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To date, a large number of research papers have been written on malware classification, identification, classification into different families, and the distinction between malware and goodware. These works have been based on captured malware samples and have attempted to analyse malware and goodware using various techniques like the analysis of malware using malware visualization. These works usually convert malware samples capturing the malware structure into image structures which are then subject to image processing. In this paper, we propose an unconventional and novel approach to malware visualization based on its dynamical analysis, subsequent complex network conversion and fractal geometry, e.g. Julia sets visualization. Very interesting images being subsequently used to classify as malware and goodware. The classification is done by deep learning network. The results of the presented experiments of fractal conversion and subsequent classification are based on a database of 6,589,997 goodware, 827,853 potentially unwanted applications and 4,174,203 malware samples provided by ESET. 1 1 https://www.eset.com. This paper aims to show a new direction in visualizing malware using fractal geometry and possibilities in analysis and classification. [Display omitted] • Introduction of a new method for malware visualization based on fractal geometry. • Show interesting results when visualy comparing classified samples using fractal geometry. • Test novel malware image classification using deep learning. • To point out and define new research directions in visual malware analysis opened by our method. [ABSTRACT FROM AUTHOR]

Image cartoon-texture decomposition is an important problem in image processing. In recent years, by exploiting low-rank priors of images, low-rank minimization methods have been widely adopted for image cartoon-texture decomposition. Since matrix rank minimization is an NP-hard problem, the convex nuclear norm is often used as a substitute for the matrix's rank to realize the low-rank minimization methods. In this paper, we utilize a generalized non-convex surrogate of the matrix rank function to develop a novel low-rank minimization model for image cartoon-texture decomposition. We design a proximal alternating algorithm to solve the non-convex model and further demonstrate the global convergence of the algorithm. Numerical experiments illustrate that the proposed method can show much better performances than the existing state-of-the-art methods for image cartoon-texture decomposition. [ABSTRACT FROM AUTHOR]

The article discusses the use of artificial intelligence (AI) technology, specifically Bing Chat, in creating images related to folk dance. It highlights the author's initial skepticism about AI, followed by their exploration and utilization of Bing Chat for generating images related to English social folk dance. It explores how AI (artificial intelligence) can be a valuable tool for creating promotional material for folk dance events providing insights and tips for using Bing Chat effectively.

That's why I believe that deep inside Apple Park, there's an underlying belief that the Camera app should be as simple as possible - and that Apple should keep building hardware to make that happen. Apple is aiming for simplicity with its Camera app. IMAGINING THE PERFECT CAMERA APP This feature got me thinking about Apple's ultimate goals in designing the Camera app. [Extracted from the article]

• Based on the expression of the generalised translation, the convolution theorem (of the first kind) in the FMT domain is derived. • To further enrich the convolution theory, the convolution theorem (of the second kind) in the FMT domain is proposed. • Based on the proposed convolution theorems, the multiplicative filter in the FMT domain is discussed. The free metaplectic transformation (FMT) is widely used in several fields, including filter design, pattern recognition, image processing, and optics. This study investigated two new convolution theorems in the FMT domain to obtain a more concise and intuitive convolution form. First, on the basis of expressing the generalized translation, we derived the convolution theorem (of the first kind) in the FMT domain, which has the elegance and simplicity of the classical Fourier transform (FT) results. Second, we provide the convolution theorem (of the second kind) in the FMT domain to further study the diversity of the convolution theory. The provided solution can be represented using a simple integral that is easy to implement in multiplying filter designs. Finally, based on this simple form of the convolution theorems, we designed a multiplicative filter in the FMT domain, demonstrating the feasibility and efficiency of the filters designed in this study through simulations. [ABSTRACT FROM AUTHOR]

The article explores knowledge graphs, a knowledge base that uses a graph-structured data model or topology to integrate data. The author suggests the importance of understanding the historical context and background of one's research area, noting that students and junior researchers are not completely aware of the source of ideas, concepts and techniques they command. Other topics discussed include the automation of reasoning, the history of graphical representations of knowledge, and how the work described in the article was funded by the Millennium Science Initiative Program.

In recent years, complementary metal-oxide semiconductor (CMOS) detectors, which have similar and slightly newer technology, have supplanted CCDs as the detector of choice. Indeed, some of the newest cameras are so good that it's becoming increasingly common to capture colorful images of galaxies, nebulae, star clusters, and comets with lots and lots of extremely short exposures, rather than several longer ones. The image uses 162 one-minute exposures recorded with a 92-mm refractor and Player One Poseidon-M camera controlled with TheSky HD. However, this may not be a significant problem, particularly if your camera and telescope combination produce an image scale larger than your mount's periodic error (PE). [Extracted from the article]

This paper proposes a novel Octonion Krawtchouk Moments (OKMs) transform to deal with a set of images in a compact manner, and based on this transform, a local zero-watermarking scheme is proposed to protect the copyright of CT medical images. The scheme first annotates regions of interest (ROIs) on seven medical images and then uses the OKMs of these ROIs to construct a single feature image called zero-watermark. This scheme adopts the gray Wolf Optimizer (GWO) algorithm to have a blind nature and to improve robustness against common image processing manipulations and attacks (zero-watermarking requirements). In addition, our scheme uses the trained U-net (R231) model to reduce the search space for the GWO algorithm and prevent this algorithm from getting stuck in a local optimal solution. The experimental results show that the proposed method is very robust against common image processing manupilations and attacks and has superiority compared with superb other zero-watermarking methods. [ABSTRACT FROM AUTHOR]

