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, Zifan Zhu, Qing An, Zhicheng Wang, , Tianxu Zhang, Ali Saleh Alshomrani
IEEE Geoscience and Remote Sensing Letters, pp 1-5; doi:10.1109/lgrs.2021.3093935

Low/high or uneven luminance results in low contrast of remotely sensed images (RSIs), which makes it challenging to analyze their contents. In order to improve the contrast and preserving fine weak details of RSIs, this letter proposes a novel enhancement framework to correct luminance guided by weighted least squares (WLS), including the following key parts. First, an image is separated into a base layer and a detail layer by employing the WLS. Then, a learning network is proposed to correct luminance for the base layer enhancement. Next, an enhancement operator for improving the detail layer is computed by using the original image and the enhanced base layer. Finally, the output image is obtained with a fusion of the enhanced base and detail components. Both quantitatively and qualitatively experimental results verify that the proposed method performs better than the state of the arts in contrast improvement and detail preservation.
, Yongyuan Qin, Zongwei Wu, Xiaowei Shen
IEEE Transactions on Instrumentation and Measurement, pp 1-1; doi:10.1109/tim.2021.3096271

Current orientation estimation system (OES) approaches can meet the Satcom-on-the-move (SOTM) accuracy requirement in low-dynamic using IMU without GNSS. However, the estimation accuracy will decrease significantly due to the influence of the high dynamics of the vehicle. This paper addresses this issue by constructing a Multi-State Constraint Optimization-based Orientation Estimation System (MSCO-OES). Our first contribution is a high-precision incremental inertial constraint, which adds the influence of the earth’s rotation to the rotation part of the pre-integration theory to maintain the integral gyro accuracy for a longer time. The second contribution is that the nonlinear optimization method is applied to orientation estimation, making better use of historical information and having higher estimation accuracy and robustness through batch optimization and iterating. In addition, we use the lie group theory to extend the weighted least square estimation algorithm to the manifold, which can update multiple historical states to the current state in real-time through incremental inertial constraints. Compared with state-of-the-art methods, the proposed orientation estimation method has higher estimation precision and a reasonable calculation amount. The performance of the proposed method is demonstrated by comparing tests and SOTM deploying tests. Several high-dynamic and long-range tests show that the proposed orientation estimation system has acceptable orientation estimation precision and reliability, which meets the high-reliable SOTM system requirements.
Jianda Xie,
IEEE Geoscience and Remote Sensing Letters, pp 1-5; doi:10.1109/lgrs.2021.3093620

It is known that the challenge for detection of small targets on the sea surface is the low signal-to-clutter ratio (SCR). In particular, for low grazing angles and high sea states, wave shading and sea spikes make the small target detection even more difficult. This letter proposed a novel phase-feature detector of small targets in sea clutter. Three phase features, which correspond to different radar scattering mechanisms between the target and sea surface, are extracted in the phase domain of radar echoes, namely, the number of phase crossing zero points, the maximum value of the phase difference probability density function, and the decorrelation time of the phase difference. A detector with a controllable false alarm rate (FAR) based on phase features is constructed using the fast convex hull learning algorithm. Experimental results on measured databases demonstrate that the proposed phase-feature detector attains better performance than the existing tri-feature detectors.
Gargi Rakshit, ,
IEEE Geoscience and Remote Sensing Letters, pp 1-5; doi:10.1109/lgrs.2021.3093827

This letter reveals the prevailing scenario of raindrop size distribution (DSD) in terms of mass-weighted mean drop diameter ( $D_{m}$ ) over a tropical metropolis, Kolkata (22.57°N, 88.37°E), India, in a contrasting aerosol environment that occurred during the COVID-19 pandemic in the absence of usual human activities. In the premonsoon months (March-May), the probability of $D_{m}$ values exceeding 2 mm has increased in 2020, indicating the dominance of large raindrops, compared to the years 2017-2019. Increased number densities of larger drops have influenced the drop fall velocity spectrum as measured by a laser precipitation monitor in terms of the percentage occurrence of high-velocity small drops (superterminal) and low-velocity large drops (subterminal) for both convective and stratiform precipitations. As measured from a Ka-band microrain Doppler radar, the mean melting layer altitude during stratiform rain has decreased by ~800 m during the premonsoon of 2020 compared to 2017-2019. According to the ERA-5 reanalysis data, changing rain microphysical characteristics are related to decreasing zero-degree isotherm height and reduced wind shear.
Fei Wang, Junyan Liu, Boyuan Dong, Guobin Liu, Mingjun Chen, Yang Wang
IEEE Transactions on Instrumentation and Measurement, pp 1-1; doi:10.1109/tim.2021.3096285

In this present study, transverse heat flow suppression (THFS) technique was proposed to enhance the ability of thermal-wave radar thermography (TWRT) to resist lateral thermal diffusion and uneven heating. The enhanced TWRT was used to detect subsurface defects of CFRP laminates. The principle of THFS was described in details by optical flow analogy. The three dimension (3D) thermal-wave model which stimulated by the uneven linear frequency modulation (LFM) thermal flow was introduced. The thermal-wave signal was processed by several different post-processing characteristic extraction algorithms (Cross-correlation algorithm, CC, Dual-orthogonal demodulation algorithm, DOD, Fractional Fourier transform, FrFT, and Principal component analysis, PCA). The comparison between normalized DOD amplitude/phase and normalized DOD-THFS amplitude/phase was carried out. The simulation results depicted THFS can significantly improve the difference between defect location and non-defect location. Nine CFRP specimens with artificial flat-bottom holes (FBHs) were prepared for nondestructive testing and evaluation (NDT&E) by enhanced TWRT. 72 FBHs were prepared to test the probability of detection (PoD) of the enhanced TWRT. Hit/miss method was used to count defect information. The results demonstrated that the enhanced TWRT can realize the effective detection of defects (90% detection probability) with a diameter depth ratio of 5.06 under 95% confidence level. Compared with two state-of-the-art approaches, the proposed DOD-THFS phase has a better defect detection SNR.
Yakun Ju, MuWei Jian, Shaoxiang Guo, Yingyu Wang, Huiyu Zhou,
IEEE Transactions on Instrumentation and Measurement, pp 1-1; doi:10.1109/tim.2021.3096282

The goal of photometric stereo is to measure the precise surface normal of a 3D object from observations with various shading cues. However, non-Lambertian surfaces influence the measurement accuracy due to irregular shading cues. Despite deep neural networks have been employed to simulate the performance of non-Lambertian surfaces, the error in specularities, shadows, and crinkle regions is hard to be reduced. In order to address this challenge, we here propose a photometric stereo network that incorporates Lambertian priors to better measure the surface normal. In this paper, we use the initial normal under the Lambertian assumption as the prior information to refine the normal measurement, instead of solely applying the observed shading cues to deriving the surface normal. Our method utilizes the Lambertian information to reparameterize the network weights and the powerful fitting ability of deep neural networks to correct these errors caused by general reflectance properties. Our explorations include: the Lambertian priors (1) reduce the learning hypothesis space, making our method learn the mapping in the same surface normal space and improving the accuracy of learning, and (2) provides the differential features learning, improving the surfaces reconstruction of details. Extensive experiments verify the effectiveness of the proposed Lambertian prior photometric stereo network in accurate surface normal measurement, on the challenging benchmark dataset.
Yongjie Zhai, Xu Yang, Qianming Wang, Zhenbing Zhao, Wenqing Zhao
IEEE Transactions on Instrumentation and Measurement, pp 1-1; doi:10.1109/tim.2021.3096600

