A new Mach-Zehnder interference temperature measuring sensor based on silica-based chip.

A new type of silicon-based Mach-Zehnder interference (MZI) temperature sensor chip with "mosquito coil" structure was designed. The sensor chip used a new MZI interference structure. After the light entered the chip, it split and interfered in the combiner of the chip. The change in the surrounding temperature will cause the refractive index of the waveguide to change, which will cause the output light intensity to change. The sensor used a frequency stabilized laser that was based on a Bragg grating fiber. The experimental results showed that this structure could achieve a resolution of 0.002 °C and measuring range of 30 °C.

Bionanotechnology and bioMEMS (BNM): state-of-the-art applications, opportunities, and challenges.

The development of micro- and nanotechnology for biomedical applications has defined the cutting edge of medical technology for over three decades, as advancements in fabrication technology developed originally in the semiconductor industry have been applied to solving ever-more complex problems in medicine and biology. These technologies are ideally suited to interfacing with life sciences, since they are on the scale lengths as cells (microns) and biomacromolecules (nanometers). In this paper, we review the state of the art in bionanotechnology and bioMEMS (collectively BNM), including developments and challenges in the areas of BNM, such as microfluidic organ-on-chip devices, oral drug delivery, emerging technologies for managing infectious diseases, 3D printed microfluidic devices, AC electrokinetics, flexible MEMS devices, implantable microdevices, paper-based microfluidic platforms for cellular analysis, and wearable sensors for point-of-care testing.

Effect of Time-Varying Multiplicative Noise on DNN- k WTA Model.

Among many k -winners-take-all ( k WTA) models, the dual-neural network (DNN- k WTA) model is with significantly less number of connections. However, for analog realization, noise is inevitable and affects the operational correctness of the k WTA process. Most existing results focus on the effect of additive noise. This brief studies the effect of time-varying multiplicative input noise. Two scenarios are considered. The first one is the bounded noise case, in which only the noise range is known. Another one is for the general noise distribution case, in which we either know the noise distribution or have noise samples. For each scenario, we first prove the convergence property of the DNN- k WTA model under multiplicative input noise and then provide an efficient method to determine whether a noise-affected DNN- k WTA network performs the correct k WTA process for a given set of inputs. With the two methods, we can efficiently measure the probability of the network performing the correct k WTA process. In addition, for the case of the inputs being uniformly distributed, we derive two closed-form expressions, one for each scenario, for estimating the probability of the model having correct operation. Finally, we conduct simulations to verify our theoretical results.

Conformal Human-Machine Integration Using Highly Bending-Insensitive, Unpixelated, and Waterproof Epidermal Electronics Toward Metaverse.

Efficient and flexible interactions require precisely converting human intentions into computer-recognizable signals, which is critical to the breakthrough development of metaverse. Interactive electronics face common dilemmas, which realize high-precision and stable touch detection but are rigid, bulky, and thick or achieve high flexibility to wear but lose precision. Here, we construct highly bending-insensitive, unpixelated, and waterproof epidermal interfaces (BUW epidermal interfaces) and demonstrate their interactive applications of conformal human-machine integration. The BUW epidermal interface based on the addressable electrical contact structure exhibits high-precision and stable touch detection, high flexibility, rapid response time, excellent stability, and versatile "cut-and-paste" character. Regardless of whether being flat or bent, the BUW epidermal interface can be conformally attached to the human skin for real-time, comfortable, and unrestrained interactions. This research provides promising insight into the functional composite and structural design strategies for developing epidermal electronics, which offers a new technology route and may further broaden human-machine interactions toward metaverse.

A flexible optoacoustic blood 'stethoscope' for noninvasive multiparametric cardiovascular monitoring.

