arXiv subjects

Jie Zhang

Publications and source records attributed to Jie Zhang.

At least 955 records · Page 53Linked to original sources

arXiv2020

Fmax = 270 GHz InAlN/GaN HEMT on Si with forming gas/nitrogen two-step annealing

In this letter, N2 and forming gas (FG) were used during ohmic contact annealing of InAlN/GaN HEMTs on Si. It is found that N2 annealing offers lower ohmic contact resistance (RC) while FG annealing features lower sheet resistance (Rsheet). Then FG/N2 two-step annealing was used to achieve a subthreshold swing (SS) of 113 mV/dec, an on/off current (Ion/Ioff) ratio of ~ 106, a transconductance (gm) peak of 415 mS/mm, a record low drain-inducing barrier lowing (DIBL) of 65 mV/V, and a record high power gain cutoff frequency (fmax) of 270 GHz on 50-nm InAlN/GaN HEMT on Si.

physics.app-ph↗

arXiv2020

Sub-60 mV/decade switching via high energy electrons tunneling in nanoscale gallium nitride field-effect transistors

Novel devices such as tunneling field-effect transistors (FETs) and ferroelectric FETs have been demonstrated to break the subthreshold swing (SS) limit with sub-60 mV/decade switching for further low voltage/low power applications. In this paper, SS of sub-60 mV/dec was firstly observed in InAlN/GaN high electron mobility transistors (HMETs): an average SS of 30 mV/dec over three orders of magnitude in drain-source (Ids) and a minimum point-by-point SS of 15 mV/dec were achieved in the InAlN/GaN HEMTs with gate length (Lg) of 40 nm. It is found that SS decreases with drain-source voltage (Vds) as well as Lg, and falls below 60 mV/dec when Lg < 100 nm. The decrease of SS as the device dimension scales down is attributed to the tunneling of high energy electrons from channel to the surface. The SS decreasing with the nanoscale gate length shows the great potential of the InAlN/GaN HEMTs to be applied in future logic switches.

physics.app-ph↗

arXiv2020

Signal strength based scheme for following mobile IoT devices in dynamic environments

The increased maturity level of technological achievements towards the realization of the Internet of Things (IoT) vision allowed sophisticated solutions to emerge, offering reliable monitoring in highly dynamic environments that lack well-defined and well-designed infrastructures. In this paper, we use a bio-inspired IoT architecture, which allows flexible creation and discovery of sensor-based services offering self-organization and self-optimization properties to the dynamic network, in order to make the required monitoring information available. The main contribution of the paper is the introduction of a new algorithm for following mobile monitored targets/individuals in the context of an IoT system, especially a dynamic one as the aforementioned. The devised technique, called Hot-Cold, is able to ensure proximity maintenance by the tracking robotic device solely based on the strength of the RF signal broadcasted by the target to communicate its sensors' data. Complete geometrical, numerical, simulation, and convergence analyses of the proposed technique are thoroughly presented, along with a detailed simulation-based evaluation that reveals the higher following accuracy of Hot-Cold compared to the popular concept of trilateration-based tracking. Finally, a prototype of the full architecture was implemented not only to demonstrate the applicability of the presented approach for monitoring in dynamic environments, but also the operability of the introduced tracking technique.

cs.NI↗

arXiv2020

Learning Improvement Heuristics for Solving Routing Problems

Recent studies in using deep learning to solve routing problems focus on construction heuristics, the solutions of which are still far from optimality. Improvement heuristics have great potential to narrow this gap by iteratively refining a solution. However, classic improvement heuristics are all guided by hand-crafted rules which may limit their performance. In this paper, we propose a deep reinforcement learning framework to learn the improvement heuristics for routing problems. We design a self-attention based deep architecture as the policy network to guide the selection of next solution. We apply our method to two important routing problems, i.e. travelling salesman problem (TSP) and capacitated vehicle routing problem (CVRP). Experiments show that our method outperforms state-of-the-art deep learning based approaches. The learned policies are more effective than the traditional hand-crafted ones, and can be further enhanced by simple diversifying strategies. Moreover, the policies generalize well to different problem sizes, initial solutions and even real-world dataset.

