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We reported a case of gnathostomiasis in a 42-year-old woman with an unclear history of eating high-risk foods and had a non-migratory skin lesion, negative serological testing and normal blood eosinophil counts. A diagnosis of gnathostomiasis was based on a live, third-stage Gnathostoma spinigerum larva that was randomly taken from the patient’s skin lesion by herself. The presenting case report demonstrates challenges in correctly diagnose cutaneous gnathostomiasis even in endemic countries due to atypical skin lesions, negative serology testing and the absence of eosinophilia and thus, the widely used classic triad of suggestive evidence of gnathostomiasis is not fulfilled.

Stereo matching is a challenging research topic in driving assistance systems. Existing stereo matching methods work well under normal day-light conditions. However, they fail to operate under adverse driving conditions, such as at night and during snowfall. This paper proposes a robust stereo matching framework using both deep-learning-based features and feature engineering. The proposed method investigates the benefits of features based on feature engineering and deep learning for solving stereo matching problems. Robust feature engineering is proposed for handling specific driving under adverse weather conditions, and a robust feature based on deep learning is considered for handling unspecific driving under extreme weather conditions. The proposed study has shown significantly improved accuracy by 8.31% for the state-of-the-art census based on semi-global matching under the reflection regions using the KITTI Stereo 2012 benchmark. Moreover, the experimental results demonstrate that the proposed system obtains more stable results than existing stereo methods based on deep learning on various stereo datasets, such as the Middlebury, EISAT, HCI, and CCD datasets.

Land subsidence occurs in Ha Long and Cam Pha cities due to human activities in which the underground coal mining exploitation plays a crucial responsibility. For mapping the spatial distribution o...

The driving assistance system is going to be true shortly. Therefore, developing a robust sub-component system, such as crosswalk detection, is necessary to support the drivers and prevent accidents. Existing crosswalk detection has been studied by using engineering feature-based methods and machine learning-based methods. However, the performance of existing systems based on engineering and machine learning still has several limitations. Therefore, this paper introduces a new approach to improving existing crosswalk detection performance by investigating the benefits of the data augmentation, unsupervised autoencoder, and state-of-the-art YOLO deep learning method. First, the input image is converted to HSV color space, LAB color space, and LUV color space. Second, the HSV, LAB, and LUV color results and raw RBC are then concatenated at the next phase. Thus, the size of the input feature is increased because we join the multiple input features. Therefore, we proposed an approach to use an auto-encoder to reduce the input dimension of the proposed system. The result yields that our proposed crosswalk detection got the detection rate and false alarm rate of 85.45% and 2.5 % under the SKKU dataset, respectively. And, we also got the detection rate and false alarm rate of 88.15% and 1.45 % under our custom fly cam dataset.

Traditional deep learning-based vehicle detection methods are often designed using a pyramid of filters with multiple scales and sizes; therefore, the processing time is slow due to the large number of scales used and because the classifier runs at all scales. Recently, a deep learning-based region proposal network was introduced to detect vehicles that only employ the network one time regardless of the size of the input image. In object detection, deep learning-based region proposal networks have achieved state-of-the-art performance in terms of accuracy. These systems achieve a very high accuracy under normal driving conditions; however, their performance decreases under difficult driving conditions such as in snow, rain, or fog. In addition, the current state-of-the-art system-based region proposal networks still fail to satisfy the real-time requirements of the driving assistant systems. More recently, the identification of local patterns has been shown to improve the performance of the traditional deep-learning systems; hence, this paper investigates local patterns in region proposal networks to improve their accuracy. Depth information is also investigated to improve the processing time of current region proposal networks. Our experimental results show that the proposed system obtains better performance than the state-of-the-art object region detection systems in terms of both accuracy and processing time.