Quantitative and multiparametric blood analysis is of great clinical importance in cardiovascular disease diagnosis. Although there are various methods to extract blood information, they often require invasive procedures, lack continuity, involve bulky instruments, or have complicated testing procedures. Flexible sensors can realize on-skin assessment of several vital signals, but generally exhibit limited function to monitor blood characteristics. Here, we report a flexible optoacoustic blood 'stethoscope' for noninvasive, multiparametric, and continuous cardiovascular monitoring, without requiring complicated procedures. The optoacoustic blood 'stethoscope' features the light delivery elements to illuminate blood and the piezoelectric acoustic elements to capture light-induced acoustic waves. We show that the optoacoustic blood 'stethoscope' can adhere to the skin for continuous and non-invasive in-situ monitoring of multiple cardiovascular biomarkers, including hypoxia, intravascular exogenous agent concentration decay, and hemodynamics, which can be further visualized with a tailored 3D algorithm. Demonstrations on both in-vivo animal trials and human subjects highlight the optoacoustic blood 'stethoscope''s potential for cardiovascular disease diagnosis and prediction.

Analysis on the inherent noise tolerance of feedforward network and one noise-resilient structure.

In the past few decades, feedforward neural networks have gained much attraction in their hardware implementations. However, when we realize a neural network in analog circuits, the circuit-based model is sensitive to hardware nonidealities. The nonidealities, such as random offset voltage drifts and thermal noise, may lead to variation in hidden neurons and further affect neural behaviors. This paper considers that time-varying noise exists at the input of hidden neurons, with zero-mean Gaussian distribution. First, we derive lower and upper bounds on the mean square error loss to estimate the inherent noise tolerance of a noise-free trained feedforward network. Then, the lower bound is extended for any non-Gaussian noise cases based on the Gaussian mixture model concept. The upper bound is generalized for any non-zero-mean noise case. As the noise could degrade the neural performance, a new network architecture is designed to suppress the noise effect. This noise-resilient design does not require any training process. We also discuss its limitation and give a closed-form expression to describe the noise tolerance when the limitation is exceeded.

Adaptive enhancement of acoustic resolution photoacoustic microscopy imaging via deep CNN prior.

Acoustic resolution photoacoustic microscopy (AR-PAM) is a promising medical imaging modality that can be employed for deep bio-tissue imaging. However, its relatively low imaging resolution has greatly hindered its wide applications. Previous model-based or learning-based PAM enhancement algorithms either require design of complex handcrafted prior to achieve good performance or lack the interpretability and flexibility that can adapt to different degradation models. However, the degradation model of AR-PAM imaging is subject to both imaging depth and center frequency of ultrasound transducer, which varies in different imaging conditions and cannot be handled by a single neural network model. To address this limitation, an algorithm integrating both learning-based and model-based method is proposed here so that a single framework can deal with various distortion functions adaptively. The vasculature image statistics is implicitly learned by a deep convolutional neural network, which served as plug and play (PnP) prior. The trained network can be directly plugged into the model-based optimization framework for iterative AR-PAM image enhancement, which fitted for different degradation mechanisms. Based on physical model, the point spread function (PSF) kernels for various AR-PAM imaging situations are derived and used for the enhancement of simulation and in vivo AR-PAM images, which collectively proved the effectiveness of proposed method. Quantitatively, the PSNR and SSIM values have all achieve best performance with the proposed algorithm in all three simulation scenarios; The SNR and CNR values have also significantly raised from 6.34 and 5.79 to 35.37 and 29.66 respectively in an in vivo testing result with the proposed algorithm.

Higher-Order Topological States in Thermal Diffusion.

Unlike conventional topological materials that carry topological states at their boundaries, higher-order topological materials are able to support topological states at boundaries of boundaries, such as corners and hinges. While band topology has been recently extended into thermal diffusion for thermal metamaterials, its realization is limited to a 1D thermal lattice, lacking access to the higher-order topology. In this work, the experimental realization is reported of a higher-order thermal topological insulator in a generalized 2D diffusion lattice. The topological corner states for thermal diffusion are observed in the bandgap of diffusion rate of the bulk, as a consequence of the anti-Hermitian nature of the diffusion Hamiltonian. The topological protection of these thermal corner states is demonstrated with the stability of their diffusion profile in the presence of amorphous deformation. This work constitutes the first realization of higher-order topology in purely diffusive systems and opens the door for future thermal management with topological protection beyond 1D geometries.