Aiming at the problems of complex background, tiny-size objects and long-tailed distribution, etc., a hybrid knowledge region-based convolutional neural network is proposed to detect multiple fittings in aerial images of transmission lines. Firstly, the structure combination rules of transmission line fittings are studied, and the relationships of co-occurrence connection and spatial location between fittings are effectively extracted through a data-driven way. Secondly, the position-sensitive score map is utilized to express the immobilized connection structure of the fittings and extract their visual features. Finally, distinct relationship forms are instantiated by the integrated knowledge modules based on graph learning. The proposed model can enhance the corresponding visual features and realize fitting classification and position regression. Experimental results show that the proposed model can accurately detect the multiple fittings on transmission lines, and the detection performance for sample-deficiency and tiny-size fittings is improved significantly compared to the commonly used high-performance model, Faster R-CNN.
Renhao Sun, , , Richang Hong, Meng Wang
IEEE Transactions on Neural Networks and Learning Systems, pp 1-11; doi:10.1109/tnnls.2021.3093419

Deep multiview clustering methods have achieved remarkable performance. However, all of them failed to consider the difficulty labels (uncertainty of ground truth for training samples) over multiview samples, which may result in a nonideal clustering network for getting stuck into poor local optima during the training process; worse still, the difficulty labels from the multiview samples are always inconsistent, and such a fact makes it even more challenging to handle. In this article, we propose a novel deep adversarial inconsistent cognitive sampling (DAICS) method for multiview progressive subspace clustering. A multiview binary classification (easy or difficult) loss and a feature similarity loss are proposed to jointly learn a binary classifier and a deep consistent feature embedding network, throughout an adversarial minimax game over difficulty labels of multiview consistent samples. We develop a multiview cognitive sampling strategy to select the input samples from easy to difficult for multiview clustering network training. However, the distributions of easy and difficult samples are mixed together, hence not trivial to achieve the goal. To resolve it, we define a sampling probability with a theoretical guarantee. Based on that, a golden section mechanism is further designed to generate a sample set boundary to progressively select the samples with varied difficulty labels via a gate unit, which is utilized to jointly learn a multiview common progressive subspace and clustering network for more efficient clustering. Experimental results on four real-world datasets demonstrate the superiority of DAICS over state-of-the-art methods.
IEEE Geoscience and Remote Sensing Letters, pp 1-5; doi:10.1109/lgrs.2021.3093502

Accurate registration of multispectral satellite images is a challenging task due to the significant and nonlinear radiometric differences between these data. To address this problem, this letter explores the strategy of geometrical similarity between triplets of feature points, and it is combined with the structural similarity between images in a feature-based image registration framework. The underlying principle is that the structural and geometrical similarities generally preserve across the images being registered. In this feature-based image registration framework, a set of control points (CPs) are first detected. Then, the geometric similarity between triplets of CPs is defined, followed by a ranking operation of these triplets of CPs. The highly ranked triplets are used to estimate a spatial transformation between images. Finally, initial matches obtained by a benchmark registration technique are refined by the estimated transformation. The experimental results demonstrate the great effectiveness of the proposed technique for registering multispectral satellite images.
Yiguo Song, Zhenyu Liu, Jiahui Wang, Ruining Tang, , Jianrong Tan
IEEE Transactions on Instrumentation and Measurement, pp 1-1; doi:10.1109/tim.2021.3096284

Surface defect detection is a challenging task in industrial manufacture. Recent methods using supervised learning need a large-scale dataset to achieve precise detection. However, the time-consuming and the difficulty of data acquisition make it difficult to build a large-scale dataset. This paper proposes a domain adaptive network, called multi-scale adversarial and weighted gradient domain adaptive network (MWDAN) for data scarcity surface defect detection. By MWDAN, the detection model trained from a small-scale dataset can gained the knowledge of transfer from another large-scale dataset. That is to say, even for a training dataset which is difficult to collect huge amounts of data, a good defect detection model can also be constructed, aided by another dataset that is relatively easy to acquire. The MWDAN is constructed in two levels. In the image level, a multi-scale domain feature adaptation approach is proposed to solve the domain shift between the source domain and target domain. In the instance level, a piecewise weighted gradient reversal layer (PWGRL) is designed to balance the weight of the backpropagation gradient for the hard-confused and easy-confused samples in domain classification and force confusion. Then, the PWGRL can reduce the local instance difference to further promote domain consistency. The experiments on mental surface defect detection show encourage results by the proposed MWDAN method.
Zicong Zhu, , Wenhui Diao, Kaiqiang Chen, Guangluan Xu, Kun Fu
IEEE Transactions on Geoscience and Remote Sensing, pp 1-16; doi:10.1109/tgrs.2021.3093557

With the development of deep convolutional neural networks, detecting rotating objects in remote-sensing images is of great significance in various fields. Existing rotating object detectors most suffer the problem of ambiguous supervision caused by inappropriate rotating object representations. This problem may result in fuzzy object localization and further lead to misclassification. In this article, we propose an Automatic Organized Points Detector (AOPDet), which derives precise localization results by applying a novel rotating object representation called nonsequential corners representation. To achieve the proposed representation, an Automatic Organization Mechanism (AOM) technique is designed to guide the model to organize points to object corners automatically. An Automatic-Organized-Points-specific (AOP-specific) head structure is also designed and equipped in the model to better focus on the rotating object detection task. On public aerial datasets, experiments show that the AOPDet achieves 17.0 mAP higher than the compared baseline model, reaching the state-of-the-art (SOTA) level. Detailed ablation experiments and error analysis strongly reveal the effectiveness of the proposed model.
, , Lijun Lu, Xingjun Luo, , ,
IEEE Transactions on Geoscience and Remote Sensing, pp 1-15; doi:10.1109/tgrs.2021.3093050

With the launch of various multipolarimetric satellites, many scholars have introduced the persistent scatterer (PS)-oriented polarimetric optimization methods and extended the persistent scatterer interferometry (PSI) method to multipolarimetric data configuration, called polarimetric PSI (PolPSI) technology. Most PolPSI methods mainly take the amplitude dispersion index (ADI) as the optimization criterion and evaluate the temporal amplitude stationarity of each polarimetric channel for finding an optimal one. However, due to the unstable statistical characteristics of the quality indicator, many non-PS pixels are easily mistaken for the PS candidates (PSCs), and the performance of interferometric phase optimization is also limited. To overcome these restrictions, in this article, a novel PolPSI method is proposed based on the following two improved innovations. First, in terms of PSC selection, the trace moment (TM)-based statistical properties of time-series polarimetric coherency matrices are utilized for selecting the scatterers with the temporal polarimetric stationarity. Second, in terms of interferometric phase optimization, all interferometric coherency matrices of multipolarization channels are added up together to construct the total power (TP) interferogram for suppressing the effect of speckle noise and decorrelation. In the experiment, 13 scenes of quad-polarization ALOS PALSAR-1 image are selected to verify the algorithm's effectiveness. The experimental results demonstrate that the proposed PolPSI method can better improve the deformation monitoring performance in three aspects than both the single-polarimetric HH and traditional exhaustive search polarimetric optimization (ESPO) methods, including phase quality improvement, density of PSs, and computational efficiency.
, Yi Chen, KaiYuan Zhu, Jian Yang, Zhiying Tan, Minzhou Luo
IEEE Transactions on Instrumentation and Measurement, pp 1-1; doi:10.1109/tim.2021.3096281