cs.AI↗

arXiv2020

Benchmarking High Bandwidth Memory on FPGAs

FPGAs are starting to be enhanced with High Bandwidth Memory (HBM) as a way to reduce the memory bandwidth bottleneck encountered in some applications and to give the FPGA more capacity to deal with application state. However, the performance characteristics of HBM are still not well specified, especially in the context of FPGAs. In this paper, we bridge the gap between nominal specifications and actual performance by benchmarkingHBM on a state-of-the-art FPGA, i.e., a Xilinx Alveo U280 featuring a two-stack HBM subsystem. To this end, we propose Shuhai, a benchmarking tool that allows us to demystify all the underlying details of HBM on an FPGA. FPGA-based benchmarking should also provide a more accurate picture of HBM than doing so on CPUs/GPUs, since CPUs/GPUs are noisier systems due to their complex control logic and cache hierarchy. Since the memory itself is complex, leveraging custom hardware logic to benchmark inside an FPGA provides more details as well as accurate and deterministic measurements. We observe that 1) HBM is able to provide up to 425GB/s memory bandwidth, and 2) how HBM is used has a significant impact on performance, which in turn demonstrates the importance of unveiling the performance characteristics of HBM so as to select the best approach. As a yardstick, we also applyShuhaito DDR4to show the differences between HBM and DDR4.Shuhai can be easily generalized to other FPGA boards or other generations of memory, e.g., HBM3, and DDR3. We will makeShuhaiopen-source, benefiting the community

cs.AR↗

arXiv2020

High-throughput injection-acceleration of electron bunches from a linear accelerator to a laser wakefield accelerator

Plasma-based accelerators (PBAs) driven by either intense lasers (laser wakefield accelerators, LWFAs) or particle beams (plasma wakefield accelerators, PWFAs), can accelerate charged particles at extremely high gradients compared to conventional radio-frequency (RF) accelerators. In the past two decades, great strides have been made in this field, making PBA a candidate for next-generation light sources and colliders. However, these challenging applications necessarily require beams with good stability, high quality, controllable polarization and excellent reproducibility. To date, such beams are generated only by conventional RF accelerators. Therefore, it is important to demonstrate the injection and acceleration of beams first produced using a conventional RF accelerator, by a PBA. In some recent studies on LWFA staging and external injection-acceleration in PWFA only a very small fraction (from below 0.1% to few percent) of the injected charge (the coupling efficiency) was accelerated. For future colliders where beam energy will need to be boosted using multiple stages, the coupling efficiency per stage must approach 100%. Here we report the first demonstration of external injection from a photocathode-RF-gun-based conventional linear accelerator (LINAC) into a LWFA and subsequent acceleration without any significant loss of charge or degradation of quality, which is achieved by properly shaping and matching the beam into the plasma structure. This is an important step towards realizing a high-throughput, multi-stage, high-energy, hybrid conventional-plasma accelerator.

physics.plasm-ph↗

arXiv2020

Single-Side Domain Generalization for Face Anti-Spoofing

Existing domain generalization methods for face anti-spoofing endeavor to extract common differentiation features to improve the generalization. However, due to large distribution discrepancies among fake faces of different domains, it is difficult to seek a compact and generalized feature space for the fake faces. In this work, we propose an end-to-end single-side domain generalization framework (SSDG) to improve the generalization ability of face anti-spoofing. The main idea is to learn a generalized feature space, where the feature distribution of the real faces is compact while that of the fake ones is dispersed among domains but compact within each domain. Specifically, a feature generator is trained to make only the real faces from different domains undistinguishable, but not for the fake ones, thus forming a single-side adversarial learning. Moreover, an asymmetric triplet loss is designed to constrain the fake faces of different domains separated while the real ones aggregated. The above two points are integrated into a unified framework in an end-to-end training manner, resulting in a more generalized class boundary, especially good for samples from novel domains. Feature and weight normalization is incorporated to further improve the generalization ability. Extensive experiments show that our proposed approach is effective and outperforms the state-of-the-art methods on four public databases.