Flexible integrated sensor with asymmetric structure for simultaneously 3D tactile and thermal sensing.

The human body detects tactile stimuli through a combination of pressure force and temperature signals via various cutaneous receptors. The development of a multifunctional artificial tactile perception system has potential benefits for future robotic technologies, human-machine interfaces, artificial intelligence, and health monitoring devices. However, constructing systems beyond simple pressure sensing capabilities remains challenging. Here, we propose an artificial flexible and ultra-thin (50 µ m) skin system to simultaneously capture 3D tactile and thermal signals, which mimics the human tactile recognition process using customized sensor pairs and compact peripheral signal-converting circuits. The 3D tactile sensors have a flower-like asymmetric structure with 5-ports and 4 capacitive elements in pairs. Differential and average signals would reveal the curl and amplitude values of the fore field with a resolution of 0.18/mm. The resistive thermal sensors are fabricated with serpentine lines and possess stable heat-sensing performance (165 mV/°C) under shape deformation conditions. Real-time monitoring of the skin stimuli is displayed on the user interface and stored on mobile clients. This work offers broad capabilities relevant to practical applications ranging from assistant prosthetics to artificial electronic skins.

A Review of Emerging Electromagnetic-Acoustic Sensing Techniques for Healthcare Monitoring.

Conventional electromagnetic (EM) sensing techniques such as radar and LiDAR are widely used for remote sensing, vehicle applications, weather monitoring, and clinical monitoring. Acoustic techniques such as sonar and ultrasound sensors are also used for consumer applications, such as ranging and in vivo medical/healthcare applications. It has been of long-term interest to doctors and clinical practitioners to realize continuous healthcare monitoring in hospitals and/or homes. Physiological and biopotential signals in real-time serve as important health indicators to predict and prevent serious illness. Emerging electromagnetic-acoustic (EMA) sensing techniques synergistically combine the merits of EM sensing with acoustic imaging to achieve comprehensive detection of physiological and biopotential signals. Further, EMA enables complementary fusion sensing for challenging healthcare settings, such as real-world long-term monitoring of treatment effects at home or in remote environments. This article reviews various examples of EMA sensing instruments, including implementation, performance, and application from the perspectives of circuits to systems. The novel and significant applications to healthcare are discussed. Three types of EMA sensors are presented: (1) Chip-based radar sensors for health status monitoring, (2) Thermo-acoustic sensing instruments for biomedical applications, and (3) Photoacoustic (PA) sensing and imaging systems, including dedicated reconstruction algorithms were reviewed from time-domain, frequency-domain, time-reversal, and model-based solutions. The future of EMA techniques for continuous healthcare with enhanced accuracy supported by artificial intelligence (AI) is also presented.

A Review of Machine Learning for Near-Infrared Spectroscopy.

The analysis of infrared spectroscopy of substances is a non-invasive measurement technique that can be used in analytics. Although the main objective of this study is to provide a review of machine learning (ML) algorithms that have been reported for analyzing near-infrared (NIR) spectroscopy from traditional machine learning methods to deep network architectures, we also provide different NIR measurement modes, instruments, signal preprocessing methods, etc. Firstly, four different measurement modes available in NIR are reviewed, different types of NIR instruments are compared, and a summary of NIR data analysis methods is provided. Secondly, the public NIR spectroscopy datasets are briefly discussed, with links provided. Thirdly, the widely used data preprocessing and feature selection algorithms that have been reported for NIR spectroscopy are presented. Then, the majority of the traditional machine learning methods and deep network architectures that are commonly employed are covered. Finally, we conclude that developing the integration of a variety of machine learning algorithms in an efficient and lightweight manner is a significant future research direction.

Extracellular-matrix-mimicked 3D nanofiber and hydrogel interpenetrated wound dressing with a dynamic autoimmune-derived healing regulation ability based on wound exudate.