To reduce the ranging error, a Field Programmable Gate Array (FPGA) pulse laser ranging method based on deep learning is proposed. By simulating the echo waveforms, the deep learning sample data are constructed to train the ranging convolutional neural networks (CNN), and the influences of different convolution kernels numbers and noise levels on the performance of the ranging neural network are analyzed. The ranging accuracy and stability of the deep learning pulse laser ranging method and the traditional pulse laser ranging method are simulated and discussed. The FPGA transplantation of ranging CNN with limited resources is realized by three modules of preprocessing, ranging CNN and distance calculation. The experimental platform has been built to collect echo data of different distances, feed the echo data to FPGA, and use the deep learning ranging method to perform the waveform range calculation. The simulation and experimental results show that the deep learning pulse laser ranging method has higher ranging accuracy and stability than traditional methods. The ranging method has been successfully implemented on FPGA, which provides the possibility for the engineering implementation of the deep learning ranging method in the future.
, Bernhard T. Rabus
IEEE Transactions on Geoscience and Remote Sensing, pp 1-23; doi:10.1109/tgrs.2021.3089131

Snow water equivalent (SWE) is an important surface parameter for understanding a number of Earth system processes. Synthetic aperture radar (SAR) has considerable potential for measuring SWE of dry-snow because SAR can penetrate through the snow to the ground surface and is both amplitude- and phase-sensitive to refraction from the snow. Previous work on refraction-based SWE measurement by SAR has utilized the repeat-pass InSAR phase signal to estimate changes in SWE that occur between SAR acquisitions. These are subject to temporal decorrelation effects and consider only the refraction that occurs along the SAR beam center rather than the entire synthetic aperture. This study examines the refractive effect of dry-snow along the synthetic aperture and its impact on SAR image formation including defocusing and phase bias of the system impulse response. Snow phase compensation during time domain processing to recover the snow-free impulse response function (IRF) is described and demonstrated. The feasibility of using the mapdrift and image sharpness autofocus methods to estimate SWE is examined, and the effect of key system parameters on the estimation performance is derived. Experimental validation of the method was conducted by acquiring L-band data with the Simon Fraser University (SFU) Airborne SAR System over a pair of corner reflectors installed on the Kluane icefield in northwestern Canada. Results from both simulations and the icefield experiment are presented and compared including an analysis of errors affecting the estimation.
, , Jian Xie, , Lixin Wu, Zelin Ma
IEEE Transactions on Geoscience and Remote Sensing, pp 1-10; doi:10.1109/tgrs.2021.3093058

It is a common method to resolve three-dimensional (3-D) deformation components associated with underground mining by incorporating Single-track interferometric synthetic aperture radar (InSAR) with a prior deformation model termed linear proportion model (LPM) (hereinafter referred to as Sin-LPM). Nevertheless, the Sin-LPM method relies on three model parameters that are needed to be in situ collected, and it neglects their dynamic changes during the period of underground extraction, narrowing the practical applications of the Sin-LPM method, and degrading the accuracy of the estimated 3-D displacements. In this article we propose a new method to resolve 3-D mining displacements from multi-track InSAR observations by incorporating with the LPM. In which, the model parameters are first considered as dynamic and further adaptively estimated from the multi-track InSAR observations using a robust solver. Following that, 3-D mining displacements are resolved from the multi-track InSAR using the conjugate gradient method (CGM). The proposed method was tested in Datong coalfield, China. The results suggest that the proposed method can well estimate 3-D mining displacements with a mean error of about 1.8 cm. Compared with the previous Sin-LPM, the proposed method can effectively improve the accuracy of the estimated 3-D displacements (e.g., 69% in this study), and can work well even over a large area where the model parameters are unknown. The proposed method offers a new insight to improve the InSAR-based retrieval of 3-D displacements induced by other anthropologic or geophysical activities.
Wen Nie, , , Pingxiang Li
IEEE Transactions on Geoscience and Remote Sensing, pp 1-15; doi:10.1109/tgrs.2021.3093474

The deep convolutional neural network (CNN) has been extensively applied to polarimetric synthetic radar (PolSAR) imagery classification. However, its success is greatly dependent on numerous labeled samples for revealing and modeling the characteristics of different targets, thus remaining a challenge in maintaining high accuracy in limited sample cases. To address this issue, a deep reinforcement learning (RL)-based PolSAR image classification framework, named deep Q-fully CNN (DQFCN), is proposed in this article. In this framework, two ways are adopted to boost the classification performance while reducing training samples. On the information utilization hand, the spatial neighboring information and polarimetric decomposition information of PolSAR data are both extracted to enrich the feature representation of the sample. Meanwhile, the 3-D CNN architecture is adopted to learn the spatial-polarimetric jointed characteristics simultaneously. On the model learning hand, two RL learning strategies are employed to promote classification performance. The first one is learning from scratch, which does not use any label information as prior knowledge but learns from its self-generated experience. Learning from pretraining is the second strategy in which the networks are sequentially trained from labeled samples and experience data to reduce the time cost. As far as we know, it is the first time that an RL-based fully CNN has been proposed for PolSAR image classification. Experiments on three benchmark datasets prove the effectiveness of the proposed framework, suggesting that the two adopted strategies achieve boosted performance in all experiments, particularly in a limited sample size.
IEEE Transactions on Electromagnetic Compatibility, pp 1-10; doi:10.1109/temc.2021.3080590

This article deals with circuit modeling of power systems and more particularly of DC–AC converters used for controlling electric motors. The objective of the model is to be able to assess conducted emission currents at both inputs and outputs for fixed operating power in order to apply appropriate mitigation design rules, such as filtering. First, we propose a linear Thevenin block model of the converter considering it as a five-port linear black box allowing possible interaction between the DC and AC ports. To this extent, the block model is made of an impedance matrix and a Thevenin voltage generators vector. We also propose theoretical relationships allowing the processing of data to generate this model from measurements. Then, we test the linear simplifications on a real DC–AC converter and compare the results to measurements. For this, S-parameter measurements in off-mode and currents at the five ports in on-mode are used for the characterization of the model on a first test setup. The model is then applied and checked by comparing circuit simulated and measured current responses on a modified test setup for which the lengths of the connection cables are modified. Finally, the capability of this block model to extrapolate by calculation the response of other installation configurations is shown considering a filter and its insertion at input/output ports of the converter with much longer cables.
IEEE Transactions on Radiation and Plasma Medical Sciences, pp 1-1; doi:10.1109/trpms.2021.3096534

