved from https://iise.confex.com/iise/2024/meetingapp.cgi/Paper/7784

Pawar, N. M., Gujar, S., Dhonde, H. B., & Valles Molina, D. (2024). Early Prediction of Characteristic Compressive Strength of Concrete Based on Mix Proportions Using Modified Dimensional Analysis (pp. 0043–0052). Las Vegas, NV, USA: IEEE. https://doi.org/10.1109/CCWC60891.2024.10427830

Smith, E. R., Rolfe, R. M., Farrell, J. W., Aslan, S., Resendiz, M. D., Li, Y., … Valles Molina, D. (2024). Development of an Augmented Reality Handwashing Tool for Children With Autism Spectrum Disorder (pp. 0249–0256). Las Vegas, NV, USA: IEEE. https://doi.org/10.1109/CCWC60891.2024.10427963

Woodman, C. J., Ridlon, A., Evelyn, C. J., Martinez, A. C., & Valles Molina, D. (2023). Integrating machine learning and infrared smart cameras into critically endangered bird production (pp. 0523–0527). IEEE. https://doi.org/10.1109/UEMCON59035.2023.10316121

Ellsworth, E. S., Rafiq, S., & Valles Molina, D. (2023). Saving lives while reducing first responder risks – with AI. Dell Technologies. Retrieved from https://www.workstationguides.com/briefs/texasstateuniversity/

Valles Molina, D., Umali, L. A. R., Paveglio, T., Brinson, J. N., Hyder, M., Jackson, G. E., … Liu, T. (2023). Data Collection and Real-Time Facial Emotion Recognition in iOS Apps With CNN-Based Models. Seattle, WA, USA: IEEE. https://doi.org/10.1109/AIIoT58121.2023.10174520

Nooruddin, M., & Valles Molina, D. (2023). An Advanced IoT Framework for Long Range Connectivity and Secure Data Transmission Leveraging LoRa and ASCON Encryption. USA: IEEE. https://doi.org/10.1109/AIIoT58121.2023.10174401

Ishola, A. A., & Valles Molina, D. (2023). Enhancing Safety and Efficiency in Firefighting Operations through Deep Learning and Temperature Forecasting Modeling in Autonomous Unit. Sensors - Advances in Intelligent Robotics Systems Based Machine Learning, 23(10). https://doi.org/10.3390/s23104628

Islam, S. B., Valles, D., Hibbits, T. J., Ryberg, W. A., & Forstner, M. R. (2023). Animal Species Recognition with Deep Convolutional Neural Network from Ecological Camera Trap Image. Animals - Use of Camera Trap for a Better Wildlife Monitoring and Conservation, 13(i9). https://doi.org/10.3390/ani13091526

Somvanshi, S., Zhu, C., Ikehata, K., Valles Molina, D., & Jin, T. (2023). Wind Speed Forecasting for Designing Sustainable Wastewater Treatment Plants (pp. 0844–0850). IEEE. https://doi.org/10.1109/CCWC57344.2023.10099313

Saha, S., & Valles Molina, D. (2023). Forecast Analysis of Visibility for Airport Operations with Deep Learning Techniques (p. pp.553-558). IEEE. https://doi.org/10.1109/CCWC57344.2023.10099100

Grimes, D., & Valles Molina, D. (2023). Performance Analysis of TensorFlow2 Object Detection API Models for Engineering Site Surveillance Applications (p. pp.547-552). IEEE. https://doi.org/10.1109/CCWC57344.2023.10099270

Alonso, D., Alonso, E., & Valles Molina, D. (2023). Classification Challenges and Analysis of Traffic Patterns for Highly Congested Areas in Central Texas (pp. 382–388). IEEE. https://doi.org/10.1109/CCWC57344.2023.10099316

Sharotry, A., Jimenez, J. A., Mendez, F. A., Wierschem, D. C., Koldenhoven, R. M., & Valles Molina, D. (2022). Manufacturing operator ergonomics: A conceptual digital twin approach to detect biomechanical fatigue. IEEE Access, (10), 12774–12791. https://doi.org/10.1109/ACCESS.2022.3145984

Tushar, S. N. B., Sarker, S., Stapleton, W. A., & Valles Molina, D. (2022). Peanut maturity classification by features extracted from selected hyperspectral components (pp. 176–183). IEEE. https://doi.org/10.1109/GHTC55712.2022.9911049

Rahman, M., Haque, A., Pujara, D. S., Mayorga, J., Kang, H. G., & Valles Molina, D. (2022). Automation of Luminescence Quantitation for High-Throughput Plant Phenotyping Using Image Processing and U-Net Segmentation (p. 117). Las Vegas, NV, USA: American Council on Science & Education. Retrieved from https://american-cse.org/static/CSCE22-book-abstracts-printing.pdf