cs.CV↗

arXiv2020

Reconstructing normal section profiles of 3D revolving structures via pose-unconstrained multi-line structured-light vision

The wheel of the train is a 3D revolving geometrical structure. Reconstructing the normal section profile is an effective approach to determine the critical geometric parameter and wear of the wheel in the community of railway safety. The existing reconstruction methods typically require a sensor working in a constrained position and pose, suffering poor flexibility and limited viewangle. This paper proposes a pose-unconstrained normal section profile reconstruction framework for 3D revolving structures via multiple 3D general section profiles acquired by a multi-line structured light vision sensor. First, we establish a model to estimate the axis of 3D revolving geometrical structure and the normal section profile using corresponding points. Then, we embed the model into an iterative algorithm to optimize the corresponding points and finally reconstruct the accurate normal section profile. We conducted real experiment on reconstructing the normal section profile of a 3D wheel. The results demonstrate that our algorithm reaches the mean precision of 0.068mm and good repeatability with the STD of 0.007mm. It is also robust to varying pose variations of the sensor. Our proposed framework and models are generalized to any 3D wheeltype revolving components.

eess.IV↗

arXiv2020

Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation

Image-level weakly supervised semantic segmentation is a challenging problem that has been deeply studied in recent years. Most of advanced solutions exploit class activation map (CAM). However, CAMs can hardly serve as the object mask due to the gap between full and weak supervisions. In this paper, we propose a self-supervised equivariant attention mechanism (SEAM) to discover additional supervision and narrow the gap. Our method is based on the observation that equivariance is an implicit constraint in fully supervised semantic segmentation, whose pixel-level labels take the same spatial transformation as the input images during data augmentation. However, this constraint is lost on the CAMs trained by image-level supervision. Therefore, we propose consistency regularization on predicted CAMs from various transformed images to provide self-supervision for network learning. Moreover, we propose a pixel correlation module (PCM), which exploits context appearance information and refines the prediction of current pixel by its similar neighbors, leading to further improvement on CAMs consistency. Extensive experiments on PASCAL VOC 2012 dataset demonstrate our method outperforms state-of-the-art methods using the same level of supervision. The code is released online.

cs.CV↗

arXiv2020

Modulation of field-like spin orbit torque in heavy metal / ferromagnet heterostructure

Recent studies rediscovered the crucial role of field-like spin orbit torque (SOT) in nanosecond-timescale SOT dynamics. However, there is not yet an effective way to control its relative amplitude. Here, we experimentally modulate the field-like SOT in W/CoFeB/MgO trilayers through tuning the interfacial spin accumulation. By performing spin Hall magnetoresistance measurement, we find that the CoFeB with enhanced spin dephasing, either generated from larger layer thickness or from proper annealing, can distinctly boost the spin absorption and enhance the interfacial spin mixing conductance G_r. While the damping-like torque efficiency increases with G_r, the field-like torque efficiency turns out to decrease with it. The results suggest that the interfacial spin accumulation, which largely contributes to a field-like torque, is reduced by higher interfacial spin transparency. Our work shows a new path to further improve the performance of SOT-based magnetic devices.

cond-mat.mes-hall↗

arXiv2020

STAN-CT: Standardizing CT Image using Generative Adversarial Network

Computed tomography (CT) plays an important role in lung malignancy diagnostics and therapy assessment and facilitating precision medicine delivery. However, the use of personalized imaging protocols poses a challenge in large-scale cross-center CT image radiomic studies. We present an end-to-end solution called STAN-CT for CT image standardization and normalization, which effectively reduces discrepancies in image features caused by using different imaging protocols or using different CT scanners with the same imaging protocol. STAN-CT consists of two components: 1) a novel Generative Adversarial Networks (GAN) model that is capable of effectively learning the data distribution of a standard imaging protocol with only a few rounds of generator training, and 2) an automatic DICOM reconstruction pipeline with systematic image quality control that ensure the generation of high-quality standard DICOM images. Experimental results indicate that the training efficiency and model performance of STAN-CT have been significantly improved compared to the state-of-the-art CT image standardization and normalization algorithms.