Dynamic regulation of wound physiological signals is the basis of wound healing. Conventional biomaterials delivering growth factors to drive wound healing leads to the passive repair of soft tissues because of the mismatch of wound healing stages. Meanwhile, the bioactivity of wound exudate is often restricted by oxidation and bacterial contamination. Herein, an extracellular matrix mimicked nanofiber/hydrogel interpenetrated network (NFHIN) was constructed with a 3D nanofibrous framework for cell immigration, and interfiled aerogel containing cross-linked hyaluronic acid and hyperbranched polyamidoamine to balance the wound microenvironment. The aerogel can collect wound exudate and transform into a polycationic hydrogel with contact-killing effects even against intracellular pathogens (bactericidal rate > 99.9% in 30 min) and real-time scavenging property of reactive oxygen species. After co-culturing with the NFHIN, the bioactivity of fibroblast in theex vivoblister fluid was improved by 389.69%. The NFHIN showed sustainable exudate management with moisture-vapor transferring rate (6000 g m-2×24 h), equilibrium liquid content (75.3%), Young's modulus (115.1 ± 7 kPa), and anti-tearing behavior similar to human skin. The NFHIN can collect and activate wound exudate, turning it from a clinical problem to an autoimmune-derived wound regulation system, showing potential for wound care in critical skin diseases.

A Low-Voltage and Power-Efficient Capless LDO Based on the Biaxially Driven Power Transistor Technique for Respiration Monitoring System.

In this study, a 0.8-V- Vin 200-mA- Io capless low-dropout voltage regulator (LDO) is developed for a wireless respiration monitoring system. The biaxially driven power transistor (BDP) technique is proposed in the LDO, with a current driven stimulation on the bulk and a voltage on the gate terminal. With the BDP technique, an adaptively biased current-driven loop (ABCL) is designed which can reduce the high threshold voltage of power transistor, thus presenting lower input voltage and reduced power consumption. Moreover, this loop can provide an improved dynamic response due to its increased discharging current. Based on an error amplifier with enhanced DC gain and gain bandwidth, the capless LDO achieves superior power supply rejection (PSR) and stability without a complex frequency compensation mechanism. The proposed LDO is fabricated in the SMIC 180 nm process with a chip area of 0.046 mm 2. Measurement results indicate that this LDO can obtain a 200-mA load current range and greater than -66 dB PSR up to 1 kHz at a supply voltage as low as 0.8 V.

Freestanding and Scalable Force-Softness Bimodal Sensor Arrays for Haptic Body-Feature Identification.

Tactile technologies that can identify human body features are valuable in clinical diagnosis and human-machine interactions. Previously, cutting-edge tactile platforms have been able to identify structured non-living objects; however, identification of human body features remains challenging mainly because of the irregular contour and heterogeneous spatial distribution of softness. Here, freestanding and scalable tactile platforms of force-softness bimodal sensor arrays are developed, enabling tactile gloves to identify body features using machine-learning methods. The bimodal sensors are engineered by adding a protrusion on a piezoresistive pressure sensor, endowing the resistance signals with combined information of pressure and the softness of samples. The simple design enables 112 bimodal sensors to be integrated into a thin, conformal, and stretchable tactile glove, allowing the tactile information to be digitalized while hand skills are performed on the human body. The tactile glove shows high accuracy (98%) in identifying four body features of a real person, and four organ models (healthy and pathological) inside an abdominal simulator, demonstrating identification of body features of the bimodal tactile platforms and showing their potential use in future healthcare and robotics.

Quantitative and Real-Time Evaluation of Human Respiration Signals with a Shape-Conformal Wireless Sensing System.

