Showing papers on "Offset (computer science) published in 2020"

Layered Antiferromagnetism Induces Large Negative Magnetoresistance in the van der Waals Semiconductor CrSBr

Abstract: The recent discovery of magnetism within the family of exfoliatable van der Waals (vdW) compounds has attracted considerable interest in these materials for both fundamental research and technological applications. However current vdW magnets are limited by their extreme sensitivity to air, low ordering temperatures, and poor charge transport properties. Here we report the magnetic and electronic properties of CrSBr, an air-stable vdW antiferromagnetic semiconductor that readily cleaves perpendicular to the stacking axis. Below its Neel temperature, $T_N = 132 \pm 1$ K, CrSBr adopts an A-type antiferromagnetic structure with each individual layer ferromagnetically ordered internally and the layers coupled antiferromagnetically along the stacking direction. Scanning tunneling spectroscopy and photoluminescence (PL) reveal that the electronic gap is $\Delta_E = 1.5 \pm 0.2$ eV with a corresponding PL peak centered at $1.25 \pm 0.07$ eV. Using magnetotransport measurements, we demonstrate strong coupling between magnetic order and transport properties in CrSBr, leading to a large negative magnetoresistance response that is unique amongst vdW materials. These findings establish CrSBr as a promising material platform for increasing the applicability of vdW magnets to the field of spin-based electronics.

MagNetO: X-vector Magnitude Estimation Network plus Offset for Improved Speaker Recognition.

Abstract: We present a magnitude estimation network that is combined with a modified ResNet x-vector system to generate embeddings whose inner product is able to produce calibrated scores with increased discrimination. A three-step training procedure is used. First, the network is trained using short segments and a multi-class cross-entropy loss with angular margin softmax. During the second step, only a reduced subset of the DNN parameters are refined using full-length recordings. Finally, the magnitude estimation network is trained using a binary crossentropy loss over pairs of target and non-target trials. The resulting system is evaluated on 4 widely-used benchmarks and provides significant discrimination and calibration gains at multiple operating points.

Decentralized control of connected automated vehicle trajectories in mixed traffic at an isolated signalized intersection

Abstract: It is concerned that system-level benefits of connected automated vehicle control might only prevail in a far-future centralized control environment, whereas the benefits could be much offset in a near-future decentralized control system. To address this concern, this paper proposes a decentralized control model for connected automated vehicle trajectory optimization at an isolated signalized intersection with a single-lane road where each connected automated vehicle aims to minimize its own travel time, fuel consumption and safety risks. To improve the computational tractability, the original complex decentralized control model is reformulated into a discrete model. A benchmark centralized control model is also formulated to compare with the decentralized control model. The DIRECT algorithm is adopted to solve the above models. Numerical results show that the decentralized control model has better computational efficiency (with an average solution time of 10 s) than the centralized control model (with an average solution time of 60 s) without significant loss of the system optimality (with an average of 3.91%). Finally, analysis on connected automated vehicle market penetration shows that the extra benefit of the centralized control model is not obvious either in under-saturated traffic (less than 1%) or at a low connected automated vehicle market penetration rate in critically-saturated and over-saturated traffic (less than 3% when the market penetration rate is lower than 20%). The results suggest that, as apposed to the earlier concern, the near-future decentralized control scheme that requires less technology maturity and infrastructure investment can achieve benefits similar to the far-future centralized control scheme with much simpler operations in under-saturated traffic, or in critically-saturated traffic and over-saturated traffic with a low connected automated vehicle market penetration rate.

Display device and driving method thereof

Abstract: A display device and driving method thereof are disclosed. The display device includes a pixel emitting light at a luminance corresponding to an output grayscale value and a color shifter for converting an input grayscale value into the output grayscale value based on output color gamut information. The color shifter includes an offset storage unit storing reference color gamut information and offset information; and a color gamut determination unit that determines the output color gamut information using the reference color gamut information and the offset information when the color shift level corresponds to a value between the reference level and the shift levels, and determines tire output color gamut information using second offset information in which the offset information is inverted and the reference color gamut information when the color shift level is not between the reference level and the shift levels.

