Non-Invasive Estimation of Plasma Sodium Concentration During Hemodialysis via Capacitively-Coupled Electrical Impedance Spectroscopy

This paper presents a compact, low-cost, and noninvasive system for real-time estimation of plasma sodium concentration ([Na]Pl) during a hemodialysis (HD) session with state-of-the-art accuracy. It is based on electrical impedance spectroscopy (EIS) performed with a capacitively-coupled impedance sensing cell and a high-frequency measurement device, both custom-built. The EIS data are processed to infer the resistance of the liquid inside the cell, which is used together with an optical hemoglobin sensor to estimate the [Na]Pl. Validation of the EIS was performed by estimating the conductivity of bloodmimicking fluid (BMF). The complete method was validated using whole bovine blood, comparing the results to those obtained with standard instruments. The system was able to estimate the [Na]Pl with sufficient accuracy (RMS error of 3.0 mol/m3 with respect to reference data) to provide clinically useful information. The proof-of-concept hardware can be converted to a cheap and compact circuit board for integration into an HD machine

Subspace Energy Monitoring for Anomaly Detection @Sensor or @Edge

The amount of data generated by distributed monitoring systems that can be exploited for anomaly detection, along with real time, bandwidth, and scalability requirements leads to the abandonment of centralized approaches in favor of processing closer to where data are generated. This increases the interest in algorithms coping with the limited computational resources of gateways or sensor nodes. We here propose two dual and lightweight methods for anomaly detection based on generalized spectral analysis. We monitor the signal energy laying along with the principal and anti-principal signal subspaces, and call for an anomaly when such energy changes significantly with respect to normal conditions. A streaming approach for the online estimation of the needed subspaces is also proposed. The methods are tested by applying them to synthetic data and real-world sensor readings. The synthetic setting is used for design space exploration and highlights the tradeoff between accuracy and computational cost. The real-world example deals with structural health monitoring and shows how, despite the extremely low computations costs, our methods are able to detect permanent and transient anomalies that would classically be detected by full spectral analysis

Adapted Compressed Sensing: A Game Worth Playing

Despite the universal nature of the compressed sensing mechanism, additional information on the class of sparse signals to acquire allows adjustments that yield substantial improvements. In facts, proper exploitation of these priors allows to significantly increase compression for a given reconstruction quality. Since one of the most promising scopes of application of compressed sensing is that of IoT devices subject to extremely low resource constraint, adaptation is especially interesting when it can cope with hardware-related constraint allowing low complexity implementations. We here review and compare many algorithmic adaptation policies that focus either on the encoding part or on the recovery part of compressed sensing. We also review other more hardware-oriented adaptation techniques that are actually able to make the difference when coming to real-world implementations. In all cases, adaptation proves to be a tool that should be mastered in practical applications to unleash the full potential of compressed sensing

An architecture for ultra-low-voltage ultra-low-power compressed sensing-based acquisition systems

Compressed Sensing (CS) has been addressed as a paradigm capable of lowering energy requirements in acquisition systems. Furthermore, the capability of simultaneously acquiring and compressing an input signal makes this paradigm perfectly suitable for low-power devices. However, the need for analog hardware blocks makes the adoption of most of standard solutions proposed so far in the literature problematic when an aggressive voltage and energy scaling is considered, as in the case of ultra-low-power IoT devices that need to be battery-powered or energy harvesting-powered. Here, we investigate a recently proposed architecture that, due to the lack of any analog block (except for the comparator required in the following A/D stage) is compatible with the aggressive voltage scaling required by IoT devices. Feasibility and expected performance of this architecture are investigated according to the most recent state-of-the-art literature

A Deep Learning Method for Optimal Undersampling Patterns and Image Recovery for MRI Exploiting Losses and Projections

Compressed Sensing was recently proposed to reduce the long acquisition time of Magnetic Resonance Imaging by undersampling the signal frequency content and then algorithmically reconstructing the original image. We propose a way to significantly improve the above method by exploiting a deep neural network to tackle both problems of frequency sub-sampling and image reconstruction simultaneously, thanks to the introduction of a new loss function to drive the training and the addition of a post-processing non-neural stage. Furthermore, we highlight how some of the quantities along the processing chain can be used as a proxy of the quality of the recovered image, thus allowing a self-assessment of the whole technique. All improvements hinge on the possibility of identifying constraints to which the final image must obey and suitably enforce them. The effectiveness of our approach is tested on real-world MRI acquisitions from the fastMRI public database and achieves an appreciable improvement in Peak Signal-to-Noise Ratio with respect to the original CS-based proposal with speed-up factors 4 and 8

Effect of Dilution on Microstructure and Wear Resistance of a Fe-Based Hardfacing Alloy with a High Amount of Carbide-Forming Elements

