A real-time global stereo-matching on FPGA

An improved global stereo matching algorithm is implemented on a single FPGA for real-time applications. Stereo matching is widely used in stereo vision systems, i.e. objects detection and autonomous vehicles. Global algorithms have much more accurate results than local algorithms, but global algorithms are not implemented on FPGA since they rely over high-end hardware resources. In this implementation the stereo pairs are divided into blocks, the hardware resources are reduced by processing one block once. The hardware implementation is based on a Xilinx Kintex 7 FPGA. Experiment results show that the proposed implementation has an accurate result for the Middlebury benchmarks and 30 frames per second (fps) @1920 × 1680 is achieved.     

Determination of optimal parameters for bilateral filter in brain MR image denoising

Noise elimination is an important pre-processing step in magnetic resonance (MR) images for clinical purposes. In the present study, as an edge-preserving method, bilateral filter (BF) was used for Rician noise removal in MR images. The choice of BF parameters affects the performance of denoising. Therefore, as a novel approach, the parameters of BF were optimized using genetic algorithm (GA). First, the Rician noise with different variances (σ = 10, 20, 30) was added to simulated T1-weighted brain MR images. To find the optimum filter parameters, GA was applied to the noisy images in searching regions of window size [3 × 3, 5 × 5, 7 × 7, 11 × 11, and 21 × 21], spatial sigma [0.1–10] and intensity sigma [1–60]. The peak signal-to-noise ratio (PSNR) was adjusted as fitness value for optimization.After determination of optimal parameters, we investigated the results of proposed BF parameters with both the simulated and clinical MR images. In order to understand the importance of parameter selection in BF, we compared the results of denoising with proposed parameters and other previously used BFs using the quality metrics such as mean squared error (MSE), PSNR, signal-to-noise ratio (SNR) and structural similarity index metric (SSIM). The quality of the denoised images with the proposed parameters was validated using both visual inspection and quantitative metrics. The experimental results showed that the BF with parameters proposed by us showed a better performance than BF with other previously proposed parameters in both the preservation of edges and removal of different level of Rician noise from MR images. It can be concluded that the performance of BF for denoising is highly dependent on optimal parameter selection.     

Automated delineation of thyroid nodules in ultrasound images using spatial neutrosophic clustering and level set

An accurate contour estimation plays a significant role in classification and estimation of shape, size, and position of thyroid nodule. This helps to reduce the number of false positives, improves the accurate detection and efficient diagnosis of thyroid nodules. This paper introduces an automated delineation method that integrates spatial information with neutrosophic clustering and level-sets for accurate and effective segmentation of thyroid nodules in ultrasound images. The proposed delineation method named as Spatial Neutrosophic Distance Regularized Level Set (SNDRLS) is based on Neutrosophic L-Means (NLM) clustering which incorporates spatial information for Level Set evolution. The SNDRLS takes rough estimation of region of interest (ROI) as input provided by Spatial NLM (SNLM) clustering for precise delineation of one or more nodules. The performance of the proposed method is compared with level set, NLM clustering, Active Contour Without Edges (ACWE), Fuzzy C-Means (FCM) clustering and Neutrosophic based Watershed segmentation methods using the same image dataset. To validate the SNDRLS method, the manual demarcations from three expert radiologists are employed as ground truth. The SNDRLS yields the closest boundaries to the ground truth compared to other methods as revealed by six assessment measures (true positive rate is 95.45 ± 3.5%, false positive rate is 7.32 ± 5.3% and overlap is 93.15 ± 5. 2%, mean absolute distance is 1.8 ± 1.4 pixels, Hausdorff distance is 0.7 ± 0.4 pixels and Dice metric is 94.25 ± 4.6%). The experimental results show that the SNDRLS is able to delineate multiple nodules in thyroid ultrasound images accurately and effectively. The proposed method achieves the automated nodule boundary even for low-contrast, blurred, and noisy thyroid ultrasound images without any human intervention. Additionally, the SNDRLS has the ability to determine the controlling parameters adaptively from SNLM clustering.     

