Memristors are one of the emerging technologies that can potentially replace state-of-the-art integrated electronic devices for advanced computing and digital and analog circuit applications including neuromorphic networks. Over the past few years, research and development mostly focused on revolutionizing the metal oxide materials, which are used as core components of the popular metal-insulator-metal memristors owing to their highly recognized resistive switching behavior. This paper outlines the recent advancements and characteristics of such memristive devices, with a special focus on (i) their established resistive switching mechanisms and (ii) the key challenges associated with their fabrication processes including the impeding criteria of material adaptation for the electrode, capping, and insulator component layers. Potential applications and an outlook into future development of metal oxide memristive devices are also outlined.

Continuous monitoring of vital signs, such as respiration and heartbeat, plays a crucial role in early detection and even prediction of conditions that may affect the wellbeing of the patient. Sensing vital signs can be categorized into: contact-based techniques and contactless based techniques. Conventional clinical methods of detecting these vital signs require the use of contact sensors, which may not be practical for long duration monitoring and less convenient for repeatable measurements. On the other hand, wireless vital signs detection using radars has the distinct advantage of not requiring the attachment of electrodes to the subject’s body and hence not constraining the movement of the person and eliminating the possibility of skin irritation. In addition, it removes the need for wires and limitation of access to patients, especially for children and the elderly. This paper presents a thorough review on the traditional methods of monitoring cardio-pulmonary rates as well as the potential of replacing these systems with radar-based techniques. The paper also highlights the challenges that radar-based vital signs monitoring methods need to overcome to gain acceptance in the healthcare field. A proof-of-concept of a radar-based vital sign detection system is presented together with promising measurement results.

Energy harvesting is an important enabling technology necessary to unleash the next shift in mm-scale and μW power computing devices, especially for wireless sensor nodes. Energy harvesting could play an important role in biomedical devices where it extends the lifetime of the system. Furthermore, it eliminates the need for periodic maintenance such as exchanging or recharging the battery. This paper presents experimental results of thermal and vibration energy harvested from human body using the thermoelectric generator and the piezo electric harvester, respectively. Contemporary research revealed that most of the published data, including harvesters datasheets, are adjusted for industrial or laboratory-setting environment. This paper focuses on obtaining experimental data from the human body using off-the-shelf harvesters, and discrete electrical components. Our experimental results showed that for 9 cm ² area of thermoelectric generator, up to 20 μW of power can be generated at 22 ^° C room temperature. In addition, 0.5 cm ³ piezo electric harvester can generate up to 3.7 μW when running at 7 mi/h. These data correspond to a power density of 2.2 μW/cm ² and 7.4 μW/cm ³ for thermoelectric generator and piezo electric harvester, respectively. As such, the harvested energy from thermal and vibration of human body could potentially power autonomous wearable and implantable devices.

This work introduces a generic, machine learning (ML)-based platform for functional reverse engineering (RE) of circuits. Our proposed platform GNN-RE leverages the notion of graph neural networks (GNNs) to: 1) represent and analyze flattened/unstructured gate-level netlists; 2) automatically identify the boundaries between the modules or subcircuits implemented in such netlists; and 3) classify the subcircuits based on their functionalities. For GNNs in general, each graph node is tailored to learn about its own features and its neighboring nodes, which is a powerful approach for the detection of any kind of subgraphs of interest. For GNN-RE , in particular, each node represents a gate and is initialized with a feature vector that reflects on the functional and structural properties of its neighboring gates. GNN-RE also learns the global structure of the circuit, which facilitates identifying the boundaries between subcircuits in a flattened netlist. Initially, to provide high-quality data for training of GNN-RE , we deploy a comprehensive dataset of foundational designs/components with differing functionalities, implementation styles, bit widths, and interconnections. GNN-RE is then tested on the unseen shares of this custom dataset, as well as the EPFL benchmarks, the ISCAS-85 benchmarks, and the 74X series benchmarks. GNN-RE achieves an average accuracy of 98.82% in terms of mapping individual gates to modules, all without any manual intervention or postprocessing. We also release our code and source data.

This paper presents the design of a fully integrated electrocardiogram (ECG) signal processor (ESP) for the prediction of ventricular arrhythmia using a unique set of ECG features and a naive Bayes classifier. Real-time and adaptive techniques for the detection and the delineation of the P-QRS-T waves were investigated to extract the fiducial points. Those techniques are robust to any variations in the ECG signal with high sensitivity and precision. Two databases of the heart signal recordings from the MIT PhysioNet and the American Heart Association were used as a validation set to evaluate the performance of the processor. Based on application-specified integrated circuit (ASIC) simulation results, the overall classification accuracy was found to be 86% on the out-of-sample validation data with 3-s window size. The architecture of the proposed ESP was implemented using 65-nm CMOS process. It occupied 0.112-mm ² area and consumed 2.78-μW power at an operating frequency of 10 kHz and from an operating voltage of 1 V. It is worth mentioning that the proposed ESP is the first ASIC implementation of an ECG-based processor that is used for the prediction of ventricular arrhythmia up to 3 h before the onset.