Andrea Ronco

Last Name

Ronco

First Name

Andrea

ORCID

0000-0003-1672-8672

Organisational unit

03827 - Gassert, Roger / Gassert, Roger

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Publications 1 - 8 of 8

Assessing the Robustness of LiDAR, Radar and Depth Cameras Against Ill-Reflecting Surfaces in Autonomous Vehicles: An Experimental Study
Lötscher, Michael; Baumann, Nicolas; Ghignone, Edoardo; et al. (2023)
2023 IEEE 9th World Forum on Internet of Things (WF-IoT)
Range-measuring sensors play a critical role in autonomous driving systems. While LiDAR technology has been dominant, its vulnerability to adverse weather conditions is well-documented. This paper focuses on secondary adverse conditions and the implications of ill-reflective surfaces on range measurement sensors. We assess the influence of this condition on the three primary ranging modalities used in autonomous mobile robotics: LiDAR, RADAR, and Depth-Camera. Based on accurate experimental evaluation the papers findings reveal that under ill-reflectivity, LiDAR ranging performance drops significantly to 33% of its nominal operating conditions, whereas RADAR and Depth-Cameras maintain up to 100% of their nominal distance ranging capabilities. Additionally, we demonstrate on a 1:10 scaled autonomous racecar how ill-reflectivity adversely impacts downstream robotics tasks, highlighting the necessity for robust range sensing in autonomous driving.
TinyssimoRadar: In-Ear Hand Gesture Recognition with Ultra-Low Power mmWave Radars
Ronco, Andrea; Schilk, Philipp; Magno, Michele (2024)
IoTDI 2024: Proceedings of the 9th ACM/IEEE Conference on Internet of Things Design and Implementation
Smart Internet of Things (IoT) devices are on the rise in popularity, with innovative use cases and applications emerging every year. Including intelligence in these novel systems presents the challenge of integrating interaction and communica tion in scenarios where traditional interfaces are not viable. Hand Gesture Recognition (HGR) has been proposed as an intuitive Human-Machine Interface, potentially suitable for controlling several classes of devices in the context of the Internet of Things. This paper proposes a low-power in-ear HGR system based on mm-wave radars, efficient spatial and temporal Convolutional Neural Networks and an energy-optimized hardware design. The design is suitable for battery-operated devices, with stringent size and energy constraints, enabling user interaction with wearable devices, but also suitable for home appliances and industrial applications. The proposed machine learning model is characterized thoroughly for robustness and generalization capabilities, achieving 94.9% (single subject) and 86.1% (Leave One-Out Cross-validation) accuracy on a set of 11+1 gestures with a model size of only 36 KiB and inference latency of 32.4 ms on a 64 MHz Cortex-M33 microcontroller, making it compatible with real-time applications. The system is demonstrated in a fully integrated, miniaturized in-ear device with a full-system average power consumption of 18.4 mW, a more than 6x improvement on the current state of the art.
Frequency Matters: Comparative Analysis of Low-Power FMCW Radars for Vital Sign Monitoring
Marty, Steven; Ronco, Andrea; Pantanella, Federico; et al. (2024)
IEEE Transactions on Instrumentation and Measurement
Vital sign monitoring is a critical step in health assessment in clinical settings. A rising interest in contactless options is pushing for innovative solutions for heart rate (HR) and respiration rate (RR) estimation. Low-power millimeter-wave radars are emerging as a solution because of their invariance to lightning conditions, subject phenotype, and privacy guarantees. On the side, the robustness of radar-based systems is still a concern due to incomplete characterization of the systems, evaluation of different frequencies, and limited evaluation on human subjects. Moreover, low-power options have not been rigorously explored, despite their potential in ubiquitous deployment. This article evaluates three low-power frequency-modulated continuous wave (FMCW) radars with the frequencies of 24, 60, and 120 GHz, investigating their performance and the influence of the carrier frequency in vital sign estimation. An initial characterization of the displacement noise is conducted using a phantom device. Various techniques to enhance the signal to noise ratio (SNR) of the radar