In this paper, the performance of two dual-ended readout PET detectors based on 5 × 5 BGO arrays were compared. The crystal elements of one BGO array have polished lateral surfaces, while the crystal elements of the other BGO array have unpolished lateral surfaces. The two ends of the BGO elements are polished. The two BGO arrays both have a pitch size of 1.6 mm and thickness of 20 mm, and BaSO4 with a thickness of 80 μm was used as the reflector. Hamamatsu S14161-0305-08 SiPM arrays were used as photodetectors. All the measurements were performed at a bias voltage of 41.0 V and a temperature of 23.5 ∘C. The flood histograms show that all the crystal elements in the two BGO arrays were clearly resolved. The detector based on the BGO array with polished lateral surfaces provides an energy resolution of 16.9 ± 1.3%, timing resolution of 3.2 ± 0.2 ns, and DOI resolution of 18.4 ± 2.2 mm. In comparison, the detector based on the BGO array with unpolished lateral surfaces provides an energy resolution of 17.7 ± 2.0%, timing resolution of 3.5 ± 0.3 ns, and DOI resolution of 3.2 ± 0.2 mm.
, Jiaqi Wang, Shumin Fei, Yajuan Liu
IEEE Transactions on Circuits and Systems II: Express Briefs, pp 1-1; doi:10.1109/tcsii.2021.3096523

The global fixed-time control problem is studied for uncertain nonlinear systems whose dead-zone input and control coefficients are unknown. Unlike the previous results, the control coefficients are unknown and their bounds are also not required. Firstly, a modified adaptive controller is proposed with the assistance of the adding a power integrator technique. And a logic switching mechanism is developed based on the framework of the fixed-time stability theorem to compensate the uncertain control coefficients and dead zone. Then it is proved that the fixed-time stability can be ensured for the closed-loop systems based on the proposed algorithm. Furthermore, the effectiveness of the proposed controller is verified by a simulation result.
Jun Wang, Haozhou Zhu, Yang Yu, Xu Liu, Eryuan Feng, Chuanzhen Lei, Yanfei Cai, Hao Zhu, , David Wei Zhang
IEEE Transactions on Circuits and Systems II: Express Briefs, pp 1-1; doi:10.1109/tcsii.2021.3096225

With the downscaling of semiconductor devices and increased fabrication complexity, the feature size and threshold voltage (Vth) of transistors are also decreased significantly. This further makes the static power of standard cell library a crucial design challenge. In this paper, transistor-level gate length biasing (TLLB) method is utilized to optimize the static power consumption of a Scan D Flip-Flop (DFF) based on the Semiconductor Manufacturing International Corporation (SMIC) 14 nm FinFET standard cell library. An improvement in both static power consumption and speed have been achieved by utilizing the TLLB optimization which can be further implemented in a variety of complex circuit designs. Furthermore, we have synthesized ARM A72 design using the standard cell library including TLLB DFF which can save 26% static power consumption compared to that with short channel DFF. The frequency is faster with shorter delay than the one using long channel DFF.
IEEE Transactions on Circuits and Systems I: Regular Papers, Volume 68, pp 3535-3535; doi:10.1109/tcsi.2021.3094567

Describes the above-named upcoming special issue or section. May include topics to be covered or calls for papers.
Li Ma, , Taofeng Liu, Shuping Gao, Pengyuan Dong
IEEE Access, pp 1-1; doi:10.1109/access.2021.3096329

Voltage sags and swells are significant issues of power quality. In order to alleviate voltage sags and swells on sensitive loads, a method is proposed to evaluate the bus and grid structure based on voltage sags/swells using voltage ellipse parameters. The voltage matrix is established for voltage sags/swells caused by four types of short-circuit faults. The voltage matrix can be converted into a minor axis matrix and a major axis matrix using the Clarke transformation, and accordingly a voltage sag matrix and a voltage swell matrix can be obtained with the interaction between voltage sags/swells and voltage ellipse parameters. The key indicator and the vulnerability indicator of bus as well as the indicator of grid structure are defined according to the voltage sag matrix and swell matrix. The buses are sorted using the indicators proposed, and the result of bus sorting provides a basis for selecting the buses that need to be managed to alleviate voltage sags/swells. The experiment results in IEEE 30-bus system and IEEE 57-bus system show that these indicators facilitate the selection of the access point for newly-added sensitive loads, and provide a theoretical guidance to the mitigation of voltage sags and swells, and help to accomplish the planning, operation and transformation of power grid.
, Lei Liang, Li Jun Wang, Li Qin, Yong Yi Chen, Yu Bing Wang, Yue Song, Yu Xin Lei, Peng Jia, Yu Gang Zeng, et al.
IEEE Access, pp 1-1; doi:10.1109/access.2021.3096519

Semiconductor optical amplifiers (SOAs) offer direct electrical injection, power consumption, integration, and anti-radiation advantages over optical fiber amplifiers. However, saturation output power and gain bandwidth have been limited in traditional structure SOAs. We demonstrate a monolithic integrated SOA with broad spectrum, high power, high gain, and small spectral linewidth expansion. The device adopts a two-stage amplified large optical cavity structure, and a lower optical field confinement factor was obtained by adjusting the thickness of the waveguide layer. The lower optical field confinement factor is conducive to improving the coupling efficiency and the maximum output power. Our device, fabricated only by standard i-line lithography with micron-scale precision, obtains excellent and stable performance. When the input power is set to 1 mW, the output power is 419 mW with a gain of 26.23 dB. When the input power is set to 25 mW at 25 °C, the output power increases to 600 mW with a gain of 13.8 dB. The corresponding gain bandwidth of 3 dB measures at least 70 nm. The spectral linewidth after the SOA is 1.15 times wider than that of the seed laser.
, Yuan Ma, Jingqiang Lin, Yuan Cao, Na Lv, Jiwu Jing
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, pp 1-1; doi:10.1109/tcad.2021.3096464

True random number generator (TRNG) as one essential hardware primitive is widely used in cryptography, Monte Carlo simulation and gambling. To evaluate the security of TRNG, the entropy of the TRNG’s output is usually estimated by the stochastic model in theory or measured off-chip after fabrication. However, the sufficiency of entropy is difficult to be guaranteed in practice due to the facts: 1) the inaccuracy of the model-based jitter measurement method, 2) the variations of the chip manufacturing process and operating environments (such as supply voltage and temperature), and 3) malicious attacks. In this work, we design a novel TRNG architecture with on-chip entropy assurance to properly solve practical security problems. In the design, we propose an on-chip entropy estimator for measuring independent jitter to quantify true randomness, which enables continuous monitoring of TRNG at runtime. Furthermore, with the cooperation of the proposed on-chip entropy estimator and a rational self-adaptive mechanism, the designed TRNG can steadily generate bitstreams with sufficient entropy (≥ 0.999 per bit) against PVT variations. We implement the TRNG architecture in FPGAs with different technology nodes (45 nm and 65 nm) and SMIC 130 nm chips. Experimental results validate that the designed TRNG has an excellent performance in terms of technology independence and environmental robustness. The generated bitstreams pass the NIST SP800-22 and Diehard statistical test suites successfully without any post-processing.
Yanhui Qiu, , Jiawei Zhao
IEEE Access, pp 1-1; doi:10.1109/access.2021.3096517