eess.IV↗

arXiv2020

Application of Structural Similarity Analysis of Visually Salient Areas and Hierarchical Clustering in the Screening of Similar Wireless Capsule Endoscopic Images

Small intestinal capsule endoscopy is the mainstream method for inspecting small intestinal lesions,but a single small intestinal capsule endoscopy will produce 60,000 - 120,000 images, the majority of which are similar and have no diagnostic value. It takes 2 - 3 hours for doctors to identify lesions from these images. This is time-consuming and increase the probability of misdiagnosis and missed diagnosis since doctors are likely to experience visual fatigue while focusing on a large number of similar images for an extended period of time.In order to solve these problems, we proposed a similar wireless capsule endoscope (WCE) image screening method based on structural similarity analysis and the hierarchical clustering of visually salient sub-image blocks. The similarity clustering of images was automatically identified by hierarchical clustering based on the hue,saturation,value (HSV) spatial color characteristics of the images,and the keyframe images were extracted based on the structural similarity of the visually salient sub-image blocks, in order to accurately identify and screen out similar small intestinal capsule endoscopic images. Subsequently, the proposed method was applied to the capsule endoscope imaging workstation. After screening out similar images in the complete data gathered by the Type I OMOM Small Intestinal Capsule Endoscope from 52 cases covering 17 common types of small intestinal lesions, we obtained a lesion recall of 100% and an average similar image reduction ratio of 76%. With similar images screened out, the average play time of the OMOM image workstation was 18 minutes, which greatly reduced the time spent by doctors viewing the images.

eess.IV↗

arXiv2020

Region-of-interest micro-focus CT based on an all-optical inverse Compton scattering source

Micro-focus computed tomography (CT), enabling the reconstruction of hyperfine structure within objects, is a powerful nondestructive testing tool in many fields. Current X-ray sources for micro-focus CT are typically limited by their relatively low photon energy and low flux. An all-optical inverse Compton scattering source (AOCS) based on laser wakefield accelerator (LWFA) can generate intense quasi-monoenergetic X/gamma-ray pulses in the keV-MeV range with micron-level source size, and its potential application for micro-focus CT has become very attractive in recent years due to the fast pace progress made in LWFA. Here we report the first experimental demonstration of high-fidelity micro-focus CT using AOCS (~70 keV) by imaging and reconstructing a test object with complex inner structures. A region-of-interest (ROI) CT method is adopted to utilize the relatively small field-of-view (FOV) of AOCS to obtain high-resolution reconstruction. This demonstration of the ROI micro-focus CT based on AOCS is a key step for its application in the field of hyperfine nondestructive testing.

physics.app-ph↗

arXiv2020

Multiform Fonts-to-Fonts Translation via Style and Content Disentangled Representations of Chinese Character

This paper mainly discusses the generation of personalized fonts as the problem of image style transfer. The main purpose of this paper is to design a network framework that can extract and recombine the content and style of the characters. These attempts can be used to synthesize the entire set of fonts with only a small amount of characters. The paper combines various depth networks such as Convolutional Neural Network, Multi-layer Perceptron and Residual Network to find the optimal model to extract the features of the fonts character. The result shows that those characters we have generated is very close to real characters, using Structural Similarity index and Peak Signal-to-Noise Ratio evaluation criterions.

cs.CV↗

arXiv2020

Achieving 50 femtosecond resolution in MeV ultrafast electron diffraction with a double bend achromat compressor

We propose and demonstrate a novel scheme to produce ultrashort and ultrastable MeV electron beam. In this scheme, the electron beam produced in a photocathode radio-frequency (rf) gun first expands under its own Coulomb force with which a positive energy chirp is imprinted in the beam longitudinal phase space. The beam is then sent through a double bend achromat with positive longitudinal dispersion where electrons at the bunch tail with lower energies follow shorter paths and thus catch up with the bunch head, leading to longitudinal bunch compression. We show that with optimized parameter sets, the whole beam path from the electron source to the compression point can be made isochronous such that the time of flight for the electron beam is immune to the fluctuations of rf amplitude. With a laser-driven THz deflector, the bunch length and arrival time jitter for a 20 fC beam after bunch compression are measured to be about 29 fs (FWHM) and 22 fs (FWHM), respectively. Such an ultrashort and ultrastable electron beam allows us to achieve 50 femtosecond (FWHM) resolution in MeV ultrafast electron diffraction where lattice oscillation at 2.6 THz corresponding to Bismuth A1g mode is clearly observed without correcting both the short-term timing jitter and long-term timing drift. Furthermore, oscillating weak diffuse scattering signal related to phonon coupling and decay is also clearly resolved thanks to the improved temporal resolution and increased electron flux. We expect that this technique will have a strong impact in emerging ultrashort electron beam based facilities and applications.