Local binary patterns and Census share similar ideas of encoding the local region by establishing the relationship between neighbor pixels to obtain robust feature transformation. Recently, LBP and its variants have been successfully applied in various applications, such as texture classification, face recognition, object detection, and segmentation, while Census has only been used to investigate stereo correspondence problem. Therefore, this paper investigates the LBP and Census using a non-local-based stereo matching method in order to analyze and discuss the main differences between LBP and Census. Moreover, as many as one hundred variants of LBP have been published to solve various problems, while only a few modifications of the Census exist for stereo matching. Comprehensive experiments with the indoor, Middlebury dataset stated that some novel LBPs that perform well in texture classification and face recognition also work well in a stereo matching application. In most cases, LBP and its variants compare favorably to Census in terms of the accuracy of the stereo method. These results proved that LBP and its variants are suitable for using in solving the stereo correspondence problem or improving the performance of existing stereo methods.

Obstacle region detection has been one of the most important tasks for detecting and classifying preceding vehicles moving along the road. Traditional U-V disparity-based obstacle detection methods consider only single disparity pixels to extract obstacle regions. They might fail to produce accurate obstacle regions when noise is encountered in the original disparity map. Recently, local patterns have been successfully applied to handle noise in many systems, such as texture classification and vehicle detection. Therefore, this paper introduces a new approach to compute U and V disparity using maximum local density encoding and ternary pattern features. Our method produced better performance than the existing U-V disparity methods in terms of detection rate and accuracy. In addition, we also evaluated the performance of the proposed method with the Faster RCNN-based object detection. Experimental results show that our method improved the detection rate of Faster RCNN by 1.65% on the KITTI dataset, and by 2.28% on the CCD dataset. In addition, the proposed method also improved the running time of Faster RCNN by 47% on the KITTI dataset.

The performance of stereo matching algorithms strongly depends on the quality of the stereo data/matching cost. Most state-of-the-art data costs require expert knowledge for the design of a transformation function, such as census for handling gray-level changes monotonically, adaptive normalized cross correlation for handling Lambertian cases, guided filtering for preserving edge information, and local density encoding for handling illumination differences. However, it is difficult to design a complex transformation function to handle unknown factors that often occur in driving conditions such as snow, rain, and sun. Therefore, this paper has investigated the deep learning strategy to develop a novel stereo matching cost model without using much expert knowledge. Experimental results show that the proposed deep learning model obtains better results than the state-of-the-art stereo matching cost as judged by the standard KITTI benchmark, Middlebury, and HCI datasets.

While it is a hot issue whether cars could drive by themselves in emergent situations without any kind of human interference, pedestrian detection is the key technology in autonomous driving cars. Though current pedestrian detection technologies have come to a point in which they are accurate in normal conditions and surroundings, existent systems are inaccurate in harsh situations, such as when there are too many pedestrians, when there is too much light or when it is too dark, or when it is raining or snowing heavily. This problem may be solved by integrating deep learning and combining a new type of local pattern with the RGB raw image as input, instead of using just the RGB image as input. We will introduce a new type of local pattern called Triangular Patterns, which is effective in extracting more detailed and stable features from local regions. Here in this paper, we propose a pedestrian detection system in which deep learning is used, along with combining the RGB raw image with Triangular Patterns for input.

This paper presents RFIC MIMO modules integrating beamformer chips (including PAs, LNAs, T/R switches, power combiners/dividers and digital controls) and smart-antenna arrays including down-converters for 5G. Several solutions: 2×2 (16 antennas) and 4×4 (64 antennas) arrays of RFIC beamformer chips are designed, fabricated and experimentally characterized. Wireless 5G links with real-time high data rate video-streaming capabilities are demonstrated using integrated cameras. Field-Field correlation-based over-the-air near-field and far-field test solutions with and without down-conversion are proposed for MIMO/Massive-MIMO phased-array systems both in frequency and time domains. Cognitive BIST solutions are combined with co-array signal-processing. Perspectives for holistic RFSOI technology solutions, including RF-analog-to-digital converters (RF-ADCs) and adaptive body-biasing systems, are drawn in comparison with BiCMOS, GaAs and GaN based technologies.