The Boost type multi-input independent generation system (IGS) with multi-winding simultaneous power supply is proposed and deeply investigated, the important conclusions are obtained. Its circuit structure is composed of a single-stage multi-winding Boost type multi-input inverter with high-frequency-link and a single-stage isolated battery charging/discharging converter connected together at the output end, its circuit topological family includes 4 types of circuit such as full-bridge etc, the maximum power energy management control strategy (EMCS) with output voltage single-loop and three-state one-cycle phase-shift modulation is adopted, and the magnetic saturation of the energy storage inductor and the distortion of output voltage of the IGS are effectively suppressed. By comparing the load power and the total input sources power, the proposed EMCS achieves smooth transition among different power supply modes. The designed and developed 2.5kVA experimental prototype has shown that the proposed IGS has the advantages such as single-stage conversion, high frequency galvanic isolation among load, multi-input sources and battery, simultaneous power supply in a high-frequency switching cycle, wide regulating range of duty cycle, small in size and light in weight, and strong load adaptability, etc.
, Piergiuseppe Di Marco, Roberto Alesii, Fortunato Santucci
IEEE Transactions on Communications, pp 1-1; doi:10.1109/tcomm.2021.3096541

In this paper, we propose a framework for cross-layer analysis of multi-static passive RFID systems. The model takes into account details of the shared wireless channel, including fading and capture effect, whereas, at the medium access control (MAC) layer, the anti-collision mechanism proposed in the EPC Generation 2 standard is taken as a reference. To address the complexity of the system model, we rely on a semi-analytical approach, that combines a moment matching approximation method to abstract the physical layer and Monte-Carlo simulations to describe the MAC dynamics. Furthermore, based on the space diversity feature offered by the multi-static settings, we introduce the concept of capture diversity and propose a modification to the standard to fully support this form of diversity. Numerical results show the impact of deployment conditions and the relative positions of interrogator, tags, and detection points on the performance of tags’ identification. We show how the number of detection points impacts the system performance under various channel conditions and MAC parameters’ settings. Finally, we validate the proposed update of the MAC protocol, showing substantial performance improvement with respect to the standard collision resolution policy.
, Daniel W. C. Ho, Tingwen Huang, Jurgen Kurths, Ljiljana Trajkovic
IEEE Access, Volume 9, pp 95083-95086; doi:10.1109/access.2021.3087971

Complex networks typically involve multiple disciplines due to network dynamics and their statistical nature. When modeling practical networks, both impulsive effects and logical dynamics have recently attracted increasing attention. Hence, it is of interest and importance to consider hybrid complex networks with impulsive effects and logical dynamics. Relevant research is prevalent in cells, ecology, social systems, and communication engineering. In hybrid complex networks, numerous nodes are coupled through networks and their properties usually lead to complex dynamic behaviors, including discrete and continuous dynamics with finite values of time and state space. Generally, continuous and discrete sections of the systems are described by differential and difference equations, respectively. Logical networks are used to model the systems where time and state space take finite values. Although interesting results have been reported regarding hybrid complex networks, the analysis methods and relevant results could be further improved with respect to conservative impulsive delay inequalities and reproducibility of corresponding stability or synchronization criteria. Therefore, it is necessary to devise effective approaches to improve the analysis method and results dealing with hybrid complex networks.
, Flavio Vinicius Cruzeiro Martins, Elizabeth Fialho Wanner, Kalyanmoy Deb
IEEE Transactions on Evolutionary Computation, pp 1-1; doi:10.1109/tevc.2021.3096669

Dominance move (DoM) is a binary quality indicator that can be used in multi-objective and many-objective optimization to compare two solution sets obtained from different simulations. The DoM indicator can differentiate the sets for certain important features, such as convergence, spread, uniformity, and cardinality. DoM does not require any reference point or any representative Pareto solution set, and it has an intuitive and physical meaning, similar to the -indicator. It calculates the minimum total move of members of one set so that all elements in another set are to be dominated or identical to at least one member of the first set. Despite the aforementioned desired properties, DoM is hard to calculate, particularly for higher dimensions. There is an efficient and exact method to calculate it in a bi-objective problems. This work proposes a novel approach to calculate DoM using a mixed-integer programming (MIP) approach, which can handle two sets with two or more objectives and is shown to overcome the issue of information loss associated with the -indicator. Experiments in the bi-objective space are done to verify the model’s correctness. Furthermore, other experiments, using 3, 5, 10, 15, 20, 25, and 30-objective problems, are performed to show how the model behaves in higher-dimensional cases. Algorithms, such as IBEA, MOEA/D, NSGA-III, NSGA-II, and SPEA2 are used to generate the solution sets, however, any other algorithm can also be used with the proposed MIP-DoM indicator. Further extensions are discussed to handle certain idiosyncrasies with some solution sets and improve the quality indicator and its use for other scenarios.
, Ben Xie, Yuxin Zhang, Shiliang Zhao, Suofei Zhang
IEEE Transactions on Cognitive and Developmental Systems, pp 1-1; doi:10.1109/tcds.2021.3096546

In this paper, we propose to use the data augmentation of batch drop-block with varying dropping ratios for constructing diversity-achieving branches in person re-identification. Since a considerable portion of input images may be dropped, this reinforces feature learning of the un-dropped region but makes the training process hard to converge. Hence, we propose a novel double-batch-split co-training approach for remedying this problem. In particular, we show that the feature diversity can be well achieved with the use of multiple dropping branches by setting individual dropping ratio for each branch. Empirical evidence demonstrates that the proposed method performs competitively on popular person Re-ID datasets, including Market-1501, DukeMTMC-reID and CUHK03, and the use of more dropping branches can further boost the performance. Source code is available at url.
Luis Claudio Soto-Ayala, Jose Antonio Cantoral-Ceballos
IEEE Latin America Transactions, Volume 19, pp 2028-2036; doi:10.1109/tla.2021.9480144

The evaluation and diagnosis of cancer related diseases can be complex and lengthy. This is exacerbated due to manual analyses based on techniques that may take copious amount of time. In the last decade, different tools have been created to detect, analyze and classify different types of cancer in humans. However, there is still a lack of tools or models to automate the analysis of human cells to determine the presence of cancer. Such a model has the potential to improve early detection and prevention of said diseases, leading to more timely medical diagnoses. In this research, we present our current effort on the development of a Deep Learning Model capable of identifying blood cell anomalies. Our results show an accuracy that meets or exceeds the current state of the art, particularly achieving lower false negative rate in comparison to previous efforts reported.
, Marcela Silva Novo
IEEE Latin America Transactions, Volume 19, pp 2062-2070; doi:10.1109/tla.2021.9480148

Deep Learning methods have important applications in digital image processing. However, the literature lacks further studies that propose machine learning models to images classification in civil construction area. For example, the vegetation recognition on facades can be relevant in identifying the degradation and abandonment of buildings. Thus, the objective of this paper is to propose an Convolutional Neural Networks (CNN) approach to vegetation images recognition in buildings. For this, a database with urban images (low altitude) captured by a drone in Zurich (Switzerland) was adopted. In addition, a rigorous hyperparameters tuning methodology for the CNN model is presented. After adjusting the hyperparameters and the final model, the system achieved 90% of accuracy in the test stage. It should also be noted that CNN correctly classified 97.8% of the positive class (with vegetation on the facade) in test images.
Luis Eduardo Barrientos Sandoval, Edson Luiz Cataldo Ferreira
IEEE Latin America Transactions, Volume 19, pp 2139-2146; doi:10.1109/tla.2021.9480157