JGR-P2O: Joint Graph Reasoning Based Pixel-to-Offset Prediction Network for 3D Hand Pose Estimation from a Single Depth Image

Abstract: State-of-the-art single depth image-based 3D hand pose estimation methods are based on dense predictions, including voxel-to-voxel predictions, point-to-point regression, and pixel-wise estimations. Despite the good performance, those methods have a few issues in nature, such as the poor trade-off between accuracy and efficiency, and plain feature representation learning with local convolutions. In this paper, a novel pixel-wise prediction-based method is proposed to address the above issues. The key ideas are two-fold: (a) explicitly modeling the dependencies among joints and the relations between the pixels and the joints for better local feature representation learning; (b) unifying the dense pixel-wise offset predictions and direct joint regression for end-to-end training. Specifically, we first propose a graph convolutional network (GCN) based joint graph reasoning module to model the complex dependencies among joints and augment the representation capability of each pixel. Then we densely estimate all pixels’ offsets to joints in both image plane and depth space and calculate the joints’ positions by a weighted average over all pixels’ predictions, totally discarding the complex post-processing operations. The proposed model is implemented with an efficient 2D fully convolutional network (FCN) backbone and has only about 1.4M parameters. Extensive experiments on multiple 3D hand pose estimation benchmarks demonstrate that the proposed method achieves new state-of-the-art accuracy while running very efficiently with around a speed of 110 fps on a single NVIDIA 1080Ti GPU (This work was supported in part by the National Natural Science Foundation of China under Grants 61976095, in part by the Science and Technology Planning Project of Guangdong Province under Grant 2018B030323026. This work was also partially supported by the Academy of Finland.). The code is available at https://github.com/fanglinpu/JGR-P2O.

Multistatic Moving Object Localization by a Moving Transmitter of Unknown Location and Offset

Abstract: This paper investigates the multistatic localization of a moving object in position and velocity using an uncoordinated moving transmitter of unknown position and velocity. The measurements are time delays and frequencies and each kind is subject to an unknown amount of offset. We have shown that incorporating the direct-path measurements between the transmitter and receivers to the indirect-path measurements from the transmitter through the object to the receivers improves the localization performance, although nuisance parameters including the transmitter position, velocity and offsets are required for estimation. The condition about the localization geometry that can eliminate the degradation due to the offsets is derived for IID Gaussian noise. Algebraic solution to localize the moving object is developed together with the performance analysis in reaching the Cramer-Rao Lower Bound accuracy under Gaussian noise over the small error region. The particular case of having time delay measurements only is examined and the optimal geometry for handling unknown transmitter position and time offset is devised. Simulations validate the theoretical developments.

Vehicle Trajectory Prediction and Cut-In Collision Warning Model in a Connected Vehicle Environment

Abstract: Side collisions caused by sudden vehicle cut-ins comprise a significant proportion of traffic accidents. Due to the complex and dynamic nature of traffic environments, the warning algorithms in advanced driving assistant systems (ADAS) often misjudge and misdiagnose risk and omit necessary warnings, because they rely solely on the sensing information of the single vehicle equipped with ADAS and have limited insights from and communication with the surrounding vehicles and traffic environment. To improve the effectiveness of ADAS in cut-in scenarios, this study established a collision warning model in a vehicle-to-vehicle (V2V) communication environment. Firstly, based on the support vector machine-recursive feature elimination (SVM-RFE) lane-change intent-recognition model, the lane-change feasibility and the change rate of the lateral offset, the logical ``and'' was used to establish a lane-change behavior prediction model, and a trajectory prediction model was established based on the long short-term memory (LSTM). Then, based on the proposed comprehensive prediction model for lane-change behavior, the driving trajectory prediction model, and the oriented bounding box (OBB) detection algorithm, a collision warning model was established for a V2V environment. Finally, based on a driving simulation platform and a real-world vehicle test, a cut-in experiment in a V2V environment was designed and implemented. By comparing the warning confusion matrix and warning time, it was found that the proposed cut-in collision warning model is superior to the traditional collision warning model. The results of this study can provide new modeling ideas and a theoretical basis for ADAS to further optimize for a cut-in scenario.

