New Measurement Method in Drug Sensing by Direct Total-Charge Detection in Voltammetry

Electrochemical biosensors are promoting point-of-care and wearable instrumentation due to their high versatility in measuring human metabolites. There is a considerable number of biological compounds that can be detected and measured through voltammetry based techniques. Voltmmetry some times requires peak identification and quantification that are non-trivial to be efficiently implemented by automatic instrumentation. To overcome the complexity of automatic peak estimation, we propose here an instrumentation circuit for edge-computing in pharmacology relying on an entirely novel measurement method via TotalCharge Detection in Cyclic voltammetry (TCDC). Namely, our TCDC method innovatively applies the coulometry measurement to the well-established voltammetry procedure. The proposed instrumentation accumulates the total charge exchanged in the faradaic process, exploiting a Nagaraj integrator as charge suppressor to fit the application-specific constraints. The work shows accurate simulations of the TCDC circuit on a set of experimental measures, acquired on paracetamol as benchmark drug. The proposed measurement technique and the developed circuit are compared to the peak detection method usually adopted in literature. The results demonstrate that the proposed system is a perfect trade-off between the doubled limit-of-detection and a tenfold reduction in measurement errors. At the same time, we eliminate any need for data oversampling and processing, promoting the TCDC as an efficient new measurement method for point-of-care and wearable monitoring of biological compounds.

Real-Time EEG-Based Cognitive Workload Monitoring on Wearable Devices

Amir Aminifar, Adriana Arza Valdes, David Atienza Alonso, Renato Zanetti

Objective: Cognitive workload monitoring (CWM)can enhance human-machine interaction by supporting task execution assistance considering the operator’s cognitive state. Therefore, we propose a machine learning design methodology and a data processing strategy to enable CWM on resource-constrained wearable devices. Methods: Our CWM solution is built upon edge computing on a simple wearable system, with only four peripheral channels of electroencephalography (EEG). We assess our solution on experimental data from 24 volunteers. Moreover, to overcome the system’s memory constraints, we adopt an optimization strategy for model size reduction and a multi-batch data processing scheme for optimizing RAM memory footprint. Finally, we implement our data processing strategy on a state-of-the-art wearable platform and assess its execution and system battery life. Results: We achieve an accuracy of 74.5% and a 74.0% geometric mean between sensitivity and specificity for cwm classification on unseen data. Besides, the proposed model optimization strategy generates a 27.5x smaller model compared to the one generated with default parameters, and the multi-batch data processing scheme reduces RAM memory footprint by 14x compared to a single batch data processing. Finally, our algorithm uses only 1.28% of the available processing time, thus, allowing our system to achieve 28.5 hours of battery life. Conclusion: We provide a reliable and optimized CWM solution using wearable devices, enabling more than a day of operation on a single battery charge. Significance: The proposed methodology enables real-time data processing on resource-constrained devices and supports real-time wearable monitoring based on EEG for applications as CWM in human-machine interaction.

2021

On the dependability of nanoscale circuits and systems

Milos Stanisavljevic

The invention of the integrated circuit and the manufacturing progress as well as continuing progress in the manufacturing process are the fundamental engines for the implementation of all technologies that support today's information society. The vast majority of microelectronic applications presented nowadays use the well-established CMOS process and fabrication technology which exhibit high reliability rates. The hypothesis of reliable components has mostly been taken in the development of electronic systems fabricated in the past four decades. The steady downscaling of CMOS technology has led to the development of devices with nanometer dimensions. For future nano-circuits, emerging nanodevices and their associated interconnects, the expected higher probabilities of failures, as well as the higher sensitivities to noise and variations, could make future chips prohibitively unreliable. The systems to be fabricated will be made of unreliable components and achieving 100% correctness will not be only extremely costly, but might be plainly impossible. The global picture is that reliability emerges as one of the most significant threats to the design of future integrated computing systems. Building reliable systems out of unreliable components will require increased cooperative involvement of the logic designers and architects, where high-level techniques will rely upon lower levels support based on novel modeling including component and system reliability as design parameters. An architecture suitable for circuit-level and gate-level redundant modules and exhibiting significant immunity to permanent and random failures, as well as unwanted fluctuation of the fabrication parameters is presented, which is based on a four-layer feed-forward topology, using averaging and thresholding as the core voter mechanisms. The architecture with both fixed and adaptable threshold is compared to triple and R-fold modular redundancy techniques, and its superiority is demonstrated based on numerical simulations as well as analytical developments. A chip implementation of the architecture is realized. Other applications of the architecture like delay variations minimization are identified and explored. A novel general method enabling introduction of fault-tolerance, and evaluation of circuit and architecture reliability is proposed. The method is based on the modeling of probability density functions (PDFs) of unreliable components and their subsequent evaluation for a given reliability architecture. PDF modeling, presented for the first in the context of realistic technology and arbitrary circuit size, is based on a cutting-edge reliability evaluation algorithm and offers scalability, speed and accuracy. Fault modeling has also been developed to support PDF modeling. In the second part of the thesis a new methodology that introduces reliability in existing design flows is proposed. The methodology consists of partitioning the whole system into reliability-optimal partitions and applying reliability evaluation and optimization at local and system level. System level reliability improvement of different fault-tolerant techniques is studied in depth. Optimal partition size analysis and redundancy optimization have been performed for the first time in the context of a large-scale system, showing that a target reliability can be achieved with low to moderate redundancy factors (R < 50) even for high defect densities (device failure rate up to 10-3). The optimal window of application of each fault-tolerant technique with respect to defect density is presented as a way to find the optimum design trade-off between the reliability and power/area. R-fold modular redundancy with distributed voting and averaging voter is selected as the most promising candidate for the implementation in trillion-transistor logic systems. Finally, a realistic circuit example of the methodology implementation is verified using simulations.

EPFL2009

Ontology-Based Resource Allocation for Internet of Things

Damiano Nunzio Arena, Zeinab Nezami

The Internet of Things has revolutionized the lifestyle in all aspects. Considering the huge number of connected objects and the plethora of real-time services, edge computing approaches have emerged. Resource allocation is one of the most important challenges in the Internet of Things. Here edge computing allows the use of resources at the edge network, hence, filling the gap between cloud and end-devices. The network resource allocation should meet users' expectations and provide optimal use of resources. Today, most of the systems are moving toward a self-x concept, such as self-organizing. As a result, these systems must be aware of users' preferences and the current state of the IoT ecosystem in order to adapt themselves to the conditions. In this context, we benefit from the employment of semantic technologies as these enhance the current systems with information modeling and reasoning capabilities which effectively support the allocation of IoT network resources.

SPRINGER INTERNATIONAL PUBLISHING AG2019

On the dependability of nanoscale circuits and systems

Milos Stanisavljevic

EPFL2009