signal and the extraction of the chest displacement signal are combined. Finally, a lightweight and accurate algorithm based on the second-order derivative of the displacement is proposed, developed, and evaluated to assess the HR and RR. The evaluation is conducted for all three radar systems on a dataset with 24 subjects. We demonstrate with the experimental evaluation that the three systems accurately estimate the RR with a mean absolute error (MAE) of less than 2 brpm (±3.05). The 60- and 120-GHz system estimates the HR accurately with an MAE of 1.8 ± 3.1 bpm and 3.2 ± 5.3 bpm, respectively, while the 24-GHz system is less effective with an MAE of 9.0 bpm, mainly due to its high noise profile. This evaluation demonstrates the feasibility of HR and RR with low-power FMCW radars, underlying the importance of the operating frequency, and the necessity of an appropriate characterization when designing the algorithms.
Towards Robust Velocity and Position Estimation of Opponents for Autonomous Racing Using Low-Power Radar
Ronco, Andrea; Baumann, Nicolas; Giordano, Marco; et al. (2023)
2023 9th International Workshop on Advances in Sensors and Interfaces (IWASI)
This paper presents the design and development of an intelligent subsystem that includes a novel low-power radar sensor integrated into an autonomous racing perception pipeline to robustly estimate the position and velocity of dynamic obstacles. The proposed system, based on the Infineon BGT60TR13D radar, is evaluated in a real-world scenario with scaled race cars. The paper explores the benefits and limitations of using such a sensor subsystem and draws conclusions based on fieldcollected data. The results demonstrate a tracking error up to 0.21 +/- 0.29 m in distance estimation and 0.39 +/- 0.19 m/s in velocity estimation, despite the power consumption in the range of 10s of milliwatts. The presented system provides complementary information to other sensors such as LiDAR and camera, and can be used in a wide range of applications beyond autonomous racing.
Investigation of mmWave Radar Technology For Non-contact Vital Sign Monitoring
Marty, Steven; Pantanella, Federico; Ronco, Andrea; et al. (2023)
2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings
Non-contact vital sign monitoring has many advan tages over conventional methods in being comfortable, unobtru sive and without any risk of spreading infection. The use of millimeter-wave (mmWave) radars is one of the most promising approaches that enable contact-less monitoring of vital signs. Novel low-power implementations of this technology promise to enable vital sign sensing in embedded, battery-operated devices. The nature of these new low-power sensors exacerbates the challenges of accurate and robust vital sign monitoring and especially the problem of heart-rate tracking. This work focuses on the investigation and characterization of three Frequency Modulated Continuous Wave (FMCW) low-power radars with different carrier frequencies of 24 GHz, 60 GHz and 120 GHz. The evaluation platforms were first tested on phantom models that emulated human bodies to accurately evaluate the baseline noise, error in range estimation, and error in displacement estimation. Additionally, the systems were also used to collect data from three human subjects to gauge the feasibility of identifying heartbeat peaks and breathing peaks with simple and lightweight algorithms that could potentially run in low-power embedded processors. The investigation revealed that the 24 GHz radar has the highest baseline noise level, 0.04 mm at 0° angle of incidence, and an error in range estimation of 3.45 ± 1.88 cm at a distance of 60 cm. At the same distance, the 60 GHz and the 120 GHz radar system shows the least noise level, 0.01 mm at 0° angle of incidence, and error in range estimation 0.64 ± 0.01 cm and 0.04 ± 0.0 cm respectively. Additionally, tests on humans showed that all three radar systems were able to identify heart and breathing activity but the 120 GHz radar system outperformed the other two.
Machine Learning In-Sensors: Computation-enabled Intelligent Sensors For Next Generation of IoT
Ronco, Andrea; Schulthess, Lukas; Zehnder, David; et al. (2022)
2022 IEEE Sensors
