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Tropical cyclones (TC) are major devastating natural disasters that lead to property destruction worth billions of dollars and threaten millions of lives. However, rapid changes in TC are the main source of the current TC forecast error. This study proposes a model for estimating the rapid weakening (RW) of TC intensity based on deep learning using infrared satellite images and sea surface temperature (DEEP_RW_TCIE). This model is in two parts: a many-to-many TC intensity estimation network, composed of spatiotemporal code and decode; and a network with multilayer perception as the core for constraining TC intensity estimation sequence, based on sea surface temperature (SST) and intensity change rate (ICR). We investigated the effects of different time series lengths, different ranges of SST, and different feature vector composition methods on the effect of the RW of the TC intensity estimation model. Moreover, we verified the rationality and feasibility of the proposed method through the analysis of experimental methods. The results show that the TC intensity at several moments before rapid TC weakening is of great significance for estimating a current rapid TC weakening, SST, and ICR. Our method greatly reduces the estimating error of the rapid TC weakening intensity. The mean of absolute errors and the root-mean-square error are 6.68 kt and 8.68 kt, respectively, which decrease by over 10%, compared to the benchmark convolutional neural network TC model.

With the development of deep learning in satellite remote sensing image segmentation, convolutional neural networks have achieved better results than traditional methods. In some full convolutional networks, the number of network layers usually increases to obtain deep features, but the gradient disappearance problem occurs when the number of network layers deepens. Many scholars have obtained multiscale features by using different convolutional calculations. We want to obtain multiscale features in the network structure while obtaining contextual information by other means. This article employs the self-attention mechanism and auxiliary loss network (SAMALNet) structure to solve the above problems. We adopt the self-attention strategy in the atrous spatial pyramid pooling module to extract multiscale features while considering the contextual information. We add auxiliary loss to overcome the gradient disappearance problem. The experimental results of extracting aquaculture areas in the Jiaozhou Bay area of Qingdao from high-resolution GF-2 satellite images show that, in general, SAMALNet achieves better experimental results compared with UPS-Net, SegNet, DeepLabv3, UNet, DeepLabv3+, and PSPNet network structures, including recall 96.34%, precision 95.91%, F1 score 96.12%, and MIoU 92.60%. SAMALNet achieved better results extracting aquaculture area boundaries than the other network structures listed above. The high accuracy of the aquaculture area can provide data support for the rational planning and environmental protection of the coastal aquaculture area and promote more rational usage of the coastal aquaculture area.

The main purpose of twin support vector regression (TSVR) is to find linear or nonlinear relationships in sample data, and then predict future data. TSVR is the decomposition of a large convex quadratic programming problem into two small convex quadratic programming problems. Therefore, TSVR not only has the advantages of fast computation and low computational complexity, but also has better regression performance. Classic SVR, TSVR is assuming that accords with mean zero, variance of noise with the variance of the gaussian distribution or not to consider the effects of noise, but in some practical applications, such as wind speed forecasting, noise characteristic is more in line with mean zero, variance for heteroscedasticity of gaussian distribution, therefore, the return of the existing technology is not the best. In this paper, the characteristics of heteroscedasticity Gaussian noise are introduced into the model TSVR, and the twin support vector regression model based on heteroscedasticity Gaussian noise (TSVR-HGN) is constructed. The Lagrange multiplier method is used to solve the problem, and the optimization algorithm is used to find the global optimization. The artificial data set, UCI data set and wind speed data set were selected for experimental comparison. The experimental results show that TSVR-HGN has better prediction accuracy.

We investigate optical property of polarization-maintaining fiber taper (PMFT) for tunable multi-wavelength laser generation in a fiber ring cavity. A panda-type polarization fiber is fused and tapered into a fiber taper with a diameter of about 2.5 μm. Optical and thermal properties of the PMFT are investigated theoretically and experimentally. As combined with a polarization dependent isolator, tunable multi-wavelength fiber laser operation is achieved by controlling of polarization-dependent loss in the fiber ring cavity. The laser wavelength number can be switched from 1 to 4. The 3 dB bandwidths of laser spectra are less than 0.05 nm for all wavelength numbers, with a maximum OSNR of about 54 dB. For single-wavelength laser operation near 1561.66 nm, the measured wavelength and power fluctuations are

Achieving accurate and reliable positioning in dynamic urban scenarios using low-cost vehicular onboard sensors, such as the global navigation satellite systems (GNSS), camera, and inertial measurement unit (IMU), is still a challenging problem. Multi-Agent collaborative integration (MCI) opens a new window for achieving this goal, by sharing the sensor measurements between multiple agents to further improve the accuracy of respective positioning. One of the major difficulties in MCI is to effectively connect all the sensor measurements arising from multiple independent agents. The popular approach is to find the overlapping areas between agents using active sensors, such as cameras. However, the performance of overlapping area detection is significantly degraded in outdoor urban areas due to the challenges arising from numerous unexpected moving objects and unstable illumination conditions. To fill this gap, this paper proposes to leverage both the camera-based overlapping area detection and the inter-ranging measurements to boost the cross-connection between multi-agents and brings the MCI to outdoor urban scenarios using low-cost onboard sensors. Moreover, a novel MCI framework is proposed to integrate the sensor measurements from the low-cost GNSS receiver, camera, IMU, and inter-ranging using state-of-the-art factor graph optimization (FGO) to fully explore their complementary properties. The proposed MCI framework is validated using two challenging datasets collected in urban canyons of Hong Kong. We conclude that the proposed MCI framework can effectively improve the positioning accuracy of the respective agents in the evaluated datasets. We believe that the proposed MCI framework has the potential to be prevalently adopted by the connected intelligent transportation systems (ITS) applications to provide robust positioning using low-cost onboard sensors in urban scenarios.

