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Two different types of three pairs of coupled lines (TPCL) structures with bandpass and all-stop filtering (ASF) characteristics, respectively, are proposed and analyzed through impedance matrix deduction and input admittance calculation. Combined with the stepped-impedance stubs, high-selectivity single-ended and balanced filters using TPCL structures have been presented. Five transmission poles (TPs) within the passband and six deep transmission zeros (TZs) from 0 to $2f_{0}$ ($f_{0}$ : passband center frequency) can be realized in both of single-ended and balanced filters. For demonstration, two sharp roll-off single-ended and balanced filter prototypes with good in-band characteristics (insertion losses and return losses) and high common-mode suppression are designed, whose measured 3-dB fractional bandwidths are of 35% and 34.4% (differential mode), respectively. Good agreement between the simulations and measurements validates the design concept.

In this paper, a high-efficiency Doherty power amplifier (DPA) using an InGaP/GaAs heterojunction bipolar transistor (HBT) process is presented. The DPA aims for the application of 2.4 GHz wireless local area network (WLAN) systems with a high peak-to-average power ratio (PAPR). The traditional bulky quarter-wavelength line is realized using a low-pass n-type network for miniaturization and low loss. A thermally compensated dynamic bias circuit is applied to increase thermal stability. Under the excitation of a 40-MHz 64-quadratic-amplitude modu-lation(QAM) signal, the proposed DPA achieves a gain of 29 dB gain, an average power of 26 dBm, and an average power added efficiency (PAE) up to 21%. Good error vector magnitude (EVM) and adjacent channel leakage ratio (ACLR) performance are also measured, which demonstrates EVM and ACLR after digital pre-distortion (DPD) are −42 dB and −50.7 dBc. respectively.

Recent years have witnessed the spread of electronic devices especially the mobile phones, which have become almost the necessities in people’s daily lives. An effective and efficient technique for blindly assessing the quality of display products is greatly helpful to improve the experiences of users, such as displaying the pictures or texts in a more comfortable manner. In this paper, we put forward a novel no-reference (NR) quality metric of display products, dubbed as NQMDP. First, we have established a new subjective photo quality database, in which 50 photos shown on three different types of display products were captured to constitute a total of 150 photos and then scored by more than 40 inexperienced observers. Second, 19 effective image features are extracted by using six different influencing factors (including complexity, contrast, sharpness, brightness, colorfulness and naturalness) on the quality of display products and then were learned with the support vector regressor (SVR) to estimate the objective quality score of each photo. Results of experiments show that our proposed method has obtained better performance than the state-of-the-art algorithms.

Recently, many fire disasters took place all over the world. Therefore, the research communities pay more attention to the algorithms that can accurately detect fire disasters in the monitoring scene. Although the current detector algorithms achieve excellent performances in the benchmark object detection task, they can not achieve high recognition accuracy in fire monitoring tasks due to the fire disaster has no distinct rules in shape, color, and size. Moreover, the traditional learning-based object detection algorithms have limitations in model size and computational cost, so it can not be well applied in resource-constrained devices and Tactile Internet. In terms of the dataset, current learning-based object detection algorithms do not consider the impact of some fair weather. The models trained by such training sets are easily affected by the weather. We propose a new Lightweight model called LFNet, which has a small size and low computational cost to solve these problems. We also published a large-scale benchmark dataset to train the fire disaster detection model. To make the learning-based model trained by the proposed dataset possess higher robustness in various weather conditions, we utilize the atmospheric scattering model to simulate the original images into pictures affected by different degrees of simulated haze and dust for training. Experimental results show that the proposed LFNet can achieve state-of-the-art performance with limited model size. The presented data augmentation method can significantly improve the robustness of the current object detection model.

Underwater image enhancement is an important low-level task for marine industry. This paper proposed an underwater image enhancement method based on colour constancy theory. Compared with many existing researches, time and space complexity of the method proposed in this paper is low and our method achieves excellent performance. Firstly, we analyzed the underwater imaging model and distortion of underwater images. Then, by compensating the specific channel and local white balance, the linear transformation is performed on the images to restore distortion in the images. Finally, the results are obtained by stretching the histogram on RGB channel and intensity channel of images. Experiments show that our method has achieved excellent performance.