Early-stage atherosclerosis detection using deep learning over carotid ultrasound images

This paper proposes a computer-aided diagnosis tool for the early detection of atherosclerosis. This pathology is responsible for major cardiovascular diseases, which are the main cause of death worldwide. Among preventive measures, the intima-media thickness (IMT) of the common carotid artery stands out as early indicator of atherosclerosis and cardiovascular risk. In particular, IMT is evaluated by means of ultrasound scans. Usually, during the radiological examination, the specialist detects the optimal measurement area, identifies the layers of the arterial wall and manually marks pairs of points on the image to estimate the thickness of the artery. Therefore, this manual procedure entails subjectivity and variability in the IMT evaluation. Instead, this article suggests a fully automatic segmentation technique for ultrasound images of the common carotid artery. The proposed methodology is based on machine learning and artificial neural networks for the recognition of IMT intensity patterns in the images. For this purpose, a deep learning strategy has been developed to obtain abstract and efficient data representations by means of auto-encoders with multiple hidden layers. In particular, the considered deep architecture has been designed under the concept of extreme learning machine (ELM). The correct identification of the arterial layers is achieved in a totally user-independent and repeatable manner, which not only improves the IMT measurement in daily clinical practice but also facilitates the clinical research. A database consisting of 67 ultrasound images has been used in the validation of the suggested system, in which the resulting automatic contours for each image have been compared with the average of four manual segmentations performed by two different observers (ground-truth). Specifically, the IMT measured by the proposed algorithm is 0.625 ± 0.167 mm (mean ± standard deviation), whereas the corresponding ground-truth value is 0.619 ± 0.176 mm. Thus, our method shows a difference between automatic and manual measures of only 5.79 ± 34.42 μm. Furthermore, different quantitative evaluations reported in this paper indicate that this procedure outperforms other methods presented in the literature.     

3D facial shape reconstruction using macro- and micro-level features from high resolution facial images

Three-dimensional (3D) facial modeling and stereo matching-based methods are widely used for 3D facial reconstruction from 2D single-view and multiple-view images. However, these methods cannot realistically reconstruct 3D faces because they use insufficient numbers of macro-level Facial Feature Points (FFPs). This paper proposes an accurate and person-specific 3D facial reconstruction method that uses ample numbers of macro- and micro-level FFPs to enable coverage of all facial regions of high resolution facial images. Comparisons of 3D facial images reconstructed using the proposed method for ground-truth 3D facial images from the Bosphorus 3D database show that the method is superior to a conventional Active Appearance Model-Structure from Motion (AAM + SfM)-based method in terms of average 3D root mean square error between the reconstructed and ground-truth 3D faces. Further, the proposed method achieved outstanding accuracy in local facial regions such as the cheek—areas where extraction of FFPs is difficult for existing methods.     

FPGA based disparity map computation with vergence control

Depth estimation in a scene using image pairs acquired by a stereo camera setup, is one of the important tasks of stereo vision systems. The disparity between the stereo images allows for 3D information acquisition which is indispensable in many machine vision applications. Practical stereo vision systems involve wide ranges of disparity levels. Considering that disparity map extraction of an image is a computationally demanding task, practical real-time FPGA based algorithms require increased device utilization resource usage, depending on the disparity levels operational range, which leads to significant power consumption. In this paper a new hardware-efficient real-time disparity map computation module is developed. The module constantly estimates the precisely required range of disparity levels upon a given stereo image set, maintaining this range as low as possible by verging the stereo setup cameras axes. This enables a parallel-pipelined design, for the overall module, realized on a single FPGA device of the Altera Stratix IV family. Accurate disparity maps are computed at a rate of more than 320 frames per second, for a stereo image pair of 640 × 480 pixels spatial resolution with a disparity range of 80 pixels. The presented technique provides very good processing speed at the expense of accuracy, with very good scalability in terms of disparity levels. The proposed method enables a suitable module delivering high performance in real-time stereo vision applications, where space and power are significant concerns.     

A new class of massively parallel direction splitting for the incompressible Navier–Stokes equations

We introduce in this paper a new direction splitting algorithm for solving the incompressible Navier–Stokes equations. The main originality of the method consists of using the operator (I ? ?_xx)(I ? ?_yy)(I ? ?_zz) for approximating the pressure correction instead of the Poisson operator as done in all the contemporary projection methods. The complexity of the proposed algorithm is significantly lower than that of projection methods, and it is shown the have the same stability properties as the Poisson-based pressure-correction techniques, either in standard or rotational form. The first-order (in time) version of the method is proved to have the same convergence properties as the classical first-order projection techniques. Numerical tests reveal that the second-order version of the method has the same convergence rate as its second-order projection counterpart as well. The method is suitable for parallel implementation and preliminary tests show excellent parallel performance on a distributed memory cluster of up to 1024 processors. The method has been validated on the three-dimensional lid-driven cavity flow using grids composed of up to 2 × 10⁹ points.     