This paper presents a new robust multiplicative watermark detector. Due to the strong robustness against various attacks, polar harmonic Fourier moment (PHFM) magnitudes are used as the employed watermark carrier. The distribution of PHFM magnitudes is highly non-Gaussian and can be properly modeled by a heavy-tailed probability density function (PDF). In this paper, we proved that Weibull distribution can suitably fit the distribution of PHFM magnitudes, and based on this, we presented a statistics-based watermark decoder by using the Weibull as a prior for the PHFM magnitudes. In watermark embedding, a multiplicative manner was used to embed watermark information in PHFM magnitudes of the highest entropy blocks to achieve better robustness and imperceptibility. In watermark detection, we developed a Weibull distribution-based statistical watermark decoder, which uses the maximum likelihood (ML) decision rule. Compared with Bessel K form (BKF), Cauchy, and generalized Gaussian (GG)-based decoders, the Weibull-based decoder demonstrates stronger robustness. In addition, the proposed watermark decoder is more robust against geometrical and common image processing attacks than existing statistical watermark decoders. [ABSTRACT FROM AUTHOR]

A common approach to constructing fuzzy rough sets (FRSs) is using t-norms. Furthermore, establishing multi-granulation fuzzy rough sets (MGFRSs) is also usually undertaken by means of t-norms. However, most of these sets cannot satisfy the property that upper approximations contain lower approximations under fuzzy binary relations, which is imperative for rough set models. The overlap function is a type of aggregate function that is widely used in multi-attribute decision-making (MADM), image processing and other fields. In this paper, two novel types of MGFRS models under n -dimensional overlap functions are established to overcome the shortcomings of existing models, which are then applied to the multi-attribute group decision-making (MAGDM) problem. First, idempotent n -dimensional overlap functions are used to establish optimistic and pessimistic MGFRSs. These new models fully preserve the important properties of the traditional MGFRS model. Second, through the theoretical analysis of MGFRS based on overlap functions (OMGFRS) and in combination with the TOPSIS method, a solution mechanism for the MAGDM method is proposed. Finally, to fully illustrate this decision-making method, an effective example is developed. A comparison with available methods indicates that this approach is more suitable and has wider adaptability, and that is can be used to handle decision-making problems. [ABSTRACT FROM AUTHOR]

A common approach to constructing fuzzy rough sets (FRSs) is using t-norms. Furthermore, establishing multi-granulation fuzzy rough sets (MGFRSs) is also usually undertaken by means of t-norms. However, most of these sets cannot satisfy the property that upper approximations contain lower approximations under fuzzy binary relations, which is imperative for rough set models. The overlap function is a type of aggregate function that is widely used in multi-attribute decision-making (MADM), image processing and other fields. In this paper, two novel types of MGFRS models under n -dimensional overlap functions are established to overcome the shortcomings of existing models, which are then applied to the multi-attribute group decision-making (MAGDM) problem. First, idempotent n -dimensional overlap functions are used to establish optimistic and pessimistic MGFRSs. These new models fully preserve the important properties of the traditional MGFRS model. Second, through the theoretical analysis of MGFRS based on overlap functions (OMGFRS) and in combination with the TOPSIS method, a solution mechanism for the MAGDM method is proposed. Finally, to fully illustrate this decision-making method, an effective example is developed. A comparison with available methods indicates that this approach is more suitable and has wider adaptability, and that is can be used to handle decision-making problems. [ABSTRACT FROM AUTHOR]

Template matching (TM) is a technique used in computer vision that helps us to identify small pieces of a predefined sub image known as template and a corresponding area inside a large image. There are several approaches in the literature that solve this problem. In spite of their acceptable results, the existing methods fail to detect templates when they are rotated in a large image. On the other hand, the structural similarity (SSIM) index is a resemblance model that allows us to evaluate the affinity between two images. Different from other similarity indexes, SSIM considers important structural information while also incorporating essential elements such as luminance and contrast terms. This paper presents a new approach for template matching when an image is rotated. Our method considers the complete process as an optimization problem where a metaheuristic algorithm is used to find the best resemblance between the template and its rotated or unrotated image. In order to obtain a robust detection, our approach considers the use of the SSIM index as an objective function. Although metaheuristic approaches produce interesting results, there is not an ideal algorithm that can competitively solve all problems. Under such conditions, our study analyzes the performance of five different metaheuristic techniques for solving the problem of TM. In the comparison, several complicated computer experiments have been conducted considering different performance indexes. The results show that the proposed technique solves the problem of TM competitively for rotated instances in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

A predictive method using physics-informed machine learning (PIML) and large eddy simulation (LES) is developed to capture the transient flow field through microscale porous media (PSPM). An image processing technique extracts the 3D geometry of the internal layers of the mask from 2D microscopy images, and then the fluid flow is first simulated numerically. The subsequently developed PIML method successfully predicts the transient flow patterns inside the porous medium. For the first time, 3D maps of time-dependent pressure, velocity, and vorticity are predicted across the fibrous porous medium. The results show that, compared to conventional computational fluid dynamics, the PIML method can reduce the computational cost by over 20 times. Further, the LES model can replicate the fine fluctuations caused by the flow passage through the porous medium. Therefore, the developed methodology allows for transient flow predictions in highly complex configurations at a substantially reduced cost. The results indicate that the PIML method can reduce the total computational time (including training and prediction) by 22.5 and 20.7 times over the standard numerical simulation, based on speeds of 0.1 and 0.5 m/s, respectively. Several factors including the inherent differences between CPUs and GPUs, algorithms and software, appear to influence this improvement. [ABSTRACT FROM AUTHOR]

The article discusses the use of artificial intelligence and machine learning in image and video processing for military and aerospace environments. Topics discussed include the latest embedded processing technologies, vision systems in military and aerospace, and the importance of open systems standards to provide vendor-agnostic platforms.