Sonar imaging has broad and critical applications in marine technologies since it is able to solve the problem that the optical system cannot operate effectively in turbid water. During the process of underwater acoustic channel transmission, sonar images are easy to degrade and thus seriously influence the resultant feature extraction and target recognition. To that end, we devise a new SOnar Image Quality Evaluation (SOIQE) method in this paper for better monitoring and guaranteeing the visual quality of sonar images. Guided by highlighting the important task objects, our proposed SOIQE method makes a pixel-wise fusion of structures extracted at multiple scales. Experimental results on the newly established sonar image quality database show that our SOIQE approach is superior to the state-of-the-art image quality models in terms of implement efficiency and evaluation performance.

Computationally efficient optimal solutions for selecting a subset of antennas to maximize the mutual information of a MIMO channel have eluded the practitioners due to its combinatorial nature, and the performance gap is widened with massive MIMO. In this work, recent advances in deep learning are leveraged to develop a deep neural network (DNN) based receive antenna selection solution for a given problem dimension. We detail the neural network structure and evaluate several relevant figures of merit via numerical simulations. This data-driven solution is shown to achieve near optimal mutual information in simple settings, but does not scale naturally with the problem dimension. For the practical scenario where the number of selected antennas is unknown a priori, hybrid greedy solutions are proposed which build on the DNN-based solution for a given dimension and then greedily increase or decrease the number of antennas to approximate the optimal solution of the new problem dimension. Numerical simulations demonstrate the effectiveness of the hybrid solutions.

Sonar technology plays an important role in the development of marine resources and military strategy. Due to the bad underwater acoustic channel, the sonar image collected by sonar technology equipment is affected by various kinds of distortions easily. To obtain high-quality sonar image, we devise a novel dual-path deep neural network (DPDNN) to measure the quality of sonar image. In these two paths, we use the batch normalization layer to reduce the training time and take the skip operation to speed up the feature extraction. Based on the above two operations, we extract the micro-scopic and macro-scopic structure of sonar image, respectively. Finally, the global average pooling layer and the fully connection layer are used to connect the above two paths. Experiments show that our DPDNN has a significant improvement in prediction performance and efficiency, respectively.

The processes of warping and rendering in Depth Image-Based Rendering (DIBR) are usually performed without a reference image. Thus, an effective and fast reference-free (RF) image quality assessment (IQA) model devised for DIBR-synthesized views is more favorable. To this aim, this research propose a novel RF IQA method of DIBR-synthesized views based on Energy Loss Estimation. Firstly, considering that the geometric distortions caused by the processes of warping and rendering might increase the high-frequency (HF) information of a DIBR-synthesized view, the influence of the geometric distortion on the visual quality of a synthesized view can be effectively evaluated by estimating the HF information energies of a given DIBR-synthesized view in the log-gabor domain. Secondly, we estimate the energy loss in the process of JPEG compression as the image complexity, which is used to overcome the problem of different amounts of HF information contained in different content images. Finally, the proposed RF DIBR-synthesized IQA metric is obtained by using the energy loss in JPEG compression to normalize the HF information energy in the log-gabor domain. Experiments conducted on two public datasets demonstrate that the proposed method is advantageous over the relevant state-of-the-art RF IQA metrics.

Two quarter-wavelength three-line coupled structures (TLCSs) are employed in the design of filtering power divider (FPD) to achieve a wide fractional bandwidth and good out-of-band rejection. The center frequency of the FPD passband can be adjusted flexibly by changing the length of the TLCS while the fractional bandwidth will remain unchanged. The frequency selectivity and passband bandwidth of the FPD can be easily controlled with the variations of the TLCS width and gap, respectively. Furthermore, the proposed FPD topology can be extended to design a four-way FPD through adding two conventional Wilkinson power dividers. This four-way FPD is fabricated with the center frequencies of 2.42 GHz and 3-dB fractional bandwidths of over 100%. The simulated and measured results are compared with good agreement.

A fifth-order bandpass filter (BPF) using three pairs of coupled lines and two shorted stepped-impedance stubs is proposed in this paper. Five transmission poles (TPs) within the passband and six deep transmission zeros (TZs) from 0 to 2f 0 (f 0 :passband center frequency) can be realized through input admittance calculation. For demonstration, a sharp roll-off BPF prototype centered at 2 GHz with good in-band characteristics (insertion losses and return losses) and high stopband rejections is designed, whose 3-dB fractional bandwidth is of 34%.

