A wireless ultrasound surgical system (WUSS) with battery modules requires efficient power consumption with appropriate cutting effects during surgical operations. Effective cutting performances of the ultrasound transducer (UT) should be produced for ultrasound surgical knives for effective hemostasis performance and efficient dissection time. Therefore, we implemented a custom-made UT with piezoelectric material and re-poling process, which is applied to enhance the battery power consumption and output amplitude performances of the WUSS. After the re-poling process of the UT, the quality factor increased from 1231.1 to 2418 to minimize the unwanted heat generation. To support this UT, we also developed a custom-made generator with a transformer and developed 2nd harmonic termination circuit, control microcontroller with an advanced reduced instruction set computer machine (ARM) controller, and battery management system modules to produce effective WUSS performances. The generator with a matching circuit in the WUSS showed a peak-to-peak output voltage and current amplitude of 166 V and 1.12 A, respectively, at the resonant frequency. The performance with non-contact optical vibrators was also measured. In the experimental data, the developed WUSS reduced power consumption by 3.6% and increased the amplitude by 20% compared to those of the commercial WUSS. Therefore, the improved WUSS performances could be beneficial for hemostatic performance and dissection time during surgical operation because of the developed UT with a piezoelectric material and re-poling process.

This paper presents a fully integrated voltage-reference circuit for implantable devices such as retinal implants. The recently developed retinal prostheses require a stable supply voltage to drive a high-density stimulator array. Accordingly, a voltage-reference circuit plays a critical role in generating a constant reference voltage, which is provided to a low-voltage-drop regulator (LDO), and filtering out the AC ripples in a power-supply rail after rectification. For this purpose, we use a beta-multiplier voltage-reference architecture to which a nonlinear current sink circuit is added, to improve the supply-independent performance drastically. The proposed reference circuit is fabricated using the standard 0.35 µm technology, along with an LDO that adopts an output ringing compensation circuit. The novel reference circuit generates a reference voltage of 1.37 V with a line regulation of 3.45 mV/V and maximum power-supply rejection ratio (PSRR) of −93 dB.

Piezoelectric transducers are important devices that are triggered by amplifier circuits in mobile ultrasound systems. Therefore, amplifier performance is vital because it determines the acoustic piezoelectric transducer performances. Particularly, mobile ultrasound applications have strict battery performance and current consumption requirements; hence, amplifier devices should exhibit good efficiency because the direct current (DC) voltage in the battery are provided to the supply voltages of the amplifier, thus limiting the maximum DC drain voltages of the main transistors in the amplifier. The maximum DC drain voltages are related with maximum output power if the choke inductor in the amplifier is used. Therefore, a need to improve the amplifier performance of piezoelectric transducers exists for mobile ultrasound applications. In this study, a post-voltage-boost circuit-supported class-B amplifier used for mobile ultrasound applications was developed to increase the acoustic performance of piezoelectric transducers. The measured voltage of the post-voltage-boost circuit-supported class-B amplifier (62 VP-P) is higher than that of only a class-B amplifier (50 VP-P) at 15 MHz and 100 mVP-P input. By performing the pulse-echo measurement test, the echo signal with the post-voltage-boost circuit-supported class-B amplifier (10.39 mVP-P) was also noted to be higher than that with only a class-B amplifier (6.15 mVP-P). Therefore, this designed post-voltage-boost circuit can help improve the acoustic amplitude of piezoelectric transducers used for mobile ultrasound applications.

With the growth of computing and communication technologies, the information processing paradigm of the healthcare environment is evolving. The patient information is stored electronically, making it convenient to store and retrieve patient information remotely when needed. However, evolving the healthcare systems into smart healthcare environments comes with challenges and additional pressures. Internet of Things (IoT) connects things, such as computing devices, through wired or wireless mediums to form a network. There are numerous security vulnerabilities and risks in the existing IoT-based systems due to the lack of intrinsic security technologies. For example, patient medical data, data privacy, data sharing, and convenience are considered imperative for collecting and storing electronic health records (EHR). However, the traditional IoT-based EHR systems cannot deal with these paradigms because of inconsistent security policies and data access structures. Blockchain (BC) technology is a decentralized and distributed ledger that comes in handy in storing patient data and encountering data integrity and confidentiality challenges. Therefore, it is a viable solution for addressing existing IoT data security and privacy challenges. BC paves a tremendous path to revolutionize traditional IoT systems by enhancing data security, privacy, and transparency. The scientific community has shown a variety of healthcare applications based on artificial intelligence (AI) that improve health diagnosis and monitoring practices. Moreover, technology companies and startups are revolutionizing healthcare with AI and related technologies. This study illustrates the implication of integrated technologies based on BC, IoT, and AI to meet growing healthcare challenges. This research study examines the integration of BC technology with IoT and analyzes the advancements of these innovative paradigms in the healthcare sector. In addition, our research study presents a detailed survey on enabling technologies for the futuristic, intelligent, and secure internet of health things (IoHT). Furthermore, this study comprehensively studies the peculiarities of the IoHT environment and the security, performance, and progression of the enabling technologies. First, the research gaps are identified by mapping security and performance benefits inferred by the BC technologies. Secondly, practical issues related to the integration process of BC and IoT devices are discussed. Third, the healthcare applications integrating IoT, BC, and ML in healthcare environments are discussed. Finally, the research gaps, future directions, and limitations of the enabling technologies are discussed.

