Vapor Deposition of Bilayer 3R MoS2 with Room-Temperature Ferroelectricity.

Two-dimensional ultrathin ferroelectrics have attracted much interest due to their potential application in high-density integration of non-volatile memory devices. Recently, 2D van der Waals ferroelectric based on interlayer translation has been reported in twisted bilayer h-BN and transition metal dichalcogenides (TMDs). However, sliding ferroelectricity is not well studied in non-twisted homo-bilayer TMD grown directly by chemical vapor deposition (CVD). In this paper, for the first time, experimental observation of a room-temperature out-of-plane ferroelectric switch in semiconducting bilayer 3R MoS2 synthesized by reverse-flow CVD is reported. Piezoelectric force microscopy (PFM) hysteretic loops and first principle calculations demonstrate that the ferroelectric nature and polarization switching processes are based on interlayer sliding. The vertical Au/3R MoS2/Pt device exhibits a switchable diode effect. Polarization modulated Schottky barrier height and polarization coupling of interfacial deep states trapping/detrapping may serve in coordination to determine switchable diode effect. The room-temperature ferroelectricity of CVD-grown MoS2 will proceed with the potential wafer-scale integration of 2D TMDs in the logic circuit.

A 36-nW Electrocardiogram Anomaly Detector Based on a 1.5-bit Non-Feedback Delta Quantizer for Always-on Cardiac Monitoring.

An always-on electrocardiogram (ECG) anomaly detector (EAD) with ultra-low power (ULP) consumption is proposed for continuous cardiac monitoring applications. The detector is featured with a 1.5-bit non-feedback delta quantizer (DQ) based feature extractor, followed by a multiplier-less convolutional neural network (CNN) engine, which eliminates the traditional high-resolution analog-to-digital converter (ADC) in conventional signal processing systems. The DQ uses a computing-in-capacitor (CIC) subtractor to quantize the sample-to-sample difference of ECG signal into 1.5-bit ternary codes, which is insensitive to low-frequency baseline wandering. The subsequent event-driven classifier is composed of a low-complexity coarse detector and a systolic-array-based CNN engine for ECG anomaly detection. The DQ and the digital CNN are fabricated in 65-nm and 180-nm CMOS technology, respectively, and the two chips are integrated on board through wire bonding. The measured detection accuracy is 90.6% ∼ 91.3% when tested on the MIT-BIH arrhythmia database, identifying three different ECG anomalies. Operating at 1 V and 1.4 V power supplies for the DQ and the digital CNN, respectively, the measured long-term average power consumption of the core circuits is 36 nW, which makes the detector among those state-of-the-art always-on cardiac anomaly detection devices with the lowest power consumption.

Mitotic defects lead to unreduced sperm formation in cdk1-/- mutants.

Unreduced gametes, that are important for species evolution and agricultural development, are generally believed to be formed by meiotic defects. However, we found that male diploid loach (Misgurnus anguillicaudatus) could produce not only haploid sperms, but also unreduced sperms, after cyclin-dependent kinase 1 gene (cdk1, one of the most important kinases in regulating cell mitosis) deletion. Observations on synaptonemal complexes of spermatocyte in prophase of meiosis and spermatogonia suggested that the number of chromosomes in some spermatogonia of cdk1-/- loach doubled, leading to unreduced diploid sperm production. Then, transcriptome analysis revealed aberrant expressions of some cell cycle-related genes (such as ppp1c and gadd45) in spermatogonia of cdk1-/- loach relative to wild-type loach. An in vitro and in vivo experiment further validated that Cdk1 deletion in diploid loach resulted in mitotic defects, leading to unreduced diploid sperm formation. In addition, we found that cdk1-/- zebrafish could also produce unreduced diploid sperms. This study provides important information on revealing the molecular mechanisms behind unreduced gamete formation through mitotic defects, and lays the foundation for a novel strategy for fish polyploidy creation by using cdk1 mutants to produce unreduced sperms, which can then be used to obtain polyploidy, proposed to benefit aquaculture.