Interpreting initial offset boosting via reconstitution in integral domain

Abstract: Initial offset boosting behaviors with homogenous, heterogeneous or extreme multistability have been reported in several nonlinear systems, but the forming mechanisms were rarely discussed. To figure out this problem, a four-dimensional (4-D) memristive system with cosine memductance is presented, which can exhibit initial offset boosting related to extreme multistability. Taking this 4-D memristive system as paradigm, a three-dimensional (3-D) system with standalone initials-related parameters is reconstructed in an integral domain. Thus, the original line equilibrium set is mapped as some periodically varied equilibrium points, which allows that the initial offset boosting is modeled as variable offset boosting with infinite topologically different attractors. Besides, the reconstituted 3-D model exhibits bi-stability or quadri-stability for fixed parameters, but it maintains the dynamics of the 4-D memristive system when initiated from the neighborhood of the origin point. Finally, circuit synthesis, PSIM simulations, and experimental measurements are carried out to validate the reconstituted variable offset boosting behaviors.

Object-based incremental registration of terrestrial point clouds in an urban environment

Abstract: Registration of terrestrial point clouds is essential for large-scale urban applications. The robustness, accuracy, and runtime are generally given the highest priority in the design of appropriate algorithms. Most approaches that target general scenarios can only fulfill some of these factors, that is, robustness and accuracy come at the cost of increased runtime and vice versa. This paper proposes an object-based incremental registration strategy that accomplishes all of these objectives without the need for artificial targets, aiming at a specific scenario, the urban environment. The key is to decompose the degrees of freedom for the SE(3) transformation to three separate but closely related steps, considering that scanners are generally leveled in urban scenes: (1) 2D transformation with matches from line primitives, (2) vertical offset compensation by robust least-squares optimization, and (3) full SE(3) least-squares refinement using uniformly selected local patches. The robustness is prioritized in the whole pipeline, as structured first by a primitive-based registration and two least-squares optimizations with robust estimations that do not require specific keypoints. An object-based strategy for terrestrial point clouds is used to increase the reliability of the first step by the line primitives, which significantly reduces the search space without affecting the recall ratio. The least-squares optimization contributes to achieve a global optimum for the accurate registration. The three coupling steps are also more efficient than segregated coarse-to-fine registration. Experimental evaluations for point clouds acquired in both a metropolis and in old-style cities reveal that the proposed methods are superior to or on par with the state-of-the-art in robustness, accuracy, and runtime. In addition, the methods are also agnostic to the primitives adopted.

Compensation of Current Measurement Offset Error for Permanent Magnet Synchronous Machines

Abstract: Thermal drift-caused current measurement offset error usually results in unfavorable torque ripples of fundamental frequency, and hence a compensation method for current offset error is proposed in this article, which is initially derived based on the nonsalient-pole machines and then extended to the salient-pole machines. By filtering the difference between the predicted currents from a simple nominal model and the measured currents, the offset error is directly estimated and then compensated with minimal effects on the current dynamics. It is proved that the estimation is accurate if the filter has unity gain at the frequency of the offset error. Therefore, the proposed method is free of the current regulator type and shows strong robustness against machine parameter deviations. It can also stably work even when the output voltage is saturated. Two kinds of filters are designed in the stationary reference frame, and the low-pass filter with low bandwidth is recommended for its easy implementation. Finally, the effectiveness of the proposed method is verified by simulations and experiments.

A stream cipher algorithm based on 2D coupled map lattice and partitioned cellular automata

Abstract: The two-dimensional coupled map lattice (2D CML) is a spatiotemporal chaotic model with complex dynamic behavior and has high potential for designing stream cipher. We propose an offset 2D CML model by adding different offsets for each lattice. The offset 2D CML model has better chaotic properties, such as larger Lyapunov exponent (LE) and more uniform chaotic sequences, than the original 2D CML model, which provides a good basis for constructing stream cipher. We combine the offset 2D CML model with the partitioned cellular automata (PCA) and propose a stream cipher algorithm. In our algorithm, the PCA is used to control the extraction of pseudo-random number from the offset 2D CML model, which effectively hides the orbit information of system and enhances the difficulty of attacking chaotic sequences. Moreover, some fast nonlinear transform operations are specially introduced into our algorithm to further improve the complexity and the running speed. Theory analysis and simulation test both confirm that the proposed stream cipher algorithm has excellent statistical performance, high security and computational efficiency. It has great potential for guaranteeing data security in the Internet.