The operating temperature of the magnetic coupler in wireless power transfer (WPT) systems for electric vehicles (EVs) determines the system reliability and service life. This paper studies the modeling and analysis of temperature characteristics of a general magnetic coupler. Firstly, a thermal circuit model is established to show the heat transfer of the magnetic coupler in the air. Secondly, the heating mechanism of the coil, core and aluminum (Al) shield plate is studied. The thermal power of each component is calculated, and the temperature distribution is qualitatively given based on the proposed thermal circuit model. Then, the temperature distribution of each component is simulated, and the results are consistent with the theoretical analysis. Finally, a 6.6 kW WPT prototype is set up, and the temperature of each component is measured after 30 minutes of operation. The temperature of the coil, 34.6 °C, is the highest among that of the coil, core, Al plate and coil base. The agreement between experimental results and the theoretical simulation results shows that the thermal field simulation can accurately predict the temperature of the magnetic coupler by reasonably setting the thermal power addition method.

Terahertz biosensing provides a suitable method to identify biomolecules due to their rich spectral fingerprint in this electromagnetic region. However, owing to the limitations of terahertz sources and detectors, the signal is weak and requires to be enhanced by particular technologies. In this paper, we propose a terahertz plasmonic biosensor based on Goos-Hänchen effect in graphene. Sample sensing can be realized by measuring the Goos-Hänchen shift of the reflected light. Numerical simulations show that the sensitivity of this biosensor can reach a high value up to 2.5 × 104μm/RIU. This graphene plasmonic configuration combined with Goos-Hänchen effect provides a novel high-sensitivity approach for future terahertz biosensing applications.

Generative adversarial network (GANs) is one of the most important research avenues in the field of artificial intelligence, and its outstanding data generation capacity has received wide attention. In this paper, we present the recent progress on GANs. First, the basic theory of GANs and the differences among different generative models in recent years were analyzed and summarized. Then, the derived models of GANs are classified and introduced one by one. Third, the training tricks and evaluation metrics were given. Fourth, the applications of GANs were introduced. Finally, the problem, we need to address, and future directions were discussed.

The attitude parameters of the 3-D movement of an aircraft are indicators of an aircraft's operating state. A method of sequence screen-spot imaging based on the laser-aided cooperative target is presented to support the measurement of aircraft attitude parameters in ground test experiments. The laseraided cooperative target is mounted on the aircraft surface and the laser spots are projected onto two fixed screen. High-speed cameras synchronously record the position of the laser spots on the screen. According to the position transformation relation between the coordinate systems, the aircraft attitude parameters can be calculated by analyzing the position of the sequence screen-spot image, which will change along with the aircraft attitude. A global calibration method based on the coplanar control points is proposed for the calibration of the measuring system. The experimental results show that the measurement accuracy of the attitude parameters for the proposed method is no more than 3' under the rotation angle from -10° to +10°. The proposed method can amplify the motion attitude of the aircraft by mounting the laser-aided cooperative target on the surface of the aircraft, which breaks the limitation of the geometric condition of the aircraft and improves the measurement accuracy.

Terahertz (THz) vortex beams carrying orbital angular momentum (OAM) with high purity and tunable topological charge (TC) will undoubtedly bring extraordinary capacities for advanced imaging or communication systems. We propose a convenient and efficient method to generate a tunable and broadband vortex beam that is excited by superradiant Smith-Purcell radiation (SSPR) on a helical grating. This scheme fully explores the advantages of natural broadband evanescent wave carried by the electron beam and the Bloch's theorem regulated helical periodic systems. An explicit relation is established between the index of the spatial harmonic wave on the grating and the topological charge of the vortex beam. The electron energy in the SSPR can be customized to ignite the specific spatial harmonics and manipulate the OAM beam, accordingly. The separated radiation region also promises the high purity of the OAM spectrum. The harmonic excitation reduces the communality between the wavelength and the device size and alleviates the difficulty of device fabrication in the THz band. The proposed vortex scheme can not only be handily scaled to microwave and mid-infrared regions, but also bring possibilities to applications based on compact tunable vortex beam sources. [ABSTRACT FROM AUTHOR]

A three-port plasmonic array is proposed to achieve a dual-field superfocusing using the forward-propagation method based on spatial Fourier transform. The algorithm of fast Fourier transform is used to facilitate the design process. Both the electric and magnetic fields can be superfocused on the focal plane of z = 0.1λ (λ is the free-space wavelength) based on an effect of radiationless electromagnetic interference. The full widths of the half-maximum of both two fields are 0.060λ and 0.072λ, respectively. Besides, the metrics of the dual-field superfocusing are 1.67 and 1.39. The proposed theory can simplify the design process of superfocusing, and the plasmonic array with brief three ports can sufficiently facilitate the construction of the desired source. This letter can be beneficial for the development of beam steering, sophisticated holography, super-resolution imaging, and other applications of near- or far-field devices. [ABSTRACT FROM AUTHOR]

This work presents a novel on- body wideband dual-polarized antenna to stabilize and enhance the link performance of wireless capsule endoscopy systems. By introducing a cross-bowtie structure with a planar compact feed method, the antenna achieves a dual-polarized performance with high port-to-port isolation. A loaded ring with additional stubs is also implemented to further enhance the bandwidth and provide a flat in-band performance from 381 to 990 MHz. The proposed antenna is measured with both homogeneous phantom and ex vivo test to validate the impedance matching performance. To validate the effectiveness of the proposed on- body antenna on the communication link, a capsule helical antenna with omnidirectional radiation pattern is designed and measured. Finally, comprehensive link studies are conducted considering the varied capsule orientations and locations, with a peak $\vert S_{21}\vert $ value of–25.5 dB at the 60 mm implant depth and an acceptable link loss due to the capsule position error. The proposed on- body antenna can be regarded as a good candidate for a practical capsule endoscopy system due to its stable and enhanced transmission performance when compared with its counterparts. [ABSTRACT FROM AUTHOR]