Three-dimensional laser scanner as a new tool for measuring lower limb volume in patients with chronic venous diseases.

OBJECTIVE: Measurement of lower limb volume in patients with chronic venous disease (CVD) is necessary for assessing severity at the time of diagnosis and evaluating response to therapy administered. Existing methods have some limitations in clinical application and accuracy. The study aimed to investigate the reliability and validity of a three-dimensional laser scanner (3DLS) in measuring the lower limb volume of patients with CVD. METHODS: A total of 30 patients with CVD (mean age, 55.6 ± 8.07 years; mean body mass index, 24.61 ± 1.87) were recruited in a vascular surgery clinic. The lower limb volumes of all participants were measured using the 3DLS and circumferential method (CM). Statistical analysis was conducted to compare the 3DLS and CM. RESULTS: There was a strong correlation between the CM and 3DLS method (r2 = 0.9065). The 3DLS had a high intraoperator and interoperator reliability. A Bland-Altman plot showed satisfactory agreement between the two methods. The 3DLS demonstrated greater bilateral limb differences than CM. CONCLUSIONS: There was satisfactory agreement between the two investigated methods. The 3DLS method was confirmed to be accurate, repeatable, and rapid in measuring the lower limb volume in patients with CVD and is, therefore, suitable for clinical use.

Lithographic Multicolor Patterning on Hybrid Perovskites for Nano-Optoelectronic Applications.

Ultrathin hybrid perovskites, with exotic properties and two-dimensional geometry, exhibit great potential in nanoscale optical and optoelectronic devices. However, it is still challenging for them to be compatible with high-resolution patterning technology toward miniaturization and integration applications, as they can be readily damaged by the organic solvents used in standard lithography processes. Here, a flexible three-step method is developed to make high-resolution multicolor patterning on hybrid perovskite, particularly achieved on a single nanosheet. The process includes first synthesis of precursor PbI2 , then e-beam lithography and final conversion to target perovskite. The patterns with linewidth around 150 nm can be achieved, which can be applied in miniature optoelectronic devices and high-resolution displays. As an example, the channel length of perovskite photodetectors can be down to 126 nm. Through deterministic vapor-phase anion exchange, a perovskite nanosheet can not only gradually alter the color of the same pattern in a wide wavelength range, but also display different colors simultaneously. The authors are optimistic that the method can be applied for unlimited perovskite types and device configurations for their high-integrated miniature applications.

Investigating the Effects of Anatomical Structures on the Induced Electric Field in the Brain in Transcranial Magnetic Stimulation.

Transcranial magnetic stimulation (TMS) is capable of stimulating neurons in the brain non-invasively and provides numerous possibilities for the treatment of various neurological disorders such as major depressive disorder, Parkinson's disease, obsessive compulsive disorder. TMS coils can affect the distribution of induced electric fields significantly, thus the design of TMS coils is always a popular topic in TMS studies. Yet the importance of the role of anatomical structures in the induced electric field has not been thoroughly investigated. Therefore, this work has compared the strength of electric fields induced from fifty realistic head models with twelve commercial or novel TMS coils to explore how anatomical structures affect the electric field. It has been found that the electric field strengths among the fifty head models showed highly correlated patterns. The coils were placed at two positions, where all the twelve coils were placed at the vertex and eight of them were placed at the dorsolateral prefrontal cortex of the head due to the coil geometry. Notably, fifty heterogeneous head models that are derived from MRI data were used in the simulations for examining the difference on the performance of TMS coils caused by different anatomical structures. A total of one thousand simulations have been conducted, providing a large amount of data for analysis. Clinical Relevance- This provides a basis to make treatment protocols or predictions in TMS clinical trials considering the different anatomical structures among subjects.

A 746 nW ECG Processor ASIC Based on Ternary Neural Network.

