Energy-Efficient Harmonic Transponder Based on On-Off Keying Modulation for Both Identification and Sensing

This paper presents a novel passive Schottky-diode frequency doubler equipped with an on-off keying (OOK) modulation port to be used in harmonic transponders for both identification and sensing applications. The amplitude modulation of the second-harmonic output signal is achieved by driving a low-frequency MOSFET, which modifies the dc impedance termination of the doubler. Since the modulation signal is applied to the gate port of the transistor, no static current is drained. A proof-of-concept prototype was manufactured and tested, operating at 1.04 GHz. An on/off ratio of 23 dB was observed in the conversion loss of the doubler for an available input power of −10 dBm. The modulation port of the circuit was excited with a square wave (fm up to 15 MHz), and the measured sidebands in the spectrum featured a good agreement with the theory. Then, the doubler was connected to a harmonic antenna system and tested in a wireless experiment for fm up to 1 MHz, showing an excellent performance. Finally, an experiment was conducted where the output signal of the doubler was modulated by a reed switch used to measure the rotational speed of an electrical motor. This work opens the door to a new class of frequency doublers, suitable for ultra low-power harmonic transponders for identification and sensing applications.

A review on magnetic and spintronic neurostimulation: challenges and prospects

In the treatment of neurodegenerative, sensory and cardiovascular diseases, electrical probes and arrays have shown quite a promising success rate. However, despite the outstanding clinical outcomes, their operation is significantly hindered by non-selective control of electric fields. A promising alternative is micromagnetic stimulation (μMS) due to the high permeability of magnetic field through biological tissues. The induced electric field from the time-varying magnetic field generated by magnetic neurostimulators is used to remotely stimulate neighboring neurons. Due to the spatial asymmetry of the induced electric field, high spatial selectivity of neurostimulation has been realized. Herein, some popular choices of magnetic neurostimulators such as microcoils (μcoils) and spintronic nanodevices are reviewed. The neurostimulator features such as power consumption and resolution (aiming at cellular level) are discussed. In addition, the chronic stability and biocompatibility of these implantable neurostimulator are commented in favor of further translation to clinical settings. Furthermore, magnetic nanoparticles (MNPs), as another invaluable neurostimulation material, has emerged in recent years. Thus, in this review we have also included MNPs as a remote neurostimulation solution that overcomes physical limitations of invasive implants. Overall, this review provides peers with the recent development of ultra-low power, cellular-level, spatially selective magnetic neurostimulators of dimensions within micro- to nano-range for treating chronic neurological disorders. At the end of this review, some potential applications of next generation neuro-devices have also been discussed.

Application of Neuromorphic Olfactory Approach for High-Accuracy Classification of Malts

Current developments in artificial olfactory systems, also known as electronic nose (e-nose) systems, have benefited from advanced machine learning techniques that have significantly improved the conditioning and processing of multivariate feature-rich sensor data. These advancements are complemented by the application of bioinspired algorithms and architectures based on findings from neurophysiological studies focusing on the biological olfactory pathway. The application of spiking neural networks (SNNs), and concepts from neuromorphic engineering in general, are one of the key factors that has led to the design and development of efficient bioinspired e-nose systems. However, only a limited number of studies have focused on deploying these models on a natively event-driven hardware platform that exploits the benefits of neuromorphic implementation, such as ultra-low-power consumption and real-time processing, for simplified integration in a portable e-nose system. In this paper, we extend our previously reported neuromorphic encoding and classification approach to a real-world dataset that consists of sensor responses from a commercial e-nose system when exposed to eight different types of malts. We show that the proposed SNN-based classifier was able to deliver 97% accurate classification results at a maximum latency of 0.4 ms per inference with a power consumption of less than 1 mW when deployed on neuromorphic hardware. One of the key advantages of the proposed neuromorphic architecture is that the entire functionality, including pre-processing, event encoding, and classification, can be mapped on the neuromorphic system-on-a-chip (NSoC) to develop power-efficient and highly-accurate real-time e-nose systems.

A Framework for Ultra Low-Power Hardware Accelerators Using NNs for Embedded Time Series Classification

In embedded applications that use neural networks (NNs) for classification tasks, it is important to not only minimize the power consumption of the NN calculation, but of the whole system. Optimization approaches for individual parts exist, such as quantization of the NN or analog calculation of arithmetic operations. However, there is no holistic approach for a complete embedded system design that is generic enough in the design process to be used for different applications, but specific in the hardware implementation to waste no energy for a given application. Therefore, we present a novel framework that allows an end-to-end ASIC implementation of a low-power hardware for time series classification using NNs. This includes a neural architecture search (NAS), which optimizes the NN configuration for accuracy and energy efficiency at the same time. This optimization targets a custom designed hardware architecture that is derived from the key properties of time series classification tasks. Additionally, a hardware generation tool is used that creates a complete system from the definition of the NN. This system uses local multi-level RRAM memory as weight and bias storage to avoid external memory access. Exploiting the non-volatility of these devices, such a system can use a power-down mode to save significant energy during the data acquisition process. Detection of atrial fibrillation (AFib) in electrocardiogram (ECG) data is used as an example for evaluation of the framework. It is shown that a reduction of more than 95% of the energy consumption compared to state-of-the-art solutions is achieved.

A Novel Design of High Performance and Robust Ultra-Low Power SRAM Cell Based on Memcapacitor

The present paper proposes a six-FinFET two-memcapacitor (6T2MC) non-volatile static random-access memory (NVSRAM). In this design, the two memcapacitors are used as non-volatile memory elements. The proposed cell is flexible against data loss when turned off and offers significant improvement in read and write operations compared to previous NVSRAMs. The performance of the new NVSRAM design is evaluated in terms of read and write operation at particular nanometric feature sizes. Moreover, the proposed 6T2MC cell is compared with 8T2R, 8T1R, 7T1R, and 7T2R cells. The results show that 6T2MC has a 5.50% lower write delay and 98.35% lower read delay compared to 7T2R and 7T1R cells, respectively. The 6T2MC cell exhibits 38.86% lower power consumption and 23.80% lower leakage power than 7T2R and 7T1R cells. The proposed cell is significantly improved in terms of HSNM, RSNM, and WSNM compared to 8T2R, 8T1R, 7T2R, and 7T1R cells, respectively. Important cell parameters, such as power consumption, data read/write delay, and SNM, are significantly improved. The superior characteristics of FinFET over MOSFET and the combination of this technology with memcapacitors lead to significant improvement in the proposed design.

Design and Implementation of an Ultra-Low-Power ECG Patch and Smart Cloud-Based Platform

<p>This paper reports a new device for electrocardiogram (ECG) signal monitoring and software for signal analysis and artificial intelligence (AI) assisted diagnosis. </p> <p>The hardware mitigates the signal loss common in previous products by enhancing the ergonomy, flexibility, and battery life. The power efficiency is optimized by design using switching converters, ultra-low-power components, and efficient signal processing. It enables 14-day of uninterrupted ECG monitoring and connectivity with a smartphone and microSD card storage.</p><p>The software is implemented in Android app and web-based platforms via Internet of Things (IoT). This component provides cloud-based and local storage and uses AI for arrhythmia detection. The arrhythmia detection algorithm shows 98.7% accuracy using Artificial Neural Network and K-Nearest Neighbors methods, and 98.1% using Decision Tree method on test data set.</p>