Row Of Pixels Research Articles

P‐4.3: Research on the effect of Vgh on LCD screen luminosity

P‐4.3: Research on the effect of Vgh on LCD screen luminosity

Laboratory Calibration of an Ultraviolet–Visible Imaging Spectropolarimeter

The ultraviolet–visible imaging spectropolarimeter (UVISP), developed by the Anhui Institute of Optics and Fine Mechanics (AIOFM), Chinese Academy of Science (CAS), is a dual-beam snapshot instrument for measuring the spectral, radiometric, and linear polarization information of absorbing aerosol in a wavelength range from 340 to 520 nm. In this paper, we propose a complete set of calibration methods for UVISP to ensure the accuracy of the measured radiation polarization data, thus guaranteeing the reliability of inversion results. In geometric calibration, we complete the assignment of the field of view (FOV) angle to each pixel of the detector using a high precision turntable and parallel light source. In addition, the geometric calibration accuracy of the S beam and P beam is also analyzed. The results show that the residuals of all row pixels are less than 0.12°. Based on geometric calibration, a spectral calibration is conducted at each spectrum of the S beam and P beam for the given FOV, and the relation between the wavelength and pixel is obtained by a linear fitting procedure. For radiometric calibration, the uniformity of spectral responsivity is corrected, and the function between spectral radiance and output digital data is established. To improve the accuracy of the polarimetric measurement, a polarimetric calibration is proposed, and validated experimental results show that the root mean square (RMS) errors for the demodulated value are all within 0.011 for the input linear polarized light with different angles of linear polarization (AoLPs). Finally, field measurements are conducted, and the absolute deviations are all within 0.01 when the UVISP and CE-318 sun–sky polarimetric radiometer (CE318N) simultaneously measure the degree of linear polarization (DoLP) of the sky at different zenith angles. These experimental results demonstrate the efficiency and accuracy of the proposed calibration methods.

Synchronous monitoring of axial vibration and rotation speed of rotating cylinder by linear array scanning

The axial vibration (AV) and rotation speed (RS) signals of a rotating cylinder contain rich condition information. In order to harness such quantitative information, an effective measurement method for synchronous monitoring of the AV and RS is highly desirable. However, traditional approaches are restricted either by limited test distance or multiple isolated sensors, which makes the approaches impractical to synchronously acquire the AV and RS signals through a single device. In this paper, a generalized visual measurement method for synchronous monitoring of the AV and RS via linear array scanning (LAS) is proposed. The basic idea is to paste a mark containing a printed black continuous bar and a black block (BCBB) on the surface of a rotating cylinder, and use a linear array camera to synchronously sample the bar and blocks. For the extraction of mark edges, we propose a single edge pixel extraction method through gray gradient of row pixels based on the observation of relatively stable gray level distribution. Line scanned signals disturbed by seam error or axial offset are automatically corrected through seam compensation and axial offset correction respectively. Experimental validation of the proposed method is presented for the cylinder under static hammering, variable speed condition and steady speed condition at a scanning distance of 500 mm. Bilinear interpolation is applied to the scanned linear array images before extracting the edge features and helps improve measurement accuracy. Analyses in the time domain and frequency domain verify the effectiveness of the LAS. The average error between the RS signal obtained by LAS and the reference value is 0.1420 rpm. The correlation coefficient between the AV time-domain signal of LAS and the reference signal is up to 0.900 and the resonance frequency error in the frequency spectrum is less than 1 Hz.

