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This work presents a low-noise amplifier (LNA) design with a wide-range gain control characteristic that integrates adjustable current distribution and output impedance techniques. For a given gain characteristic, the proposed LNA provides better wideband interference rejection performance than conventional LNA. Moreover, the proposed LNA also has a wider gain control range than conventional LNA. Therefore, it is suitable for satellite communications systems. The simulation results demonstrate that the voltage gain control range is between 14.5 and 34.2 dB for such applications (2600 MHz); the input reflection coefficient is less than −18.9 dB; the noise figure (NF) is 1.25 dB; and the third-order intercept point (IIP3) is 4.52 dBm. The proposed LNA consumes 23.85–28.17 mW at a supply voltage of 1.8 V. It is implemented by using TSMC 0.18-um RF CMOS process technology. [ABSTRACT FROM PUBLISHER]

This work develops a low-power up-conversion mixer. The developed mixer integrates the LC parallel resonant technique, the inverter amplifier technique, the resistive-feedback technique, and the resistive source degeneration technique. Therefore, it has higher conversion gain, low power consumption, a large IF bandwidth, and gain control functionality, which provides favorable linearity. The output radio frequency of the proposed mixer is between 1950 MHz and 2000 MHz, and the input frequencies are 50 MHz to 100 MHz. The measured conversion gains of the mixer in high-gain mode and low-gain mode are 9.5 dB and 1 dB, respectively; the measured input third-order intercept points (IIP3) in high-gain and low-gain modes are 0 dBm and 7 dBm, respectively. The DC operation point of the proposed mixer does not differ between the modes. The mixer dissipates 2 mW of power at a supply voltage of 1.2 V, and is used in a Taiwan Semiconductor Manufacturing Company 90 nm RF CMOS process. The area of the chip is 0.7 mm 2 . [ABSTRACT FROM AUTHOR]

In this paper, a differential multi-band CMOS low noise amplifier (LNA), operated in a range of 800–1700 MHz, is proposed. In this design, the LNA is integrated wide-band interference rejection (input band selection technology) and capacitive cross-coupling topologies, which can improve the interference rejection and noise figure preference. Moreover, the conventional notch filter technology only rejected the specified frequency. In this experiment, by using the proposed wide-band interference rejection technology, the LNA can reject unwanted signals (out-of-band signals) and image signals from different frequency. Thus, the LNA has good linearity and interference rejection performance. With the increasing use of frequency spectrum, the proposed technology is even more important. The post-simulation results of proposed LNA show that the voltage gain is 13–17.5 dB, the noise figure (NF) is less than 3.4 dB, and the third-order intercept point (IIP3) is 7.36 dBm. The LNA consumes 8.96 mW under 1.8 V supply voltage in TSMC 0.18-μm RF CMOS process. [ABSTRACT FROM AUTHOR]

Rain removal from a single color image is a challenging problem as no temporal information among successive images can be obtained. In this paper, we propose a single-color-image-based rain removal framework by properly formulating rain removal as an image decomposition problem based on sparse representation. In our framework, an input color image is first decomposed into a low-frequency part and a high-frequency part by using the guided image filter so that the rain streaks would be in the high-frequency part with nonrain textures/edges, and the high-frequency part is then decomposed into a rain component and a nonrain component by performing dictionary learning and sparse coding. To separate rain streaks from the high-frequency part, a hybrid feature set, including histogram of oriented gradients, depth of field, and Eigen color, is employed to further decompose the high-frequency part. With the hybrid feature set applied, most rain streaks can be removed; simultaneously nonrain component can be enhanced. To the best of our knowledge, compared with the state-of-the-art approaches, the proposed method is among the first to focus on the problem of single color image rain removal and achieves promising results with not only the rain component being removed more completely, but also the visual quality of restored images being improved. [ABSTRACT FROM AUTHOR]

Highlights: [•] Focus of interests in videos is localized by 3D spatiotemporal visual attention modeling. [•] Salient feature points are detected in visual attention regions. [•] Geometric constraint measurement is used for conducting bi-directional point matching. [•] Video matching is transformed to frame-based time-series linear search problem. [•] Our proposed approach achieves promising high detection rate. [ABSTRACT FROM AUTHOR]

Abstract: In nighttime driving brake lights are particularly important because they offer a warning signal to prevent potential collisions. In this work, we propose a novel visual-based approach for nighttime brake light detection using three-dimensional Nakagami imaging to analyze tail lights of vehicles in front. Rather than heuristic features, such as symmetry of taillights and appearance of the third brake light, the proposed approach extracts invariant features by modeling the scattering of brake lights, thus allowing detection to proceed in a part-based manner. Experiments from extensive datasets show that the proposed system can effectively detect vehicle braking under different lighting and traffic conditions, making it a realistic option for real-world applications. [Copyright &y& Elsevier]