Objective: Interventional MRI (i-MRI) is crucial for MR image-guided therapy. Current image reconstruction methods for dynamic MR imaging are mostly retrospective that may not be suitable for real-time i-MRI. Therefore, an algorithm to reconstruct images without a temporal pattern as in dynamic imaging is needed for i-MRI. Methods: We proposed a low-rank and sparsity (LS) decomposition algorithm with framelet transform to reconstruct the interventional feature with a high temporal resolution. Different from the existing LS-based algorithms, the spatial sparsity of both the low-rank and sparsity components was used. We also used a primal dual fixed point (PDFP) method for optimization of the objective function to avoid solving sub-problems. Intervention experiments with gelatin and brain phantoms were carried out for validation. Results: The LS decomposition with framelet transform and PDFP could provide the best reconstruction performance compared with those without. Satisfying reconstruction results were obtained with only 10 radial spokes for a temporal resolution of 60 ms. Conclusion and Significance: The proposed method has the potential for i-MRI in many different application scenarios. [ABSTRACT FROM AUTHOR]

Due to the ability to handle low-light environments, poor weather conditions and privacy protection, person identification based on radar micro-Doppler (m-D) signatures has emerged as a research interest. Moreover, as it is always difficult to construct large-scale radar data sets, we propose an approach to recognize a person’s identity with limited training sample. In which, the fine m-D signal processing technology is first used to obtain high-quality m-D data spectrograms. A three-layer convolutional principal component analysis network with a dimension optimization architecture (CPCAN-3) is then designed to learn the highly discriminative features and address the identification problem. The model has few network parameters, is easy to train, and has low dependence on a large number of training samples. Different daily activities data are captured in an indoor environment to evaluate the performance of proposed method, and the state-of-the-art algorithms are chosen for comparison. The experimental results show that the proposed scheme performs better performance than the others on small data sets. Especially when the motion of “running” is adopted to identify persons, the model achieves 98% accuracy on the identification of ten people. [ABSTRACT FROM AUTHOR]

Many high-resolution maritime radars for sea-surface small target detection transmit pulses with large time-bandwidth-products to increase transmitter power and to use the pulse compression technique for high range resolution. On the sea surface, besides small targets of interest, there exist many uninteresting large objects such as big ships, reefs, and offshore platforms for oil production. Large objects have too strong radar returns and their range sidelobes often mask wide range intervals. In these range intervals, masked small targets are difficult to be detected directly. In this article, a complete scheme is proposed to find small targets masked by range sidelobes of large objects in high-resolution maritime radars. In this scheme, high-precision reconstruction of radar returns of large objects is crucial because tiny reconstruction errors are enough to mask weak returns of small targets of interest. An algorithm using the linear programming and oversampled high-resolution range profile model of a large object is given to obtain high-precision reconstruction of the strong radar returns of a large object. From residual radar returns removed the reconstructed strong returns, a near-optimal coherent detector under sea clutter modeled by the spherically invariant random vector with inverse Gamma texture plus colored Gaussian noise is constructed to find masked small targets. Simulated experiments and real measured radar data are used to verify the effectiveness of the proposed scheme and method and reveal their limitations. The results show that the proposed scheme and method are all effective unless small targets fall into the range mainlobe regions of large objects. [ABSTRACT FROM AUTHOR]

A 1 bit single-polarization broadband space-time-coding digital metasurface element with a zero cross polarization is proposed. This element consists of a modified patch, an electronic switch, a dc-bias network, a ground plane, and two-layer dielectrics. The upper patch with edge loading and centered square hole is fully optimized to achieve broadband and stable phase difference between two states. By electronically controlling the diode, the element achieves a 1 bit phase shift with a relative bandwidth of 20% within 180° ± 30° from 7.71 to 9.43 GHz. Moreover, a negligible cross polarization is obtained due to the uniaxial symmetry of the structure, and the amplitude loss is less than 0.8 dB for two states. The experimental results measured in a rectangular waveguide are well matched with the simulations, further demonstrating validity of the proposed element. [ABSTRACT FROM AUTHOR]

Terahertz biosensing provides a suitable method to identify biomolecules due to their rich spectral fingerprint in this electromagnetic region. However, owing to the limitations of terahertz sources and detectors, the signal is weak and requires to be enhanced by particular technologies. In this paper, we propose a terahertz plasmonic biosensor based on Goos-Hänchen effect in graphene. Sample sensing can be realized by measuring the Goos-Hänchen shift of the reflected light. Numerical simulations show that the sensitivity of this biosensor can reach a high value up to $2.5\times 10^4\mu$ m/RIU. This graphene plasmonic configuration combined with Goos-Hänchen effect provides a novel high-sensitivity approach for future terahertz biosensing applications. [ABSTRACT FROM AUTHOR]

A differentially fed capsule antenna with a complex input impedance is proposed in this letter. The antenna maintains a circular flat shape with a diameter of 10 mm and operates at the desired band of the RF chip (2.4–2.525 GHz). With the utilization of an inductively coupled fed structure and dual meandered radiators, a –10 dB impedance bandwidth of 10.2% is achieved. In the meantime, the high impedance tunability can be realized by adjusting corresponding structural parameters. The operating mechanisms and parametric studies are explored in detail to investigate the characteristic of the proposed structure. The capsule antenna prototype is fabricated, assembled, and measured according to the practical scenario. The measured results highly agree with the simulation ones, which indicate that the proposed antenna could be a good candidate for wireless capsule endoscopy applications. [ABSTRACT FROM AUTHOR]