The aim of this paper is perform the synthesis of sung Spanish vowels considering the soprano vocal category of lyrical singers, including variation of sustained pitches with vibrato and tremolo effects, considering sounds from Spanish language. The Fant source-filter theory is used to model the production of the sung vowels: the source is based on the Rosenberg glottal pulse model and the filter (the vocal tract) is composed by an all-pole filter model with formant frequencies and bandwidths from the vowels of the Spanish language, obtained through experimental voice signals from two soprano singers. All the sounds synthesized are available to be accessed and they were submitted to a group of listeners which gave a very good evaluation with respect to the intelligibility and naturalness of the sounds.
Fabio Viegas, , , Wesllei Felipe Heckler
IEEE Latin America Transactions, Volume 19, pp 2019-2027; doi:10.1109/tla.2021.9480143

Crime is one of the most critical problems in urban centers, especially in large cities. In this sense, technological solutions are needed to provide security to citizens, contributing to the reduction of crime rates. The present work proposes the UFollower model (Ubiquitous Follower) which meets this scenario. The scientific contribution of this work consists of the use of Context Histories and User Profiles for data analysis focused on ubiquitous security. The use of prediction mechanisms and historical contexts allowed to reach up to 70.35% of inference rate for a particular crime (domestic violence). The comparison with the related works indicates that UFollower is the only proposal that presents the issue of public security with context histories and user profile management. The evaluation was conducted through scenarios, allowing to evaluate the related hypotheses. In this sense, a context simulator was built where twenty objects interacted to allow the evolution. Among them, there are people, vehicles, cameras, and wearables.
Sisi You, Hantao Yao,
IEEE Transactions on Circuits and Systems for Video Technology, pp 1-1; doi:10.1109/tcsvt.2021.3096237

Multi-object tracking is a challenging task due to the occlusion of different targets. Existing methods focus on inferring a robust and discriminative feature for data association based on the targets generated by the existing detector. Unlike existing methods that consider each target independently during generating the trajectories, we propose a novel Spatial-Temporal Topology-based Detector (STTD) algorithm that treats the target and its nearest neighbors as a cluster and introduces a topology structure to describe the dynamics of moving targets belonging to the same cluster. With the public detections and the tracked objects in the previous frame, STTD firstly refines them by regression of detector to obtain the candidate proposals in the current frame. After that, the temporal topology constraint is proposed to recover the missed objects by considering the continuity and consistency of the topological structure. Based on the assumption that the targets belonging to the same topology should have a consistent characteristic, the spatial topology constraint is proposed to remove the inaccurate targets. Then we can obtain new candidate objects and construct the cost matrix used for data association. The evaluations on three MOTChallenge benchmarks verify the effectiveness of the proposed method.
Bruna De Oliveira Busson, Leticia De Oliveira Santos, , Clodoaldo De Oliveira Carvalho Filho
IEEE Latin America Transactions, Volume 19, pp 2079-2086; doi:10.1109/tla.2021.9480150

Photovoltaic (PV) modules convert part of solar radiation into electrical energy. Another fraction of the incident energy causes an increase of the PV module operating temperature, leading to an electrical performance reduction. In the present paper is proposed the passive cooling of a floating PV (FPV) module using 5 fixed heat bridges to reduce the operating temperature and increase the energy conversion efficiency. The modeling developed for a FPV module operating temperature with heat bridges predicts the cooling capacity of the plant. The proposed model is nonlinear algebraic and equations require iterative numerical solution. Experimental tests allowed to compare thermal and electrical behavior of a FPV module and a rooftop (conventional) PV module, both in Fortaleza, Brazil. The FPV module temperature was 3.2C lower than the conventional module temperature, on average. The model developed for FPV module with heat bridges may predict its operating temperature with error around 5%. According to the measurements, the FPV module productivity was 26.1% higher than conventional PV module productivity, on average. Thus, the modeling developed is in condition to predict the thermal behavior and prove the effectiveness of passive cooling.
, Weisi Lin, Qingming Huang
IEEE Transactions on Circuits and Systems for Video Technology, pp 1-1; doi:10.1109/tcsvt.2021.3096528

Image quality assessment (IQA) plays a central role in many image processing algorithms and systems. Although many popular IQA models achieves high performance on existing released databases, they are still not well accepted in practical applications due to the not-always satisfactory accuracy on real-world data and situations. In this paper, we revisit the IQA research, and point out an ignored but interesting problem in IQA: the coarse-grained (i.e., when quality variation is sufficiently big, as the setting of most IQA databases up to date) statistical results evaluated on existing databases mask the fine-grained differentiation. Accordingly, we present a survey on image quality assessment from a new perspective: fine-grained image quality assessment (FG-IQA). Recent FG-IQA research on five major kinds of images is introduced, and some popular IQA methods are analyzed from FG-IQA perspective. The potential problems for current IQA research based on existing coarse-grained databases are analyzed and the necessity of more FG-IQA research is justified. Finally, we discuss some challenges and possible directions for future works in FG-IQA.
Ke Guo, Yang Qi, Jiale Yu, David Frey,
IEEE Transactions on Smart Grid, pp 1-1; doi:10.1109/tsg.2021.3096638

To facilitate the transformation from conventional power systems towards smart grids, the concept of microgrids has been widely applied in practice, serving as the medium to accommodate renewable generators. One crucial problem is the stability associated with microgrids. This paper proposes a converter-based power system stabilizer (CBPSS), acting as a supplementary control loop, to enhance the stability of the islanded microgrids. The goal of the proposed CBPSS is to stabilize the critical microgrid mode with the generation of a damping torque, and it is achieved with the identification of the forward loop from the CBPSS to the microgrid. Then, the parameters of the proposed CBPSS can be designed accordingly to compensate the phase lag of the identified forward loop. Besides, an eigenvalue-mobility-based method is presented to identify the optimal installation location of the CBPSS in microgrids. As a consequence, the maximum stabilizing effect can be realized with the least control effort. Finally, modal analysis and time-domain simulations as well as hardware experimental results confirm the effectiveness of the proposed method.
, Hessam Sokooti, Mohamed S. Elmahdy, Irene M. Lips, Mohammad T. Manzuri Shalmani, Roel T. Zinkstok, Frank J.W.M. Dankers, Marius Staring
IEEE Access, pp 1-1; doi:10.1109/access.2021.3096270

Manual or automatic delineation of the esophageal tumor in CT images is known to be very challenging. This is due to the low contrast between the tumor and adjacent tissues, the anatomical variation of the esophagus, as well as the occasional presence of foreign bodies (e.g. feeding tubes). Physicians therefore usually exploit additional knowledge such as endoscopic findings, clinical history, additional imaging modalities like PET scans. Achieving his additional information is time-consuming, while the results are error-prone and might lead to non-deterministic results. In this paper we aim to investigate if and to what extent a simplified clinical workflow based on CT alone, allows one to automatically segment the esophageal tumor with sufficient quality. For this purpose, we present a fully automatic end-to-end esophageal tumor segmentation method based on convolutional neural networks (CNNs). The proposed network, called Dilated Dense Attention Unet (DDAUnet), leverages spatial and channel attention gates in each dense block to selectively concentrate on determinant feature maps and regions. Dilated convolutional layers are used to manage GPU memory and increase the network receptive field. We collected a dataset of 792 scans from 288 distinct patients including varying anatomies with air pockets, feeding tubes and proximal tumors. Repeatability and reproducibility studies were conducted for three distinct splits of training and validation sets. The proposed network achieved a DSC value of 0.79±0.20, a mean surface distance of 5.4±20.2mm and 95% Hausdorff distance of 14.7±25.0mm for 287 test scans, demonstrating promising results with a simplified clinical workflow based on CT alone. Our code is publicly available via
Peixuan He, , Jiayu Yang, Qiudong Xia, Jianqing Liu, David S. L. Wei
IEEE Transactions on Network and Service Management, pp 1-1; doi:10.1109/tnsm.2021.3096428