Architectural and mathematical fundamentals of improvement neural networks for classification of images

The article proposes ways to solve the problem of structural synthesis of neural networks architectures; based on non-traditional approaches to their analytical formalization and application of new ones data processing operations. The example of the CIFAR10 dataset proves the possibility of improving the accuracy of the solution image classification tasks within an alternative architecture with expandable input and narrowing structures of trained neural networks. The results of the application of input expanding image taken with Resizing and Conv2DTranspose layers at the input trained neural networks indicate the effectiveness of solving such classification problems by example CIFAR10 dataset. Achieved on the basis of the Xception neural network, the average classification accuracy of 10 classes CIFAR10 images are 97.3%. The side effect of pre-scaling images is leveling the accuracy of the classification of different classes, which allows us to consider such a change in size as an option data augmentation in the dataset. Tensor-matrix methods have been introduced for further development of this approach formalization of the description of neural networks on the basis of the penetrating end product of matrices and its block ones modifications. On this basis, a number of new convolution and maxpooling operations are proposed, as well as combinations on input of the narrowing segment not only symmetrically enlarged image, but also its variants obtained on the basis of a generalized penetrating product. It is about drawing images expanded by rows of pixels (horizontally) and columns (vertically), as well as a combination of several different options symmetrical image extensions on the principle of constructing a pyramidal segment of the PSPNet neural network.

Position-adjusted subset tracking analysis coupled with a double circle marker

A novel digital image displacement measurement system using a position-adjusted subset tracking analysis (PASTA) method that tracks subsets in the x- and y-directions coupled with double circular markers is proposed in this study. The PASTA method realizes the subpixel resolution in x- and y-directions based on differences in the luminance of subsets. In this method, a subset is defined as a part of the diameter of the inner circular marker (size of the subset = 1 × d or d × 1), which includes the two-colored edges of the double circular marker. The circular marker makes it easy to track the location of the subset in the x- or y-direction. This enables the calculation of the displacement in the x- and y-directions with high resolution and reduces errors. In numerical simulations, images with displacements of -2–2 pixels in one- or two-dimensions were prepared, and the measurement resolution, measurement range, and validity were verified. By focusing on the pixel rows or columns of the diameter of the circular marker and determining the subset position, we confirmed that we can determine the luminance change affected only by the displacement in the x- or y-direction. We experimentally verified the validity of the PASTA method and evaluated the robustness of the measurement system. The mean absolute error was less than approximately 15 μm within the measurement range and approximately 40 μm outside the measurement range. Within the measurement range, the relationship between the change in the luminance value and the displacement is linear, and stable measurement is possible. The PASTA method can measure a displacement of 50 × 10−3 mm with sufficient tracking.

A new color image encryption algorithm using multiple chaotic maps with the intersecting planes method

This paper proposes a new and efficient color image encryption algorithm based on multiple chaotic maps (Logistic map, Sine map, and Chebyshev map) and the intersecting planes method inside a cube. All maps were used to fill the three adjacent faces of the cube. These faces represent the three channels of the color image (red, green, and blue). The first phase of our method begins with extracting all the pixels from the original image and looking for the corresponding values of each pixel on the three faces of the cube. Then, we used a circular rotation operation based on the position of each pixel (row, column). This rotation prevents two identical pixels to have the same encrypted value. Afterward, we used the intersecting planes method with the corresponding face to encrypt the pixels. For the confusion part, we chose the 2D transformation Arnold Cat Map to shuffle all the pixels and change their positions according to the parameters calculated from all the pixels of the original image. We evaluated the performance of our algorithm against various differential and statistical attacks and based on multiple factors, such as histogram, entropy, correlation coefficient, UACI, NPCR, and PSNR. We compared the proposed method with several efficient methods in the field. The various results got by our method show its performance and reliability in terms of safety, precision, confidentiality, and robustness.

Operation Status Monitoring of Transmission Tower in Power System based on Data Fusion

This paper studies the application of data fusion technology in power system to solve some difficult problems in this complex energy system. A transmission tower identification and bird nest detection method based on corner, line, color and shape features is proposed. Through LSD (Line Segment Detection) and Harris corner detection method, the straight line segment and corner point in the image are extracted respectively. Combined with triangle method, the actual tilt angle of tower is measured; According to the nesting rule of birds in transmission towers, the basic unit segmentation algorithm of transmission towers is proposed, and the basic unit segmentation of transmission towers is realized by using the local maximum of the target pixel row statistical histogram. The algorithm proposed in this paper can effectively solve the problems of on-line measurement of tilt angle of transmission tower and on-line detection of bird's nest, which will lay a theoretical foundation for on-line monitoring of transmission tower status.