Mapping logical quantum circuits to Noisy Intermediate-Scale Quantum (NISQ) devices is a challenging problem which has attracted rapidly increasing interests from both quantum and classical computing communities. This article proposes an efficient method by (i) selecting an initial mapping that takes into consideration the similarity between the architecture graph of the given NISQ device and a graph induced by the input logical circuit and (ii) searching, in a filtered and depth-limited way, a most useful swap combination that makes executable as many as possible two-qubit gates in the logical circuit. The proposed circuit transformation algorithm can significantly decrease the number of auxiliary two-qubit gates required to be added to the logical circuit, especially when it has a large number of two-qubit gates. For an extensive benchmark set of 131 circuits and IBM's current premium Q system, viz., IBM Q Tokyo, our algorithm needs, in average, 0.3801 extra two-qubit gates per input two-qubit gate, while the corresponding figures for three state-of-the-art algorithms are 0.4705, 0.8154, and 1.0066, respectively. [ABSTRACT FROM AUTHOR]

Reflective metasurfaces are utilized as a bridge linking propagating waves and surface waves. However, the effective constitutive parameters (permittivity and permeability) cannot be retrieved by conventional theory without transmission coefficients. In this paper, an innovative method based on a quasi-one-port network model is proposed to robustly retrieve the effective electromagnetic parameters ($\varepsilon _{\mathrm {eff}}$ , $\mu _{\mathrm {eff}}$ , $n_{\mathrm {eff}}$ , and $z_{\mathrm {eff}}$) for reflective metasurfaces with isotropic unit cells. The validity of the method is demonstrated by imping TE-polarized terahertz waves normally and obliquely (10°) on the multiple-layer unit cells. The effective boundary of the multiple-layer cells is determined by minimizing the deviation of the effective refractive indices from 0.1018 to 0.0260 using a genetic algorithm, which increases the consistency of the retrieval results. The sensitivity of the effective constitutive parameters to the accuracy of reflection coefficients and nonzero incident angle $\theta _{i}$ is also discussed. This method is beneficial for the design of reflective metasurfaces or multichannel reflectors, the coupling of spoof space plasmon polaritons, and other applications of anomalous reflection. [ABSTRACT FROM AUTHOR]

Defogging is an essential preprocessing technique for object detection in computer vision-based systems and has been widely used in outdoor surveillance system applications. This paper proposes an efficient defogging algorithm for both static images and videos. Considering real-time applications, the proposed defogging algorithm involves a low-cost hardware oriented design that is based on an atmospheric scattering model and dark channel prior. Compared with previous low-complexity techniques, simulation results indicated that the proposed design demonstrated superior performance in terms of quantitative and qualitative evaluations. A weighting technique and a contour preserving estimation approach are adopted alternately to refine the factors in the defogging process. Furthermore, in the atmospheric light estimation, an adjuster is applied to the video for preventing “flicker” which means that the brightness changes dramatically between two neighbor frames in the video. Hence, the proposed algorithm is suitable for video defogging applications, which have not been dealt with in previous approaches. To achieve the requirement of real-time applications for both static and dynamic images, an implementation of seven-stage very-large-scale integration architecture for the proposed algorithm is presented. By using TSMC 0.13- $\mu {\mathrm{ m}}$ technology, the design yielded a processing rate of approximately 200 Mpixels/s. [ABSTRACT FROM AUTHOR]

The development of a G-band traveling wave tube for wireless communications and airborne radars is described. The maximum output power of this device is over 20 W. This device provides a saturation output power over 15 W and a saturation gain over 32 dB with a bandwidth of 7.6 GHz. A folded waveguide circuit with modified circular bends is utilized to eliminate the band-edge instability caused by the high beam voltage of 20 kV. A pencil beam of 50 mA with a transmission ratio over 95 % is realized, making the device capable of operating in continuous wave mode. Periodic permanent magnets are employed for compactness. Chemical vapor deposition diamond disc is used in the input and output RF windows to minimize the loss and voltage standing wave ratios of the traveling wave tube. The weight and the size of the device are 2 kg and 300 mm $\times70$ mm $\times70$ mm, respectively. The efficiency of the device is over 5 %. [ABSTRACT FROM AUTHOR]

Quantum algorithm design usually assumes access to a perfect quantum computer with ideal properties like full connectivity, noise-freedom, and arbitrarily long coherence time. In noisy intermediate-scale quantum (NISQ) devices, however, the number of qubits is highly limited and quantum operation error and qubit coherence are not negligible. Besides, the connectivity of physical qubits in a quantum processing unit (QPU) is also strictly constrained. Thereby, additional operations like SWAP gates have to be inserted to satisfy this constraint while preserving the functionality of the original circuit. This process is known as quantum circuit transformation. Adding additional gates will increase both the size and depth of a quantum circuit and, therefore, cause further decay of the performance of a quantum circuit. Thus, it is crucial to minimize the number of added gates. In this article, we propose an efficient method to solve this problem. We first choose by using simulated annealing an initial mapping which fits well with the input circuit and then, with the help of a heuristic cost function, stepwise apply the best-selected SWAP gates until all quantum gates in the circuit can be executed. Our algorithm runs in time polynomial in all parameters, including the size and the qubit number of the input circuit, and the qubit number in the QPU. Its space complexity is quadratic to the number of edges in the QPU. The experimental results on extensive realistic circuits confirm that the proposed method is efficient and the number of added gates of our algorithm is, on average, only 57% of that of state-of-the-art algorithms on IBM Q20 (Tokyo), the most recent IBM quantum device. [ABSTRACT FROM AUTHOR]