High-speed routers design using data stream distributor unit

As the line rates standards are changing frequently to provide higher bit rates, the routers design has become very challenging due to the need for new wire-speed router's network processor (NP) unit. Typically, designing new NPs could take a long time and is very costly. In this work, we are presenting a new approach in high-speed routers design. Our approach is to use a data stream distributor (or DSD) to split the high bit rate line to few lower rate lines. These low rate lines will be processed by existing NPs that are already in use with today routers that are designed to support such low line rates. Such approach will allow the developing of routers in a short time and at a low cost. Clearly, there are many design challenges associated with this approach of routers design such as load balancing, buffer managing, and traffic distribution.This paper discusses the concept, advantages, and the architecture of the DSD approach. Also, we highlight the implementation of the DSD chip design using a Virtex Xilinx System-On-Chip (SOC) and specifically the Virtex XCV 150 chip. The cycle's accurate simulation has shown that the designed DSD chip is capable of splitting a 2.5 Gb/s line rate to four low bit rate lines of 622 Mb/s. The chip has 118,065 gates and runs at 70 MHz.     

Design,simulation and experiment of electroosmotic microfluidic chip for cell sorting

A microfluidic cell sorting chip has been developed using micromachining technology, where electroosmotic flow (EOF) is exploited to drive and switch cells. For this electroosmotically driven system, it is found that the effect of induced hydrostatic pressure caused by unequal levels in solution reservoirs is not negligible. In this work, the numerical simulation of EOF and opposing pressure induced flow in microchannels is presented and the velocity profiles in the microchannels are measured experimentally using microparticle imaging velocimetry (PIV) system. The result shows that the final resulting velocity is the superposition of the two flows. A total volume of 0.305 μl is transported in the 50 μm microchannel and the back flow occurs after 240 s transportation. The task of sorting cells is realized at the switching structure by adjusting the electric fields in the microchannels. The performance of the cell sorting chip is optimized by investigating the effect of different switching structures. A Y-junction switching structure with 90° switching angle is highly recommended with simulated leakage distance of 53 μm and switching time of 8 ms.     

Automatic urban modeling using volumetric reconstruction with surface graph cuts

The demand for 3D city-scale models has been significantly increased due to the proliferation of urban planning, city navigation, and virtual reality applications. We present an approach to automatically reconstruct buildings densely spanning a large urban area. Our method takes as input calibrated aerial images and available GIS meta-data. Our computational pipeline computes a per-building 2.5D volumetric reconstruction by exploiting photo-consistency where it is highly sampled amongst the aerial images. Our building surface graph cut method overcomes errors of occlusion, geometry, and calibration in order to stitch together aerial images and yield a visually coherent texture-mapped result. Our comparisons show similar quality to the manually modeled buildings of Google Earth, and show improvements over naive texture mapping and over space-carving methods. We have tested our algorithms with a 12 sq km area of Boston, MA (USA), using 4667 images (i.e., 280 GB of raw image data) and producing 1785 buildings.     

A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition

This paper presents a novel adaptive cuckoo search (ACS) algorithm for optimization. The step size is made adaptive from the knowledge of its fitness function value and its current position in the search space. The other important feature of the ACS algorithm is its speed, which is faster than the CS algorithm. Here, an attempt is made to make the cuckoo search (CS) algorithm parameter free, without a Levy step. The proposed algorithm is validated using twenty three standard benchmark test functions. The second part of the paper proposes an efficient face recognition algorithm using ACS, principal component analysis (PCA) and intrinsic discriminant analysis (IDA). The proposed algorithms are named as PCA + IDA and ACS–IDA. Interestingly, PCA + IDA offers us a perturbation free algorithm for dimension reduction while ACS + IDA is used to find the optimal feature vectors for classification of the face images based on the IDA. For the performance analysis, we use three standard face databases—YALE, ORL, and FERET. A comparison of the proposed method with the state-of-the-art methods reveals the effectiveness of our algorithm.     