• We improved the background model to extract the background features of the object. • A frequency-domain processing method is proposed to predict the objects' position. • A novel fusion framework is introduced for salient object detection. Salient object detection is a fundamental problem in image processing and computer vision. Many saliency detection algorithms based on the background and frequency-domain are used to extract salient object clues. However, the former causes the real object to be submerged in the detected object areas, especially in complex or small object scenes. While the latter will lead to the loss of some object information when detecting large objects. To solve these problems and achieve better object detection results, we propose a fusion framework for salient object detection by fusing background and frequency-domain features. The background features of the image are extracted by an improved background model. This model represents the spatial layout of the image area with respect to the image boundaries. Meanwhile, we present a new frequency-domain processing method to obtain multiscale frequency-domain features and mark the saliency of the object at different scales. Within our framework, inspired by human visual attention, we use the idea of a self-attention mechanism to capture the intrinsic relation between background and multiscale frequency-domain features. In addition, this fusion framework provides a three-dimensional Gaussian convolution kernel, which expands two-dimensional local information to three dimensions for feature fusion, thus producing more accurate salient objects. Experiment results demonstrate that the proposed method consistently outperforms eleven state-of-the-art methods on five challenging and complicated datasets in terms of four evaluation metrics. [ABSTRACT FROM AUTHOR]

In this brief, we consider the problem of descriptors construction for the task of content-based image retrieval using deep neural networks. The idea of neural codes, based on fully connected layers’ activations, is extended by incorporating the information contained in convolutional layers. It is known that the total number of neurons in the convolutional part of the network is large and the majority of them have little influence on the final classification decision. Therefore, in this brief, we propose a novel algorithm that allows us to extract the most significant neuron activations and utilize this information to construct effective descriptors. The descriptors consisting of values taken from both the fully connected and convolutional layers perfectly represent the whole image content. The images retrieved using these descriptors match semantically very well to the query image, and also, they are similar in other secondary image characteristics, such as background, textures, or color distribution. These features of the proposed descriptors are verified experimentally based on the IMAGENET1M dataset using the VGG16 neural network. For comparison, we also test the proposed approach on the ResNet50 network. [ABSTRACT FROM AUTHOR]

BOTTOM LINE Most people wondering whether they should buy the iPhone 14 will be upgrading from a much older iPhone than the iPhone 13, but those same people would be wise to consider the iPhone 13 as an alternative since the iPhone 13 is still on sale. If the new camera features of the iPhone 14 are attractive to you, then you may want to consider spending a little more to get the iPhone 14 Pro, which brings a 48MP camera and a 3X telephoto lens along with all of the new iPhone 14 camera features. The Geekbench 4 battery tests indicated just under 9 hours for the iPhone 14, 9:44 for the iPhone 14 Pro, 8:11 for the iPhone 13, and 9:15 for the iPhone 13 Pro. [Extracted from the article]

The iPhone 14 Pro Max is the about same size as the 12 Pro Max, as well as the iPhone 13 Pro Max it replaces (fave.co/3xWPTlb). And the iPhone 14 Pro Max lasted 3 hours and 43 minutes longer than the iPhone 14 Pro - that's 30 percent more battery life from a battery that is 35 percent larger than the smaller iPhone 14 Pro. However, if battery life is your main reason for choosing a large iPhone, Apple does have another iPhone that has recently arrived - the iPhone 14 Plus, which according to Apple's estimates can beat the 14 Pro Max in terms of battery life. The yearly iPhone release, and indeed most smartphone releases, have gotten kind of boring: The latest model is a little faster, the camera's better, and maybe there's some stand-out gimmick that will feature in all the ads but won't really improve the way you use your phone. [Extracted from the article]

ADJUST THE DISPLAY SETTINGS Depending on what previous iPhone you may have had, you may not have found much use for some of the display settings that could be much more useful on an iPhone 14. Open the Camera app, switch to Photo mode, and swipe upward to show the little camera control buttons. CHECK OUT THE NEW CAMERA MODES One of the iPhone 14's coolest new camera features is Action mode, which applies really heavy video stabilization to your hand-held shots. [Extracted from the article]

The performance of nanoscale semiconductor technologies has become susceptible to high temperatures and aging phenomena. While guard-bands have conventionally been used to combat degradation-induced timing violations, approximations have recently been leveraged to compensate for degradations in lieu of adding timing guard-bands, without a loss in performance. However, only simple approximation techniques such as truncation have been considered in prior work. In this paper, a wide range of approximate arithmetic circuits including adders and multipliers using various sophisticated approximation techniques are investigated to cope with aging- and temperature-induced degradations. To this end, approximate circuits are first characterized for their delay increase under degradations. With this, we then determine the approximation level required to compensate for guard-bands under different degradations. Degradation-aware logic synthesis results show that the simple use of truncated arithmetic circuits leads to a higher quality loss compared to using other approximate circuits. However, a truncated multiplier has the lowest error distance towards a reliable operation in 10 years. The approximate multipliers with configurable error recovery are most suitable when the level of degradation is higher, e.g., at a temperature of 70 °C. The characterization of degradation at the circuit level is then used for design exploration at the architecture level without the need for further gate-level simulations. For three different image processing applications, experimental results show that guard-bands can be mitigated while maintaining an output result with a high visual quality. [ABSTRACT FROM AUTHOR]