The aging phenomenon in current society is getting more and more serious, and the health status of the elderly is facing challenges. A fracture or other disease caused by a fall, or a fall due to a sudden illness, may result in serious consequences if not treated in time. According to the difference between fall and normal activities, this paper proposes a method for detecting human fall by ELM based on inertial measurement unit. By placing the inertial measurement unit on the human body, the human acceleration and angular velocity data are sampled and the human body attitude is calculated. The ELM training is performed on the human characteristic data to obtain the ELM classification function, and the measurement data is classified by the ELM classification function to determine whether it is in a falling state. The human fall experiment is carried out, and the result shows that the ELM method can accurately detect the fall state of the human. The use and promotion of this method is of great significance for the treatment of falls and reduction of casualties.

Pedestrian navigation is an essential element in people's daily life. In some places where GPS signals are missing or weak, it is difficult to perform normal high precision navigation. As a strong supplement, inertial navigation does not depend on any external signals, but it has a large cumulative error. In this paper, a pedestrian navigation method with multi-sensor correction is studied based on the zero-velocity correction principle. The error source of pedestrian inertial navigation is analyzed and a multi-sensor error correction system is constructed. Based on the inertial sensor gyroscope and accelerometer, the magnetometer and altimeter are introduced to calculate the orientation and height. In the stance phase of pedestrian walking process the velocity and angular rate are reset and the orientation and height from navigation solution are corrected. The Kalman filter method is used to track and estimate the correction error for compensating the inertial navigation result. A pedestrian walking experiment was carried out. The results show that the constructed multi-sensor pedestrian navigation correction system can effectively track the pedestrian trajectory with a horizontal average error of 1.82% and a height average error of 2.53%. Therefore, the method can accurately perform pedestrian navigation and positioning.

The general tracking algorithm is vulnerable to noise because of using a single feature, makes the performance and robustness of the those algorithms greatly limited. In this paper, in order to achieve the robust and pretty performance, we propose a novel multiple feature fused model in correlation filter framework for visual tracking. The adoption of complementarity between different features can effectively eliminate the effects of noise and maintain their advantages of different features. While the correlation filter framework can provide a fast training and locate mechanism. In addition, we give a simple but effective scale detection method, which can appropriately handle the scale variation in the tracking sequences. We evaluate our tracker on OTB2013 benchmark, which include 51 video sequences. On this dataset, our results show that the proposed approach achieves a promising performance.

Robust visual object tracking is one of the most challenging issues in the field of computer vision. Because of the circular shifts strategy, correlation filter-based trackers show a great efficiency in tracking task and thus receive lots of attentions. However, most of the correlation filter-based trackers fix the scale of the targets in each frame and use single template to update the filters, which makes the trackers unreliable in the tracking task. In this paper, we intend to promote the robustness of the kernelized correlation filters (KCF) in the tracking task, through a fast scale pyramid solution to solve the scale variations problems. Furthermore, we introduce a sparse model selection scheme on template sets to solve the problem of contaminated templates in single template methods. We test our method on OTB-2013 dataset and the experimental results show the robustness of our method. The proposed tracker achieves promising performance both in terms of accuracy and speed comparing with the state-of-the-art trackers.

The accurate localization of a object target is a challenging research issue in visual tracking. Most correlation filter based tracking algorithms has been degraded their performances because of the weaknesses of their search strategy. This paper investigates the problem of accurate location the object target in visual tracking sequences. We propose a novel particle filter re-detection tracking approach for target re-location, when the kernelized correlation filters tracking result becomes unreliable. Additionally, we give a new target scale evaluation. Different from other proposed scale search strategies, our method merely consider the difference between the maximum value of the response map of adjacent frames. Extensive experiments are performed on the OTB2013 dataset. On the result of this benchmark, the proposed approach achieves a pretty performance.

Based on the carbon fiber cylinder solid phase extraction technology, a system was researched, which can be used to complete step elution, separation and continuous/selective injection for different polar compounds. The process was realized through the valve switching and more than one peristaltic pump controlled by single chip microcomputer. The connector between liquid chromatography (LC) and UV-detector is also available in this paper. The experimental results show that, this combined system meet the requirement of QA/QC in the aspect of simple operation, high precision and repeatability. It can be widely used in online analysis of different polar compounds with liquid chromatography in the fields of environment analysis, drug analysis, life science, etc.

Based on the research of gas flow liquid phase microextraction (GF-LPME) system, a combining interface between GF-LPME system and gas chromatography (GC) and an auto-sampling equipment have been researched and developed, which can be used for rapid extraction, separation, enrichment and sampling for volatile or semi-volatile analyte. The control system of the combining technique is designed through virtual instrument technology, and the working conditions of the control system detected by control panel. Experiments show that, this device with the advantages of simple extraction structure, convenient operation, high accuracy and extensible functions, can be used in gas chromatography analysis for complex samples of trace compounds in the area of medicine, agriculture, food, environment, etc.