Ishola, A. A., & Valles, D. (2022). Using Machine Learning and Regression Analysis to Classify and Predict Danger Levels in Burning Sites (pp. 453–459). IEEE. https://doi.org/10.1109/AIIoT54504.2022.9817232

Sefat, Md. S., & Shahjahan, Md. (2022). [Review of Ensemble Training With Classifiers Selection Mechanism, by M. Rahman & D. Valles Molina] (pp. 0131–0136). IEEE. https://doi.org/10.1109/UEMCON53757.2021.9666676

Thapa, K., McClellan, S. A., & Valles Molina, D. (2022). Supervised Machine Learning in Inter-Level, Ultra-Low Frequency Power Line Communications. International Journal On Advances in Telecommunications, 14(1 & 2), 51:69.

Sharotry, A., Jimenez, J., Wierschem, D. C., Mendez, F. A., Koutitas, G., Valles Molina, D., … Rolfe, R. M. (2021). A Digital Twin Framework for Real-Time Analysis and Feedback of Repetitive Work in the Manual Material Handling Industry (pp. 2637–2648). IEEE. https://doi.org/10.1109/WSC48552.2020.9384043

Thapa, K., McClellan, S. A., & Valles Molina, D. (2021). Supervised machine learning in digital power line communications (pp. 16–21).

Paveglio, T., & Valles Molina, D. (2021). Second Sight: MobileNet v1 Integration in Dynamic and Time Critical Scenarios. IEEE. https://doi.org/10.1109/IEMCON53756.2021.9623152

Alam, H., & Valles Molina, D. (2021). Debris Object Detection Caused by Vehicle Accidents Using UAV and Deep Learning Techniques. IEEE. https://doi.org/10.1109/IEMCON53756.2021.9623110

Valles Molina, D. (2021). Democratizing access to data science boosts university’s research. Dell Technologies. Retrieved from https://www.delltechnologies.com/asset/en-us/products/workstations/customer-stories-case-studies/texas-state-university-dell-dsw-case-study.pdf

Saeed, F. S., Bashit, A. A., Viswanathan, V. R., & Valles Molina, D. (2021). An Initial Machine Learning-Based Victim’s Scream Detection Analysis for Burning Sites. Applied Sciences, 11(18). https://doi.org/10.3390/app11188425

Valles Molina, D., & Matin, R. (2021). An Audio Processing With Ensemble Learning Approach for Speech-Emotion Recognition for Children With ASD (pp. 0055–0061). IEEE. https://doi.org/10.1109/AIIoT52608.2021.9454174

Pillai, U. K., & Valles Molina, D. (2021). An Initial Deep CNN Design Approach for Identification of Vehicle Color and Type for Amber and Silver Alerts. IEEE. https://doi.org/10.1109/CCWC51732.2021.9375917

Jimenez, J., Aslan, S., Valles Molina, D., Koutitas, G., Mendez, F. A., & Wierschem, D. C. (2021). Using industry 4.0 digital twins to model human labor in smart material handling systems.

Islam, S., & Valles Molina, D. (2020). Performance Analysis and Evaluation of LSTM and GRU Architectures for Houston toad and Crawfish frog Call Detection (pp. 0106–0111). IEEE. https://doi.org/10.1109/UEMCON51285.2020.9298170

Sharma, P., & Valles Molina, D. (2020). Backbone Neural Network Design of Single Shot Detector from RGB-D Images for Object Detection (pp. 0112–0117). IEEE. https://doi.org/10.1109/UEMCON51285.2020.9298175

Islam, S., Valles Molina, D., & Forstner, M. R. (2020). A Houston Toad Call Detection Initial Approach Using Gated Recurrent Units for Conservational Efforts. IEEE. https://doi.org/10.1109/IETC47856.2020.9249158

Islam, S. B., Valles Molina, D., & Forstner, M. R. (2020). Identification of Wild Species in Texas from Camera-trap Images using Deep Neural Network for Conservation Monitoring (pp. 1–6). IEEE. https://doi.org/10.1109/IETC47856.2020.9249141

Matin, R., & Valles Molina, D. (2020). A Speech Emotion Recognition Solution Based on Support Vector Machine for Children with Autism Spectrum Disorder to Help Identify Human Emotions. IEEE. https://doi.org/10.1109/IETC47856.2020.9249147

Hernandez, M., Valles Molina, D., Wierschem, D. C., Rolfe, R. M., Koutitas, G., Mendez, F. A., … Jimenez, J. (2020). An Initial Julia Simulation Approach to Material Handling Operations from Motion Captured Data. In 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) (pp. 0718–0722). IEEE. https://doi.org/10.1109/iemcon51383.2020.9284829

Pillai, U. K., & Valles Molina, D. (2020). Vehicle Type and Color Classification and Detection for Amber and Silver Alert Emergencies Using Machine Learning. IEEE. https://doi.org/10.1109/IEMTRONICS51293.2020.9216368

Carroll, T., Hernandez, G., Koutitas, G., Wierschem, D. C., Mendez, F. A., Valles Molina, D., … Jimenez, J. (2020). Comparison of inverse kinematics algorithms for digital twin industry 4.0 applications (pp. 0300–0305). IEEE.

