Real-time phonocardiogram analysis on embedded devices is a key enabler for scalable and accessible cardiovascular diagnostics, particularly considering portable systems designed for low-income countries. This work introduces a combination of compressive sensing and deep learning to build portable, efficient and effective diagnostic tools for widespread cardiac screenings. The proposed classification framework is tailored for deployment on ultra-low power STM32 microcontrollers equipped with the novel Neural-ART accelerator. Experimental evaluation on the CirCor Digiscope Phonocardiogram dataset demonstrates that even with a compression ratio exceeding 100×, a classification model can achieve up to 97.3% F1-score. A similar level of performance was obtained on the PhysioNet 2016 dataset, which was used to assess the robustness and generalization capability of the developed architectures. Compared to the state-of-the-art, our final edge solution achieves 94.3% accuracy, an inference time of 18.7 ms and an energy requirement of just 1.51 mJ per input window of 4,096 samples, confirming its suitability for real-time, energy-constrained medical applications.

The integration of compressive sensing with realtime embedded systems opens new possibilities for efficient, low-power biomedical signal acquisition. This paper presents a custom hardware platform based on the RP2350 microcontroller, tailored for synchronized multi-modal biomedical monitoring. The system is capable of capturing cardiopulmonary sounds, along with biopotential signals such as phonocardiography (PCG), electrocardiography (ECG) and electromyography (EMG), photoplethysmography (PPG), and inertial measurement unit (IMU) data for posture recognition. To ensure sampleaccurate synchronization, a Sub-1GHz radio system is used across multiple nodes. Wi-Fi and Bluetooth connectivity enable centralized data aggregation. Experimental results demonstrate the achieved decrease in power consumption when using compressive sensing, efficient multi-node synchronization, and scalability for wireless biomedical monitoring applications. The compact form factor and low-cost design make it suitable for various medical applications, including remote healthcare and long-term monitoring.

In music performance research, conservatory pianists have been a subject of interest. However, previous studies have often relied on data captured from unsynchronised devices, leaving a significant portion of potential data unexplored. This paper introduces a synchronisation method for a multimodal sensor setup to address this gap. The primary focus of this paper is the implementation of a synchronization mechanism. The proposed technique is a cheap and robust way to synchronise the required multimodal setup. The insights derived from this research can improve future studies and methodologies in musical performance research and are more generically applicable towards any multimodal sensor setup.

In this paper we present a flexible low-cost sonar sensor platform that can be used for a wide range of biomimetic sonar experiments and autonomous sonar navigation targeted at robotics applications. The navigation abilities of bats using ultrasound (sonar) in unknown cluttered environments are very effective and can be distilled into a sensor architecture and accompanying control methodology that lends itself to be implemented on cost efficient hardware. The sensor architecture and processing methodology of this sensing platform mimics that of bats. In this paper we specifically focused on the common big-eared bat (Micronycteris microtis) although this could be transferred to other bat species or even other echolocating animals since the experimental platform was designed for flexibility. Using this platform we were able to implement a control system using a subsumption architecture that features different behavior patterns based solely on the sonar sensor as a source of exteroceptive information. In order to validate the combination of our autonomous navigation control system and our developed sonar sensor platform, the hardware was mounted on the P3DX robotics platform that was introduced in an unknown testing environment and have it drive autonomously. These experiments were used to validate our assumption of the efficacy of these relatively simple biomimetic control mechanisms and thus alleviating the need for expensive sensing platforms for certain robotics applications.

In this paper we will propose a method to evaluate the performance of a certain ultra-wide band fixed anchor configuration in complex indoor environments by making use of the mutual information as the performance metric. Furthermore we will introduce an incremental algorithm that will determine an optimized anchor configuration for complex indoor environments. By making use of heuristics we are able to ensure that the time required to complete the algorithm is feasible on commercial grade computers, even for large-scale floor plans.

In this paper we describe a system for passive acoustic sound source localization in 3D using a distributed microphone array. By distributing the microphones into small-scale and large-scale arrays we can exploit array processing algorithms that use near-field and far-field properties of the sound source with respect to the array. The microphones on the small-scale device are sampled using a single, simultaneously sampling analog to digital converter which results in time synchronization between these microphone channels up to a small fraction of the signal's phase. The large-scale array consists of multiple small-scale devices and can be deployed over a much larger capture volume. We create the distributed microphone arrays in a manner that is scale-free with regard to the amount of microphones in the system, the capture volume, the type of sound and the type of sensors/microphones. In this paper we describe the hardware topology, the localization algorithms and the obtained results.