Underwater image processing has been shown to exhibit significant potential for exploring underwater environments. It has been applied to a wide variety of fields, such as underwater terrain scanning and autonomous underwater vehicles (AUVs)-driven applications, such as image-based underwater object detection. However, underwater images often suffer from degeneration due to attenuation, color distortion, and noise from artificial lighting sources as well as the effects of possibly low-end optical imaging devices. Thus, object detection performance would be degraded accordingly. To tackle this problem, in this article, a lightweight deep underwater object detection network is proposed. The key is to present a deep model for jointly learning color conversion and object detection for underwater images. The image color conversion module aims at transforming color images to the corresponding grayscale images to solve the problem of underwater color absorption to enhance the object detection performance with lower computational complexity. The presented experimental results with our implementation on the Raspberry pi platform have justified the effectiveness of the proposed lightweight jointly learning model for underwater object detection compared with the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

• A novel end-to-end framework is designed for satellite super resolution. • A local linear embedding block is proposed to embed sparse patches into the high-level feature space. • A hierarchical non-local block is proposed to facilitate weight learning. • Experiments demonstrate the superiority of the proposed method. Recent years have seen great progress in many deep learning-based super-resolution methods that learn global responses through the non-local strategy. However, improper linear fitting tends to produce unsatisfactory results with notable artifacts. To relieve the improper linear fitting and reuse the hierarchical features, we propose an end-to-end locally linear embedding super-resolution network, termed as LLE-Net, based on the assumption that the local geometric relationship in the low-resolution manifold space also exists in high-level feature manifold space. The proposed LLE-Net includes two major components: 1) the local linear embedding block (LLEB) and 2) the hierarchical non-local block (HNB). In particular, LLEB searches sparse and similar feature maps and embeds the geometric relationship into the high-level feature space, allowing more attention to be paid to objects' textural details. To promote the representation ability, the proposed HNB is able to explore layer- and pixel-level interdependencies. We conduct extensive experiments to evaluate the superiority of LLE-Net via two groups of experiments: (1) super-resolution tasks on two satellite image datasets and a satellite video image dataset, and (2) the subsequent high-level image processing tasks (i.e., satellite semantic segmentation). The proposed method achieves an 0.7 dB improvement in PSNR value on the Draper dataset and 0.07% segmentation accuracy improvement. The source code of the proposed LLE-Net is publicly available. [ABSTRACT FROM AUTHOR]

In this study, an autonomous capture framework was proposed for a tumbling target satellite with a space manipulator. First, the motion model of a tumbling target was constructed and the motion properties were analyzed. Subsequently, a predictive motion control method was introduced to compensate for the time delay owing to image processing and to predict the tumbling motion of the target. Stability analysis was conducted using a Lyapunov function. To address the problem of large position and attitude deviations of the tumbling target, different autonomous capture strategies, that is, non-fixed-point position-based capture and area-based capture, were proposed to capture different parts of the target, such as the docking ring and side surfaces, which are important for practical applications. The capture conditions were created and compared, where the non-fixed-point position capture strategy and area-based capture strategy had a larger pose tolerance than the traditional fixed-point position methods. Finally, the framework was tested using Adams/Simulink co-simulations. The results validated the autonomous capture process and proposed alternatives for practical applications. [ABSTRACT FROM AUTHOR]

This 4K TV builds on last year's EISA Award-winning C825, with improved wide-angle viewing and anti-reflection abilities adding to an enhanced brilliance in picture performance. Primare A35.8 Designed for both the multichannel audio and hi-fi worlds, Primare's flagship 35 Series amplifier deploys eight bridgeable channels of potent, dynamic Hypex Ncore amplification, and then lets the user decide what to do with them. EISA, or the Expert Imaging and Sound Association, is an organization representing 60 of the most respected special interest publications and websites from 29 countries that cover Hi-Fi, Home Theater Video, Home Theater Audio, Photography, Mobile Devices, and In-Car Electronics. [Extracted from the article]

Enhancing the quality of low-light (LOL) images plays a very important role in many image processing and multimedia applications. In recent years, a variety of deep learning techniques have been developed to address this challenging task. A typical framework is to simultaneously estimate the illumination and reflectance, but they disregard the scene-level contextual information encapsulated in feature spaces, causing many unfavorable outcomes, e.g., details loss, color unsaturation, and artifacts. To address these issues, we develop a new context-sensitive decomposition network (CSDNet) architecture to exploit the scene-level contextual dependencies on spatial scales. More concretely, we build a two-stream estimation mechanism including reflectance and illumination estimation network. We design a novel context-sensitive decomposition connection to bridge the two-stream mechanism by incorporating the physical principle. The spatially varying illumination guidance is further constructed for achieving the edge-aware smoothness property of the illumination component. According to different training patterns, we construct CSDNet (paired supervision) and context-sensitive decomposition generative adversarial network (CSDGAN) (unpaired supervision) to fully evaluate our designed architecture. We test our method on seven testing benchmarks [including massachusetts institute of technology (MIT)-Adobe FiveK, LOL, ExDark, and naturalness preserved enhancement (NPE)] to conduct plenty of analytical and evaluated experiments. Thanks to our designed context-sensitive decomposition connection, we successfully realized excellent enhanced results (with sufficient details, vivid colors, and few noises), which fully indicates our superiority against existing state-of-the-art approaches. Finally, considering the practical needs for high efficiency, we develop a lightweight CSDNet (named LiteCSDNet) by reducing the number of channels. Furthermore, by sharing an encoder for these two components, we obtain a more lightweight version (SLiteCSDNet for short). SLiteCSDNet just contains 0.0301M parameters but achieves the almost same performance as CSDNet. Code is available at https://github.com/KarelZhang/CSDNet-CSDGAN. [ABSTRACT FROM AUTHOR]

The article explores the power of digital storytelling through images in education, emphasizing the rapid processing of visual content and its role in collective storytelling. Topics include the educational benefits of digital storytelling, the intersection of digital literacy and collective storytelling, and a practical activity where students collaboratively create narratives based on curated images.