The two input filter inductors of the previously proposed current fed multi-resonant converter (CFMRC) occupy too much volume and hence restrict the power density of the converter. In this paper, an integrated magnetic structure is adopted to the CFMRC's input inductors to cut down the size, cost and loss of the CFMRC. With proper design, the inverse coupling of input inductors apparently reduces the ripple of the current flowing through the inductors. Thereby, the inductance can be reduced to further decrease the core size and copper length, and hence farther improve the power density as well as the efficiency of the converter. The above mentioned merits are verified by a 150 W prototype with integrated input inductors operating at 255 kHz switching frequency, the volume of the input inductors is reduced from 25410 mm3 to 7056 mm3 after integrating, and the full load efficiency is increased from 95.4% to 95.9%.

A high step-up current fed multi-resonant converter (CFMRC) has been proposed recently for renewable energy harvesting where the sustainable power sources such as PV panels and fuel cells are characterized by low-voltage, high-current output. An output voltage doubler is applied to the previously proposed CFMRC for the purpose of reducing the turns-ratio of the transformer. Compared to the CFMRC with full bridge rectifier, the CFMRC with output voltage doubler simplifies the transformer structure and hence reduces the cost. By applying the interleaving technique, the ac resistances of the transformer windings are greatly reduced. When running a 150 W prototype at 255 kHz with 23 V input and 350 V output, a peak efficiency of 95.95% is achieved.

A high efficiency high step-up dc-dc converter is proposed for renewable energy harvesting where the sustainable power sources such as PV panels and fuel cells are characterized by low-voltage, high-current output. The proposed converter employs the current fed half-bridge structure to generate a square wave current source. Then, a multi-resonant tank is used to provide high voltage gain, absorb the parasitic parameters of the transformer and create zero-current switching (ZCS) condition for all primary switches. By applying single capacitor instead of LC for the output filter, the maximum voltage of the resonant capacitor is clamped to the output voltage which minimizes the circulating energy within the circuit and offers ZCS to the rectifying diodes. The performance of the proposed converter was verified by a 150 W prototype. A peak efficiency of 95.2% was achieved by running the prototype at 255 kHz with 23 V input and 350 V output.

A high efficiency high step-up dc-dc converter is proposed for renewable energy harvesting where the sustainable power sources such as PV panels and fuel cells are characterized by low-voltage, high-current output. The proposed converter employs the current fed half-bridge structure to generate a square wave current source. Then, a multi-resonant tank is used to provide high voltage gain, absorb the parasitic parameters of the transformer and create zero-current switching (ZCS) condition for all primary switches. By applying single capacitor instead of LC for the output filter, the maximum voltage of the resonant capacitor is clamped to the output voltage which minimizes the circulating energy within the circuit and offers ZCS to the rectifying diodes. The performance of the proposed converter was verified by a 150 W prototype. A peak efficiency of 95.2% was achieved by running the prototype at 255 kHz with 23 V input and 350 V output.

In the development of grid-connected PV systems, efficiency plays a key role for solar farmers to shorten their capital payoff. Moreover, it is found that the architecture of a grid-connected PV system affects its efficiency greatly. After an overview for kinds of power architectures in grid-connected PV system, multi-string configuration attracts us most attention for its best compromise between efficiency and cost. However, strong anticipation for efficiency improvement continuously drives the development of grid-connected PV system. A new architecture for supper high efficiency maximum power point tracking (MPPT) based on multi-string configuration is proposed in this paper. This new architecture with the compensating concept for MPPT is theoretically developed and a 2.8 kW experimental PV platform is built. Experiment results verified the 99% high efficiency merit of the proposed PV architecture under complex climate condition for whole year operation.

LLC resonant converter has become an excellent candidate for front-end dc-dc converter because of its high efficiency and capability of optimizing the converter for normal operation condition over a wide input voltage and load range. However, lack of suitable driving scheme for the synchronous rectifier (SR) limits the efficiency improvement of LLC in the wide input voltage condition. Although corporations like International Rectifier (IR) and STMicroelectronics (ST) have offered some so-called idea SR driver like IR1167, the 500 kHz switching frequency restriction and the cost pressure of IR1167 limit its application. In this paper, a new structure for LLC converter which enables self-driven for its synchronous rectification is proposed. A 400 kHz, 1 kW LLC resonant converter with wide input voltage range (300 V to 400 V) and 12 V output is designed and simulated to verify the function of the proposed circuit.