physics.acc-ph↗

arXiv2020

3 m$\times$3 m heterolithic passive resonant gyroscope with cavity length stabilization

Large-scale high sensitivity laser gyroscopes have important applications for ground-based and space-based gravitational wave detection. We report on the development of a 3 m$\times$3 m heterolithic passive resonant gyroscope (HUST-1) which is installed on the ground of a cave laboratory. We operate the HUST-1 on different longitudinal cavity modes and the rotation sensitivity reaches $1.6\times10^{-9}$ rad/s/$\rm \sqrt{Hz}$ beyond 1 Hz. The drift of the cavity length is one of the major sensitivity limits for our gyroscope in the low frequency regime. By locking cavity length to an ultra-stable reference laser, we achieve a fractional cavity length stability of $5.6\times10^{-9}$ m$/\rm \sqrt{Hz}$ at 0.1 mHz, a four orders of magnitude improvement over the unconstrained cavity in the low frequency regime. We stabilize the cavity length of a large-scale heterolithic passive resonant gyroscope through active feedback and realize long-term operation. The rotation sensitivity reaches $1.7\times10^{-7}$ rad/s/$\sqrt{\rm{Hz}}$ at 0.1 mHz, a three orders of magnitude improvement, which is no longer limited by the cavity length drift in this frequency range.

physics.ins-det↗

arXiv2020

Model Watermarking for Image Processing Networks

Deep learning has achieved tremendous success in numerous industrial applications. As training a good model often needs massive high-quality data and computation resources, the learned models often have significant business values. However, these valuable deep models are exposed to a huge risk of infringements. For example, if the attacker has the full information of one target model including the network structure and weights, the model can be easily finetuned on new datasets. Even if the attacker can only access the output of the target model, he/she can still train another similar surrogate model by generating a large scale of input-output training pairs. How to protect the intellectual property of deep models is a very important but seriously under-researched problem. There are a few recent attempts at classification network protection only. In this paper, we propose the first model watermarking framework for protecting image processing models. To achieve this goal, we leverage the spatial invisible watermarking mechanism. Specifically, given a black-box target model, a unified and invisible watermark is hidden into its outputs, which can be regarded as a special task-agnostic barrier. In this way, when the attacker trains one surrogate model by using the input-output pairs of the target model, the hidden watermark will be learned and extracted afterward. To enable watermarks from binary bits to high-resolution images, both traditional and deep spatial invisible watermarking mechanism are considered. Experiments demonstrate the robustness of the proposed watermarking mechanism, which can resist surrogate models learned with different network structures and objective functions. Besides deep models, the proposed method is also easy to be extended to protect data and traditional image processing algorithms.

cs.MM↗

arXiv2020

Influence of Lorentz Invariation Violation on Arbitrarily Spin Fermions Tunneling Radiation in the Vaidya-Bonner Spacetime

In the spacetime of non-stationary spherical symmetry Vaidya-Bonner black hole, an accurate modification of Hawking tunneling radiation for fermions with arbitrarily spin is researched. Considering a light dispersion relationship derived from string theory, quantum gravitational theory and Rarita-Schwinger Equation in the non-stationary spherical symmetry spacetime, we derive an accurately modified dynamic equation for fermions with arbitrarily spin. By solving the equation, modified tunneling rate of fermions with arbitrarily spin, Hawking temperature and entropy at the event horizon of Vaidya-Bonner black hole are presented. We find the Hawking temperature will increase, but the the entropy will decrease comparing with the case without Lorentz Invariation Violation modification.

hep-th↗