Only now are Mac webcams getting to be on par with the USB webcam I've used for a decade! MAC USER There was a time when Apple really cared about Macs having better video chat quality than any other computer you could buy. [Extracted from the article]

Recently, some authors studied the distributive laws of continuous t-norms and some families of the common classes of uninorms over overlap and grouping functions in [33] , [41] , but until now a complete characterization of the distributivity on idempotent uninorms over overlap or grouping functions widely used in image processing is still unresolved. Moreover, authors in [55] characterized the distributivity equations of uni-nullnorms with continuous Archimedean underlying operators over the above two functions. In this paper, we proceed with the distributivity characterization of idempotent uni-nullnorms over them which obviously generalizes the corresponding results of idempotent uninorms over these two functions. During the process, we introduce a class of weak overlap and grouping functions with weak coefficients, and obtain the full characterizations of the above functions by considering the different values of the underlying uninorms' associated functions of idempotent uni-nullnorms on the interval endpoints and comparing the values with the weak coefficients. These obtained results totally answer the question on the distributive solutions of idempotent uninorms over overlap functions, which have been mentioned as their future works in [33]. In additions, we also obtain that overlap and grouping functions have particular structures with a constant domain where its value equals to the neutral element of the idempotent uninorm when they are distributive over idempotent uninorms. [ABSTRACT FROM AUTHOR]

Approximate computing (AC) has been considered as a promising paradigm to improve the energy-efficiency of computing hardware for error-tolerant applications, with negligible quality degradation to the output. Dividers frequently limit the performance of a computing system; however, they have not received as much attention as multipliers and adders in AC. In this paper, an energy-efficient and high-performance approximate divider is proposed based on logarithmic conversion and piecewise constant approximation. In this design, the range for the conversion between binary and logarithmic numbers is first expanded from $\mathbf {[{0,1}]}$ to $\mathbf {[-0.5,1]}$. A heuristic search algorithm is then devised to find the most accurate constant set to approximate the reciprocal of the divisor, by minimizing a statistical error. The hardware implementation is presented for both floating-point (FP) and integer dividers. With a high configurability, the proposed divider results in a mean relative error distance (MRED) from 2.78% to 0.046%, indicating a high accuracy among state-of-the-art approximate dividers. Compared to the half-precision FP divider, the proposed divider with a MRED of 0.74% can achieve nearly $\mathbf {90\times }$ improvement in PDP. Moreover, compared to state-of-the-art approximate dividers, the proposed design is in the Pareto Frontier in terms of power delay product (PDP) and MRED. The three image processing application results demonstrate that the proposed divider can result in the highest peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) even with truncation. [ABSTRACT FROM AUTHOR]

Image resizing is a basic tool in image processing, and in literature, we have many methods based on different approaches, which are often specialized in only upscaling or downscaling. In this paper, independently of the (reduced or enlarged) size we aim to get, we approach the problem at a continuous scale where the underlying function representing the image is globally approximated by its Lagrange–Chebyshev I kind interpolation polynomial corresponding to suitable (tensor product) grids of first kind Chebyshev zeros. This is a well-known approximation tool widely used in many applicative fields due to the optimal behavior of the related Lebesgue constants. Here we aim to show how Lagrange–Chebyshev interpolation can be fruitfully applied also for resizing any digital image in both downscaling and upscaling at any desired size. The performance of the proposed method has been tested in terms of the standard SSIM (Structured Similarity Index Measurement) and PSNR (Peak Signal to Noise Ratio) metrics. The results indicate that, in upscaling, it is almost comparable with the classical Bicubic resizing method with slightly better metrics, but in downscaling a much higher performance has been observed in comparison with Bicubic and other recent methods too. Moreover, for all downscaling cases with an odd scale factor, we give a theoretical estimate of the MSE (Mean Squared Error) of the output image produced by our method, stating that it is certainly null (hence PSNR equals infinite and SSIM equals one) if the input image's MSE is null. [ABSTRACT FROM AUTHOR]

This article showcases three new products in the field of astronomy. The first is the Zeus 455M PRO deep-sky camera by Player One Astronomy, which features a 61-megapixel sensor and advanced cooling technology. The second is the Odyssey observing station by Unistellar Optics, which combines a reflector telescope with a color CMOS detector and a smartphone-controlled mount. The third is the EQ-26 motorized equatorial telescope mount by Orion Telescopes & Binoculars, designed for telescopes weighing up to 26 pounds and featuring stepper motors and a built-in polar finderscope. These products offer innovative features for amateur astronomers and can be further explored for more information. [Extracted from the article]

Palmprint recognition techniques have been widely applied in security authentication. As a typical image processing method, convolutional neural network (CNN) has been applied to extract features from palmprint images. However, most existing CNN methods cannot meet the demand in recognition speed due to their high computational complexity. This paper proposes a simple yet effective network (SYEnet) for palmprint recognition, which is a lightweight neural network. SYEnet is composed of four learnable feature extraction networks, which can extract discriminative information from 9 patches. In addition, a dedicated network for palmprint ROI extraction (PREnet) is designed. PREnet extracts ROI from the original image, generating ROI images of consistent dimensions, which can be adapted to complex environments without limitations of lighting, gesture, image quality, and resolution. Moreover, a new contactless palmprint database is collected to validate our method. The database contains 12,800 images from 400 palms and provides data supplementation for current research on palmprint recognition. The proposed SYEnet is evaluated on eight real-world palmprint databases, in comparison to other related methods. Experimental results prove that our method is more efficient and has higher recognition accuracy. [ABSTRACT FROM AUTHOR]

