Binary Pictures Research Articles

Interproximal tooth cleaning operated by a tactile robot. An in vitro analysis.

New technologies such as tactile robots and artificial intelligence (AI) are finding their way into clinical practice in dentistry and could contribute to the improvement of oral health care in the future. The hypothesis of the present in vitro pilot study trial was that a collaborative tactile robot programmed by a dental student could remove interproximal artificial plaque as effectively as a human operator. Model teeth were fully covered with artificial plaque and set into phantom jaws. First, a robot was programmed by a dental student to perform interproximal cleaning with an interproximal brush. Second, the teeth were covered with artificial plaque again, and the dental student performed the interproximal cleaning manually. Both experiments were repeated five times. Residual plaque was measured with binary pictures. Surface coverage was reported, and the comparison of methods was performed with significance defined at α = 0.05. No statistically significant difference was found in the cleaning result between the robot and the human operator. The results of the present in vitro pilot study indicate that a tactile robot with integrated AI programmed by a dental student can perform interproximal cleaning as effectively as a dental student. In the future, the use of robot assistants to support oral hygiene, eg, in patients with reduced motor skills or impaired vision, should be fur ther investigated.

Bi-level binary coded fully connected classifier based on residual network 50 with bottom and deep level features for bearing fault diagnosis

Among the existing bearing fault diagnosis algorithms, the diagnosis time of algorithms with high accuracy is relatively long, and the accuracy of some lightweight methods is low compared to other methods. This work proposes a bi-level binary coded fully connected classifier based on residual network 50 with bottom and deep level features (Bi-BUR) for bearing fault diagnosis, enabling both high accuracy and high-speed characteristics. The Bi-BUR has two levels of classifiers. The first level begins with a signal-to-picture method based on integer binary coding to convert the bearing fault signals into binary pictures; then, the first classification of the converted pictures is performed by the underlying feature extraction-residual network 50, which can extract both the underlying and the advanced features. The second level performs modal decomposition of the signals that are not classified successfully in the first level, performs the second classification with a fully connected classifier inserted into the underlying feature extraction module, and exports the final classification results after weighted summation of the results of each type. The Bi-BUR is simulated on the Case Western Reserve University motor, the self-priming centrifugal pump, and the axial piston hydraulic pump bearing failure datasets. All three experiments achieved more than 95% accuracy in the first level of categorization and 100% diagnosis after the second level of categorization. And the Bi-BUR is faster than other high-accuracy methods.

Discrete element study of the effect of particle shape on critical state characteristics and constitutive parameters of sand

Based on the discrete element particle flow software PFC3D, four irregular particles with the same aspect ratio, convexity, and sphericity as the binary pictures are constructed. To examine the influence mechanisms of particle shape on mechanical properties, microstructure, and critical characteristics, an index F characterizing the regularity of the particle shape is introduced. By examining the link between the regularity index and the constitutive parameter, a statistical model is developed to predict the peak friction angle while considering the impacts of relative density and particle shape. The findings demonstrate that decreasing the value of the regularity index always causes an increase in the stress ratio, shear dilatancy, and void ratio. In p'-q, e-logp’, and MCN-logp’ spaces, the distribution pattern of critical state lines is affected by the particle shape. At the same strain instant, when the particle becomes more irregular, the distribution direction of the maximum contact force of irregular particles tends to be more horizontal to resist the greater external loads. In comparison to spherical particles, the Δq/p' and Δφp of irregular particles increase linearly with the increase of ΔF. The measured and predicted values of the peak friction angle for the statistical model are essentially consistent. The critical parameters ecs、ηcs、φcs、MCNcs decrease linearly with the increase of the regularity index. The above conclusions serve as a guide for revising the empirical formula.

Plant Disease Detection using Deep Learning in Banana and Sunflower

In recent years plant disease detection and classification is finding a lot of scope in the field of agriculture. The use of image pre-processing along with deep learning techniques is making the role of farmers easy in the process of plant leaf disease detection. In this paper we propose a deep learning technique, ResNet-50 for the identification and classification of leaf diseases mainly in banana and sunflower. Images for the training and testing purpose are collected by visiting the farms and from village dataset for normal, leaf spot, leaf blight, powdery mildew, bunchy top, sigatoka, panama wilt. Pre-processing is done to remove eliminate the noise in the image by converting the RGB input to HSV image. Binary pictures are retrieved to separate the diseased and unaffected portions based on the hue and saturation components. A clustering method is utilized to separate the diseased region from the normal portion and the background. Classification of the disease is carried out using ResNet-50 algorithm. The experimental results obtained are compared with CNN, machine learning algorithms like SVM, KNN, DT and Ensemble algorithm like RF and XG booster. The proposed algorithm provided maximum efficiency compared to other algorithms.

