The development of real-time, reliable, low-cost automatic phonocardiogram (PCG) analysis systems is critical for the early detection of cardiovascular diseases (CVDs), especially in countries with limited access to primary health care programs. Once the raw PCG acquired by the stethoscope has been preprocessed, the first key task is its segmentation into the fundamental heart sounds. For this purpose, an optimized hardware implementation of the segmentation algorithm is essential to attain a computer-aided diagnostic system based on PCGs. This article presents the optimization of a U-Net-based segmentation algorithm for its implementation in a low-end field-programmable gate array (FPGA) using low-resolution fixed-point data types. The optimization strategies seek to reduce the system latency while maintaining a constrained consumption of FPGA resources, aiming for a real-time response from the stethoscope data acquisition to the CVD detection. Experimental results prove a 64% decrease in latency compared to a baseline version, a 3.9% reduction of block random access memory (BRAM), which is the limiting resource of the design, and a 70% reduction in energy consumption. To the best of our knowledge, this is the first work to exhaustively study different optimization strategies for implementing a large 1-D U-Net-based model, achieving real-time fully characterized performance.

The continuous development of more accurate and selective bio- and chemo-sensors has led to a growing use of sensor arrays in different fields, such as health monitoring, cell culture analysis, bio-signals processing, or food quality tracking. The analysis and information extraction from the amount of data provided by these sensor arrays is possible based on Machine Learning techniques applied to sensor fusion. However, most of these computing solutions are implemented on costly and bulky computers, limiting its use in in-situ scenarios outside complex laboratory facilities. This work presents the application of machine learning techniques in food quality assessment using a single Field Programmable Gate Array (FPGA) chip. The characteristics of low-cost, low power consumption as well as low-size allow the application of the proposed solution even in space constrained places, as in food manufacturing chains. As an example, the proposed system is tested on an e-nose developed for beef classification and microbial population prediction.

Nowadays, most of the automatized measurement processes are carried out by VISA (Virtual Instrument Software Architecture) compatible instruments, that execute the instructions provided by a host computer connected through wired standard buses, as USB (Universal Serial Bus), GPIB (General-Purpose Instrumentation Bus), PXI (PCI eXtensions for Instrumentation) or Ethernet. To overcome the intrinsic limitations associated to these wired systems, this work presents an instrumentation control system based on the IEEE 802.11 wireless communications standard. Intended for instruments having a USB control port, this port is connected to a gateway based on a compact Raspberry Single Board Computer (SBC) and thus the instrument can be connected to the host computer via Wireless Fidelity (WiFi), easily allowing the deployment of an ad-hoc instruments communication network in the working area or its connection to a previously deployed general purpose WiFi network. Developed under Python, the operation commands, wireless link protocol, and USB connection allow two modes of operation to provide system flexibility: a live mode, where commands are sent individually from the host computer to the selected instrument; and a standalone mode, where a full measurement process can be entirely downloaded in the gateway to be autonomously executed on the instrumentation. The system performance in both operation modes, distance of operation, time latencies, and operating lifetime in battery operation have been characterized.

The growing development of communication technologies has given rise to the Internet of Things, which has led to the emergence of new cities, smart grids, and smart buildings, and the development of energy generation using renewable sources, as well as the emergence of new electrical loads such as the electric car. These advances give rise to the need for new media devices with remote communication, and require a greater control and monitoring of the state of the electrical grid in order to verify its correct state, as well as the detection of faults or alterations that are occurring in it due to these new generation systems or new loads. These remote, unsupervised measurement devices require galvanic isolation to protect the measurement and communication system, so that even if there is a break in the isolation, the integrity of the measurement and communication system is maintained. In addition, as it is a device prepared for multipoint measurement, the cost of the probe must be contained. This article details the design, implementation, and validation of a low-cost remote isolated differential voltage probe. This probe is intended for monitoring at network supply points, as well as for the verification of the European standard EN 50160 as a means of detecting disturbances in network behaviour. Its characteristics as a differential and isolated probe provide it with the possibility of floating voltage averaging, guaranteeing the integrity of the electronics of the low-voltage probe, i.e., the digitalisation and communication system. The measurements collected are sent via an MQTT protocol, which makes the remote probe a device compatible with the Internet of Energy. For the validation of the probe, a full functional test is performed, including FFT spectral analysis to verify the compliance of the mains voltage with the aforementioned European standard EN 50160.

The measurement of the magnetic field generated by a flowing current constitutes a non-invasive sensing technique for online energy consumption monitoring. In this work, based on the use of low-cost linear Hall effect sensors, a low-form-factor custom contactless ammeter probe is presented. The differential configuration of the sensor module and the subsequent fully digital programmability in range and sensitivity, together with the included self-calibration and compensation circuits for mismatching, managed by a microcontroller, allow for optimum detection for both continuous and mains current with a resolution of 10 mA for input ranges of 2 A. The proposed ammeter power consumption and measurement accuracy in different scenarios are tested, including the power monitoring of an IoT-based device, obtaining results matched to those featured by a commercial oscilloscope current probe, which validates its suitability and reliability as autonomous low-cost probe for portable contactless power monitoring.