This paper presents an ultra-low power electrocardiography (ECG) processor application-specific integrated circuit (ASIC) for the real-time detection of abnormal cardiac rhythms (ACRs). The proposed ECG processor can support wearable or implantable ECG devices for long-term health monitoring. It adopts a derivative-based patient adaptive threshold approach to detect the R peaks in the PQRST complex of ECG signals. Two tiny machine learning classifiers are used for the accurate classification of ACRs. A 3-layer feed-forward ternary neural network (TNN) is designed, which classifies the QRS complex's shape, followed by the adaptive decision logics (DL). The proposed processor requires only 1 KB on-chip memory to store the parameters and ECG data required by the classifiers. The ECG processor has been implemented based on fully-customized near-threshold logic cells using thick-gate transistors in 65-nm CMOS technology. The ASIC core occupies a die area of 1.08 mm2. The measured total power consumption is 746 nW, with 0.8 V power supply at 2.5 kHz real-time operating clock. It can detect 13 abnormal cardiac rhythms with a sensitivity and specificity of 99.10% and 99.5%. The number of detectable ACR types far exceeds the other low power designs in the literature.

Ultra-Robust and Extensible Fibrous Mechanical Sensors for Wearable Smart Healthcare.

Fibrous material with high strength and large stretchability is an essential component of high-performance wearable electronic devices. Wearable electronic systems require a material that is strong to ensure durability and stability, and a wide range of strain to expand their applications. However, it is still challenging to manufacture fibrous materials with simultaneously high mechanical strength and the tensile property. Herein, the ultra-robust (≈17.6 MPa) and extensible (≈700%) conducting microfibers are developed and demonstrated their applications in fabricating fibrous mechanical sensors. The mechanical sensor shows high sensitivity in detecting strains that have high strain resolution and a large detection range (from 0.0075% to 400%) simultaneously. Moreover, low frequency vibrations between 0 and 40 Hz are also detected, which covers most tremors that occur in the human body. As a further step, a wearable and smart health-monitoring system has been developed using the fibrous mechanical sensor, which is capable of monitoring health-related physiological signals, including muscle movement, body tremor, wrist pulse, respiration, gesture, and six body postures to predict and diagnose diseases, which will promote the wearable telemedicine technology.

A 0.66mW 400 MHz/900 MHz Transmitter IC for In-Body Bio-Sensing Applications.

A sub-1GHz transmitter (TX) integrated chip (IC) with ultra-low power consumption and moderately high adjacent channel power rejection (ACPR) is presented for in-body bio-sensing applications. The 400 MHz 12-phase digital power amplifier (DPA) is implemented with the proposed 16QAM modulation scheme to improve the energy efficiency. The TX IC also contains a 900 MHz FSK TX realized with a symmetrical edge-combiner, which can be used in the low accuracy mode. A fully digital modulator with band shaping is integrated on the chip for the improvement of ACPR performance. Fabricated in 65-nm CMOS process, the chip occupies an active area of 0.75 mm2. Under 0.5 V supply voltage, the TX consumes less than 0.66 mW power consumption while delivering -15 dBm of output power when operating at both bands. The presented TX has an energy efficiency performance comparable to the state-of-the-arts low power designs, with the measured average energy consumption of 64.5/220 pJ/bit, and the measured figure-of-merit (FoM) of 2.04/6.98 nJ/(bit · mW) for the two bands. Compared with the state-of-the-arts sub-1mW designs in literatures, the ACPR is improved by at least 13 dB.

The Soft-Strain Effect Enabled High-Performance Flexible Pressure Sensor and Its Application in Monitoring Pulse Waves.

Flexible and wearable pressure sensors attached to human skin are effective and convenient in accurate and real-time tracking of various physiological signals for disease diagnosis and health assessment. Conventional flexible pressure sensors are constructed using compressible dielectric or conductive layers, which are electrically sensitive to external mechanical stimulation. However, saturated deformation under large compression significantly restrains the detection range and sensitivity of such sensors. Here, we report a novel type of flexible pressure sensor to overcome the compression saturation of the sensing layer by soft-strain effect, enabling an ultra-high sensitivity of ~636 kPa-1 and a wide detection range from 0.1 kPa to 56 kPa. In addition, the cyclic loading-unloading test reveals the excellent stability of the sensor, which maintains its signal detection after 10,000 cycles of 10 kPa compression. The sensor is capable of monitoring arterial pulse waves from both deep tissue and distal parts, such as digital arteries and dorsal pedal arteries, which can be used for blood pressure estimation by pulse transit time at the same artery branch.