Respiration signals reflect many underlying health conditions, including cardiopulmonary functions, autonomic disorders and respiratory distress, therefore continuous measurement of respiration is needed in various cases. Unfortunately, there is still a lack of effective portable electronic devices that meet the demands for medical and daily respiration monitoring. This work showcases a soft, wireless, and non-invasive device for quantitative and real-time evaluation of human respiration. This device simultaneously captures respiration and temperature signatures using customized capacitive and resistive sensors, encapsulated by a breathable layer, and does not limit the user's daily life. Further a machine learning-based respiration classification algorithm with a set of carefully studied features as inputs is proposed and it is deployed into mobile clients. The body status of users, such as being quiet, active and coughing, can be accurately recognized by the algorithm and displayed on clients. Moreover, multiple devices can be linked to a server network to monitor a group of users and provide each user with the statistical duration of physiological activities, coughing alerts, and body health advice. With these devices, individual and group respiratory health status can be quantitatively collected, analyzed, and stored for daily physiological signal detections as well as medical assistance.

Wavefront shaping: A versatile tool to conquer multiple scattering in multidisciplinary fields.

Optical techniques offer a wide variety of applications as light-matter interactions provide extremely sensitive mechanisms to probe or treat target media. Most of these implementations rely on the usage of ballistic or quasi-ballistic photons to achieve high spatial resolution. However, the inherent scattering nature of light in biological tissues or tissue-like scattering media constitutes a critical obstacle that has restricted the penetration depth of non-scattered photons and hence limited the implementation of most optical techniques for wider applications. In addition, the components of an optical system are usually designed and manufactured for a fixed function or performance. Recent advances in wavefront shaping have demonstrated that scattering- or component-induced phase distortions can be compensated by optimizing the wavefront of the input light pattern through iteration or by conjugating the transmission matrix of the scattering medium. This offers unprecedented opportunities in many applications to achieve controllable optical delivery or detection at depths or dynamically configurable functionalities by using scattering media to substitute conventional optical components. In this article, the recent progress of wavefront shaping in multidisciplinary fields is reviewed, from optical focusing and imaging with scattering media, functionalized devices, modulation of mode coupling, and nonlinearity in multimode fiber to multimode fiber-based applications. Apart from insights into the underlying principles and recent advances in wavefront shaping implementations, practical limitations and roadmap for future development are discussed in depth. Looking back and looking forward, it is believed that wavefront shaping holds a bright future that will open new avenues for noninvasive or minimally invasive optical interactions and arbitrary control inside deep tissues. The high degree of freedom with multiple scattering will also provide unprecedented opportunities to develop novel optical devices based on a single scattering medium (generic or customized) that can outperform traditional optical components.

Deep and Domain Transfer Learning Aided Photoacoustic Microscopy: Acoustic Resolution to Optical Resolution.

Acoustic resolution photoacoustic micros- copy (AR-PAM) can achieve deeper imaging depth in biological tissue, with the sacrifice of imaging resolution compared with optical resolution photoacoustic microscopy (OR-PAM). Here we aim to enhance the AR-PAM image quality towards OR-PAM image, which specifically includes the enhancement of imaging resolution, restoration of micro-vasculatures, and reduction of artifacts. To address this issue, a network (MultiResU-Net) is first trained as generative model with simulated AR-OR image pairs, which are synthesized with physical transducer model. Moderate enhancement results can already be obtained when applying this model to in vivo AR imaging data. Nevertheless, the perceptual quality is unsatisfactory due to domain shift. Further, domain transfer learning technique under generative adversarial network (GAN) framework is proposed to drive the enhanced image's manifold towards that of real OR image. In this way, perceptually convincing AR to OR enhancement result is obtained, which can also be supported by quantitative analysis. Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) values are significantly increased from 14.74 dB to 19.01 dB and from 0.1974 to 0.2937, respectively, validating the improvement of reconstruction correctness and overall perceptual quality. The proposed algorithm has also been validated across different imaging depths with experiments conducted in both shallow and deep tissue. The above AR to OR domain transfer learning with GAN (AODTL-GAN) framework has enabled the enhancement target with limited amount of matched in vivo AR-OR imaging data.