Abstract: State of the art methods for video resizing usually produce perceivable visual discontinuities. Therefore, how to preserve the visual continuity in video frames is one of the most critical issues. In this paper, we propose a novel approach for modeling dynamic visual attention based on spatiotemporal analysis in order to detect the focus of interest automatically. The continuously varied co-sited blocks in a video cube are first detected and their variations are characterized as visual cubes, which are further employed to determine a proper extent of salient regions in video frames. Once the proper extent through video cubes is determined, the resizing process then can be conducted to find the global optimum. Our experiment shows that the proposed content-aware video resizing based on spatiotemporal visual cubes can effectively generate resized videos while keeping their isotropic manipulation and the continuous dynamics of visual perception. [Copyright &y& Elsevier]

Abstract: Analyzing human crowds is an important issue in video surveillance and is a challenging task due to their nature of non-rigid shapes. In this paper, optical flows are first estimated and then used for a clue to cluster human crowds into groups in unsupervised manner using our proposed method of adjacency-matrix based clustering (AMC). While the clusters of human crowds are obtained, their behaviors with attributes, orientation, position and crowd size, are characterized by a model of force field. Finally, we can predict the behaviors of human crowds based on the model and then detect if any anomalies of human crowd(s) present in the scene. Experimental results obtained by using extensive dataset show that our system is effective in detecting anomalous events for uncontrolled environment of surveillance videos. [Copyright &y& Elsevier]

Due to the difficulty in locating high-resistance grounding faults, this paper proposes a novel fault location method for HVdc transmission lines by considering double-end unsynchronized using Hilbert–Huang transform and one-dimensional convolutional neural network (1D-CNN). After the fault signal is collected at both ends, the proposed method can achieve high-precision fault location, requiring only the two ends data transmission without time synchronization. After Empirical Mode Decomposition (EMD), the high-frequency components of the double-terminal fault signals are connected in series to make a characteristic waveform. This waveform contains characteristics of different fault types and distances, which can be learned by CNN. The trained CNN can then be used to achieve fault location effectively. As a comparison, two fault location methods based on traditional traveling wave and machine learning are introduced. Electromagnetic transient simulation software PSCAD/EMTDC has been used to carry out various types of fault simulation on the ± 500 kV HVdc transmission system. The results show that the proposed method can reliably and accurately locate line faults under fault resistance up to 5200 Ω. [ABSTRACT FROM AUTHOR]

Gender recognition is a challenging task in real life images and surveillance videos due to their relatively lowresolution, under uncontrolled environment and variant viewing angles of human subject. Therefore, in this paper, a system of real-time gender recognition for real life images is proposed. The contribution of this work is three-fold. In order to make the system robust, a mechanism of decision making based on the combination of surrounding face detection, context-regions enhancement and confidence-based weighting assignment is designed. Experiment results obtained by using extensive dataset show that our system is effective and efficient in recognizing genders for uncontrolled environment of real life images. [ABSTRACT FROM PUBLISHER]

• Adaptive calibration in fault localization enhanced by semantic segmentation. • Precise fault localization with multiscale analysis at macro and micro levels. • Approach validated through extensive simulations and field tests for accuracy. • An industrial prototype demonstrating the superiority of our approach over existing methods. Fault localization is crucial for ensuring stability, particularly in high impedance faults (HIF) characterized by low current levels and prolonged transient processes (TP). Existing methods predominantly analyze differences in the fixed-length transient waveform, potentially causing delays in triggering or failure in HIF scenarios. To address these challenges, a novel AI application paradigm for HIF localization was introduced, incorporating both adaptive TP calibration and multiscale correlation analysis. Based on 1D-Unet, the TP of the zero-sequence voltage (ZSV) can be adaptively calibrated to maximize the utilization of transient information. Subsequently, the differential zero-sequence voltage (DZSV) and transient zero-sequence current (TZSC) can be acquired to facilitate multiscale correlation analysis. Combined with a sliding window strategy, the micro correlation between DZSV and TZSC is articulated through the local correlation degree (LCD). The comprehensive correlation degree (CCD) between DZSV and TZSC is then formulated to realize fault feeder/ section localization at the macro level. The 1D-Unet model achieved a classification accuracy of 99.2 % for sample points in test datasets and showed robustness with an accuracy exceeding 93.5 % in the presence of 20 dB noise interference. When integrated with the well-trained 1D-Unet, the proposed approach underwent further validation using simulation data and field recordings. These tests confirmed the model's resilience to noise interference up to 20 dB and its efficacy across networks of diverse topologies, such as the IEEE-13 and 34-node distribution networks. Additionally, an industrial prototype applying this framework identified all fault conditions without false positives or omissions, outperforming existing methods under various fault scenarios, including those involving high impedance materials and different resistance levels across multiple feeders. [ABSTRACT FROM AUTHOR]

Many sensors like digital fault indicators (DFIs) have been applied and promoted in distribution systems. The sensors can provide a technical mean for single‐line‐to‐ground (SLG) fault section location, but there are still some feature extraction and fault diagnosis problems. A novel SLG fault section location method utilizing auto‐encoder (AE) and fuzzy C‐means (FCM) clustering is presented in this work. Taking advantage of abundant information provided by DFIs, striking features can be extracted by the AE network, which is different from the artificially designed features that rely on prior knowledge. Compared with the learning‐based methods requiring massive training data, the proposed method only requires the data from one SLG fault. By applying the AE network to the zero‐sequence current measured by DFIs, the SLG fault section location's striking features could be obtained. Through feature classification by FCM clustering without setting threshold, the positional relationship between each detection node and the fault point would be distinguished to locate the fault section. Considering the abnormal communication of DFIs, the experiment proves that the proposed method can work effectively under various fault conditions. [ABSTRACT FROM AUTHOR]