Inversion symmetry broken in 2H phase vanadium-doped molybdenum disulfide.

Two-dimensional (2D) layered transition metal dichalcogenides (TMDs) have received much attention in nonlinear optical applications due to their unique crystal structures and second harmonic generation (SHG) efficiency. However, SHG signals in TMDs show a layer-dependent behavior, consistent with the presence (absence) of inversion symmetry in even-layer (odd-layer) of TMDs. Herein, we synthesized monolayer and bilayer 2H and 3R phase vanadium (V)-doped MoS2 crystal. Raman spectroscopy, XPS, and STEM were used to identify the chemical composition and crystalline structure of as-grown nanoflakes. SHG measurement was used to research the symmetry of V-doped MoS2 crystals with different stacking orders. Significantly, the SHG efficiency in bilayer 2H phase V-doped MoS2 is equivalent to the 3R phase, indicating an inversion symmetry broken lattice structure caused by the in situ V substitute for Mo sites. This study will be conducive to promote the development of promising nonlinear optical devices based on 2D material.

A 2.63 µW ECG Processor With Adaptive Arrhythmia Detection and Data Compression for Implantable Cardiac Monitoring Device.

An ultra-low power ECG processor ASIC (application specific integrated circuit) with R-wave detection and data compression is presented, which is designed for the long-term implantable cardiac monitoring (ICM) device for arrhythmia diagnosis. An adaptive derivative-based detection algorithm with low computation overhead for potential arrhythmia recording is proposed to detect arrhythmia with the occasional abnormal heart beats. In order to save as much as possible cardiac information with the limited memory size available in the ICM device, a hierarchical data buffer structure is proposed which saves 3 types of data, including the raw ECG data segments of 2 seconds, compressed ECG data segments of 45 seconds, and R-peak values and interval lengths of >2000 beat cycles. A modified swinging-door-trending (SDT) method is proposed for the ECG data compression. The ASIC has been implemented based on fully-customized near-threshold standard cells using the thick-gate transistors in 65-nm CMOS technology for low dynamic power consumption and leakage. The ASIC core occupies a die area of 1.77 mm2. The measured total power is 2.63 µW, which is among the ECG processors with the lowest core power consumption. It exhibits a relatively high positive precision rate (P+) of 99.3% with a sensitivity of 98.2%, in contrast to the similar designs in literature with the same core power consumption level. Also, an ECG data compression ratio (CR) of up to 17.0 has been achieved, with a good trade-off between the compression efficiency and loss.

A 4-µW Analog Front End Achieving 2.4 NEF for Long-Term ECG Monitoring.

An ultra-low-power low-noise analog front end (AFE) is presented in this work, aiming for long-term ECG with clear P-waves for clinical diagnose. The chopper amplifier with passive noise filter and PWM based offset cancellation results in an input-reference noise of 0.39 µVrms, which shows 3.7X noise improvements among the state-of-the-art designs. With the digital offset cancellation by the pulse-width modulation wave, the AFE achieves a low input-referred dc offset of 0.4 µV among 9 tested chips and a low drift under 30 µV. A dynamic scale ADC with low-power comparator strategy prevents the instability and signal-loss, achieving an SFDR of 71.6 dB. The proposed AFE achieves a noise-efficient-factor (NEF) of 2.4 with a power consumption of 4 µW. The fabricated chip is demonstrated in a miniature prototype for long-term ECG monitoring application, presenting a clear ECG waveform with visible P-wave. The simultaneously ECG recording with a medical grade 12-lead ECG Holter shows the effective acquisition of the prototype, proofing the better noise performance.

Integrated proteomic and transcriptomic analysis reveals that polymorphic shell colors vary with melanin synthesis in Bellamya purificata snail.