High Sensitivity Differential Giant Magnetoresistance (GMR) Based Sensor for Non-Contacting DC/AC Current Measurement.

Abstract: This paper presents the design and implementation of a high sensitivity giant magnetoresistance (GMR) based current sensor with a broad range of applications. The novelty of our approach consists in using a double differential measurement system, based on commercial GMR sensors, with an adjustable biasing system used to linearize the field response of the system. The work aims to act as a fully-operational proof of concept application, with an emphasis on the mode of operation and methods to improve the sensitivity and linearity of the measurement system. The implemented system has a broad current measurement range from as low as 75 mA in DC and 150 mA in AC up to 4 A by using a single setup. The sensor system is also very low power, consuming only 6.4 mW. Due to the way the sensors are polarized and positioned above the U-shaped conductive band through which the current to be measured is flowing, the differential setup offers a sensitivity of about between 0.0272 to 0.0307 V/A (signal from sensors with no amplifications), a high immunity to external magnetic fields, low hysteresis effects of 40 mA, and a temperature drift of the offset of about −2.59×10−4 A/°C. The system provides a high flexibility in designing applications where local fields with very low amplitudes must be detected. This setup can be redesigned for a wide range of applications, thus allowing further specific optimizations, which would provide an even greater accuracy and a significantly extended operation range.

DMVOS: Discriminative Matching for Real-time Video Object Segmentation

Abstract: Though recent methods on semi-supervised video object segmentation (VOS) have achieved an appreciable improvement of segmentation accuracy, it is still hard to get an adequate speed-accuracy balance when facing real-world application scenarios. In this work, we propose Discriminative Matching for real-time Video Object Segmentation (DMVOS), a real-time VOS framework with high-accuracy to fill this gap. Based on the matching mechanism, our framework introduces discriminative information through the Isometric Correlation module and the Instance Center Offset module. Specifically, the isometric correlation module learns a pixel-level similarity map with semantic discriminability, and the instance center offset module is applied to exploit the instance-level spatial discriminability. Experiments on two benchmark datasets show that our model achieves state-of-the-art performance with extremely fast speed, for example, J&F of 87.8% on DAVIS-2016 validation set with 35 milliseconds per frame.

A Novel Rapid-Flooding Approach With Real-Time Delay Compensation for Wireless-Sensor Network Time Synchronization.

Abstract: One-way-broadcast-based flooding time synchronization algorithms are commonly used in wireless-sensor networks (WSNs). However, the packet delay and clock drift pose a challenge to accuracy, as they entail serious by-hop error accumulation problems in the WSNs. To overcome this, a rapid-flooding multibroadcast time synchronization with real-time delay compensation (RDC-RMTS) is proposed in this article. By using a rapid-flooding protocol, flooding latency of the referenced time information is significantly reduced in the RDC-RMTS. In addition, a new joint clock skew-offset maximum-likelihood estimation (MLE) is developed to obtain the accurate clock parameter estimations and the real-time packet delay estimation. Moreover, an innovative implementation of the RDC-RMTS is designed with an adaptive clock offset estimation. The experimental results indicate that the RDC-RMTS can easily reduce the variable delay and significantly slow the growth of by-hop error accumulation. Thus, the proposed RDC-RMTS can achieve accurate time synchronization in large-scale complex WSNs.

Iterative Doppler Frequency Offset Estimation in Satellite High-Mobility Communications

Abstract: Satellite communication systems are able to provide diverse services for ground terminals in ubiquitous global coverage, which play a vital role in high-mobility communication environments Existing technologies developed primarily for satellite communications cannot be readily applied to satellite high-mobility communication scenarios, since high Doppler frequency offset caused by the fast movement of wireless terminals, and low signal-to-noise ratio (SNR) circumstances caused by limited link budgets in satellites incur more difficulty of the synchronization, especially for short burst transmission To solve such a problem in satellite high-mobility communications, we propose a novel method named GP-MASO-MLE, which consists of a coarse estimation algorithm based on the Gaussian process (GP) model and Newton-Raphson method, and a fine correction algorithm based on the improved maximum likelihood estimation (MLE) jointly with turbo decoding iterations Simulation results show that the proposed algorithm can approach to the bit error rate (BER) performance bound of ideal Doppler frequency offset correction within 01 dB, which can be well applied in code-aided (CA) satellite high-mobility communication systems for its good performance In addition, the computational complexity of the proposed algorithm is lower than other traditional turbo synchronization algorithms