Pose estimation systems have seen some big developments in the last two decades due to technological advances and a greater need for these systems. The industry that thrives and simultaneously popularizes these developments is the entertainment industry. One of the latest developments in home entertainment are Virtual Reality systems that lets users have an immersive experience when playing video games. In order to fully achieve this immersive experience, objects in the ‘real world’ can be used to manipulate objects in the virtual world. Therefore the ‘real world’ objects need to be located in the user's environment to a great extent. One of these virtual reality kits utilizes an optical solution that incorporates dual-axis rotating laser sweeps on the transmitter side and photodiodes on the receiver side to achieve the positioning of these objects. Since the transmitters are stand-alone systems that can be purchased at a relatively low price, we propose to use this hardware in combination with custom low-cost receiver hardware to achieve an affordable yet precise and accurate six degrees-of-freedom human body pose estimation system.

This paper describes an alternative approach to giving people with limited hand and arm movement the ability to select objects on a computing device using head movement (head mouse). To allow for better filtering of unintentional head movement, and to allow for a faster update rate of the head mouse, the full six-DoF pose of the head is estimated using a low-cost camera and IR markers, a 3-axis accelerometer, and a 3-axis magnetometer. The pose estimation problem was cast in a probabilistic fashion in which information from the different sensors is fused into a single a-posteriori distribution for the sensor pose. Simulations were run to analyze the influence of the proposed sensor fusion algorithm on the stability and accuracy of the pose estimation, and thus on the ability to point a mouse cursor to a specified location on a screen. Experiments were then performed validating the results from the simulations on real sensors. The proposed algorithm was shown to give more accurate and stable results than using only a camera to estimate the six-DoF pose.

This paper introduces and validates the performance of an alternative input device for people with limited hand/arm movement and control. A summary is provided of the current state of the art in alternative input devices. Based on this, a low-cost solution is proposed that allows for 1) low latency and high operating speed and accuracy, 2) use from different viewing angles without recalibration, and 3) the ability to seamlessly control multiple devices. A prototype of this system was built and tested to determine the accuracy of the system (usingapan-tilt system), and to analyze the performance of the system compared to the state of the art (through user tests, based on the ISO 9241-411 test). The proposed system allows for great accuracy (σX= 0.28 px, σY= 0.29 px, σXY= 0.02 px), a decent performance compared to the state of the art (throughput = 1.52 bits/s, error rate = 31%), and good results in the ISO 9241-411 independent rating scale. The results from the performed experiments show that the proposed system leads great promise in real-world applications. A low-cost head-mounted camera could be used as an alternative human interface device for people with limited hand/arm movement and control, allowing them to participate in the ongoing trend of computing devices gaining importance in our everyday activities.

Throughout the last decades, human motion capture systems have become an important tool for various sectors. Besides the entertainment sector, which is probably the most known sector to use these systems through popular movies and video games, the medical sector has adopted this technology as an analysis tool. These clinical analyses measure the human body posture and movement for various purposes including rehabilitation and sports, and require high accuracy and precision. Although there is a variety of products that offer these capabilities, there is not yet an affordable system that meets the requirements for medical implementations. We therefore propose using an ultrasonic six degrees-of-freedom sensor system which combines a distributed array of ultrasonic emitters and a small array of ultrasonic receivers. In order to support low-cost human pose estimation, while retaining the possibility for re-use and expandability, the need for a flexible embedded hardware platform naturally emerged. The proposed hardware design supports the requirements of a high accuracy and high precision pose estimation system while keeping its overall system cost low.

Passive acoustic localization is an important technique in a wide variety of monitoring applications, ranging from healthcare over biological survey to structural health monitoring of buildings. If the subject of interest emits a recognizable sound and is picked up by a distributed array of microphones it is possible to measure the difference in arrival times and reconstruct the source positions. This requires the microphones to be synchronized up to a fraction of the expected time differences of arrivals in order for the system to be able to produce accurate location estimates. In this paper we take a closer look at the techniques required to synchronize a network of sensors, connected through a low-power RF-communication link, in a distributed manner, ie. without the presence of a master node. The absence of a master node makes the network more robust against the failure of a single node. We took inspiration from the synchronization technique observed in some species of fireflies. A synchronized pseudo random number is embedded in the captured data as a marker to re-align the data-streams in time in a post-processing stage.

This paper presents a new method to automatically generate posynomial symbolic expressions for the performance characteristics of analog integrated circuits. The coefficient set as well as the exponent set of the posynomial expression are determined based on SPICE simulation data with device-level accuracy. We prove that this problem corresponds to solving a non-convex optimization problem without local minima. The presented method is capable of generating posynomial performance expressions for both linear and nonlinear circuits and circuit characteristics. This approach allows the automatic generation of an accurate sizing model that composes a geometric program that fully describes the analog circuit sizing problem. The automatic generation avoids the time-consuming nature of hand-crafted analytic model generation. Experimental results illustrate the capabilities and effectiveness of the presented modeling technique.