The snail Bellamya purificata is an ecologically and economically important freshwater gastropod species. However, limited genomic resources are available for this snail. In this study, the transcriptome of mantle tissues and proteome of shells of B. purificata with two shell colors (namely light-cyan line (LC) and light-purple line (LP)) were deeply sequenced and characterized. A total of 5.72 million contigs were assembled into 157,015 unigenes, 21,455 (13.66%) of these unigenes were significantly matched to NR, Swiss-Prot, KOG, GO and KEGG database. 1807 differentially expressed genes (DEGs) were identified between the two different shell color lines. These DEGs were significantly enriched in five KEGG pathways including tyrosine metabolism, tryptophan metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, and histidine metabolism, which suggested that the shell color polymorphism in B. purificata was a result of melanin synthesis variation. A total of 1521 proteins were identified in B. purificata here as well. The differentially expressed protein analysis showed that the tyrosinase content in LP was significantly decreased in comparison to LC, which agreed with the transcriptome analysis results. This study provides valuable genomic resources of B. purificata and improves our understanding of molecular mechanisms of biomineralization and shell color polymorphism in snail.

Long-Term Bowel Sound Monitoring and Segmentation by Wearable Devices and Convolutional Neural Networks.

Bowel sounds (BSs), typically generated by the intestinal peristalses, are a significant physiological indicator of the digestive system's health condition. In this study, a wearable BS monitoring system is presented for long-term BS monitoring. The system features a wearable BS sensor that can record BSs for days long and transmit them wirelessly in real-time. With the system, a total of 20 subjects' BS data under the hospital environment were collected. Each subject is recorded for 24 hours. Through manual screening and annotation, from every subject's BS data, 400 segments were extracted, in which half are BS event-contained segments. Thus, a BS dataset that contains 20 × 400 sound segments is formed. Afterwards, CNNs are introduced for BS segment recognition. Specifically, this study proposes a novel CNN design method that makes it possible to transfer the popular CNN modules in image recognition into the BS segmentation domain. Experimental results show that in holdout evaluation with corrected labels, the designed CNN model achieves a moderate accuracy of 91.8% and the highest sensitivity of 97.0% compared with the similar works. In cross validation with noisy labels, the designed CNN delivers the best generability. By using a CNN visualizing technique-class activation maps, it is found that the designed CNN has learned the effective features of BS events. Finally, the proposed CNN design method is scalable to different sizes of datasets.

A Supervised Speech Enhancement Method for Smartphone-Based Binaural Hearing Aids.

It is essential but quite challenging to alleviate speech information loss and distortion while developing the speech processing algorithms in hearing aids. Recently, many speech enhancement methods based on deep learning are proven effective. However, most of the algorithms fail to achieve real-time processing, which is significant for hearing aids, especially for a smartphone-centered binaural hearing aid system. A supervised speech enhancement method based on an RNN structure is proposed to address the real-time problem. The problem is explored as a resource-constrained speech intelligibility improvement problem with the target of improving speech intelligibility at low SNR situations. The objective experimental result using the standard evaluation metrics has verified the superiority of the proposed method. The trial use by a small number of volunteers also indicates that the user experience has been improved with the proposed method.

A Wi-Fi-Based Wireless Indoor Position Sensing System with Multipath Interference Mitigation.

Wi-Fi-based indoor position sensing solutions have the advantages of easy integration in mobile phones and low cost by using existing Wi-Fi access points. The mainstream methods are commonly based on the received signal strength indicator (RSSI), which suffers from multipath interference in complicated indoor environments. Through the in-depth analysis of the multipath interference, an RSSI-assisted time difference of arrival (TDoA) method is proposed for Wi-Fi-based indoor position sensing in this work. The key idea is to compensate for the multipath interference in the received signals based on the coarse estimation using RSSI and TDoA calculation. A prototype system has been implemented to validate the proposed method. Experimental results have demonstrated the effectiveness of the proposed method, especially for handling the multipath interference with small propagation delay difference. Experimental results show that the indoor position sensing system can achieve a 90th percentile error of 0.3 m. The proposed method can also achieve moderate computational complexity and moderate real-time performance compared to other methods.