Dynamic analysis, FPGA implementation, and cryptographic application of an autonomous 5D chaotic system with offset boosting

Abstract: An autonomous five-dimensional (5D) system with offset boosting is constructed by modifying the well-known three-dimensional autonomous Liu and Chen system. Equilibrium points of the proposed autonomous 5D system are found and its stability is analyzed. The proposed system includes Hopf bifurcation, periodic attractors, quasi-periodic attractors, a one-scroll chaotic attractor, a double-scroll chaotic attractor, coexisting attractors, the bistability phenomenon, offset boosting with partial amplitude control, reverse period-doubling, and an intermittency route to chaos. Using a field programmable gate array (FPGA), the proposed autonomous 5D system is implemented and the phase portraits are presented to check the numerical simulation results. The chaotic attractors and coexistence of the attractors generated by the FPGA implementation of the proposed system have good qualitative agreement with those found during the numerical simulation. Finally, a sound data encryption and communication system based on the proposed autonomous 5D chaotic system is designed and illustrated through a numerical example.

DEM Generation With a Scale Factor Using Multi-Aspect SAR Imagery Applying Radargrammetry

Abstract: Digital elevation model (DEM) generation using multi-aspect synthetic aperture radar (SAR) imagery applying radargrammetry has become a hotspot. The traditional radargrammetric method is to solve the rigorous radar projection equations to obtain the three dimensional coordinates of targets. In this paper, we propose a new DEM generation method based on the offset between multi-aspect images formed on ground plane. The ground object will be projected to different positions from different viewing aspect angles if the height of object is not equal to the height of imaging plane. The linear relationship between the offset of imaging positions and height of the object is derived and scale factor is obtained finally. Height information can be retrieved from offset of imaging positions directly through the DEM extraction model presented in this paper. Thus the solution to nonlinear equations point by point can be avoided. Real C band airborne circular SAR images is used to verify the proposed approach. When extracted DEM applied in multi-aspect imaging process, superimposition of multi-aspect images will no longer be defocusing and can achieve finer observation of the scanned scene.

Wireless position sensing and normalization of embedded resonant sensors using a resonator array

Abstract: In this work, four square, planar resonators with unique frequency windows were used to form a 2 by 2 array for wireless position determination and normalization of position-dependent, embedded resonant sensors. First, a master table of S|21| gain and phase data was collected at 8100 positions. Automated scripts extracted the characteristic gain and phase peaks and used cubic interpolation to expand the master table to 7,157,160 unique angle and coordinate positions. An unknown position is then determined by comparing its S|21| measurements to this table. To further improve the position accuracy, multiple measurements are collected on linear flyby trajectories. The average and standard deviation of predicted position offset from true value using this method were 3.2 and 2.3 mm, respectively. To test normalization of a position dependent sensor, a spiral resonant sensor was placed underneath the square array. The sensor signal was modulated using varying amounts of water on the sensor surface. A corrected reading was determined using four different flyby trajectories using the position array data to adjust the signal based on position. We found that average errors of the normalized signals were between 0.04 to 0.15 MHz at lower water volume (0.5 mL) and -0.53 to -0.74 MHz at higher water volume (2.0 mL). In its current state, the positional array can be used for asset tracking or feedback control and the sensor normalization can be used to improve the measurement accuracy of embedded sensors. This technique can be further improved by collecting more accurate master calibration data using an automated system.

Robust Nonlinear Control Using Barrier Lyapunov Function Under Lateral Offset Error Constraint for Lateral Control of Autonomous Vehicles

Abstract: In this study, we propose a robust nonlinear control method using the barrier Lyapunov function (BLF) under the lateral offset error constraint for lateral control of autonomous vehicles. Furthermore, for the application of the BLF-based control method to lateral control, we propose a second-order lateral dynamics scheme based on the look-ahead distance of the vehicle. In the second-order lateral dynamics, the system functions with unknown parameters, and the external disturbances can be lumped into a disturbance term. The proposed method consists of an extended state observer (ESO) and a nonlinear controller. The ESO is designed to estimate the full state variable and disturbance, including the system modeling and external disturbance. The nonlinear controller is developed using the BLF to compensate for the disturbances and to guarantee the constraint for the lateral offset error. Consequently, the proposed method satisfies the output constraint in the presence of disturbances and modeling uncertainties as well as improves the lateral control performance using only the lateral offset error at the look-ahead distance. The lateral control performance of the proposed method is validated using CarSim and MATLAB/Simulink.