In this communication, we present an effective solution for reconfigurable matching circuits for tuning medical implant communications service (MICS) band implantable antennas in various tissues. We use only two lumped components, a shunt inductor and a series varactor with bias in the proposed circuits, to match a spiral antenna on the neurostimulator platforms. By properly tuning the bias voltage of the varactor, impedance matching of the same antenna is achieved in different tissues with a broad relative permittivity range from 5 to 60, which indicates superior antenna performance in the dramatically changeable biological environments. With the merits of simple configuration and easy voltage programming, the proposed circuits are accurately designed on impedance trace of the Smith Charts. In clinical applications, we integrate the reconfigurable matching circuit with the neurostimulator platform. The proposed circuits with implantable antenna are numerically analyzed and experimentally validated at the system level. We achieve good antenna impedance matching performance in various types of tissues, i.e., the reflection coefficients are less than −20 dB, and a 6–12 dB benefit of link budget is achieved in different tissues. [ABSTRACT FROM AUTHOR]

The new vision-based object sorting system is a fundamental module in the construction and demolition waste recycling industry, where image deblurring is vital as the system often fails due to the heavily blurred images caused by the vibration of the conveyor belt that carries solid wastes. This paper proposes a novel blind deblurring approach in which a novel penalty function is formulated as the regularization term in the total energy function. This regularization term is based on sparse prior and solved as part of a mathematical optimization problem, which is operated on the dark channel of the input image. The method not only reserves the structure information of the image but also avoids over-smoothing in the final restoration. On synthetic and natural blurred images, the method outperforms other popular methods. Even with less iterations, the convergence rate and quality of the results are superior. We apply this approach for solid waste image restoration and achieve remarkable results with high validity and reliability. [ABSTRACT FROM AUTHOR]

In today's datacenter topologies, there exist multiple equal-cost paths between each pair of communicating virtual machines. Yet, splitting flows and routing them along multiple paths may lead to packet reordering, which may affect the performance of TCP. In this paper, we propose Promenade, a new protocol that uses random network coding to mitigate the negative effects of packet reordering, while at the same time achieving weighted proportional fairness in bandwidth allocation across different tenants. To achieve weighted proportional fairness when allocating bandwidth to tenants, the problem of rate control is formulated as a convex optimization problem, and Promenade uses its distributed solution as a theoretical foundation to design its bandwidth allocation protocol. With our real-world implementation of Promenade in the Mininet testbed, we are able to show that Promenade is able to achieve weighted proportional fairness in its rate control when individual flows are split into multiple paths. [ABSTRACT FROM AUTHOR]

A dual-mode antenna with cooperative matching (co-matching) was designed for implantable neurostimulators functioning within the Medical Implant Communications Service (MICS) band. In the proposed matching method, the first and third modes of a spiral monopole were appropriately designed and cooperatively matched for the patients with different body shapes. For obese patients, the neurostimulator was implanted in the fat layer (low permittivity), operating in the first mode. For slim patients, the neurostimulator was placed around the muscle tissue (high permittivity), operating in the third mode. Although there were different modes of operation in modified environments, the antenna was consistently well matched using a single lumped inductor to cover the desired band. The proposed matching strategy was validated by experiments, which followed the findings of our simulation data, exhibiting potential applications for implantable neurostimulators in different body types. [ABSTRACT FROM AUTHOR]

This paper introduces an analytical framework for assessing the cost-effectiveness of intervention strategies to reduce total joint replacement (TJR) readmissions. In such a framework, a machine learning-based readmission risk prediction model is developed to predict an individual TJR patient's risk of hospital readmission within 90 days post-discharge. Specifically, through data sampling and boosting techniques, we overcome the class imbalance problem by iteratively building an ensemble of models. Then, utilizing the results of the predictive model, and by taking into account the imbalanced misclassification costs between readmitted and nonreadmitted patients, a cost analysis framework is introduced to support decision making in selecting cost-effective intervention policies. Finally, using this framework, a case study at a community hospital is presented to demonstrate the applicability of the analysis. [ABSTRACT FROM AUTHOR]

A magnetic-focused X-band traveling wave tube (TWT) based on carbon nanotube (CNT) cathode is presented. The CNT TWT has a simple structure of the electron gun, consisting of a CNT emitter electrode and a ring-shaped anode, with no metallic mesh extracting electrode, nor electrostatic focusing electrode. The field emission electrons are extracted directly by the ring-shaped anode, while the electron beam focusing is realized by an extra magnetic field. Apart from the CNT electron gun, the TWT mainly has a high-frequency interaction circuit and a collector. All these components are totally immersed in a uniform axial magnetic field, and thus, electrons are confined by the Lorentz force during their traveling process from the cathode surface toward the collector. The fabricated CNT TWT was tested in a superconducting system providing a uniform axial magnetic field. The dc mode testing showed about 86% of the emission electrons can reach collector, which is better than a common gate-controlled CNT electron gun. [ABSTRACT FROM AUTHOR]