Blake, N. A., Sehin, O., Partain, J. W., Valles Molina, D., Marquez, A., Jimenez, J., … Davis, R. (2020). Cross-cultural engineering skill development at an international engineering summer boot camp. https://doi.org/10.18260/1-2--34357

Sharotry, A., Jimenez, J., Wierschem, D. C., Mendez, F. A., Koutitas, G., Valles Molina, D., … Rolfe, R. M. (2020). A digital twin framework of a material handling operator in industry 4.0 environments (pp. 45–52).

Sharma, P., & Valles Molina, D. (2020). Deep Convolutional Neural Network Design Approach for 3D Object Detection for Robotic Grasping (pp. 0311–0316). IEEE. https://doi.org/10.1109/CCWC47524.2020.9031186

Jaradat, F., & Valles Molina, D. (2020). A Victims Detection Approach for Burning Building Sites Using Convolutional Neural Networks (pp. 0280–0286). IEEE. https://doi.org/10.1109/CCWC47524.2020.9031275

Johnson, D., & Valles Molina, D. (2020). A Non-Linear GPU Performance Modeling Approach and Consolidated Linear Hardware Model Performance Evaluation of the LEAP Cluster (pp. 0517–0523). IEEE. https://doi.org/10.1109/CCWC47524.2020.9031282

Islam, S., & Valles Molina, D. (2020). Houston Toad and Other Chorusing Amphibian Species Call Detection Using Deep Learning Architectures (pp. 0511–0516). IEEE. https://doi.org/10.1109/CCWC47524.2020.9031223

Islam, S. B., & Valles Molina, D. (2020). Identification of Wild Species in Texas from Camera-trap Images Using Deep Neural Network for Conservation Monitoring (pp. 0537–0542). IEEE. https://doi.org/10.1109/CCWC47524.2020.9031190

Hernandez, G., Valles Molina, D., Wierschem, D. C., Rolfe, R. M., Koutitas, G., Aslan, S., … Jimenez, J. (2020). Machine Learning Techniques for Motion Analysis of Fatigue from Manual Material Handling Operations Using 3D Motion Capture Data (pp. 0300–0305). IEEE. https://doi.org/10.1109/CCWC47524.2020.9031222

Bashit, A., & Valles Molina, D. (2020). MFCC based Houston Toad call Detection using LSTM (pp. D3-3-1-D3-3–6). IEEE. https://doi.org/10.1109/ISMCR47492.2019.8955667

Bashit, A. A., & Valles Molina, D. (2020). A Solar Powered Raspberry Pi Houston Toad Call Detection System Using Neural Network Model (pp. 1024–1027). IEEE Computer Society. https://doi.org/10.1109/CSCI46756.2018.00198

Pinales, A., & Valles Molina, D. (2020). AESV Integration of IMU and Implementation of Interleaved Data Acquisition and Transmission Method (p. pp 541-544). IEEE Computer Society. https://doi.org/10.1109/CSCI46756.2018.00110

DasGupta, B., Valles Molina, D., & McClellan, S. A. (2020). A Comparison of MLA Techniques for Classification of Network Bandwidth Loss (pp. 771–775). IEEE Computer Society. https://doi.org/10.1109/CSCI46756.2018.00155

Jaradat, F., & Valles Molina, D. (2020). A Human Detection Approach for Burning Building Site Using Deep Learning Techniques (pp. 1434–1435). IEEE Computer Society. https://doi.org/10.1109/CSCI46756.2018.00277

Dasgupta, B., Valles Molina, D., & McClellan, S. A. (2019). Estimating TCP RTT with LSTM Neural Networks (pp. 192–198).

Johnson, D. A., & Valles Molina, D. (2019). A Linear Approach to Network Performance Modeling and a Consolidation of Linear Performance Models of the LEAP Cluster (pp. 132–135).

