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Electrical neurostimulation has been used successfully as a technique in both research and clinical contexts for over a century. Despite significant progress, inherent problems remain, hence there has been a drive for novel neurostimulation modalities including ultrasonic, magnetic, and optical, which have the potential to be less invasive, have enhanced biointegration, deeper stimulus penetration from the probe, and higher spatiotemporal resolution. Optogenetics—the optical stimulation of genetically photosensitized neurons, enables highly precise genetic targeting of the stimulus. Specifically, it allows for selective optical excitation and inhibition via different wavelengths. As such, optogenetics has become a prominent tool for neuroscience. Herein, the complementarity between different forms of neurostimulation is explored with a focus on cranial magnetic and optogenetic stimulation. Magnetic stimulation is complementary to optogenetics in that it does not require an electrochemical tissue interface like in the case of electrical stimulation. Furthermore, if incorporated onto the same probe as one with light emitters, its stimulation field can be orthogonal to the light emission field—allowing for complementary stimulus fields. Herein, dual optogenetic and magnetic modalities are proposed that can unite to yield a powerful and versatile tool for neural engineering.

Aiming at the self-association feature of the Hopfield neural network, we can reduce the\ud need for extensive sensor training samples during human behavior recognition. For a\ud training algorithm to obtain a general activity feature template with only one time data\ud preprocessing, this work proposes a data preprocessing framework that is suitable for\ud neuromorphic computing. Based on the preprocessing method of the construction matrix\ud and feature extraction, we achieved simplification and improvement in the classification of\ud output of the Hopfield neuromorphic algorithm. We assigned different samples to neurons\ud by constructing a feature matrix, which changed the weights of different categories to\ud classify sensor data. Meanwhile, the preprocessing realizes the sensor data fusion\ud process, which helps improve the classification accuracy and avoids falling into the\ud local optimal value caused by single sensor data. Experimental results show that the\ud framework has high classification accuracy with necessary robustness. Using the\ud proposed method, the classification and recognition accuracy of the Hopfield\ud neuromorphic algorithm on the three classes of human activities is 96.3%. Compared\ud with traditional machine learning algorithms, the proposed framework only requires\ud learning samples once to get the feature matrix for human activities, complementing\ud the limited sample databases while improving the classification accuracy.

In this letter, we present an integrated readout chip for magnetoresistive (MR) sensors consisting of a readout chain that comprises a DC-coupled fully differential difference amplifier (FDDA) followed by a programmable gain amplifier (PGA), as well as a low-noise current biasing scheme for the MR sensor. The current bias scheme features a 10-bit digital-toanalog converter (DAC) to compensate for process variations of the MR sensing element as well as to calibrate for variations in the DC bias field of the sensor. The bias current source achieves a very low current noise floor of 2.2pA/√Hz for bias currents up to 1mA. The readout chip is manufactured in 180nm SOI CMOS and consumes a total power of 38mW. The paper is an extended version of 1, incorporating additional modeling details and measurement results.

This paper proposes a multi-sensing Human Activity Recognition framework, which uses Neuromorphic computing to processing from Sensors and Radars of different type signals for data fusion and classification. At this point, Inertial Measurement Unit sensors and Universal Software-defined Radio Peripheral, and Radar devices are used to collect human activities signals separately. The feature extraction and selection process the sensors signal to dimension reduction without time factor by design an attention mechanism. And then, following Expectation-Maximization calculation to achieve a binary feature pattern that fits the discrete Hopfield neural network input. Depend on the Neuromorphic computing of associative memory function and similarity calculation to the neurons’ feedback output. It finally achieves human activity recognition with one-shot learning. There are explores multi-sensing human activity recognition between limited dataset and ensures accuracy without dropping. The technique can be extended to include more hardware signal processing to the system.

Cardiovascular diseases (CVDs) remain the dominant cause of mortality globally and are of an increasing trend. Generally, coronary stent placement is a pervasive treatment to combat CVDs. However, the cardiovascular stent employed as the passive expanded support in the blocked artery frequently introduce a postoperative complication called in-stent restenosis. The innovative self-reported stents integrated with biosensors can offer effective diagnosis and treatment of in-stent restenosis. In addition, the use of biotelemetry is essential in the interaction with physiological signals in the vessel's lumen, ensuring continuous monitoring of internal conditions within a vessel. This paper presents two compact dual-band microstrip antennas with encapsulation and without encapsulation that can implement power and data transmission. In particular, the 1.4 GHz Wireless Medical Telemetry Service (WMTS) band and 2.45 GHz Industrial Scientific and Medical (ISM) band have been allocated for wireless power transfer and biotelemetry, respectively. The proposed stent antennas employ meander slots on the radiating patch, which can achieve an ultra-small volume of 5 × 5 × 0.64 mm 3 . Moreover, the antenna configurations, reflection coefficient values, and surface current distribution plots are compared between the encapsulated and unencapsulated antennas.

As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), Artificial Intelligence (AI) — including Machine Learning (ML) and Big Data analytics — as well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic. This paper provides a blend of discussions on the contribution of these digital technologies, propose several complementary and multidisciplinary techniques to combat COVID-19, offer opportunities for more holistic studies, and accelerate knowledge acquisition and scientific discoveries in pandemic research. First, four areas where IoT can contribute are discussed, namely, i) tracking and tracing, ii) Remote Patient Monitoring (RPM) by Wearable IoT (WIoT), iii) Personal Digital Twins (PDT), and iv) real-life use case: ICT/IoT solution in Korea. Second, the role and novel applications of AI are explained, namely: i) diagnosis and prognosis, ii) risk prediction, iii) vaccine and drug development, iv) research dataset, v) early warnings and alerts, vi) social control and fake news detection, and vii) communication and chatbot. Third, the main uses of robotics and drone technology are analyzed, including i) crowd surveillance, ii) public announcements, iii) screening and diagnosis, and iv) essential supply delivery. Finally, we discuss how Distributed Ledger Technologies (DLTs), of which blockchain is a common example, can be combined with other technologies for tackling COVID-19.