Hardfacing is a widely diffused technique adopted to increase service life of parts for heavy-duty applications. Even though hardfacing alloys feature optimized chemistry and microstructure for specific service conditions, dilution with substrate modifies the resulting properties along a significant fraction of the deposit thickness. In particular, C and B diffusion to the substrate alters hypereutectic alloys reducing the carbide-forming ability andmodifying the solidification sequence. In the present paper, the effect of dilution on a hypereutectic Fe-C-B based alloy containing Cr and Mo was investigated. The effect of dilution on the reference alloy was studied by producing laboratory castings with an increased amount of Fe, up to 50 mass %. The obtained results were compared with the dilution of the hardfacing alloy cast on steel substrates. The microstructural evolution was analyzed by XRD (X-ray diffraction), differential scanning calorimetry (DSC), optical microscopy (OM), and scanning electron microscopy (SEM), whereas mechanical behaviour was evaluated by hardness measurements and wear resistance by pin-on-disc tests

Precarización del empleo y nuevas barreras de exclusión: una mirada al mercado laboral de La Pampa

El objetivo del presente trabajo consiste en interpretar las formas de vinculación entre la oferta y la demanda de empleo en el mercado laboral pampeano, especialmente en el sector privado. En esta búsqueda, de tipo exploratoria, identificamos mecanismos que refuerzan, incluso en lapsos de relativa reactivación económica, las barreras de exclusión laboral y social. La oferta de empleo local, en los últimos cinco años, evidencia el desarrollo de un complejo abanico de componentes manifiestos y latentes que inciden en la incorporación o exclusión de los aspirantes. Las prácticas empresariales estudiadas cobran materialidad en contextos de elevada precarización, y dan cuenta de aspectos contradictorios al percibir y manifestar sus propias demandas organizacionales. Esto genera comportamientos cada vez más centrífugos de los sectores más vulnerables en el mercado laboral. A este cuadro se le añaden los efectos de políticas gubernamentales de larga data, con un fuerte carácter asistencialista, viciadas de clientelismo, constituyendo un escenario que adquiere matices más complejos aún. La práctica profesional conduce inevitablemente a la toma de posición, en una Argentina de la crisis que cuestiona y reposiciona el rol de los intelectuales. En este sentido, por tratarse del mercado de trabajo, de alto impacto en la calidad de vida y la inclusión social; avanzar en una conceptualización e intervención simultáneas, constituye un desafío que requiere ser priorizado.Facultad de Humanidades y Ciencias de la Educació

Deep Neural Oracles for Short-Window Optimized Compressed Sensing of Biosignals

The recovery of sparse signals given their linear mapping on lower-dimensional spaces can be partitioned into a support estimation phase and a coefficient estimation phase. We propose to estimate the support with an oracle based on a deep neural network trained jointly with the linear mapping at the encoder. The divination of the oracle is then used to estimate the coefficients by pseudo-inversion. This architecture allows the definition of an encoding-decoding scheme with state-of-the-art recovery capabilities when applied to biological signals such as ECG and EEG, thus allowing extremely low-complex encoders. As an additional feature, oracle-based recovery is able to self-assess, by indicating with remarkable accuracy chunks of signals that may have been reconstructed with a non-satisfactory quality. This self-assessment capability is unique in the CS literature and paves the way for further improvements depending on the requirements of the specific application. As an example, our scheme is able to satisfyingly compress by a factor of 2.67 an ECG or EEG signal with a complexity equivalent to only 24 signed sums per processed sample

Streaming Algorithms for Subspace Analysis: Comparative Review and Implementation on IoT Devices

Subspace analysis is a widely used technique for coping with high-dimensional data and is becoming a fundamental step in the early treatment of many signal processing tasks. However, traditional subspace analysis often requires a large amount of memory and computational resources, as it is equivalent to eigenspace determination. To address this issue, specialized streaming algorithms have been developed, allowing subspace analysis to be run on low-power devices such as sensors or edge devices. Here, we present a classification and a comparison of these methods by providing a consistent description and highlighting their features and similarities. We also evaluate their performance in the task of subspace identification with a focus on computational complexity and memory footprint for different signal dimensions. Additionally, we test the implementation of these algorithms on common hardware platforms typically employed for sensors and edge devices

Low-power fixed-point compressed sensing decoder with support oracle

Approaches for reconstructing signals encoded with Compressed Sensing (CS) techniques, and based on Deep Neural Networks (DNNs) are receiving increasing interest in the literature. In a recent work, a new DNN-based method named Trained CS with Support Oracle (TCSSO) is introduced, relying the signal reconstruction on the two separate tasks of support identification and measurements decoding. The aim of this paper is to improve the TCSSO framework by considering actual implementations using a finite-precision hardware. Solutions with low memory footprint and low computation requirements by employing fixed-point notation and by reducing the number of bits employed are considered. Results using synthetic electrocardiogram (ECG) signals as a case study show that this approach, even when used in a constrained-resources scenario, still outperform current state-of-art CS approaches