Valles Molina, D., & Haque, Md. I. A. (2019). Facial Expression Recognition Using DCNN and Development of an iOS App for Children with ASD to Enhance Communication Abilities (pp. 0476–0482). IEEE. https://doi.org/10.1109/UEMCON47517.2019.8993051

McClellan, S. A., Valles Molina, D., & Koutitas, G. (2019). Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning. Applied Sciences, 9(14). https://doi.org/https://doi.org/10.3390/app9142902

Valles Molina, D., & McClellan, S. A. (2019). Using machine learning to optimize Linux networking. Linux Journal, (298), 128–138. Retrieved from https://secure2.linuxjournal.com/ljarchive/LJ/298/12625.html

Bashit, A. A., & Valles Molina, D. (2019). A Mel-Filterbank and MFCC-based Neural Network Approach to Train the Houston Toad Call Detection System Design. IEEE. https://doi.org/10.1109/IEMCON.2018.8615076

Haque, M. I. A., & Valles Molina, D. (2019). A Facial Expression Recognition Approach using DCNN for Autistic Children to Identify Emotions. IEEE. https://doi.org/10.1109/IEMCON.2018.8614802

Pinales, A., & Valles Molina, D. (2019). Autonomous Embedded System Vehicle Design on Environmental, Mapping and Human Detection Data Acquisition for Firefighting Situations. IEEE. https://doi.org/10.1109/IEMCON.2018.8615022

DasGupta, B., Valles Molina, D., & McClellan, S. A. (2019). A K-Means Algorithm Approach for Classifying Wireless Signal Loss Using RTT and Bandwidth. IEEE. https://doi.org/10.1109/IEMCON.2018.8615015

Johnson, D. A. S., & Valles Molina, D. (2019). An Initial Scale-Factor Linear Polynomial Regression Model Approach for Hardware Performance on an HPC Compute-Node. IEEE. https://doi.org/10.1109/IEMCON.2018.8614937

Haque, M. I., & Valles Molina, D. (2018). Facial Expression Recognition from Different Angles Using DCNN for Autistic Children to Recognize Emotional Patterns. IEEE Computer Society.

Valles Molina, D., Apple, M. E., & Andrews, C. (2018). Visual Simulation Correlate Plant Functional Trait Distribution with Elevation and Temperature in the Cairngorm Mountains of Scotland (pp. 1252–1258). IEEE Computer Society. https://doi.org/10.1109/CSCI.2017.220

Jaradat, F., & Valles Molina, D. (2018). An Exponential Smoothing Embedded System Approach to Dangerous Temperature Detection for Firefighter Safety (pp. 41–44). United States of America: CSREA Press. Retrieved from https://americancse.org/events/csce2018/proceedings

Bashit, A. A., & Valles Molina, D. (2018). An Embedded Approach for Controlling Automatic Water Pump and Monitoring Real-Time Remote Data on Desktop, Android, and Web-based Application (pp. 33–36). United States of America: CSREA Press. Retrieved from https://americancse.org/events/csce2018/proceedings

Azami, N., & Valles Molina, D. (2018). An Electrical Vehicle Charging Station Monitoring Embedded Design (pp. 58–61). United States of America: CSREA Press. Retrieved from https://americancse.org/events/csce2018/proceedings

Haque, M. I. U., & Valles Molina, D. (2018). Design of a Sensor-Based Adaptive Smart Home System Using ARM Cortex-M3 (pp. 22–25). United States of America: CSREA Press. Retrieved from https://americancse.org/events/csce2018/proceedings

Jewel, M. U., DasGupta, B., & Valles Molina, D. (2018). Gas and Air Quality Detection, Monitoring, and Alerting Using Embedded System for Nanofabrication Facility (pp. 45–48). United States of America: CSREA Press. Retrieved from https://americancse.org/events/csce2018/proceedings

DasGupta, B., & Valles Molina, D. (2018). IP Packet Loss and RTT Calculation Simulation Using Low-Cost Embedded Real-Time Systems (pp. 54–57). United States of America: CSREA Press. Retrieved from https://americancse.org/events/csce2018/proceedings

Freedman, R. J., & Valles Molina, D. (2018). A Communication Benchmark Tailored to Intel Broadwell Nodes and Tuned to the DEAC Cluster (pp. 502–508). IEEE. https://doi.org/10.1109/CCWC.2018.8301671

Freedman, R. J., & Valles Molina, D. (2017). A Modeling Approach to Hardware Analysis of the Heterogeneous DEAC Cluster (pp. 1408–1409). IEEE. https://doi.org/10.1109/CSCI.2016.0272

Valles Molina, D., Apple, M. E., Dick, J., Andrews, C., Gutierrez-Giron, A., & Pauli, H. (2015). Modeling Plant Functional Traits and Elevation in the Cairngorn Mountains of Scotland.

Valles Molina, D. (2013). A Numerical Modeling MATLAB Approach to Memory Behavior on a Multi-core Architecture on a Beowulf Cluster Single-Node.

Valles Molina, D., Williams, D., & Nava, P. (2012). Scheduler Modifications for Improvement of Performance on a Beowulf Cluster Single Node.

Valles Molina, D., Williams, D., & Nava, P. (2012). Load Balancing Approach Based on Limitations and Bottlenecks of Multi-core Architecture on a Beowulf Cluster Compute-Node.

Valles Molina, D., Williams, D., & Nava, P. (2009). Performance and Timing Measurements in a Beowulf Cluster Compute-Node.