Overlap functions, which can be characterized as a type of non-associative binary aggregation operators, have emerged as one of the most extensively utilized aggregation operators in numerous applications, including image processing, information fusion, and classification problems. At the same time, fuzzy rough sets have also been widely used in these fields due to their excellent ability to handle continuous and uncertain information. However, the variable precision fuzzy rough set model based on overlap functions and its applications have not been fully studied. For instance, some basic properties are invalid and there is a lack of practical applications. In this paper, both overlap functions and precision parameters are introduced into the fuzzy rough sets, namely the overlap function-based variable precision fuzzy rough set (OVPFRS), which is then used in the practical problem of tumor classification. First, considering the existing overlap function-based rough set models, the OVPFRS model is established, and some underlying properties of this model are explored. Second, on the basis of the proposed model, a method for attribute reduction is developed. Finally, the new method is applied to the classification of tumor data from the real world. Through experimentation and comparison with other attribute reduction methods, it has been demonstrated that our model is flexible and the algorithm is viable and effective. [ABSTRACT FROM AUTHOR]

This research aims to revolutionize complex image clipping using quantum computing, particularly in line clipping algorithms. Traditional methods like Cohen-Sutherland, Liang-Barsky, Cyrus-Beck, and Fast Clipping have limitations in scalability and efficiency. The proposed quantum line clipping algorithm uses quantum computing's unique properties to redefine image processing. It views images as vectors, allowing for computation of pixel distances through quantum inner products. The introduction of Q-AND and Q-OR gates within a nine-section quantum display unit plays a pivotal role in decision-making for line clipping, enabling the exclusion of lines beyond the viewport. Comparing this algorithm with classical methods, the quantum algorithm on IBM, Google, and Rigetti's platforms demonstrated superior runtime complexities, demonstrating the immense potential of quantum computation in addressing intricate image clipping challenges. The Quantum Fourier Transform (QFT) acts as a quantum black box, generating high probability amplitudes for substantial vectors. The research also introduces mathematical theorems, such as the quantum image clipping equivalence theorem, which establishes a robust equivalence relation between quantum pixels in frame buffers, the quantum way line clipping equivalence theorem, and the quantum support line clipping optimality theorem. The practical implementation of the proposed quantum line clipping algorithm is presented in Appendix-B, providing valuable resources for researchers and practitioners seeking to explore and experiment with quantum line clipping techniques. [ABSTRACT FROM AUTHOR]

Overlap functions are an important class of aggregation operators on [0,1] that have been proposed for applications in image processing, classification, etc. Later, Paiva et al. lifted overlap functions on [0,1] to complete lattices. In this paper, we continue to study overlap functions on bounded posets so as to lift the continuity in the notion of overlap functions from [0,1] to bounded posets mainly from the topological aspects. More precisely, we introduce the notion of overlap functions on bounded posets. And then, we consider the multiplicative generator triples of overlap functions on bounded posets, and investigate constructions of overlap functions by multiplicative generator triples. [ABSTRACT FROM AUTHOR]

Writing high-performance code on modern machines requires not just locally optimizing inner loops, but globally reorganizing computations to exploit parallelism and locality--doing things such as tiling and blocking whole pipelines to fit in cache. This is especially true for image processing pipelines, where individual stages do much too little work to amortize the cost of loading and storing results to and from off-chip memory. As a result, the performance difference between a naïve implementation of a pipeline and one globally optimized for parallelism and locality is often an order of magnitude. However, using existing programming tools, writing high-performance image processing code requires sacrificing simplicity, portability, and modularity. We argue that this is because traditional programming models conflate the computations defining the algorithm with decisions about intermediate storage and the order of computation, which we call the schedule. We propose a new programming language for image processing pipelines, called Halide, that separates the algorithm from its schedule. Programmers can change the schedule to express many possible organizations of a single algorithm. The Halide compiler then synthesizes a globally combined loop nest for an entire algorithm, given a schedule. Halide models a space of schedules which is expressive enough to describe organizations that match or outperform state-of-the-art hand-written implementations of many computational photography and computer vision algorithms. Its model is simple enough to do so often in only a few lines of code, and small changes generate efficient implementations for x86, ARM, Graphics Processors (GPUs), and specialized image processors, all from a single algorithm. Halide has been public and open source for over four years, during which it has been used by hundreds of programmers to deploy code to tens of thousands of servers and hundreds of millions of phones, processing billions of images every day. [ABSTRACT FROM AUTHOR]

Approximate multipliers are used in error-tolerant applications, sacrificing the accuracy of results to minimize power or delay. In this paper we investigate approximate multipliers using static segmentation. In these circuits a set of $m$ contiguous bits (a segment of $m$ bits) is extracted from each of the two $n$ -bits operand, the two segments are in input to a small $m\times m$ internal multiplier whose output is suitably shifted to obtain the result. We investigate both signed and unsigned multipliers, and for the latter we propose a new segmentation approach. We also present simple and effective correction techniques that can significantly reduce the approximation error with reduced hardware costs. We perform a detailed comparison with previously proposed approximate multipliers, considering a hardware implementation in 28 nm technology. The comparison shows that static segmented multipliers with the proposed correction technique have the desirable characteristic of being on (or close to) the Pareto-optimal frontier for both power vs normalized mean error distance and power vs mean relative error distance trade-off plots. These multipliers, therefore, are promising candidates for applications where their error performance is acceptable. This is confirmed by the results obtained for image processing and image classification applications. [ABSTRACT FROM AUTHOR]

