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9 pages, 394 KB

Open AccessProceeding Paper

From Human-Computer Interaction to Human-Robot Manipulation

by Shuwei Guo, Cong Yang, Zhizhong Su, Wei Sui, Xun Liu, Minglu Zhu and Tao Chen

Eng. Proc. 2025, 110(1), 1; https://doi.org/10.3390/engproc2025110001 - 25 Sep 2025

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Abstract

The evolution of Human–Computer Interaction (HCI) has laid the foundation for more immersive and dynamic forms of communication between humans and machines. Building on this trajectory, this work introduces a significant advancement in the domain of Human–Robot Manipulation (HRM), particularly in the remote operation of humanoid robots in complex scenarios. We propose the Advanced Manipulation Assistant System (AMAS), a novel manipulation method designed to be low cost, low latency, and highly efficient, enabling real-time, precise control of humanoid robots from a distance. This method addresses critical challenges in current teleoperation systems, such as delayed response, expensive hardware requirements, and inefficient data transmission. By leveraging lightweight communication protocols, optimized sensor integration, and intelligent motion mapping, our system ensures minimal lag and accurate reproduction of human movements in the robot counterpart. In addition to these advantages, AMAS integrates multimodal feedback combining visual and haptic cues to enhance situational awareness, close the control loop, and further stabilize teleoperation. This transition from traditional HCI paradigms to advanced HRM reflects a broader shift toward more embodied forms of interaction, where human intent is seamlessly translated into robotic action. The implications are far-reaching, spanning applications in remote caregiving, hazardous environment exploration, and collaborative robotics. AMAS represents a step forward in making humanoid robot manipulation more accessible, scalable, and practical for real-world deployment. Full article

(This article belongs to the Proceedings of The 2nd International Conference on AI Sensors and Transducers)

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9 pages, 1130 KB

Open AccessProceeding Paper

Development of an Integrated Satellite-Based Estimation Method for Water Transparency and Algal Beds in the Mekong River

by Tomonari Masuzaki, Shuta Murakami, Supachai Prainetr and Ganbat Davaa

Eng. Proc. 2025, 110(1), 2; https://doi.org/10.3390/engproc2025110002 - 14 Oct 2025

Viewed by 214

Abstract

This study presents an integrated satellite-based approach for estimating water transparency and algal bed distribution in the dynamic environment of the Mekong River using satellite imagery. Recognizing that previous research has predominantly focused on marine settings, this work addresses the unique challenges inherent in riverine systems like the Mekong, where low and variable transparency demands specialized techniques. Utilizing Sentinel-2 satellite data, a system was developed that compensates for water depth effects through the Bottom Index—a metric derived from the reflective properties of the riverbed. This approach effectively minimizes the influence of water depth variability on remote sensing data and enhances the accuracy of bottom characterization. Concurrently, water transparency is estimated via a ratio-based statistical model that correlates reflectance values from two selected bands to compute the Secchi-Disk Depth. Field experiments conducted in both coastal waters demonstrated that the estimated results align with in situ observations in algal bed distribution. Full article

(This article belongs to the Proceedings of The 2nd International Conference on AI Sensors and Transducers)

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9 pages, 7778 KB

Open AccessProceeding Paper

Adaptive IoT-Based Platform for CO₂ Forecasting Using Generative Adversarial Networks: Enhancing Indoor Air Quality Management with Minimal Data

by Alessandro Leone, Andrea Manni, Andrea Caroppo and Gabriele Rescio

Eng. Proc. 2025, 110(1), 3; https://doi.org/10.3390/engproc2025110003 - 30 Oct 2025

Viewed by 411

Abstract

Monitoring indoor air quality is vital for health, as CO₂ is a major pollutant. An automated system that accurately forecasts CO₂ levels can optimize HVAC management, preventing sudden increases and reducing energy waste while maintaining occupant comfort. Traditionally, such systems require extensive datasets collected over months to train algorithms, making them computational expensive and inefficient. To address this limitation, an adaptive IoT-based platform has been developed, leveraging a limited set of recent data to forecast CO₂ trends. Tested in a real-world setting, the system analyzed parameters such as physical activity, temperature, humidity, and CO₂ to ensure accurate predictions. Data acquisition was performed using the Smartex WWS T-shirt for physical activity data and the UPSense UPAI3-CPVTHA environmental sensor for other measurements. The chosen sensor devices are wireless and minimally invasive, while data processing was carried out on a low-power embedded PC. The proposed forecasting model adopts an innovative approach. After a 5-day training period, a Generative Adversarial Network enhances the dataset by simulating a 10-day training period. The model utilizes a Generative Adversarial Network with a Long Short-Term Memory network as the generator to predict future CO₂ values based on historical data, while the discriminator, also a Long Short-Term Memory network, distinguishes between actual and generated CO₂ values. This approach, based on Conditional Generative Adversarial Networks, effectively captures data distributions, enabling more accurate multi-step probabilistic forecasts. In this way, the framework maintains a Root Mean Square Error of approximately 8 ppm, matching the performance of our previous approach, while reducing the need for real training data from 10 to just 5 days. Furthermore, it achieves accuracy comparable to other state-of-the-art methods that typically requires weeks or even months of training. This advancement significantly enhances computational efficiency and reduces data requirements for model training, improving the system’s practicality for real-world applications. Full article

(This article belongs to the Proceedings of The 2nd International Conference on AI Sensors and Transducers)

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9 pages, 4397 KB

Open AccessProceeding Paper

Extract Temperature Coefficients of LGS for High-Temperature Applications Based on the Finite Element Method

by Danyu Mu, Hong Zhang, Jikai Zhang, Yan Feng, Hao Jin and Shurong Dong

Eng. Proc. 2025, 110(1), 4; https://doi.org/10.3390/engproc2025110004 - 24 Nov 2025

Viewed by 118

Abstract

Surface-acoustic-wave (SAW) sensors with Langasite (LGS) substrate have broad prospects in the field of wireless passive temperature sensing in harsh environments. However, there are still challenges in terms of accuracy regarding the material temperature coefficient of LGS and the temperature simulation of heavy mass load electrodes. This paper presents a method for fitting the material temperature coefficient of LGS based on a combination of finite element simulation (FEM) and measured data. Eleven different cuts of LGS SAW resonators were fabricated, and the frequency response of each cut device at 30–800 °C was obtained through experiments. Some of the data were used in the training dataset and the material temperature coefficient of LGS was obtained through comsol simulation fitting. The remaining data were used as a test dataset to verify the accuracy of the results. The results show that the material coefficient obtained using this method has good accuracy in the frequency prediction of thick electrode LGS SAW sensors at different temperatures with different cuts. Full article

(This article belongs to the Proceedings of The 2nd International Conference on AI Sensors and Transducers)

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