To reduce the duplicated traffic and improve the performance of distributing massive volumes of multimedia contents, in-network caching has been proposed recently. However, as in-network content caching can be directly utilized to respond users’ requests, multimedia content retrieval is beyond content providers’ control and makes it hard for them to implement access control and service accounting. In this paper, we propose a Fine-grained Accountable and Space-Efficient access control scheme, called FASE, for multimedia content distribution. FASE allows content providers to be fully offline while making the best of in-network caching. In FASE, the attribute-based encryption at multimedia content provider side and access policy based authentication at the edge router side jointly ensure secure fine-grained access control. Our scheme is efficient in both space and time. By designing one time chameleon signature (OTCS), users can keep anonymous during the authentication, and their privileges can be conveniently revoked when needed. Besides, secure service accounting is implemented by letting edge routers collect service credentials generated during users’ request process. Through formal security analysis, we prove the security of our scheme. Simulation results demonstrate that our scheme is efficient with acceptable overhead.
, Lei Wang, Jing Huo, Yinghuan Shi, Xin Geng, Yang Gao
IEEE Transactions on Circuits and Systems for Video Technology, pp 1-1; doi:10.1109/tcsvt.2021.3096668

In person re-identification (Re-ID), supervised methods usually need a large amount of expensive label information, while unsupervised ones are still unable to deliver satisfactory identification performance. In this paper, we introduce a novel person Re-ID task called unsupervised cross-camera person Re-ID, which only needs the within-camera (intra-camera) label information but not cross-camera (inter-camera) labels which are more expensive to obtain. In real-world applications, the intra-camera label information can be easily captured by tracking algorithms and few manual annotations. In this situation, the main challenge becomes the distribution discrepancy across different camera views, caused by the various body pose, occlusion, image resolution, illumination conditions, and background noises in different cameras. To address this situation, we propose a novel Adversarial Camera Alignment Network (ACAN) for unsupervised cross-camera person Re-ID. It consists of the camera-alignment task and the supervised within-camera learning task. To achieve the camera alignment, we develop a Multi-Camera Adversarial Learning (MCAL) to map images of different cameras into a shared subspace. Particularly, we investigate two different schemes, including the existing GRL (i.e., gradient reversal layer) scheme and the proposed scheme called “other camera equiprobability” (OCE), to conduct the multi-camera adversarial task. Based on this shared subspace, we then leverage the within-camera labels to train the network. Extensive experiments on five large-scale datasets demonstrate the superiority of ACAN over multiple state-of-the-art unsupervised methods that take advantage of labeled source domains and generated images by GAN-based models. In particular, we verify that the proposed multi-camera adversarial task does contribute to the significant improvement.
Xingming Yang, Fei Xu, , Zhao Xie, Yongxuan Sun
IEEE Access, pp 1-1; doi:10.1109/access.2021.3096553

Social relation, as the basic relation in our daily life, is vital for social action analysis. However, how to learn the social feature between people is still not tackled. In this work, we propose a gaze-aware graph convolutional network (GA-GCN) for social relation recognition, which targets discovering the context-aware social relation inference with gaze-aware attention. To predict the gaze direction, we apply a convolutional network trained with gaze direction loss. Then, we build a graph convolutional inference module, which is a two-stream graph inference with both gaze-aware attention and distance-aware attention. The attention can pick up relevant context objects for context-aware representation. We further introduce additional scene features and construct a multiple feature fusion module, which can adaptively learn social relation representation from both scene feature and context-aware feature. Extensive experiments on the PISC and the PIPA datasets demonstrate that our GA-GCN can find interesting contextual objects and achieves state-of-the-art performances.
Sudarshan Nandy,
IEEE Sensors Journal, pp 1-1; doi:10.1109/jsen.2021.3096425

The outbreak of the coronavirus is in its growing stage due to the lack of standard diagnosis for the patients. The situation of any populous area in a geographic location is very critical due to the quick virus spread from an infected individual to the rest. Currently, medical administration is at a crisis point due to the rapidly increasing number of cases and limited medical facilities. Thus, it is time to explore and design an intelligent model to monitor patient health symptoms remotely and predict and detect the abnormality of the patient’s health status in quick succession. Thus, the health status of a coronavirus-affected patient can be identified via a well-adjusted predictive model by analyzing the observed parameters of the health. In the proposed model, an Auto-regressive Integrated Moving Average is incorporated to design a predictive model to find the kth forecast of the observed health symptoms of a patient, and Akaike Information Criteria based selection is introduced to find the current best-fit prediction model. Further, the features are extracted from the forecast over each symptom to find a pattern of each patient, and the patterns are learned by the K-Means algorithm to detect the symptomatic and asymptomatic patient intelligently. To demonstrate the efficiency of the proposed model, we evaluate the model using a synthetic dataset, generated from the health symptoms of 400 patients and compare the performance of the model with the standard methods.
Xiao Ma, , Yu Zhou, Xueyi Hu, Fadong Li
IEEE Sensors Journal, pp 1-1; doi:10.1109/jsen.2021.3095623

When the electromagnetic rail gun accelerates the armature, the rail is in a harsh condition of high temperature, high pressure and powerful current which will cause a series of grooving phenomenon. In order to detect the inner bore profile, we developed a measuring device using laser triangulation method and basically realized the measurement of the inner bore profile measurement. However, except for the translation and deflection errors, we found that the actual measurement cross-section would be rotated due to the unsteady movement compared with the ideal section. In addition, the previous calibration needs a high-resolution image sensor to get deflection information, it means high cost. In this paper, we analyzed the 3 kinds of deviations and provide a method to calibrate the rotation error using a calibration block. An improved calibration system was designed in low cost and experiment verified the feasibility of the calibration method. The calibration system can be divided into 3 parts, the outside global laser light provides a constant central position, a distance detecting device with a cone laser generator and an inside calibration block. After experiment, the calibration method was effective and the deviation of the profile can be corrected.
Chao Xu, , Deyong Chen, Jian Chena, Bowen Liu, Wenjie Qi, Tian Liang, Xu She
IEEE Sensors Journal, pp 1-1; doi:10.1109/jsen.2021.3096496

Seismic sensors are the key sensitive components in geophysical exploration, and the new-type electrochemical seismic sensors have gradually aroused researchers’ interest for their superior performance in low-frequency domain and large working inclination. In this paper, a method was developed to fabricate the MEMS (micro-electro-mechanical systems) based integrated electrodes for the electrochemical seismic sensors. The proposed integrated electrodes which employed three-layer anodic bonding structure of silicon-glass-silicon served as the substitutes for multilayer manual assembly structures. Compared to previous counterparts, this integrated structure has the advantages of simplified assembly processes of sensitive unit and high consistency as the no requirement of manual alignment. The results shown that the cross-correlation coefficient between two proposed devices was quantified as 0.998 with the sensitivity of 5956 V/(m/s) @1Hz. This electrode is so far the sensitive structure which realize both high sensitivity and high integration in the electrochemical seismic sensors.
, Dzuhri Radityo Utomo, Byeonghun Yun, Hafiz Usman Mahmood, Sang-Gug Lee
IEEE Access, pp 1-1; doi:10.1109/access.2021.3096423