This paper presents an overview of methods to automatically generate posynomial response surface models for the performance characteristics of analog integrated circuits based on numerical simulation data. The methods are capable of generating posynomial performance expressions for both linear and nonlinear circuits and circuit characteristics, at SPICE-level accuracy. This approach allows for automatic generation of an accurate sizing model for a circuit that composes a geometric program that fully describes the analog circuit sizing problem. The automatic generation avoids the time-consuming and approximate nature of handcrafted analytic model generation. The methods are based on techniques from design of experiments and response surface modeling. Attention is paid to estimating the relative "goodness-of-fit" of the generated models. Experimental results illustrate the capabilities and effectiveness of the presented methods.

This paper presents an new direct--fitting method to generate posynomial response surface models with arbitrary constant exponents for linear and nonlinear performance parameters of analog integrated circuits. Posynomial models enable the use of efficient geometric programming techniques for circuit sizing and optimization. The automatic generation avoids the time--consuming nature and inaccuracies of handcrafted analytic model generation. The technique is based on the fitting of posynomial model templates to numerical data from SPICE simulations. Attention is paid to estimating the relative `goodness--of--fit' of the generated models. Experimental results illustrate the significantly better accuracy of the new approach.

This paper presents a novel method to automatically generate symbolic expressions for both linear and nonlinear circuit characteristics using a template-based fitting of numerical, simulated data. The aim of the method is to generate convex, interpretable expressions. The posynomiality of the generated expressions enables the use of efficient geometric programming techniques when using these expressions for circuit sizing and optimization. Attention is paid to estimating the relative 'goodness-of-fit' of the generated expressions. Experimental results illustrate the capabilities of the approach.

Presents a circuit simplification method for the symbolic analysis of a linear or linearized (small-signal) analog circuit. Its goal is to generate simplified signal flow graphs describing the circuit's behavior in well-defined frequency intervals. These frequency subranges are automatically constructed based on numerical calculation of the system's poles and zeroes. The circuit reduction has been implemented using graph manipulation techniques. The order in which these manipulations are applied is based on a tradeoff between the error and the level of simplification they introduce. The technique can be used to symbolically localize poles and zeroes and inspect their observability. This allows generating symbolic expressions for poles and zeroes, which in optimal conditions lead to simple, interpretable expressions. The technique can also be used as preprocessor to simplify a circuit before analyzing it with standard approximate symbolic analysis techniques. In that case, it helps overcoming the time and memory constraints related to those techniques. Experimental results show the effectiveness of the approach.

This paper presents a method to automatically generate posynomial response surface models for the performance parameters of analog integrated circuits. The posynomial models enable the use of efficient geometric programming techniques for circuit sizing and optimization. To avoid manual derivation of approximate symbolic equations and subsequent casting to posynomial format, techniques from design of experiments and response surface modeling in combination with SPICE simulations are used to generate signomial and posynomial models in an automatic way. Attention is paid to estimating the relative 'goodness-of-fit' of the generated models. Experimental results allow one to assess both the quality of the generated models as well as the strengths and the limitations of the presented approach.

This paper presents a D/A converter with a 14-bit intrinsic linearity in 0.5µm CMOS technology, which has been designed using a systematic design methodology for current-steering D/A converters. A flexible architecture is proposed for which the design parameters are calculated using a performance-driven top-down design methodology. The layout of the regular structure typical for D/A converters is automatically generated. Measurement results are reported. Due to the systematic design methodology, the design was realized in less than one month total accumulated person effort.

This paper presents a method to reduce the complexity of a linear or linearized (small-signal) analog circuit. The reduction technique, based on quality-error ranking, can be used as a standard reduction engine that ensures the validity of the resulting network model in a specific (set of) design point(s) within a given frequency range and a given magnitude and phase error. It can also be used as an analysis engine to extract symbolic expressions for poles and zeroes. The reduction technique is driven by analysis of the signal flow graph associated with the network model. Experimental results show the effectiveness of the approach. 1

This paper describes a new modeling methodology that allows to derive systematically behavioral signal path models of operational amplifiers. Combined with symbolic simulation, these models provide high qualitative insight into the small-signal functioning of a circuit. The behavioral signal path model provides compact interpretable expressions for the poles and zeros that constitute the signal path. These expressions show which design parameters have dominant influence on the position of a pole/zero and thus enable a designer to control a manual interactive sizing process. The methodology consists of the application of a sequence of abstractions, so that one gradually progresses from a full device to a full behavior circuit representation. During this translation, qualitative insight and design requirements are obtained. The methodology is implemented in an open tool called EF2ef. The behavioral signal path model is also used for optimization based sizing in order to achieve pole placement in an efficient way. For optimization based siting, a new strategy for hierarchical penalty function composition is proposed, which allows sequential pruning of the design space. Combined with an operating point driven DC formulation and local minimax optimization, a fast sizing method is obtained which can be used for interactive design space exploration. Experimental results of both modeling and siting are shown.