Fair bandwidth allocation in datacenter networks has received a substantial amount of research attention, as multiple tenants are hosted by virtual machines in a public cloud. In the context of private datacenters, link bandwidth is shared among applications running data parallel frameworks, such as MapReduce, instead. In this paper, we introduce the rigorous definition of performance-centric fairness, with the guiding principle that the performance that data parallel applications will enjoy should be proportional to their weights. We first investigate the problem of maximizing application performance while maintaining strict performance-centric fairness. We then present an inherent tradeoff between fairness and efficiency, which is interpreted from the perspectives of bandwidth utilization and social welfare, respectively. From the first perspective, we propose an algorithm to improve bandwidth utilization by introducing an extended version of fairness. From the second perspective, we formulate an optimization problem of bandwidth allocation that maximizes the social welfare across all the applications, allowing a tunable degree of relaxation on performance-centric fairness. A distributed algorithm is then presented to solve the problem, based on dual based decomposition. With extensive simulations, we demonstrate the effectiveness of our algorithms in improving efficiency and application performance (by up to $1.4X$ ), with flexible degree of relaxation on the performance-centric fairness. [ABSTRACT FROM AUTHOR]

Two-dimensional exponential correlation rough surfaces characterized by textures are combined with the small-slope approximation (SSA) method to comparatively study electromagnetic (EM) scattering features of textured surfaces. The normalized copolarized radar cross section from 2-D exponential correlation rough surfaces characterized by stripe texture and block texture, respectively, is analyzed. Several numerical results show the effects of incident angle, texture angle, correlation length, and root-mean-square height on the copolarimetric scattering from the textured rough surface. The validity of the SSA method is verified by comparisons of theoretical value and measured data. Moreover, normalized amplitude distributions of backscattering fields from cells in a scene are studied through its statistical distribution and space correlation function, which are particularly useful for analysis and simulation of remote sensing images. Finally, based on the statistical distribution and space correlation function, the zero memory nonlinear transformation method is utilized to simulate EM scattering from very large scenes. The simulated scene coincides with the original one quite well. [ABSTRACT FROM PUBLISHER]

This paper presents a signal processing method for the electrophysiology simultaneously recorded during deep brain stimulation (DBS) as a research tool. Regarding the local field potential (LFP) signals recorded during stimulation, a novel method was proposed for removal of stimulation artifacts caused by the much stronger stimulating pulse compared to typical LFP. This artifact suppression method was tested and evaluated in an in vitro situation. The results indicate that the stimulation artifacts are well suppressed by this method. Secondly, this method was tested in vivo in Parkinson’s disease (PD) patients. It was used to process the LFP signals recorded intraoperatively from PD patients to preliminarily explore the quantitative dependencies of beta band synchronization variations in the subthalamic nucleus (STNs) on the applied DBS parameters, including stimulation voltage, frequency and pulse width. The results confirm that DBS therapy can suppress excessive beta frequency activity and that the degree of attenuation increases with increasing DBS voltage within a range of 1–3 V and increasing DBS frequency within a range of 60–120 Hz. The proposed artifact suppression method provides technical support for exploring the direct effect of electrical stimulation on the brain activities. [ABSTRACT FROM PUBLISHER]

We propose a new method to generate orbital angular momentum (OAM) mode by superposing multiple LP11 modes with microphase difference distributed along fiber propagation. This way, OAM mode can be generated by superposing a series of LP11 mode with phase difference much less than π/2, which is necessary for schemes using two degenerated LP11 mode superposition but is difficult to generate and hard to keep stable. We demonstrate this principle experimentally by utilizing few-mode fiber long period grating (FMF-LPG), in which a series of LP11 modes with microphase difference distribution can be generated by twisting FMF-LPG. This scheme provides a potential all-fiber, compact, and low-attenuation scheme to generate stable OAM modes. [ABSTRACT FROM PUBLISHER]

The article focuses on a computer security system for the complex computer network architectures needed for Web-based information systems operated by large institutions such as corporations, government, and the military. These systems are termed inhomogeneous information systems (IIS). The architecture model employs Web-service technology to provide security functions and maintain systems interoperability. The government of China has employed the first system to be based on this model.

We have studied the time-dependence degradation of ON current of amorphous silicon thin-film transistors (a-Si:H TFTs), which is a function of stress duration, stress temperature, and stress bias. A simple method with stretched-exponential equation and current-voltage function is used to characterize and predict the TFT performance. Bias-temperature stress at different stress voltages has been performed on a-Si:H TFTs. A new method using ON current degradation to analyze TFT device performance is presented, which is different from the conventional threshold-voltage shift method. We have also observed that the beta value in the ON current degradation method compared to the threshold-voltage shift method, with a stretched-exponential stress time, is related to beta~beta0-TST/T0. Finally, we have also used the new equation to evaluate the performance of the gate-driver-on-array circuit in our products. If the limitation of the current for the pull-down device is 1times10-6 A, then the operation time of the pull-down device can be estimated to about 1219 h when key pulled-down TFT is operating at 60degC. [ABSTRACT FROM PUBLISHER]

The appropriate external stress can enhance a device and circuit performance. The 7.4% speed enhancement is achieved for the 250-nm node ring oscillator under uniaxial tensile strain for a mutually perpendicular layout of the NFET and the PFET. The speed enhancement is less than 1.5% for the conventional parallel layout of the NFET and the PFET. A 180-nm node transimpedance amplifier has a ∼ 5% bandwidth enhancement using a biaxial tensile strain or a uniaxial tensile strain parallel to the NFET channel to tune the peaking frequency of active inductor in the circuit. The package strain can provide an extra useful parameter for the future digital and analog circuit design. [ABSTRACT FROM AUTHOR]

We propose a novel approach based on modeled blazed grating (MBG) to realize the mode conversion, multiplexing, demultiplexing, and switching in few-mode transmission fibers. The proposed grating can distribute different modes to different diffraction orders. We demonstrate the generation, multiplexing, demultiplexing, and high efficient conversion and switching of independent coaxial modes in a few-mode transmission fiber using MBG experimentally. [ABSTRACT FROM PUBLISHER]