Wearable devices can be used for monitoring vital physical and physiological signs remotely, as well as for interacting with computers. Widespread adoption of wearables is somewhat hindered by the duration time they can be used without re‐recharging. To ensure uninterrupted operation, these devices need a constant and battery‐less energy supply. Scavenging energy from the wearable's surroundings is, therefore, an essential step towards achieving genuinely autonomous and self‐powered devices. While energy harvesting technologies may not completely eliminate the battery storage unit, they can ensure a maximum duration of use. Piezoelectric energy harvesting is a promising and efficient technique to generate electricity for powering wearable devices in response to body movements. Consequently, we systematically survey the range of technologies used for scavenging energy from the human body, with a particular focus on the upper‐limb area. According to our review and in comparison to other upper limb locations, highest power densities can be achieved from piezoelectric transducers located on the wrist. For short and fast battery charging needs, we therefore review the range of materials, architectures and devices used to scavenge energy from these upper‐limb areas. We provide comparisons as well as recommendations and possible future directions for harvesting energy using this promising technique.

In this work, a different insight into the absorption characteristics of the chalcogenide thin film over the visible-wavelength range is studied in detailed. A comparative study of the lossy mode resonance (LMR) dips stem from the evanescent-wave absorption is theoretically and experimentally investigated towards refractive index measurement in aqueous environment. The absorbing layer of thermally evaporated arsenic trisulfide chalcogenide (As2S3) thin film coated onto the low OH etched optical fiber (ChOF) is prepared. X-ray diffraction (XRD), scanning electron microscopy (SEM), and Raman spectroscopy, were used to characterize the chemical species, the crystal structure, and the morphology of the degradation layer confirming the As2S3, thin-film deposition on the curved optical fiber surface. The LMR dips condition hinging on the adoption of the chalcogenide layer was comprehensively explored by referring to the absorption spectra as a function of the wavelength and its thicknesses. Taking the advantage of the chalcogenide absorptive features within the visible spectral range, the results verified that coating even a thin subwavelength thickness of As2S3 chalcogenide on a bare has the ability to provide liquid refractometer with a versatile absorbing features. Our results point to a new path for the development of LMR-based chemical sensors, environmental sensors, or to be combined to the integrated micro-photonics device biosensors.

This paper presents proof-of-principle of a miniatured low noise, low power, and high-sensitive malaria detection method based on the magnetoresistance (MR) sensor with a CMOS analog front-end (AFE) readout circuit for the detection of paramagnetic hemozoin particles. COMSOL Multiphysics® is employed for the finite-element (FEM) simulation of hemozoin particles to prove that the magnetic field generated from a multi-hemozoin particles system is within the sensing range of MR sensors. A CMOS AFE circuit is designed to convert the tiny current (approximately 60 µA) from MR sensors into a strong voltage signal able to be sampled and suppress high frequency and large amplitude noises stemming from the shift of the resultant magnetic field during the malaria diagnostic process. This CMOS AFE circuit is composed of a transimpedance amplifier (TIA) and a pair of Butterworth filters. This TIA can achieve a 98.5 dB dc gain and a 2.823 MHz bandwidth with low power consumption (375.65 µW) at a 3.3 V voltage supply and low input-referred noise (21.3857 nA/√Hz at 100 Hz). Butterworth filters can significantly reduce the high frequency and large amplitude noises caused by the unexpected shift of the magnetic field. The experimental results prove that the system provides an immediate response to samples with hemozoin particles and has the potential to achieve malaria parasite detection.

In the teaching of electronic engineering, some practical projects need to be added in order to connect various courses together. To strengthen the connections between courses, the project-based teaching method proposed in this paper advocates linking the knowledge of different courses through the combination of theory and practice. With this as a guide, projects have been set up for students. One of them is about designing and making a managed ethernet switch. In the process of making and completing this project, the students’ ability has been significantly improved, which fully proves the benefits of the teaching method.

A Hall sensor array system for magnetic field detection and analysis is realized in X-FAB 0.18 μm CMOS technology. Magnetic field detection is attributed to the magnetization of metal coils to metal particles and the sensing characteristics of the Hall sensor array. The system puts forward a complete solution from Hall sensors, analog front-end circuit, analog-to-digital converter (ADC) to microcontroller unit. Using Ansoft Maxwell and COMSOL Multiphysics software for simulation verification, the minimum diameter of magnetic particles that can be detected in the system is 2 μm. The measured signal to noise and distortion ratio, spurious free dynamic range, and effective number of bits of the proposed ADC are 70.61 dB, 90.08 dB, and 11.44-bit, respectively. The microsystem based on STM32 combines hardware and software design, which can effectively adjust the motion parameters and realize the real-time display in the LCD screen of the magnetic field and voltage information. Compared to the prior system, the portability, cost, and efficiency have been considerably improved, which is aimed at the rapid measurement of heavy metal particles such as Fe, Co, and Ni in ambient air and blood.