3D visual comfort assessment by measuring the vertical disparity tolerance

Misalignment in stereo images leads to 3D discomfort, but the visual tolerance for disparities varies with viewing environment and stimulus. The aim of the study was twofold: first, to assess if vertical disparity tolerance (VDT) could be a reliable indicator of 3D visual comfort under certain restrained condition when vertical disparity is induced; second, to be able to predict how viewing conditions can affect visual comfort using an analytical model. Two viewing condition parameters were considered: luminance and stimulus angular size. The study was carried out in two experiments involving 17 subjects. In Experiment 1, visual comfort and vertical disparity tolerance were measured by a series of psychophysical tests for different stimulus angular sizes and luminance. Based on a regression analysis of this data, a model was proposed to estimate VDT as a function of luminance and stimulus angular size. In Experiment 2, a validation test was carried out to assess the quality of the model. Results confirm that for given viewing conditions (luminance, angular size, induced vertical disparity), the visual comfort measured is in agreement with the one predicted (ρ = 1.0008, p = 0.0026). VDT is a recognized reliable indicator of visual comfort due to vertical disparity and the model can be used to predict visual comfort for given viewing conditions.     

Efficient multicast schemes for 3-D Networks-on-Chip

3-D Networks-on-Chip (NoCs) have been proposed as a potent solution to address both the interconnection and design complexity problems facing future System-on-Chip (SoC) designs. In this paper, two topology-aware multicast routing algorithms, Multicasting XYZ (MXYZ) and Alternative XYZ (AL + XYZ) algorithms in supporting of 3-D NoC are proposed. In essence, MXYZ is a simple dimension order multicast routing algorithm that targets 3-D NoC systems built upon regular topologies. To support multicast routing in irregular regions, AL + XYZ can be applied, where an alternative output channel is sought to forward/replicate the packets whenever the output channel determined by MXYZ is not available. To evaluate the performance of MXYZ and AL + XYZ, extensive experiments have been conducted by comparing MXYZ and AL + XYZ against a path-based multicast routing algorithm and an irregular region oriented multiple unicast routing algorithm, respectively. The experimental results confirm that the proposed MXYZ and AL + XYZ schemes, respectively, have lower latency and power consumption than the other two routing algorithms, meriting the two proposed algorithms to be more suitable for supporting multicasting in 3-D NoC systems. In addition, the hardware implementation cost of AL + XYZ is shown to be quite modest.     

An intelligent strategy for checking the annual inspection status of motorcycles based on license plate recognition

License plate recognition techniques have been successfully applied to the management of stolen cars, management of parking lots and traffic flow control. This study proposes a license plate based strategy for checking the annual inspection status of motorcycles from images taken along the roadside and at designated inspection stations. Both a UMPC (Ultra Mobile Personal Computer) with a web camera and a desktop PC are used as hardware platforms. The license plate locations in images are identified by means of integrated horizontal and vertical projections that are scanned using a search window. Moreover, a character recovery method is exploited to enhance the success rate. Character recognition is achieved using both a back propagation artificial neural network and feature matching. The identified license plate can then be compared with entries in a database to check the inspection status of the motorcycle. Experiments yield a recognition rate of 95.7% and 93.9% based on roadside and inspection station test images, respectively. It takes less than 1 s on a UMPC (Celeron 900 MHz with 256 MB memory) and about 293 ms on a PC (Intel Pentium 4 3.0 GHz with 1 GB memory) to correctly recognize a license plate. Challenges associated with recognizing license plates from roadside and designated inspection stations images are also discussed.     

Speech authentication system using digital watermarking and pattern recovery

The objective of this paper is to detect speech forgery using digital audio watermarking and pattern recovery techniques. A digital watermark pattern has been attached with the speech signal to detect three kinds of alterations or forgeries such as substitution, insertion, and removal. The watermark pattern will be modified if some changes have been made to the speech contents. Modification and forgery can be measured and detected by pattern recovery. The proposed method uses the cyclic pattern embedding to overcome synchronizing problems of previous detection techniques. In addition, pattern recovery enhances the robustness to compression. This method has been tested and verified using six recording devices, which was used for collecting verbal data. The speech signals were sampled at the rate of 8 kHz and digitized at 16 bits resolution. Randomly chosen regions were substituted, removed, and compressed in MP3 at the rate of 16 kbps as well as in CELP at the rate of 11.5 kbps. The experiment shows the perfect detection for three kinds of forgeries and it proved the validity of the proposed method.     