Speckle-Based Optical Cryptosystem and its Application for Human Face Recognition via Deep Learning.

Face recognition has become ubiquitous for authentication or security purposes. Meanwhile, there are increasing concerns about the privacy of face images, which are sensitive biometric data and should be protected. Software-based cryptosystems are widely adopted to encrypt face images, but the security level is limited by insufficient digital secret key length or computing power. Hardware-based optical cryptosystems can generate enormously longer secret keys and enable encryption at light speed, but most reported optical methods, such as double random phase encryption, are less compatible with other systems due to system complexity. In this study, a plain yet highly efficient speckle-based optical cryptosystem is proposed and implemented. A scattering ground glass is exploited to generate physical secret keys of 17.2 gigabit length and encrypt face images via seemingly random optical speckles at light speed. Face images can then be decrypted from random speckles by a well-trained decryption neural network, such that face recognition can be realized with up to 98% accuracy. Furthermore, attack analyses are carried out to show the cryptosystem's security. Due to its high security, fast speed, and low cost, the speckle-based optical cryptosystem is suitable for practical applications and can inspire other high-security cryptosystems.

An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements.

HIGHLIGHTS: Carbon-based gradient resistance element structure is proposed for the construction of multifunctional touch sensor, which will promote wide detection and recognition range of multiple mechanical stimulations. Multifunctional touch sensor with gradient resistance element and two electrodes is demonstrated to eliminate signals crosstalk and prevent interference during position sensing for human-machine interactions. Biological sensing interface based on a deep-learning-assisted all-in-one multipoint touch sensor enables users to efficiently interact with virtual world. Human-machine interactions using deep-learning methods are important in the research of virtual reality, augmented reality, and metaverse. Such research remains challenging as current interactive sensing interfaces for single-point or multipoint touch input are trapped by massive crossover electrodes, signal crosstalk, propagation delay, and demanding configuration requirements. Here, an all-in-one multipoint touch sensor (AIOM touch sensor) with only two electrodes is reported. The AIOM touch sensor is efficiently constructed by gradient resistance elements, which can highly adapt to diverse application-dependent configurations. Combined with deep learning method, the AIOM touch sensor can be utilized to recognize, learn, and memorize human-machine interactions. A biometric verification system is built based on the AIOM touch sensor, which achieves a high identification accuracy of over 98% and offers a promising hybrid cyber security against password leaking. Diversiform human-machine interactions, including freely playing piano music and programmatically controlling a drone, demonstrate the high stability, rapid response time, and excellent spatiotemporally dynamic resolution of the AIOM touch sensor, which will promote significant development of interactive sensing interfaces between fingertips and virtual objects.

A Silicon-Based Adaptable Edge Coherent Radar Platform for Seamless Health Sensing and Cognitive Interactions With Human Subjects.

Empowered by the rapid advancements of semiconductor techniques, emerging areas such as industry 4.0, precise healthcare, pervasive communications, intelligent robots, and smart buildings are to be realized, which put substantial demands on low-power and high-performance cognitive edge sensors. Capabilities of precise sensing and seamless interactions with human subjects are pivotal to boosting versatile Internet of Everything (IoE) applications. However, it is challenging to attain various kinds of intuitive sensing based on one edge sensor. A novel silicon-based phased-array coherent radar sensing platform is proposed to attain versatile application-driven capabilities by focusing the wideband radar beams accurately at the target's direction to attain precise sensing. The coherent radar platform can support a maximum 60° field-of-view sensing range with smaller than 2° optimum steering step resolution and -70-dBm sensitivity. The silicon-based mixed-signal coherent radar chip platform is fabricated by a 65-nm CMOS process and owns the convenience for massive deployments at the edge. A series of experiments were conducted to validate the integrated radar platform's adaptable capabilities and salient performances on human subject detection, vital signs monitoring, and motion recognition. Notably, the adaptable multifunction integrated radar platform opens up the enticing possibility for next-generation monolithic edge devices supporting seamless health sensing and cognitive interactive functions with human subjects.