This paper proposes a simultaneous output power- and gain-matching technique in a sub- THz power amplifier (PA) design based on a maximum achievable gain (Gmax) core. The optimum combination of three-passive-elements-based embedding networks for implementing the Gmax-core is chosen considering the small- and large-signal performances at the same time. By adopting the proposed technique, the simultaneous output power- and gain-matching can be achieved, maximizing the small-signal power gain and large-signal output power simultaneously. A 150 GHz single-ended two-stage PA without power combining circuit is implemented in a 65-nm CMOS process based on the proposed technique. The amplifier achieves a peak power gain of 17.5 dB, peak power added efficiency (PAE) of 13.3 and 16.1 %, saturated output power (Psat) of 10.3 and 9.4 dBm, and DC power consumption of 86.3 and 52.4 mW, respectively, under the bias voltage of 1.2 and 1 V, which corresponds to the highest PAE, gain per stage and Pout per single transistor among other reported CMOS D-band PAs.
, Sergio Trilles, Joaquin Torres-Sospedra, Antonio Iera, Giuseppe Araniti
IEEE Sensors Journal, pp 1-1; doi:10.1109/jsen.2021.3096730

Emerging communication network applications require a location accuracy of less than 1m in more than 95% of the service area. For this purpose, 5G New Radio (NR) technology is designed to facilitate high-accuracy continuous localization. In 5G systems, the existence of high-density small cells and the possibility of the device-to-device (D2D) communication between mobile terminals paves the way for cooperative positioning applications. From the standardization perspective, D2D technology is already under consideration (5G NR Release 16) for ultra-dense networks enabling cooperative positioning and is expected to achieve the ubiquitous positioning of below one-meter accuracy, thereby fulfilling the 5G requirements. In this survey, the strengths and weaknesses of D2D as an enabling technology for cooperative cellular positioning are analyzed (including two D2D approaches to perform cooperative positioning); lessons learned and open issues are highlighted to serve as guidelines for future research.
Jamshed Ahmad, Abdul Wahid Khan, Habib Ullah Khan
IEEE Access, pp 1-1; doi:10.1109/access.2021.3096663

In software engineering field, requirement change management is a challenging job. Ignoring incoming changes results in customer displeasure. It may also result in late product transportation. Managing requirement changes in poor way is the main cause of product failure. It has more diverse effect in global software outsourcing. In software quality requirement change management, it is necessary to address success factors in order to accomplish the requirements of the customers. In this paper, systematic literature review approach is used for documentation and scrutinization of success factors. Total sixteen success factors were recognized having great impact on quality software requirement change management. Our identified success factors like ‘Proper Requirement Change Management’, ‘Rapid Delivery’, ‘Quality Software Product, Access to Market’, ‘Project Management’, ‘Skills and Methodologies’, ‘Low Cost/Effort Estimation’, ‘Clear Plan and Road Map’, ‘Agile Processes’, ‘Low Labor Cost’, ‘User Satisfaction’, ‘Communication/Close Coordination’, ‘Proper Scheduling and Time Constraints’, ‘Frequent Technological Changes’, ‘Robust Model’, ‘Geographical juncture/Cultural differences’ are the crucial factors that affect software quality requirement change. Company size and different database have been used for the analysis of success factors. The databases/search engine used are Google scholar, Science Direct, IEEE Explore and Springer for the exploration of success factors. Companies are analyzed on the basis of their size such as small, medium and large.
Ali Momeni Asl, , Seyed Javad Mirabedini
IEEE Access, pp 1-1; doi:10.1109/access.2021.3096224

Cryptography is one of the most important security mechanisms for transmitting digital media on the Internet. Most cryptographic image methods proposed based on chaotic maps are dependent upon image sizes and work on square images. This study presents a scale-invariant color image encryption method in three-dimensional space to solve this problem. First, a two-dimensional color image is converted into a three-dimensional space. Red, green, and blue color spectrums are divided into a set of gray-level square sub-images. Then, the 3D substitution and 3D permutation operation are performed on the sub-images to have confusion and diffusion properties. In substitution operations, the pixel values of the sub-images are changed with appropriate keys by XOR and circular shift operators. In permutation operation, the positions of the pixels are changed using modular three-dimensional chaos mappings. The sub-images are divided into one or more windows with equal size to have scale-invariant three-dimensional permutation, and 3D modular chaotic map operations are performed on each window with separate keys. Depending on the number of sub-images, there may be two last windows which have overlapping. The steps of a 3D modular chaotic map on the windows can be implemented in parallel to increase the speed of color image encryption. The proposed approach, in comparison to similar color image encryption algorithms, can increase the key space and improve standard parameters such as entropy, sensitivity, adjacent pixel correlations, and histogram uniformity.
IEEE Access, Volume 9, pp 1-1; doi:10.1109/access.2021.3096279

Transport emissions, including road, rail, air, and marine transportation, account for a large part of the overall emissions; hence, there is a need to review strategies for managing associated issues and coping with negative impacts. A simultaneous improvement in economic efficiency can help us achieve our desired objectives in the concerned context. Sharing economy, i.e., a peer-to-peer-based sharing of access to assets, can help reduce the total resources required and consequently reduce carbon footprints. In line with this objective, we propose an intelligent model to study carbon dioxide emissions from road transport using taxi trips in Dublin, Ireland. The proposed method is a hybrid unsupervised learning approach tailored for the particular structure of the problem. We present how an intelligent approach can be implemented to model CO2 emissions from road transport. The model categorizes taxis based on different features related to the emissions they release. Five clusters are detected, which can be attributed to varying levels of emissions. Accordingly, those vehicles labeled as the highest emitters can be targeted for further improvements in reducing CO2, i.e., replacing pollutant cars with electric cars or including them in the taxi fleet as sharing ones only.
, Oktay Karakus, Robin Holmes, Alin Achim
IEEE Access, pp 1-1; doi:10.1109/access.2021.3096643

In representation learning, Convolutional Sparse Coding (CSC) enables unsupervised learning of features by jointly optimising both an ℓ2-norm fidelity term and a sparsity enforcing penalty. This work investigates using a regularisation term derived from an assumed Cauchy prior for the coefficients of the feature maps of a CSC generative model. The sparsity penalty term resulting from this prior is solved via its proximal operator, which is then applied iteratively, element-wise, on the coefficients of the feature maps to optimise the CSC cost function. The performance of the proposed Iterative Cauchy Thresholding (ICT) algorithm in reconstructing natural images is compared against algorithms based on minimising standard penalty functions via soft and hard thresholding as well as against the Iterative Log-Thresholding (ILT) method. ICT outperforms the Iterative Hard Thresholding (IHT), Iterative Soft Thresholding (IST), and ILT algorithms in most of our reconstruction experiments across various datasets, with an average Peak Signal to Noise Ratio (PSNR) of up to 11.30 dB, 7.04 dB, and 7.74 dB IST, IHT, and ILT respectively. The source code for the implementation of the proposed approach is publicly available at
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