Graph-Based Depth Denoising & Dequantization for Point Cloud Enhancement.

A 3D point cloud is typically constructed from depth measurements acquired by sensors at one or more viewpoints. The measurements suffer from both quantization and noise corruption. To improve quality, previous works denoise a point cloud a posteriori after projecting the imperfect depth data onto 3D space. Instead, we enhance depth measurements directly on the sensed images a priori, before synthesizing a 3D point cloud. By enhancing near the physical sensing process, we tailor our optimization to our depth formation model before subsequent processing steps that obscure measurement errors. Specifically, we model depth formation as a combined process of signal-dependent noise addition and non-uniform log-based quantization. The designed model is validated (with parameters fitted) using collected empirical data from a representative depth sensor. To enhance each pixel row in a depth image, we first encode intra-view similarities between available row pixels as edge weights via feature graph learning. We next establish inter-view similarities with another rectified depth image via viewpoint mapping and sparse linear interpolation. This leads to a maximum a posteriori (MAP) graph filtering objective that is convex and differentiable. We minimize the objective efficiently using accelerated gradient descent (AGD), where the optimal step size is approximated via Gershgorin circle theorem (GCT). Experiments show that our method significantly outperformed recent point cloud denoising schemes and state-of-the-art image denoising schemes in two established point cloud quality metrics.

Row anchor selection classification method for early-stage crop row-following

The field navigation tasks of agricultural robots are the basis for implementing field management projects in the early stages of seedlings guided by precision agriculture policies. High accuracy and execution speed of navigation tasks are needed for the escalating field machine implementations in a significantly unstructured and irregular field environment, contributing to the difficulty and urgency of achieving a field navigation solution with high navigation accuracy while maintaining proper execution speed. In our study, the row anchor selection classification method (RASCM) is presented to track early-stage crop rows, and the early corn seedling dataset (E-dataset) and LRC-dataset are developed for the measurement of the experimental results. RASCM is divided into three parts, including a row anchor selection method (RasM), a lightweight deep convolutional neural network (DCNN) network GhostNet, and a combined loss function (C-loss). It is suggested that RASCM has achieved good results in terms of calculation speed and accuracy by comparing the end-to-end regression operation based on the steering wheel angle (E2Er) and crop row pixel segmentation (SeG) approaches with RASCM on the LRC-dataset, which can be effectively applied in actual projects. Finally, a class activation map (CAM) is used to visualize the internal working mechanism of GhostNet, and the demonstrated effectiveness of GhostNet is not limited to the RasM labeling area. In contrast, broad global features are learned, and the previous noise is highly mitigated. Strong generalization performance for different environments is displayed by using RACSM, contributing to the effectiveness of early-stage crop row-following. The main contribution of our paper is the proposal of RASCM, which is a novel method in the field of early-stage crop row-following and can be used as a basic reference for subsequent work in this research field.

X-ray imaging of moving objects using on-chip TDI and MDX methods with single photon counting CdTe hybrid pixel detector

X-ray imaging of moving objects using line detectors remains the most popular method of object content and structure examination with a typical resolution limited to 0.4–1 mm. Higher resolutions are difficult to obtain as, for the detector in the form of a single pixel row, the narrower the detector is, the lower the image Signal to Noise Ratio (SNR). This is because, for smaller pixel sizes, fewer photons hit the pixel in each time unit for a given radiation intensity.To overcome the trade-off between the SNR and spatial resolution, a two-dimensional sensor, namely a pixel matrix can be used. Imaging of moving objects with a pixel matrix requires time-domain integration (TDI). Straightforward TDI implementation is based on the proper accumulation of images acquired during consecutive phases of an object’s movement. Unfortunately, this method is much more demanding regarding data transfer and processing. Data from the whole pixel matrix instead of a single pixel row must be transferred out of the chip and then processed.The alternative approach is on-chip TDI implementation. It takes advantage of photons acquired by multiple rows (a higher SNR), but generates similar data amount as a single pixel row and does not require data processing out of the chip. In this paper, on-chip TDI is described and verified by using a single photon counting two-dimensional (a matrix of 128 × 192 pixels) CdTe hybrid X-ray detector with the 100 µm × 100 µm pixel size with up to four energy thresholds per pixel. Spatial resolution verification is combined with the Material Discrimination X-ray (MDX) imaging method.