We propose a novel fuzzy encoding pattern that fuzzily encodes the relative orders between pixel pairs. An image window is divided into disjoint neighboring pixel sets for the window’s center pixel, and the relative order is established not only between the center pixel and its neighbors but also between the pixel pairs in each neighboring pixel set. The relative orders are fuzzily encoded to extract more detailed information from a local structure. We successfully apply the pattern as a matching cost function for stereo correspondence under severe radiometric variations. We conduct experiments using the proposed matching cost function and compare it with functions employing the census transform, supporting local binary pattern, and adaptive normalized cross correlation, as well as a mutual information-based matching cost function, using different stereo data sets. Compared with the census transform, the proposed function reduces the error from 33.1% to 16.9% in the Middlebury data set and from 17.6% to 9.5% in the Kitti data set. The experimental results indicate that the proposed function is superior to the state-of-the-art functions under radiometric variations. In addition, the proposed function is faster than recently developed functions, such as the adaptive normalized cross correlation, a mutual information-based function, and support local binary pattern.

This paper introduces a general framework for detection, recognition, and tracking preceding vehicles and pedestrians based on a deep learning approach. The proposed framework combines a novel deep learning approach with the use of multiple sources of local patterns and depth information to yield robust on-road vehicle and pedestrian detection, recognition, and tracking. The proposed system is first based on robust obstacle detection to identify obstacles appearing along the road that are likely to be vehicles and pedestrians, implemented as an efficient adaptive U-V disparity algorithm. Second, the results from the obstacle detection stage are input into a novel vehicle and pedestrian recognition system based on a deep learning model that processes multiple sources of depth information and local patterns. Finally, the results from the recognition stage are used to track detected vehicles or pedestrians in the next frame by means of a proposed tracking and validation model. The proposed framework has been thoroughly evaluated by inputting several vehicle and pedestrian data sets that were collected under various driving conditions. Experimental results show that this framework provides robust vehicle and pedestrian detection, recognition, and tracking with high accuracy, and also satisfies the real-time requirements of driver assistance systems.

Real-world stereo images are inevitably affected by radiometric differences, including variations in exposure, vignetting, lighting, and noise. Stereo images with severe radiometric distortion can have large radiometric differences and include locally nonlinear changes. In this paper, we first introduce an adaptive orthogonal integral image, which is an improved version of an orthogonal integral image. After that, based on matching by tone mapping and the adaptive orthogonal integral image, we propose a robust and accurate matching cost function that can tolerate locally nonlinear intensity distortion. By using the adaptive orthogonal integral image, the proposed matching cost function can adaptively construct different support regions of arbitrary shapes and sizes for different pixels in the reference image, so it can operate robustly within object boundaries. Furthermore, we develop techniques to automatically estimate the values of the parameters of our proposed function. We conduct experiments using the proposed matching cost function and compare it with functions employing the census transform, supporting local binary pattern, and adaptive normalized cross correlation, as well as a mutual information-based matching cost function using different stereo data sets. By using the adaptive orthogonal integral image, the proposed matching cost function reduces the error from 21.51% to 15.73% in the Middlebury data set, and from 15.9% to 10.85% in the Kitti data set, as compared with using the orthogonal integral image. The experimental results indicate that the proposed matching cost function is superior to the state-of-the-art matching cost functions under radiometric variation.

In the machine learning technology, making computers read letters, characters and digits have been a hot issue in many areas of research. Among them, the recognition of handwritten numbers has still a long way to go, unlike the recognition of printed digits or the recognition of handwritten English sentences. In this paper we will introduce to you a multiple handwritten digit recognition system using deep learning.

Stereo correspondence is challenging under realistic conditions due to uncontrolled factors that affect input images, including illumination inconsistencies and radiometric variations. Many local and global models have been suggested to address these problems; however, their performance is often degraded due to the assumption of color consistency between the left and right images. Therefore, we present a new local pattern, local density encoding, for stereo matching measurements to improve the performance of existing stereo methods. Our experimental results indicate that the proposed method is less sensitive to illumination changes and radiometric variations. Moreover, in the cases with normal and severe illumination changes, the proposed method is more robust than state-of-the-art data costs.