A Wireless Visualized Sensing System with Prosthesis Pose Reconstruction for Total Knee Arthroplasty.

The surgery quality of the total knee arthroplasty (TKA) depends on how accurate the knee prosthesis is implanted. The knee prosthesis is composed of the femoral component, the plastic spacer and the tibia component. The instant and kinetic relative pose of the knee prosthesis is one key aspect for the surgery quality evaluation. In this work, a wireless visualized sensing system with the instant and kinetic prosthesis pose reconstruction has been proposed and implemented. The system consists of a multimodal sensing device, a wireless data receiver and a data processing workstation. The sensing device has the identical shape and size as the spacer. During the surgery, the sensing device temporarily replaces the spacer and captures the images and the contact force distribution inside the knee joint prosthesis. It is connected to the external data receiver wirelessly through a 432 MHz data link, and the data is then sent to the workstation for processing. The signal processing method to analyze the instant and kinetic prosthesis pose from the image data has been investigated. Experiments on the prototype system show that the absolute reconstruction errors of the flexion-extension rotation angle (the pitch rotation of the femoral component around the horizontal long axis of the spacer), the internal-external rotation (the yaw rotation of the femoral component around the spacer vertical axis) and the mediolateral translation displacement between the centers of the femoral component and the spacer based on the image data are less than 1.73°, 1.08° and 1.55 mm, respectively. It provides a force balance measurement with error less than ±5 N. The experiments also show that kinetic pose reconstruction can be used to detect the surgery defection that cannot be detected by the force measurement or instant pose reconstruction.

A 0.0014 mm2 150 nW CMOS Temperature Sensor with Nonlinearity Characterization and Calibration for the -60 to +40 °C Measurement Range.

This work presents a complementary metal-oxide-semiconductor (CMOS) ultra-low power temperature sensor chip for cold chain applications with temperatures down to -60 °C. The sensor chip is composed of a temperature-to-current converter to generate a current proportional to the absolute temperature (PTAT), a current controlled oscillator to convert the current to a frequency signal, and a counter as the frequency-to-digital converter. Unlike the conventional linear error calibration method, the nonlinear error of the PTAT current under the low temperature range is fully characterized based on the device model files provided by the foundry. Simulation has been performed, which clearly shows the nonlinear model is much more accurate than the linear model. A nonlinear error calibration method, which requires only two-point calibration, is then proposed. The temperature sensor chip has been designed and fabricated in a 0.13 µm CMOS process, with a total active die area of 0.0014 mm2. The sensor only draws a 140 nA current from a 1.1 V supply, with the key transistors working in the deep subthreshold region. Measurement results show that the proposed nonlinear calibration can decrease the measurement error from -0.9 to +1.1 °C for the measurement range of -60 to +40 °C, in comparison with the error of -1.8 to +5.3 °C using the conventional linear error calibration.

A 3-D Surface Reconstruction with Shadow Processing for Optical Tactile Sensors.

An optical tactile sensor technique with 3-dimension (3-D) surface reconstruction is proposed for robotic fingers. The hardware of the tactile sensor consists of a surface deformation sensing layer, an image sensor and four individually controlled flashing light emitting diodes (LEDs). The image sensor records the deformation images when the robotic finger touches an object. For each object, four deformation images are taken with the LEDs providing different illumination directions. Before the 3-D reconstruction, the look-up tables are built to map the intensity distribution to the image gradient data. The possible image shadow will be detected and amended. Then the 3-D depth distribution of the object surface can be reconstructed from the 2-D gradient obtained using the look-up tables. The architecture of the tactile sensor and the proposed signal processing flow have been presented in details. A prototype tactile sensor has been built. Both the simulation and experimental results have validated the effectiveness of the proposed 3-D surface reconstruction method for the optical tactile sensors. The proposed 3-D surface reconstruction method has the unique feature of image shadow detection and compensation, which differentiates itself from those in the literature.