In passive bistatic radars, weak target echoes may often be masked by direct path interference, multipath components, and strong target echoes, making weak target detection a challenging problem. The conventional 1-D adaptive cancelation algorithms, such as the normalized least mean square (NLMS), cannot effectively suppress strong target echoes when their Doppler frequencies spread. In addition, the continuous distribution of the NLMS weight vector does not match the sparse characteristics of strong multipath components and target echoes, thus resulting in degraded cancelation performance. Motivated by this fact, a novel 2-D sparse-weight NLMS filtering scheme is proposed by extending the NLMS to a 2-D structure, in which the weight vector is sparsely distributed and adaptively adjusted based on the sparse strong multipath components and target echoes. [ABSTRACT FROM PUBLISHER]

As an increasing number of infrastructure-as-a-service (IaaS) cloud providers start to provide cloud computing services, they form a competition market to compete for users of these services. Due to different resource capacities and service workloads, users may observe different finishing times for their cloud computing tasks and experience different levels of service qualities as a result. To compete for cloud users, it is critically important for each cloud service provider to select an "optimalâ' price that best corresponds to their service qualities, yet remaining attractive to cloud users. To achieve this goal, the underlying rationale and characteristics in this competition market need to be better understood. In this paper, we present an in-depth game theoretic study of such a competition market with multiple competing IaaS cloud providers. We characterize the nature of noncooperative competition in an IaaS cloud market, with a goal of capturing how each IaaS cloud provider will select its optimal prices to compete with the others. Our analyses lead to sufficient conditions for the existence of a Nash equilibrium, and we characterize the equilibrium analytically in special cases. Based on our analyses, we propose iterative algorithms for IaaS cloud providers to compute equilibrium prices, which converge quickly in our study. [ABSTRACT FROM AUTHOR]

Multi-component hybrid nanostructures containing two nanoscaled components of FePt and ZnO were successfully fabricated through seed mediated growth. The preformed FePt nanoparticles, which were fabricated either by the reduction of \Pt(acac)2 and the decomposition of Fe(CO)5 or by simultaneous chemical reduction of \Pt(acac)2 and \Fe(acac)_3 by 1,2-hexadecanediol at high temperature, work as the hetero-nucleation seeds for the preparation of hybrid nanostructures. The end products can be either FePt@ZnO core/shell nanoparticle assembly or FePt/ZnO nanocomposites, depending on the seeding particle size. If the seeding particle size is larger than 3.5 nm, core/shell nanoparticle assembly was formed, while if the seeding particle is smaller than 2 nm, FePt/ZnO nanocomposites were formed. For the FePt@ZnO core/shell, HRTEM showed a quasi-epitaxial growth between the FePt core and the ZnO shell. The ZnO shell was highly deformed. The core/shell nanoparticle assembly exhibits both semiconducting and magnetic properties which is superparamagnetic at room temperature. For the nanocomposites, the as-synthesized ultra-small 1.9 nm \FePt_3 nanoparticles are superparamagnetic. After embedding into the ZnO matrix, those superparamagnetic nanoparticles become magnetically hard with coercivity field of 650 Oe at room temperature. First-principles calculations indicate a giant interfacial anisotropic energy, induced by the strong spin-orbit interaction between Pt and O at interface, leading to room-temperature permanent ferromagnetism. The findings shed light on research for new material designs with giant interfacial anisotropy for various applications. [ABSTRACT FROM PUBLISHER]

In this letter, we report the enhanced fringe Capacitance in FinFETs when compared to the equivalent planar MOSFETs at the 22-nm node. We show that this increase is due to the 3-D nature of the device and also due to the close proximity of the source/drain (Sm) epitaxial (epi) region to the metal gate. Using well-calibrated 3-D mixed-mode simulations, we show that this will cause the performance of FinfETs to be significantly degraded, unless proper device optimizations are carried out. Our results also indicate that the selective epi growth of SID may adversely affect the overall performance of FinFETs, although it is effective in reducing series resistance. The increased parasitic components in FinFETs can be a serious issue for FinFET circuits with a large fan-out, and the solution lies in the aggressive fin pitch reduction, as shown in this letter. [ABSTRACT FROM AUTHOR]

In this letter, the authors introduce a novel self-aligned etch-stopper sidewall-contact hydrogenated amorphous silicon (a-Si : H) thin-film transistor (ESSC-TFT), which reduces the photo leakage current by more than one order of magnitude and increases the ON-OFF ratio to seven orders of magnitude under back light illumination. Such a TFT will enable high-resolution and high-brightness liquid-crystal displays (LCDs) for next-generation TV, monitor, notebook, and mobile-phone applications. This ESSC-TFT design reduces the volume of a-Si film in which the active region can totally be shielded by the gate metal resulting in the prevention from direct back light illumination. With the sidewall contact, the hole current is reduced due to the smaller contact area between drain/source and a-Si layer. As well as the source, drain parasitic intrinsic resistance of a-Si can be also lessened by the ESSC-TFT structure. Although the defects between etched a-Si and n+ a-Si film may degrade the ON current, the ESSC-TFT still exhibits higher ON-OFF ratio and lower leakage than the one in traditional etch-stopper (ES)-TFT structure. The ESSC-TFT structure can be used not only for TFT-LCD application but also for the applications that demand high ON-OFF ratio and low-leakage device, such as X-ray image sensor. [ABSTRACT FROM AUTHOR]