A chaotic map generally employed to generate chaotic sequence is key element for an image encryption algorithm (IEA). In this study, an IEA using novel 2D chaotic map, which is based on Euler and Pi numbers so-called eπ -map, is presented. eπ -map exploits infinity diversity attribute of these numbers. Moreover, a diffusion operation referred to as "bit reversion" in which the bits of the pixels are symmetrically reverted is proposed for manipulating the pixel value. eπ -map is exhaustively examined through bifurcation and trajectory diagrams, Lyapunov exponent (LE), sample entropy (SE), permutation entropy (PE) and 0-1 test. The encryption performance of the IEA is then investigated across various cryptanalysis such as key-space, key sensitivity, histogram, information entropy, correlation coefficient, differential attack, cropping attack, noise attack and encryption execution time. Furthermore, the results are compared with the most recent literature. It is demonstrated that eπ -map has superior hyperchaotic performance in views of ergodicity, complexity and randomness. The IEA based on eπ -map and bit reversion is a secure and reliable algorithm thanks to its outperforming cryptanalysis results. [ABSTRACT FROM AUTHOR]

This paper proposes an analytical design procedure for a class of 2D filters with a directional characteristic in the frequency plane. The method starts from two types of low-pass prototypes, namely a Gaussian filter and a wide-band filter. The two prototypes and the resulted 2D filters are zero-phase, a very useful property in image processing tasks. The prototypes are parametric and can be scaled along the frequency axis, thus resulting narrower or wider. A 1D to 2D frequency mapping, based on accurate approximations, is derived and applied to each prototype, obtaining the desired directional 2D frequency response with specified selectivity and orientation. The designed filters are adjustable, with a frequency response in factored matrix form depending on specifications. The Gaussian prototype yields very selective directional filters, used in detecting oriented straight lines in images, as show simulation results on several test images. Using oriented wide-band filters, other useful 2D filters result, namely square and parallelogram filters. The designed filters have accurate frequency responses, good directional selectivity and high linearity at relatively low order. An advantage of this analytical approach is that the filter transfer function results in parametric form and allows for easy tunability. [ABSTRACT FROM AUTHOR]

A multi-purpose mechanical platform to track moving objects in three-dimensional space has been developed. It is composed of one main microcontroller board that processes all system data, two cameras, three motors, and one secondary microcontroller board to position a platform with three degrees of freedom. The system computes the optical flow and moves the cameras accordingly, tracking motion within the visual scene. The platform operates autonomously. To the best of our knowledge, there are no similar systems reported with low-resolution image sensors and low-cost microcontrollers. Existing solutions rely on personal computers and advanced FPGAs to process image data. This article concludes that the optical flow operation is efficient even using an image sensor with very low resolution. Thus, the system complexity and image data processing are alleviated significantly. The platform can be easily adapted to different application scenarios by adding new peripherals, sensors, or image processing algorithms. A detailed description of the system design and experimental results are provided. [ABSTRACT FROM AUTHOR]

The article reports that the U.S. Department of Defense (DOD) has a bevy of technological hardware and software at its disposal to provide warfighters with the most up-to-date intelligence. Topics include information on the ability to process images captured by a range of devices; and capture details on high-performance embedded computing image and video processors rely heavily on general-purpose graphics processing units (GPGPUs).

Time-series data are widespread in real-world industrial scenarios. To recover and infer missing information in real-world applications, the problem of time-series prediction has been widely studied as a classical research topic in data mining. Deep learning architectures have been viewed as next-generation time-series prediction models. However, recent studies have shown that deep learning models are vulnerable to adversarial attacks. In this study, we prospectively examine the problem of time-series prediction adversarial attacks and propose an attack strategy for generating an adversarial time series by adding malicious perturbations to the original time series to deteriorate the performance of time-series prediction models. Specifically, a perturbation-based adversarial example generation algorithm is proposed using the gradient information of the prediction model. In practice, unlike the imperceptibility to humans in the field of image processing, time-series data are more sensitive to abnormal perturbations and there are more stringent requirements regarding the amount of perturbations. To address this challenge, we craft an adversarial time series based on the importance measurement to slightly perturb the original data. Based on comprehensive experiments conducted on real-world time-series datasets, we verify that the proposed adversarial attack methods not only effectively fool the target time-series prediction model LSTNet, they also attack state-of-the-art CNN-, RNN-, and MHANET-based models. Meanwhile, the results show that the proposed methods achieve a good transferability. That is, the adversarial examples generated for a specific prediction model can significantly affect the performance of the other methods. Moreover, through a comparison with existing adversarial attack approaches, we can see that much smaller perturbations are sufficient for the proposed importance-measurement based adversarial attack method. The methods described in this paper are significant in understanding the impact of adversarial attacks on a time-series prediction and promoting the robustness of such prediction technologies. [ABSTRACT FROM AUTHOR]

The only real competition for Apple's most affordable iPad (which starts at $329) is the Kindle Fire models and some deeply disappointing Android tablets that have retail prices in the $150 range and are often used as free giveaways when you buy service subscriptions or other more expensive tech gear. When it comes to tablets, at least budget tablets, there's iPad and there's everything else. IT'S TIME FOR A FACELIFT Outside of a faster processor (which most won't notice), double the storage (which they will noticed), and a much better front camera (which is very nice), the 9th-generation iPad is about the same as the 8th-generation iPad - which was about the same as the 7th-generation iPad. [Extracted from the article]

Recently, single-particle cryo-electron microscopy (cryo-EM) has become an indispensable method for determining macromolecular structures at high resolution to deeply explore the relevant molecular mechanism. Its recent breakthrough is mainly because of the rapid advances in hardware and image processing algorithms, especially machine learning. As an essential support of single-particle cryo-EM, machine learning has powered many aspects of structure determination and greatly promoted its development. In this article, we provide a systematic review of the applications of machine learning in this field. Our review begins with a brief introduction of single-particle cryo-EM, followed by the specific tasks and challenges of its image processing. Then, focusing on the workflow of structure determination, we describe relevant machine learning algorithms and applications at different steps, including particle picking, 2-D clustering, 3-D reconstruction, and other steps. As different tasks exhibit distinct characteristics, we introduce the evaluation metrics for each task and summarize their dynamics of technology development. Finally, we discuss the open issues and potential trends in this promising field. [ABSTRACT FROM AUTHOR]