Simulation and experiment research on vaporizing liquid micro-thruster

In the present work, a micro-thruster chip with dimension of 19.5 mm × 9.5 mm was fabricated with MEMS technologies for the experiment study of vaporizing liquid micro-thruster. In addition, a full 3D computational model was constructed to simulate the aft section of a vaporizing liquid micro-thruster for investigating flow characteristics. The results show that there were four distinct flow patterns observed in this study including snake flow, vapor-droplet flow, vapor-droplet-jet flow, and vapor flow. To prevent the failure of micro-thruster chip from generating of snake flow, the heating treatment of an empty micro-thruster chip at 300 °C for 2 h was the key factor. The generation of vapor flow preliminarily proved that the concept of vaporizing liquid micro-thruster chip was feasible. Furthermore, the numerical model in this study successfully provided the thrust estimation. The channel cross-section of 1 mm × 100 μm designed in this study was fit for developing a micro-thruster of O(mN) (ranging from 1 to 6 mN approximately). The numerical simulation could match better with the experiment results for the vapor flow cases if the flow oscillation was taken into consideration, and the heating channel of micro-thruster was lengthened to completely vaporize the liquid water.     

Design and fabrication of a new MEMS capacitive microphone using a perforated aluminum diaphragm

In this paper, a novel single-chip MEMS capacitive microphone is presented. The novelties of the method relies on the moveable aluminum (Al) diaphragm positioned over the backplate electrode, where the diaphragm includes a plurality of holes to allow the air in the gap between the electrode and the diaphragm to escape and thus reducing acoustical damping in the microphone. Spin-on-glass (SOG) was used as a sacrificial and isolating layer. Backplate is monocrystalline silicon wafer, that it is more stiff. This work will focus on design, simulation, fabrication and characterization of the microphone. The structure has a diaphragm thickness of 3 μm, a diaphragm size of 0.5 mm × 0.5 mm, and an air gap of 1.0 μm. The results show that the pull-in voltage is 105 V, the initial stress of evaporated aluminum diaphragm is around 1500 MPa and the zero bias capacitance of microphone is 2.12 pF. Comparing with the previous works, this microphone has several advantages: the holes have been made on diaphragm, therefore no need of KOH etching to make back chamber, in this way the chip size of each microphone is reduced. The fabrication process uses minimal number of layers and masks to reduce the fabrication cost.     

Continuous blood pressure estimation based on multiple parameters from eletrocardiogram and photoplethysmogram by Back-propagation neural network

The cuff-less continuous blood pressure monitoring provides reliable and invaluable information about the individuals’ health condition. Conventional sphygmomanometer with a cuff measures only the value of the blood pressure intermittently and the measurement process is sometimes inconvenient. In this work, a systematic approach with multi-parameter fusion has been proposed to estimate the non-invasive beat-to-beat systolic and diastolic blood pressure with high accuracy. The methods involve real-time monitoring of the electrocardiogram (ECG) and photoplethysmogram (PPG), and extracting the R peak from the ECG and relevant feature parameters from the synchronous PPG. Also, it covers the creation of the topological model of back-propagation neural network that has fifteen neurons in the input layer, ten neurons in the single interlayer, and two neurons in the output layer, where all the neurons are fully connected. As for the results, the proposed method was validated on the volunteers. The reference blood pressure (BP) is from Finometer (MIDI, Finapres Medical System, Netherlands). The results showed that the mean ± S.D. for the estimated systolic BP (SBP) and diastolic BP (DBP) with the proposed method against reference were −0.41 ± 2.02 mmHg and 0.46 ± 2.21 mmHg, respectively. Thus, the continuous blood pressure algorithm based on Back-Propagation neural network provides a continuous BP with a high accuracy.     

FPGA implementation of reversible watermarking in digital images using reversible contrast mapping

Reversible contrast mapping (RCM) and its various modified versions are used extensively in reversible watermarking (RW) to embed secret information into the digital contents. RCM based RW accomplishes a simple integer transform applied on pair of pixels and their least significant bits (LSB) are used for data embedding. It is perfectly invertible even if the LSBs of the transformed pixels are lost during data embedding. RCM offers high embedding rate at relatively low visual distortion (embedding distortion). Moreover, low computation cost and ease of hardware realization make it attractive for real-time implementation. To this aim, this paper proposes a field programmable gate array (FPGA) based very large scale integration (VLSI) architecture of RCM-RW algorithm for digital images that can serve the purpose of media authentication in real-time environment. Two architectures, one for block size (8 × 8) and the other one for (32 × 32) block are developed. The proposed architecture allows a 6-stage pipelining technique to speed up the circuit operation. For a cover image of block size (32 × 32), the proposed architecture requires 9881 slices, 9347 slice flip-flops, 11291 number 4-input LUTs, 3 BRAMs and a data rate of 1.0395 Mbps at an operating frequency as high as 98.76 MHz.