Integrating Hexagonal Image Processing with Evidential Probabilistic Supervised Classification Technique to Improve Image Retrieval Systems

This paper presents a suggested approach to treat a major issue in images classification namely uncertainty. Uncertainty in image classification means some pixels within each cluster are more or less likely to actually belong to this cluster. So, techniques have been used in this paper to deal with the pixels that do not belong to specific regions, helping to raise image retrieval performance. This was done by merging one of the artificial intelligence techniques, which is image processing, with one of the statistical techniques for probability, which is evidential probabilistic. In such contexts, it may be advantageous to resort to two branches: hexagonal image processing based on partial down-sampling of the image resolution in both directions by half using weighted average performance then shifting the remaining pixels in alternate rows. The other is an evidential theory which is rich and flexible formalisms for representing and manipulating uncertain information. Both hexagonal image processing and evidential theory are used to obtain high accuracy in images classification. The hierarchical nature of the hexagonal image processing addressing scheme is exploited to extract features from the image efficiently.

Vibro-tactile display for the visually impaired using miniature electrostatic transducers

A vibro-tactile electronic display is being developed to aid the visually impaired community by increasing access to graphical and textual information. A high-density vibro-tactile arrayed display would improve the user experience while moving away from external displays on the market. We propose to use millimeter scale electrostatic transducers operating at frequencies well below resonance as candidate array pixel elements. Motion detectable by a fingertip, mechanical impedance of a fingertip, and frequency at which fingertips can detect contact were considered. Motion of 5μm with a fingertip touching the device and 10 μm without a finger touching the device is desired with the device at a mechanical impedance of 480N/m, which is twice the mechanical impedance of the fingertip (240N/m measured by Diller and Kyung). Transducer membrane thickness, force on the fingertip, and gap width are being optimized through prototyping and finite element analysis. A prototyped row of pixels was manufactured using 0.051 mm thick metalized Mylar film with an air gap of 0.02mm and a diameter of 1 to 2 mm per pixel, driven by AC electrostatics at 400 V peak to peak. Motion analysis is being conducted using a laser vibrometer. Preliminary results demonstrate motion of 10 μm peak to peak at low frequencies, with further analysis and optimization ongoing.

Method for hiding text data in an image

The main problem: The article deals with the issues of hiding text information in a graphic file. A formula for hiding text information in image pixels is proposed. A steganography scheme for embedding secret text in random image pixels has been developed. Random bytes are pre-embedded in each row of pixels in the source image. As a result of the operations performed, a key image is obtained. The text codes are embedded in random bytes of pixels of a given RGB channel. To form a secret message, the characters of the ASCII code table are used. Demo encryption and decryption programs have been developed in the Python 3.5.2 programming language. A graphic file is used as the decryption key. Purpose: To develop an algorithm for embedding text information in random pixels of an image. Methods: Among the methods of hiding information in graphic images, the LSB method of hiding information is widely used, in which the lower bits in the image bytes responsible for color encoding are replaced by the bits of the secret message. Analysis of methods of hiding information in graphic files and modeling of algorithms showed an increase in the level of protection of hidden information from detection. Results and their significance: Using the proposed steganography scheme and the algorithm for embedding bytes of a secret message in a graphic file, protection against detection of hidden information is significantly increased. The advantage of this steganography scheme is that for decryption, a key image is used, in which random bytes are pre-embedded. In addition, the entire pixel bits of the container image are used to display the color shades. It can also be noted that the developed steganography scheme allows not only to transmit secret information, but also to add digital fingerprints or hidden tags to the image.