Offset-free MPC strategy for nonzero regulation of linear impulsive systems

Abstract: In various biomedical applications, drug administration treatment can be modeled as an impulsive control system. Despite the development of different control strategies for impulsive systems, the elimination of the offset generated by a plant-model mismatch has not yet been researched. In biomedical systems, this mismatch is a consequence of physiological changes and can result in inaccurate treatment of patients. Therefore, control techniques that accomplish the objectives by compensating the effect of variations are required. The present paper proposes and substantiates a novel offset-free model predictive control (MPC) strategy for impulsive systems. To that aim, an impulsive disturbance model is introduced, and an observer design is developed that includes new observability criteria for estimating the disturbance and the state. Further, it is demonstrated that the proposed control strategy achieves zero offset tracking from an analysis of the observer and the controller at steady state. Additionally, the controller incorporates a recent MPC formulation to steer the state to an equilibrium set using artificial/intermediary variables to achieve nonzero regulation. Finally, these results are evaluated and illustrated using a dynamical model for type 1 diabetic patients.

Initial tool path selection of the iso-scallop method based on offset similarity analysis for global preferred feed directions matching

Abstract: The iso-scallop method has been long adopted to achieve a shorter overall machining length. The efficiency and machining performance of this method are largely dominated by the initial tool path. The preferred direction field supplies the local best feed directions. Usually, the offset paths generated by the iso-scallop method largely deviate from the preferred directions, even though the initial path is strictly along the preferred directions. The matching degree of the offset paths and preferred direction field should be taken into consideration when selecting an initial tool path. This paper presents a novel initial path selection method for the iso-scallop method to make whole iso-scallop tool paths and the preferred direction field consistent as much as possible. A surface is re-parameterized to keep the conformality between the surface and parametric domain, which leads to the more regular offset paths on the new parametric domain. By fitting the vector field, streamlines are generated for representing the preferred feed direction on the parametric domain. The offset similarity metric defined by the initial path and streamlines is constructed to measure the matching degree between offset paths and preferred feed directions. Then the feasible path with the best offset similarity for the streamlines will be selected as the initial tool path. In our case study, feed directions with the maximum strip width are chosen. The test results have shown that the tool paths generated by the proposed method achieved a better matching for the selected feed directions and a shorter overall length compared with some existing tool path generation methods.

Robust Clock Skew and Offset Estimation for IEEE 1588 in the Presence of Unexpected Deterministic Path Delay Asymmetries

Abstract: IEEE 1588, built on the classical two-way message exchange scheme, is a popular clock synchronization protocol for packet-switched networks. Due to the presence of random queuing delays in a packet-switched network, the joint recovery of the clock skew and offset from the timestamps of the exchanged synchronization packets can be treated as a statistical estimation problem. In this paper, we address the problem of clock skew and offset estimation for IEEE 1588 in the presence of possible unknown asymmetries between the deterministic path delays of the forward master-to-slave path and reverse slave-to-master path, which can result from incorrect modeling or cyber-attacks. First, we develop lower bounds on the mean square estimation error for a clock skew and offset estimation scheme for IEEE 1588 assuming the availability of multiple master-slave communication paths and complete knowledge of the probability density functions (pdf) describing the random queuing delays. Approximating the pdf of the random queuing delays by a mixture of Gaussian random variables, we then present a robust iterative clock skew and offset estimation scheme that employs the space alternating generalized expectation-maximization (SAGE) algorithm for learning all the unknown parameters. Numerical results indicate that the developed robust scheme exhibits a mean square estimation error close to the lower bounds.

Robust control of a not gate by composite pulses

Abstract: We present a general procedure to implement a not gate by composite pulses robust against both offset uncertainties and control field variations. We define different degrees of robustness in this two-parameter space, namely, along one, two, or all directions. We show that the phases of the composite pulse satisfy a nonlinear system and can be computed analytically or numerically.