Smart sensors are an emerging technology that allows combining the data acquisition with the elaboration directly on the Edge device, very close to the sensors. To push this concept to the extreme, technology companies are proposing a new generation of sensors allowing to move the intelligence from the edge host device, typically a microcontroller, directly to the ultra-low-power sensor itself, in order to further reduce the miniaturization, cost and energy efficiency. This paper evaluates the capabilities of a novel and promising solution from STMicroelectronics. The presence of a floating point unit and an accelerator for binary neural networks provide capabilities for in-sensor feature extraction and machine learning. We propose a comparison of full-precision and binary neural networks for activity recognition with accelerometer data generated by the sensor itself. Experimental results have demonstrated that the sensor can achieve an inference performance of 10.7 cycles/MAC, comparable to a Cortex-M4-based microcontroller, with full-precision networks, and up to 1.5 cycles/MAC with large binary models for low latency inference, with an average energy consumption of only 90 μJ/inference with the core running at 5 MHz.
In-Ear-Voice: Towards Milli-Watt Audio Enhancement With Bone-Conduction Microphones for In-Ear Sensing Platforms
Schilk, Philipp; Polvani, Niccolò; Ronco, Andrea; et al. (2023)
IoTDI '23: Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation
The recent ubiquitous adoption of remote conferencing has been accompanied by omnipresent frustration with distorted or otherwise unclear voice communication. Audio enhancement can compensate for low-quality input signals from, for example, small true wireless earbuds, by applying noise suppression techniques. Such processing relies on voice activity detection (VAD) with low latency and the added capability of discriminating the wearer's voice from others - a task of significant computational complexity. The tight energy budget of devices as small as modern earphones, however, requires any system attempting to tackle this problem to do so with minimal power and processing overhead, while not relying on speaker-specific voice samples and training due to usability concerns. This paper presents the design and implementation of a custom research platform for low-power wireless earbuds based on novel, commercial, MEMS bone-conduction microphones. Such microphones can record the wearer's speech with much greater isolation, enabling personalized voice activity detection and further audio enhancement applications. Furthermore, the paper accurately evaluates a proposed low-power personalized speech detection algorithm based on bone conduction data and a recurrent neural network running on the implemented research platform. This algorithm is compared to an approach based on traditional microphone input. The performance of the bone conduction system, achieving detection of speech within 12.8ms at an accuracy of 95% is evaluated. Different SoC choices are contrasted, with the final implementation based on the cutting-edge Ambiq Apollo 4 Blue SoC achieving 2.64mW average power consumption at 14uJ per inference, reaching 43h of battery life on a miniature 32mAh li-ion cell and without duty cycling.
Demo Abstract: In-Ear-Voice - Towards Milli-Watt Audio Enhancement With Bone-Conduction Microphones for In-Ear Sensing Platforms
Schilk, Philipp; Polvani, Niccolò; Ronco, Andrea; et al. (2023)
IoTDI '23: Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation
This demonstration presents a custom-developed research platform for low-power wireless earbuds based on the cutting-edge Ambiq Apollo 4 Blue SoC, and targeted at applications in in-ear sensing and on-the-edge data processing. The earbud shown is currently equipped with a novel, commercial MEMS bone-conduction microphone. Such microphones can record the wearer's speech with much greater isolation, enabling personalized voice activity detection and further audio enhancement applications. The device is running a specialized, TinyML-based, voice activity detection algorithm, indicating the wearer's speech using an onboard LED. A second identical earbud attempts to do the same detection using a traditional air-conduction microphone is also shown to underline the advantage the bone-conduction microphone provides. Overall the platform achieves 2.64mW average power consumption at 14uJ per inference, reaching 43h of battery life on a miniature 32mAh li-ion cell without duty cycling.

Publications 1 - 8 of 8

Andrea Ronco

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