Significant progress has been made in the field of micro/nano-retinal implant technologies. However, the high pixel range, power leakage, reliability, and lifespan of retinal implants are still questionable. Active implantable devices are safe, cost-effective, and reliable. Although a device that can meet basic safety requirements set by the Food and Drug Administration and the European Union is reliable for long-term use and provides control on current and voltage parameters, it will be expensive and cannot be commercially successful. This study proposes an economical, fully controllable, and configurable wireless communication system based on field-programmable gated arrays (FPGAs) that were designed with the ability to cope with the issues that arise in retinal implantation. This system incorporates hexagonal biphasic stimulation pulses generated by a digital controller that can be fully controlled using an external transmitter. The integration of two separate domain analog systems and a digital controller based on FPGAs is proposed in this study. The system was also implemented on a microchip and verified using in vitro results.

Voice-activated artificial intelligence (AI) technology has advanced rapidly and is being adopted in various devices such as smart speakers and display products, which enable users to multitask without touching the devices. However, most devices equipped with cameras and displays lack mobility; therefore, users cannot avoid touching them for face-to-face interactions, which contradicts the voice-activated AI philosophy. In this paper, we propose a deep neural network-based real-time sound source localization (SSL) model for low-power internet of things (IoT) devices based on microphone arrays and present a prototype implemented on actual IoT devices. The proposed SSL model delivers multi-channel acoustic data to parallel convolutional neural network layers in the form of multiple streams to capture the unique delay patterns for the low-, mid-, and high-frequency ranges, and estimates the fine and coarse location of voices. The model adapted in this study achieved an accuracy of 91.41% on fine location estimation and a direction of arrival error of 7.43° on noisy data. It achieved a processing time of 7.811 ms per 40 ms samples on the Raspberry Pi 4B. The proposed model can be applied to a camera-based humanoid robot that mimics the manner in which humans react to trigger voices in crowded environments.

This paper introduces an ambient light rejection (ALR) circuit for the autonomous adaptation of a subretinal implant system. The sub-retinal implants, located beneath a bipolar cell layer, are known to have a significant advantage in spatial resolution by integrating more than a thousand pixels, compared to epi-retinal implants. However, challenges remain regarding current dispersion in high-density retinal implants, and ambient light induces pixel saturation. Thus, the technical issues of ambient light associated with a conventional image processing technique, which lead to high power consumption and area occupation, are still unresolved. Thus, it is necessary to develop a novel image-processing unit to handle ambient light, considering constraints related to power and area. In this paper, we present an ALR circuit as an image-processing unit for sub-retinal implants. We first introduced an ALR algorithm to reduce the ambient light in conventional retinal implants; next, we implemented the ALR algorithm as an application-specific integrated chip (ASIC). The ALR circuit was fabricated using a standard 0.35-μm CMOS process along with an image-sensor-based stimulator, a sensor pixel, and digital blocks. As experimental results, the ALR circuit occupies an area of 190 µm2, consumes a power of 3.2 mW and shows a maximum response time of 1.6 s at a light intensity of 20,000 lux. The proposed ALR circuit also has a pixel loss rate of 0.3%. The experimental results show that the ALR circuit leads to a sensor pixel (SP) being autonomously adjusted, depending on the light intensity.

Viruses infect millions of people worldwide each year, and some can lead to cancer or increase the risk of cancer. As viruses have highly mutable genomes, new viruses may emerge in the future, such as COVID-19 and influenza. Traditional virology relies on predefined rules to identify viruses, but new viruses may be completely or partially divergent from the reference genome, rendering statistical methods and similarity calculations insufficient for all genome sequences. Identifying DNA/RNA-based viral sequences is a crucial step in differentiating different types of lethal pathogens, including their variants and strains. While various tools in bioinformatics can align them, expert biologists are required to interpret the results. Computational virology is a scientific field that studies viruses, their origins, and drug discovery, where machine learning plays a crucial role in extracting domain- and task-specific features to tackle this challenge. This paper proposes a genome analysis system that uses advanced deep learning to identify dozens of viruses. The system uses nucleotide sequences from the NCBI GenBank database and a BERT tokenizer to extract features from the sequences by breaking them down into tokens. We also generated synthetic data for viruses with small sample sizes. The proposed system has two components: a scratch BERT architecture specifically designed for DNA analysis, which is used to learn the next codons unsupervised, and a classifier that identifies important features and understands the relationship between genotype and phenotype. Our system achieved an accuracy of 97.69% in identifying viral sequences.