A High Offset Distribution Tolerance High Resolution ISFET Array With Auto-Compensation for Long-Term Bacterial Metabolism Monitoring

Abstract: This paper presents a CMOS ion-sensitive-field-effect-transistor (ISFET) array with superior offset distribution tolerance, resolution and linearity for long-term bacterial metabolism monitoring. A floating gate ISFET is adopted as the sensing front end to maximize ion sensitivity and support ultra-long-term measurement. To solve the DC offset issue induced by trapped chargers and drifts in each ISFET sensor, a complementary readout scheme with column offset compensation is proposed. P-type and N-type source followers are combined to cover a wide range of input DC offsets while maintaining small area and high linearity. The DC offset is digitally compensated during signal readout to facilitate global amplification and quantization. Fabricated in 0.18 μm standard CMOS process, the ISFET array can tolerate an offset distribution beyond power supply with a linear pH-to-output response. Due to high ion sensitivity and low circuit noise, the whole system achieves a high resolution of 0.017 pH. The proposed ISFET system has successfully demonstrated an accurate pH monitoring of normal Escherichis coli growth for 11 hours and its response to antibiotics, showing long-term bacterial metabolism monitoring capability.

Offset or Onset Frame: A Multi-Stream Convolutional Neural Network with CapsuleNet Module for Micro-expression Recognition

Abstract: Micro-expression is a spontaneous facial expression, which may reveal people's real emotions. The micro-expression recognition has recently attracted much attention in psychology and computer vision community. In this paper, we designed a multi-stream Convolutional Neural Network (CNN) combined with the Capsule Network(CapsNet) module.named CNNCapsNet, to improve the performance of micro-expression recognition. Firstly, both vertical and horizontal optical flow are computed from the onset to the apex, and from the apex to the offset frame respectively, which is the first time that the offset frame information has been taken into account in the field of micro-expression recognition. Secondly, these four optical flow images and the grayscale image of apex frame are input into the five-stream CNN model to extract features. Finally, CapsNet completes micro-expression recognition by learning the features extracted by CNN. The method proposed in this paper are evaluated using the Leave-One-Subject-Out (LOSO) cross-validation protocol on CASME II. The results show that the offset information, which is often neglected, is more important than onset information for the recognition task. Our CNNCapsNet framework can achieve the accuracy of 64.63% for the five-class micro-expression classification.

Crashworthiness optimisation for the rectangular tubes with axisymmetric and uniform thicknesses under offset loading

Abstract: This study is aimed to compare and optimise the crashing performances of an axisymmetric rectangular tube (ART) and a uniform thickness tube (UTT) under offset loading. Both ART and UTT are strengthened with diaphragms. Numerical simulations for the initial UTT with diaphragms under offset loading were firstly performed based on the experimentally validated finite element model (FEM), which illustrated that deformation process varied from stable mode to bending-sliding mode with an increase offset distance of 0–60 mm. Then, design of experiment (DOE) method was employed to determine the design domains of UTT and ART under various offset distances. Based on the results of DOE, three deformation regions were identified and parametric studies showed that the thicknesses of tubes and diaphragms had significant effects on their crashing performances. Subsequently, the irregular design domains derived from the impact condition with offset distance of 45 mm were selected to search the optimal thickness configurations of both UTT and ART. Finally, the multi-objective optimisation (MOD) was conducted by using non-dominated sorting genetic algorithm (NAGA-II) to minimise the initial peak crushing force (IPCFd) and maximise the specific energy absorption (SEAd) under multiple offset loadings. The obtained Pareto fronts showed that ART generally achieved higher IPCFd and SEAd; however, ART outperformed UTT for both IPCFd and SEAd in certain design domains. The outcomes provide insights into the design and selection of thin-walled structures for engineering applications.

A Symmetric Controllable Hyperchaotic Hidden Attractor

Abstract: By introducing a simple feedback, a hyperchaotic hidden attractor is found in the newly proposed Lorenz-like chaotic system. Some variables of the equilibria-free system can be controlled in amplitude and offset by an independent knob. A circuit experiment based on Multisim is consistent with the theoretic analysis and numerical simulation.