Pedestrian detection systems have become a crucial topic due to their contribution to numerous applications, such as surveillance, self-driving cars, tracking systems, and robotics. Therefore, we have developed an efficient pedestrian detection algorithm using a support vector machine (SVM) and a histogram of oriented gradients (HOG) feature. This paper introduces a system that is capable of reducing the set of candidate detection regions and classifying pedestrians and non-pedestrians in urban traffic by using the stixel world and HOG + SVM, respectively. The stixel world computation in this proposal is used to compute the region of interest of the input image. We also introduce our new human dataset, including more than 3100 human images, and a method used for classifying the dataset. By implementing on a CPU only, we reach high-quality detection at 56 fps in urban traffic scenarios. Our experimental results demonstrate the effectiveness of reducing the set of detection regions to improve the processing time of our system.

The local binary pattern (LBP) and its variants have been widely investigated in many image processing and computer vision applications due to their robust ability to capture local image structures and their computational simplicity. The existing LBPs extract local structure information by establishing a relationship between the central pixel and its adjacent pixels. However, most LBPs miss the relationship among all of the pixels in the local region. Therefore, this paper proposes a novel model to establish this relationship by introducing a support LBP. The proposed model improves the performance of the existing LBP methods and results in lower sensitivity to illumination changes and radiometric variations. Moreover, the proposed model has been successfully investigated in two applications: disparity map generation and texture classification. For disparity map generation, the proposed model reduces the root mean square (RMS) error by 23.6% (in Baby1 dataset, Middlebury), and 16.58% (in Aloe dataset, Middlebury) as compared with the standard LBP under radiometric variation conditions. Moreover, the proposed model reduces the RMS by 28.11% as compared with the standard LBP under the Gaussian noise condition in the ESATS dataset. For texture classification applications, the proposed model improves the classification results from 96.26% to 98.13% on the Outext database, from 88.03% to 91.41% on the Xu database, and from 94.00% to 96.67% on the KTH-TIPS database as compared with the completed LBP.

The evolutionary algorithm (EA) is an effective method for solving various problems because it can search through very large search spaces and can quickly come to nearly optimal solutions. However, existing EA-based methods for vehicle detection cannot achieve high performance because their fitness functions depend on sensitive information, such as edge or color information on the preceding vehicle. This paper focuses on improving the performance of existing evolutionary-based methods for vehicle detection by introducing an effective fitness function that can more accurately capture a vehicle's information by combining a disparity map, edge information, and the position and motion of the preceding vehicle. The proposed method can detect multiple vehicles by using a turn-back genetic algorithm (GA) and can prevent false detection by using motion detection. Our fitness function is designed in a typical manner along with the fitness parameters. These parameters are usually selected using heuristic methods, making the choice of optimal parameters difficult. Therefore, this paper proposes a new approach to estimating optimal fitness parameters using EA and the least squares method. Robustness testing showed that the proposed method provides detection rate (DR) results close to those obtained using a state-of-the-art system and outperforms other dominant vehicle-detection-based EAs.

Anisotropic Liquid Crystal Polymer (LCP) substrates are used to design 3D conformal antenna elements and filtering structures in Laser Direct Structuring technology (LDS) for applications including 5G connected objects and massive Internet of Things (IoT): e.g., wearable (smart watches/rings), mobile (smartphones/tablets), home (appliances/lighting) devices. Based on the experimentally extracted optical absorption and refraction properties, identification of chemical and/or morphologic composition of the characterized samples (solid and liquid materials) are targeted. Perspectives for macro-modeling of composite materials and nano-Particles using homogeneization techniques are drawn.

International audience; In this paper we present experimental characterization of packaged switch devices in terms of their RF attributes isolation, insertion loss, power consumption, and linearity. Packaging and Board assembly significantly reduce their RF and mm-Wave performances. A broadband experimental setup is developed for the qualification of packaged switch devices accounting for deembedding effects both with on-board/on-package and on-chip probing. Module-based switch devices have been measured then, plastic molding, Si cap, and bonding wires have been sequentially removed to investigate their influences. Different challenges with packaged switch devices are identified and effective solutions are proposed for their qualification. © 2016 IEEE.