Nowadays, smart monitoring devices such as digital fault indicator (DFI) have been installed in distribution systems to provide sufficient information for fault location. However, it is still a challenge to extract effective features from massive data for single-line-to-ground (SLG) fault-section location. This work proposes a novel method of fault-section location using a 1-D convolutional neural network (1-D CNN) and waveform concatenation. After SLG fault occurs, DFI measures the transient zero-sequence currents at double-ends of the line section, which could be concatenated to construct characteristic waveform. The features of characteristic waveforms would be extracted adaptively by 1-D CNN to locate the fault section. Furthermore, the problem where the on-site recorded data are hard to collect would be solved because 1-D CNN only needs a small number of samples for training in practical applications. The experimental results verified that the proposed method could work effectively under various fault conditions, even if a few DFIs are out of order. [ABSTRACT FROM AUTHOR]

The diagnosis of high-impedance fault (HIF) is a critical challenge due to the presence of faint signals that exhibit distortion and randomness. In this study, we propose a novel diagnostic approach for HIF based on semantic segmentation of the signal envelope (SE) and Hilbert marginal spectrum (HMS). The proposed approach uses 1D-UNet to identify the transient process of potential fault events in zero-sequence voltage to judge fault inception. Longer timescale zero-sequence voltage is then used to extract SE and HMS, representing HIF distortion and randomness characteristics. These features are transformed into images, and ResNet18 is employed to detect the presence of HIF. An industrial prototype of the proposed approach has been implemented and validated in a 10 kV test system. The experimental results indicate that the proposed approach outperforms the comparison by a significant margin regarding triggering deviation and detection accuracy, particularly in resonant distribution networks. [ABSTRACT FROM AUTHOR]

Abstract Because of the limited reflected energy and incoming illumination in an individual band, the reflected energy captured by a hyperspectral sensor might be low and there is inevitable noise that significantly decreases the performance of the subsequent analysis. Denoising is therefore of first importance in hyperspectral image (HSI) analysis and interpretation. However, most HSI denoising methods remove noise with the important spectral information being severely distorted. This paper presents an HSI denoising method using trainable spectral difference learning with spatial initialization (called HDnTSDL) aimed at preserving the spectral information. In the proposed HDnTSDL model, a key band is automatically selected and denoised. The denoised key band acts as a starting point to reconstruct the rest of the non-key bands. Meanwhile, a deep convolutional neural network (CNN) with trainable non-linearity functions is proposed to learn the spectral difference mapping. Then, the rest of the non-key bands are denoised under the guidance of the learned spectral difference with the key band as a starting point. Experiments have been conducted on five databases with both indoor and outdoor scenes. Comparative analyses validate that the proposed method: (i) presents superior performance in spatial recovery and spectral preservation, and (ii) requires less computational time than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper presents a new symmetrical SURF descriptor to detect vehicles on roads and then proposes a novel sparsity-based classification scheme to recognize their makes and models. First, for vehicle detection, this paper proposes a symmetry transformation on SURF points to detect all possible matching pairs of symmetrical SURF points. Then, each desired ROI of vehicle can be located very accurately from the set of symmetrical matching pairs through a projection technique. The advantages of this scheme are no need of background subtraction and its extreme efficiency in real-time detection tasks. After that, two challenges in vehicle make and model recognition (MMR) should be addressed, i.e ., the multiplicity and ambiguity problems. The multiplicity problem stems from one vehicle model often having different model shapes on the road. The ambiguity problem means vehicles even made from different companies often share similar shapes. To treat the two problems, a dynamic sparse representation scheme is proposed to represent a vehicle model in an over-complete dictionary whose base elements are the training samples themselves. With the dictionary, a novel Hamming distance classification scheme is proposed to classify vehicle makes and models to detailed classes. Because of the sparsity of the representation and the nature of Hamming code highly tolerant to noise, different vehicle makes and models can be recognized with high accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes a novel system to analyze human-object interaction events happening between hands and faces in real time. Two challenging problems in this event analysis must be addressed, i.e. , there is no prior knowledge (like shape, color, size, and texture) about the handheld objects, and there are large spatial–temporal variations in event representation. For the first challenge, a novel ratio histogram is proposed to find important color bins to locate handheld objects and their trajectories via a code book technique. This scheme is different from other boosted methods which require very time-consuming estimations to search reliable body configurations. For the second challenge, a mixture of HMMs is proposed to describe an event not only from its dynamic context but also its multiplicity context. It can be performed in real time because an exhaustive search process is avoided to find possible interaction pairs between objects and body parts. [ABSTRACT FROM AUTHOR]