In recent years, overlap functions, as a class of bivariate aggregation operators that are widely used in various application problems (see, e.g., in decision-making, image processing, classifications etc.), have been generalized to many forms. In particular, Paiva et al. (R. Paiva, E. Palmeira, R. Santiago, B. Bedregal, Lattice-valued overlap and quasi-overlap functions, Information Sciences 562 (2021) 180–199.) have generalized the overlap functions to the so-called quasi-overlap functions lately. In the meantime, considering aggregation operators on finite chains, especially the commonly bivariate aggregation operators (see, e.g., t-norms, t-conorms, uninorms, t-operators etc.) has become an important research topic in the fields of aggregation operators. In this paper, we take into account this research topic for quasi-overlap functions. First of all, we give the concept of quasi-overlap functions on a finite chain L with n + 2 elements and its arbitrary subchains together with three generalized forms of quasi-overlap functions on any subchain of L. And then, we show some examples of quasi-overlap functions on L along with some of its specific subchains and study the idempotent property, Archimedean property and cancellation law of quasi-overlap functions on L. Finally, we obtain two construction methods of quasi-overlap functions on L , one of them is the ordinal sum construction. [ABSTRACT FROM AUTHOR]

Image denoising is an important topic in image processing. This paper proposes a novel approach to speckle noise removal using a combination of nonlocal and local variable p (x , [ ∇ u ]) -exponents. The existence of a strong solution for the regularization problem is shown through Galerkin's approximation. Furthermore, the denoising model is rigorously evaluated against various benchmark datasets, and performance metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) are computed. These statistical measures offer quantitative evidence of the chosen variable exponent parameter p (x , [ ∇ u ]) denoising model's superior performance compared to existing methods. The model demonstrates its efficacy in preserving image quality, reducing noise, and enhancing visual fidelity, thus validating the effectiveness of the chosen parameter. [ABSTRACT FROM AUTHOR]

Face-attribute synthesis is a typical application of neural network technology. However, most current methods suffer from the problem of uncontrollable attribute intensity. In this study, we proposed a novel intensity-controllable generation network (ICGNet) based on covering learning for face attribute synthesis. Specifically, it includes an encoder module based on the principle of homology continuity between homologous samples to map different facial images onto the face feature space, which constructs sufficient and effective representation vectors by extracting the input information from different condition spaces. It then models the relationships between attribute instances and representational vectors in space to ensure accurate synthesis of the target attribute and complete preservation of the irrelevant region. Finally, the progressive changes in the facial attributes by applying different intensity constraints to the representation vectors. ICGNet achieves intensity-controllable face editing compared to other methods by extracting sufficient and effective representation features, exploring and transferring attribute relationships, and maintaining identity information. The source code is available at https://github.com/kllaodong/-ICGNet. • We designed a new encoder module to map face images of different condition spaces into face feature space to obtain sufficient and effective face feature representation. • Based on feature extraction, we proposed a novel Intensity-Controllable Generation Network (ICGNet), which can realize face attribute synthesis with continuous intensity control while maintaining identity and semantic information. • The quantitative and qualitative results showed that the performance of ICGNet is superior to current advanced models. • New encoder module maps varied face images into a unified feature space. • ICGNet innovatively synthesizes face attributes with intensity control. • ICGNet maintains identity and semantics in face attribute generation. • Outperforms advanced models in quantitative and qualitative measures. [ABSTRACT FROM AUTHOR]

Currently, weapon detection through video surveillance has been extensively studied using deep learning techniques by researchers. However, limited attention has been given to the detection of weapons in night time or dark scenarios. This paper aims to address this gap by proposing a novel approach for weapon detection specifically modified for low-light conditions. The authors demonstrate accurate and robust detection of weapons in challenging nighttime environments by modifying the deep learning model YOLOv7. The YOLOv7-DarkVision model has been developed by combining a brightening algorithm with the advanced image processing techniques and architecture of YOLOv7. A dataset of 15,367 images were collected for training of the model, along with five dark videos from various sources for performance evaluation. The derived detection model, which has a precision score of 95.50% and an F1-Score of 93.41%, performs absolutely well as a weapon detector. [ABSTRACT FROM AUTHOR]

Recently, integrating ferroelectric materials with nanotransistors such as carbon nanotube field-effect transistors (CNTFETs) has opened new doors for demonstrating a new generation of ultra-miniature circuits and systems. Utilizing the negative differential resistance effect in negative capacitance CNTFETs (NC-CNTFETs) has spurred the efforts for designing ultra-compact ternary circuits and systems similar to their binary structures. This paper presents an ultra-efficient ternary image edge detection hardware using NC-CNTFET technology. The proposed hardware is endowed with a noise reduction circuitry to mitigate the noise effects. Using four $1\times 3$ kernels and concatenating the image pixels, the proposed ternary hardware has been designed using only 50 transistors. The proposed ternary hardware at the circuit level shows, on average, 74% improvements regarding power delay-product (PDP) compared to the CNTFET-based counterparts. Our comprehensive simulations indicate that the proposed NC-CNTFET-based hardware shows a 40% improvement in data loss, 2.2 times improvement in performance ratio, and 1.14 times improvement in Pratt’s figure-of-merit, respectively, compared to the related designs. Our results accentuate that the proposed NC-CNTFET-based ternary hardware is a breakthrough achievement in demonstrating ultra-efficient and noise-immune ternary image processing circuits beyond the conventional binary counterparts. [ABSTRACT FROM AUTHOR]