SiGe BiCMOS design solutions for Lange Couplers operating in the mm-Wave domain are proposed. Various circuit topologies are designed, fabricated, and experimentally compared in terms of their RF performances. Effect of grounding strategies and influence of DTI pattering are studied both for CPS and CPW topologies to evaluate dependence of obtained RF performances on Die back-side grounding strategies. Perspectives for feasibility of distributed Chip-Package-PCB Co-integration of Lange coupler devices are drawn for loss reduction and improved matching to external input sources and antennas.

In this paper, modeling and experimental characterization of bond-wire arrays are proposed based on dedicated pilote carriers designed and fabricated for RF and microwave applications. Effects of the number of wire elements, connected in parallel to build bond-wire arrays, on the values of extracted broadband parasitics are investigated. Physical characterization of molding compound materials is proposed for their structural analysis and for broadband extraction of their dielctric properties (complex permittivity). Influence of molding compound properties on extracted parasitics of bond-wire arrays is highlighted.

SiGe BiCMOS design solutions for Lange Couplers operating in the mm-Wave domain are proposed. Various circuit topologies are designed, fabricated, and experimentally compared in terms of their RF performances. Effect of grounding strategies and influence of DTI pattering are studied both for CPS and CPW topologies to evaluate dependence of obtained RF performances on Die back-side grounding strategies. Perspectives for physics-based broadband equivalent circuit model extraction are proposed for lumped elements implementation of Lange Couplers using custom variation-aware RLC library elements.

International audience; The work is related to an Agile Filter which is a specific RF block to be inserted between an antenna and a first stage of pre-amplification in future on board antenna systems or any future multi-purpose communications terminals. It will be able to process signals in the frequency range between 2 and 18 GHz with target insertion loss below 3dB. The basic idea of the demonstrator studied and developed in this project is to evaluate a SiP approach combining heterogeneous technologies and 3D integration and packaging. This paper presents the process flow used to design, fabricate and integrate in 3D a tunable RF filter which tunability is obtained by the use of MEMS varactors based on a copper on glass technology and stacked on micro-PCD for the electronic control functions. MEMS+ parameterized structures for RF MEMS varactor designs are presented with dimension constraints to be compatible with the space available at the end of each combline sub-filter. Indeed, the MEMS capacitors are composed of a mobile square membrane of 210μm by side and overall size of 600μm. In addition, these RF MEMS tunable capacitor models were automatically transferred into VerilogA for rapid system-level simulation usable further in ADS environment to verify the RF performances.

International audience; This paper presents a model for the crossover area of two crossing lines. The model was compared to 3D full wave electromagnetic simulation with good agreement up to 20 GHz.

International audience; This paper describes a simple technique for on wafer passive multi-port circuits characterization. This method gives an efficient measurement of the scattering S-parameters of these devices at their ports reference planes. Furthermore, it provides cost saving in terms of used wafer surface and equipments by using an universal two-port Vector Network Analyzer (VNA). This method is applied to the characterization of a branch-line coupler designed on a lossy silicon substrate at millimeter waves (30 GHz). The de-embedding and the experimental procedures are presented in this paper. The experimental measurements extracted from our method show a good agreement with those made with a four-port VNA.

This paper is devoted to shape optimization for Partial Differential Equations (PDE) systems related to Computational Fluid Dynamics (CFD). Numerical approximation of the PDE's relies on schemes satisfying discrete maximum principles and using unstructured meshes generated from the shape parameters. The theory of control is applied to the discrete design problem with the resulting constrained optimization problem solved by gradient based algorithms. An automatic differentiation procedure for Fortran codes is extensively used to carry out the CFD sensitivity analysis.

Spatiotemporal stereo matching has attracted much interest over the last few years. The problem is to maintain the temporally consistent disparity of video sequences and to avoid the flickering-artifacts occurring in consecutive disparity maps. In this paper, we propose a constant time spatiotemporal local stereo matching based on the information permeability method that was recently proposed and achieves good stereo results for static image pairs. The proposed three-pass aggregation method takes into account multiple preceding and following frames of the video instead of a single image pair as in the conventional method. Consequently, the temporal disparity consistency is enforced without requiring an explicit motion estimation as several spatiotemporal approaches have done. Experiments showed that our method improves the disparity consistency compared to the conventional information permeability method, and that it outperforms existing spatiotemporal stereo matching techniques.