Optimization of acquisition parameters of slurry coating characteristics based on multiple yarn simultaneous detection

A simultaneous detection method was proposed to detect slurry coating characteristics based on the same spatial resolution of starch-based sized and raw yarns. A dynamic image acquisition device was improved to capture the apparent images of multiple yarns simultaneously after the starch–iodine color reaction. The slurry coating characteristics were calculated utilizing image-processing technology. For the sake of effective and stable detection, the effects of illumination conditions on the concentration of I2-KI solution were studied through the absorbance value test. The effects of image-processing parameters on the experimental results were analyzed respectively by measuring six kinds of yarn of different counts and size pick-up. The results showed that the illumination conditions within an illumination time of 0–60 min, illumination intensity of 0–80,000 lux and illumination distance of 0.01–0.20 m had no significant effects on the concentration of I2-KI solution. Eight rows of pixels in the central axis areas of the yarn were determined as the effective statistical areas. It was determined that the sample size can meet the needs of stability test in 200 pieces. A dynamic threshold method, which served the intersection of the gray histogram of sized and raw yarn under the same spatial resolution as the threshold, can effectively segment the slurry coating areas. On the above basis, the optimization acquisition parameters of the detection method were obtained. The proposed method can meet the requirements of the efficiency test, common applicability and ease of operation.

An Ultra-High Frame Rate Ion Imaging Platform Using ISFET Arrays With Real-Time Compression.

In this paper, a Lab-on-Chip platform with ultra-high throughput and real-time image compression for high speed ion imaging is presented. The sensing front-end comprises of a CMOS ISFET array with sensors biased in velocity saturation for a linear pH-to-current conversion and high spatial and temporal resolution. An array of 128 × 128 pixels is designed with a pixel size of 13.5 μm × 10.5 μm. In-pixel reset switches are applied for offset compensation, by asynchronously resetting the floating gate of the ISFET to a known fixed potential. Additionally, each row of pixels is processed by a current mode signal pipeline with auto zeroing functionality to remove fixed pattern noise, followed by an on-chip 1 MS/s 8-bit row-parallel single slope ADC. Fabricated in standard TSMC 180 nm BCD process, the entire system-on-chip occupies a silicon area of 2 mm × 2 mm, and achieves a frame rate of 6100 fps (7800 fps from simulation). A high speed 25 ms-latency readout platform based on a USB 3.0 interface and standard JPEG is presented for real-time ion imaging and image compression respectively, while an optimised JPEG algorithm is also designed and verified for a higher compression ratio without sacrificing image quality. We demonstrate real-time ion image visualisation by sensing high speed ion diffusion at 6100 fps, which is more than two times faster than the current state-of-the-art.

Enhancing the performance of optical camera communication via accumulative sampling.

Deemed as a practical approach to realize Visible Light Communication on commercial-off-the-shelf devices, the Optical Camera Communication (OCC) is attracting increasing attention, thanks to its readiness to be built purely upon ubiquitous LED illuminating infrastructure and handy smartphones. However, limited by the low sampling ability of the built-in camera on a smartphone, the performance of existing OCC systems is still far away from the requirements of practical applications. To this end, we further investigate the reception ability of the smartphone's camera and propose an accumulative sampling scheme to improve the performance of the OCC system. Essentially, the proposed scheme can use all the grayscale information of the pixels projected by the LED transmitter, whereas the conventional ones normally use single row (or column) pixels for demodulating. By implementing the lightweight demodulation algorithm with accumulative sampling, we experimentally verify its effectiveness for supporting higher transmission frequency hence better performance in terms of data rate. Extensive evaluations have shown the BERs of the proposed method are over 87% and 96% lower than that provided by the baselines at a maximum transmission frequency of 5 kHz for the Samsung S8 and iPhone 8 Plus receivers, respectively.

The Utilization of Artificial Neural Network Equalizer in Optical Camera Communications.