Surface anisotropy that opposes crystalline anisotropy can become significant for highly packed grains. In this paper, we present an estimation of FePt grain size limit in the presence of surface anisotropy. The estimated grain size is optimized for rectangular, circular and hexagonal grain, respectively. In this manner, the thermally stable granular media can be attained for high areal density magnetic recording system. Micromagnetic simulations are performed to predict the required switching field for FePt granular media in the presence of surface anisotropy. The results reveal possible engineering challenges faced in designing high density magnetic recording medium. [ABSTRACT FROM AUTHOR]

Existing localization approaches are divided into two groups: 1) range based and 2) range free. The range-free schemes often suffer from poor accuracy and low scalability, whereas the range-based localization approaches heavily depend on extra hardware capabilities or on the absolute received signal strength indicator (RSSI) values, which is far from practical. In this paper, we propose a mobile-assisted localization scheme called Perpendicular Intersection (PI), setting up a delicate tradeoff between range-free and range-based approaches. Instead of directly mapping RSSI values into physical distances, by contrasting RSSI values from the mobile beacon to a sensor node, PI utilizes the geometric relationship of a perpendicular intersection to compute node positions. We have implemented the prototype of PI with 100 TelosB motes and evaluated PI in both indoor and outdoor environments. Through comprehensive experiments, we show that PI achieves high accuracy, significantly outperforming the existing range-based and the mobile-assisted localization schemes. [ABSTRACT FROM PUBLISHER]

In the three-dimensional video system, the depth image-based rendering is a key technique for generating synthesized views, which provides audiences with depth perception and interactivity. However, the inaccuracy of depth information leads to geometrical rendering position errors, and the compression distortion of texture and depth videos degrades the quality of the synthesized views. Although existing quality enhancement methods can eliminate the distortions in the synthesized views, their huge computational complexity hinders their applications in real-time multimedia systems. To this end, a residual distillation enhanced network (RDEN)-guided lightweight synthesized view quality enhancement (SVQE) method is proposed to minimize holes and compression distortions in the synthesized views while reducing the model complexity. First, a rethinking on the deep-learning-based SVQE methods is performed. Then, a feature distillation attention block is proposed to effectively reduce the distortions in the synthesized views and make the model fulfill more real-time tasks, which is a lightweight and flexible feature extraction block using an information distillation mechanism and a lightweight multi-scale spatial attention mechanism. Third, a residual feature fusion block is proposed to improve the enhancement performance by using the feature fusion mechanism, which efficiently improves the feature extraction capability without introducing any additional parameters. Experimental results prove that the proposed RDEN efficiently improves the SVQE performance while consuming few computational complexities compared with the state-of-the-art SVQE methods. [ABSTRACT FROM AUTHOR]

The introduction of defects into FePt is an effective method to lower the ordering temperature of L10 FePt. The residual defects, however, are inevitable to affect the magnetic properties of L10 FePt. Using first-principles calculations, we systematically study the effect of defects (vacancy, antisite and doping) on the saturation magnetization and magnetic anisotropy field of L10 FePt. Our results show that the Fe-rich composition has higher saturation magnetization than Pt-rich composition. The uniaxial anisotropy field decreases with the introduction of defects except for the vacancy defects which, however, are difficultly formed due to the high formation energy. [ABSTRACT FROM AUTHOR]

With the proliferation of multi-touch mobile devices, such as smartphones and tablets, users interact with devices in non-conventional gesture-intensive ways. As a new way to interact with mobile devices, gestures have been proven to be intuitive and natural with a minimal learning curve, and can be used in interactive multimedia applications. In order for multiple users to collaborate in an interactive manner, we propose that gestures can be streamed in multiple broadcast sessions, with each session corresponding to one of the users as the source of a gesture stream. During the interactive session, the Quality of Experience (QoE) of mobile users hinges upon delays from when gestures are entered by the source to when they are recognized by each of the receivers, which we refer to as gesture recognizing delays. In this paper, we present the design of GestureFlow, a gesture broadcast protocol designed specifically for concurrent gesture streams in multiple broadcast sessions, such that the gesture recognizing delay in each session is minimized. We motivate the effectiveness and practicality of using inter-session network coding, and address challenges introduced by the linear dependence of coded packets. We evaluate our protocol design using an extensive array of real-world experiments on mobile devices, involving a new gesture-intensive interactive multimedia application, called MusicScore, that we developed from scratch. [ABSTRACT FROM AUTHOR]

Several high-level quantum programming languages have been proposed in the previous research. In this paper, we define a low-level flowchart language for quantum programming, which can be used in implementation of high-level quantum languages and in design of quantum compilers. The formal semantics of the flowchart language is given, and the notion of correctness for programs written in this language is introduced. A structured quantum programming theorem is presented, which provides a technique of translating quantum flowchart programs into programs written in a high-level language, namely, a quantum extension of the while-language. [ABSTRACT FROM AUTHOR]

Learning long-range contextual dependence is important for remote sensing (RS) image segmentation in complex patterns. Meanwhile, exploring local context information is conducive to the discrimination of fine details. Only underlining either global semantic correlations or local context details is insufficient to achieve accurate segmentation. In this letter, we propose an architecture with the global-local self-attention (GLSA) mechanism, called GLSANet, which can simultaneously consider both global and local contexts for segmentations. Particularly, the GLSA mechanism consists of the global atrous self-attention (GASA) and local window self-attention (LWSA) mechanisms. GASA can learn long-range semantic relations in a gapped manner, while LWSA can locally capture contextual details. As a bridge between the two self-attention (SA) branches, a context fusion module (CFM) is further designed to adaptively integrate global and local contexts. The experiments with public datasets show that the proposed GLSANet significantly refines semantic segmentation and outperforms other competing methods. [ABSTRACT FROM AUTHOR]