High dynamic range conditions are major obstacles to the implementation of practical stereovision systems in real scenes. We address this problem by introducing an adaptive local ternary-derivative pattern (ALTDP) which is a fusion of the local ternary pattern (LTP) and local derivative pattern (LDP). We make three main contributions in this study: (i) ALTDP encodes more detail information than LDP by extending to eight directions; (ii) ALDTP is better at discriminating and less sensitive to noise in uniform regions with three-value encoding (-1,0,1) without using a pre-defined threshold; and (iii) ALTDP significantly improves the performance of hierarchical belief propagation (BP) by substituting ALTDP data cost for the different intensity data cost. Moreover, our proposed method performs slightly better than LBP and LDP with three datasets: synthetic sequences (set 2) in the EISATS dataset, bright differences sequences (set 5) in the EISATS dataset, and the bumblebee xb3 dataset.

A new approach for vehicle detection and distance estimation based on stereo vision and evolutionary algorithm (SEA) is described in this paper. First, we reuse our recent work on FPGA implementation of census-based correlations for stereo matching. Next, the SEA uses the gray scale left image and disparity information obtained from the FPGA system to detect the preceding vehicle and estimate its distance. This paper introduces an effective fitness function that allows our proposed method to have an improved performance and higher accuracy when compared with the existing evolutionary algorithm (EA) based methods. A new crossover type, tourna-ment crossover, is introduced to reduce the convergence time of our proposed. This paper also introduces a new approach for estimating the fitness function parameters. This estimation differs from the traditional EA because these parameters were generally created via experiments. Moreover, the processing time and accuracy of SEA can be improved by converting the global search to the local search with V disparity map. The robust experiments have proved that SEA successfully detects vehicles in front and sustains noise from different objects appearing along the road. The detection range is 10m-140m, the detection rate is 95 % and the average processing-time is approximately 31 ms/frame on CPU. These results prove that SEA is suitable for a real-time system.

In this paper, we propose a blocking-matching approach to the problem of correspondence in stereo matching. In blocking-matching methods, the correspondence or difference between pixels of a stereo pair is measured by a local window. Despite some area-based stereo matching algorithms have been developed and work well in a number of kinds of regions such as textureless or object boundary regions, their performance can debase once working in some sorts of radiometric conditions. Our proposed algorithm, in which non-parametric transform is used in the pre-processing step and an edge-preserving filter is used in the post-processing step, is an improved method of a shiftable window method and can work robustly in various radiometric conditions. Input images first are pre-processed by the census transform that makes our method robustly when the image pair is captured in various light sources or camera uncovering conditions. The window cost in our approach is calculated from the transformed images using in the Hamming distance, and the similarity is finally selected by a Winner-Takes-All strategy. The experimental results for the Middleburry images show that our proposed algorithm outperforms test local stereo algorithms in radiometrically dissimilarity images.

In this paper, we proposed two window-based methods, spatial weight shiftable window and spatial weight multiple window, for correspondence problem in stereo matching. The spatial weight shiftable window is an improvement of a shiftable window method while the spatial weight multiple window is an enhancement of a multiple window method. They combine spatial weighted window for each support window, and they hence can work well in the regions of disparity discontinuity or object boundaries. The window costs in our approaches is calculated by deploying spatial weighted window for each support window, and the similarity is finally selected by a Winner-Takes-All strategy. The experimental results for the Middleburry images illustrated that the proposed algorithms outperform test local stereo algorithms.

In window-based stereo matching algorithms, a local window is used to measure the similarity (or dissimilarity) between pixels of a stereo pair. An implicit assumption in local stereo matching is that all pixel within a support window have the same (or similar) disparity, and many local methods have been proposed to satisfy this assumption. However, objects in real-word images have arbitrary sizes and shapes, and hence this assumption can be violated frequently. In this paper, we propose spatial fixed window, spatial shiftable window, spatial multiple window, and spatial variable window methods which are improved methods of fixed window, shiftable window, multiple window, and variable window, respectively. We also experiment these improved algorithms in gray and color images, and the experimental results using the Middleburry images show that the proposed methods outperform their corresponding original methods.