In this paper, we propose and validate an artificial neural network-based equalizer for the constant power 4-level pulse amplitude modulation in an optical camera communications system. We introduce new terminology to measure the quality of the communications link in terms of the number of row pixels per symbol , which allows a fair comparison considering the progress made in the development of the current image sensors in terms of the frame rates and the resolutions of each frame. Using the proposed equalizer, we experimentally demonstrate a non-flickering system using a single light-emitting diode (LED) with of 20 and 30 pixels/symbol for the unequalized and equalized systems, respectively. Potential transmission rates of up to 18.6 and 24.4 kbps are achieved with and without the equalization, respectively. The quality of the received signal is assessed using the eye-diagram opening and its linearity and the bit error rate performance. An acceptable bit error rate (below the forward error correction limit) and an improvement of ~66% in the eye linearity are achieved using a single LED and a typical commercial camera with equalization.

A novel quality assurance procedure for trajectory log validation using phantom-less real-time latency corrected EPID images.

The use of trajectory log files for routine patient quality assurance is gaining acceptance. Such use requires the validation of the trajectory log itself. However, the accurate localization of a multileaf collimator (MLC) leaf while it is in motion remains a challenging task. We propose an efficient phantom‐less technique using the EPID to verify the dynamic MLC positions with high accuracy. Measurements were made on four Varian TrueBeams equipped with M120 MLCs. Two machines were equipped with the S1000 EPID; two were equipped with the S1200 EPID. All EPIDs were geometrically corrected prior to measurements. Dosimetry mode EPID measurements were captured by a frame grabber card directly linked to the linac. All leaf position measurements were corrected both temporally and geometrically. The readout latency of each panel, as a function of pixel row, was determined using a 40 × 1.0 cm2 sliding window (SW) field moving at 2.5 cm/s orthogonal to the row readout direction. The latency of each panel type was determined by averaging the results of two panels of the same type. Geometric correction was achieved by computing leaf positions with respect to the projected isocenter position as a function of gantry angle. This was determined by averaging the central axis position of fields at two collimator positions of 90° and 270°. The radiological to physical leaf end position was determined by comparison of the measured gap with that determined using a feeler gauge. The radiological to physical leaf position difference was found to be 0.1 mm. With geometric and latency correction, the proposed method was found to be improve the ability to detect dynamic MLC positions from 1.0 to 0.2 mm for all leaves. Latency and panel residual geometric error correction improve EPID‐based MLC position measurement. These improvements provide for the first time a trajectory log QA procedure.

An efficient medical image encryption using hybrid DNA computing and chaos in transform domain.

In this growing era, a massive amount of digital electronic health records (EHRs) are transferred through the open network. EHRs are at risk of a myriad of security threats, to overcome such threats, encryption is a reliable technique to secure data. This paper addresses an encryption algorithm based on integer wavelet transform (IWT) blended with deoxyribo nucleic acid (DNA) and chaos to secure the digital medical images. The proposed work comprises of two phases, i.e. a two-stage shuffling phase and diffusion phase. The first stage of shuffling starts with initial block confusion followed by row and column shuffling of pixels as the second stage. The pixels of the shuffled image are circularly shifted bitwise at the first stage of diffusion to enhance the security of the system against differential attack. The second stage of diffusion operation is based on DNA coding and DNA XOR operations. The experimental analyses have been carried out with 100 DICOM test images of 16-bit depth to evaluate the strength of the algorithm against statistical and differential attacks. By the results, the maximum entropy has been obtained an average of 15.79, NPCR of 99.99, UACI of 33.31, and larger keyspace of 10140, which infer that our technique overwhelms various other state-of-the-art techniques.

P‐13.10: AMOLED IR‐drop Automatic Compensation Based on Real ELVDD

This paper introduces a scheme to compensate IR drop in real time based on the real ELVDD in display. The real ELVDD of each row of pixels at the time of charge and discharge is obtained by designing detection circuits on both sides of the display, and the pixel voltage Vdata is adjusted by the detection voltage. In this way, the display problem caused by local and global IR drop can be solved in a direct and effective way.