Driver Drowsiness Detection using Artificial Intelligence

The goal of the research is to show how artificial intelligence may be used to identify driver tiredness using visual processing. Experts estimate that over a quarter of all serious car accidents are brought on by drivers who are too sleepy to pay attention to the road. As a result, we know that tiredness is a more common contributor to car accidents than intoxication. Vision-based ideas were used to design the Drowsiness Detection System. The gadget relies on a small camera to detect drowsiness in drivers by examining their eyes and scanning their face. The Viola-Jones and Hough transform are the techniques utilised by the system to first scan the driver's face, then the eyes, and then check whether the eyes are open or closed using artificial intelligence software. The system works with binary pictures to scan the sides of the face, reducing the space where the eyes will be located. Let's say that the eyes are shown to be closed for five or more consecutive frames. When this occurs, the system tracks the driver's level of activity and determines that the driver is dozing off, so it sounds an alert or produces an alarm signal to wake him up.

System of Automatic Recognition of Video Text Amazigh based on the Random Forest

In this paper; we introduce a system of automatic recognition of Video Text Amazigh based on the Random Forest. After doing some pretreatments on the video and picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives. These characteristics are linked to pixels’ densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the convolutional neural network (CNN) and the Random Forest method. We carried out the experiments with a database containing 3300 samples collected from different writers. The experimental results show that our proposed OCR system is very efficient and provides good recognition accuracy rate of handwriting characters images acquired via Video camera phone.

Protein Desorption Kinetics Depends on the Timescale of Observation.

The presence of so-called reversible and irreversible protein adsorption on solid surfaces is well documented in the literature and represents the basis for the development of nanoparticles and implant materials to control interactions in physiological environments. Here, using a series of complementary single-molecule tracking approaches appropriate for different timescales, we show that protein desorption kinetics is much more complex than the traditional reversible-irreversible binary picture. Instead, we find that the surface residence time distribution of adsorbed proteins transitions from power law to exponential behavior when measured over a large range of timescales (10-2-106 s). A comparison with macroscopic results obtained using a quartz crystal microbalance suggested that macroscopic measurements have generally failed to observe such nonequilibrium phenomena because they are obscured by ensemble-averaging effects. These findings provide new insights into the complex phenomena associated with protein adsorption and desorption.

Processualism and substantialism as two philosophical paradigms

The subject of this research is the advantage of the ontology of process over the ontology of substance in description of the ontological structure of the world. Analysis is conducted on the comparative heuristic potential of essentialism and substantialism on the one hand, and philosophy of the process on the other hand. Essentialism postulates stable, mature, already constituted objects with objectively given essential characteristics that form profound structure of the object. The clearly differentiated array of concepts and categories that reflects the structure of reality and the nature of things is placed on the reality in the flow of becoming. The philosophy of process, in turn, insists on the dynamic and relational nature of reality, and considers time to be the factor constitutive of reality. The fabric of reality is comprised of the processes that relate and interact with other processes forming their dynamic context. The article demonstrates the applicability of the ontology of process in biology, namely on the example of the phenomenon of symbiosis and holobionts. The arguments are provided that the object can be ontologically described as a process; however, a range of processes (for example, photosynthesis) cannot be analyzed in the object categories. Criticism is expressed towards the concept of essential properties of the substance, taking into account perspectivism and variants of the processes of with objects. It is indicated that gradualism characteristics to the philosophy of process allows removing dichotomies and introducing nuances into the binary picture of reality. The novelty of this work consists in highlighting the questions faced by the philosophy of process, namely the question of determination of the status of time essential for determining the process as the basic ontological entity.

Real Time Vehicle Recognition System for Secure Environment

An automatic system based on “Digital Image Processing” technology is developed using MATLAB tool to recognize the vehicle by identifying the license plate. A database is linked to the MATLAB processor, which takes input as the detected number plate. Information about the owner is extracted and electronic format of the challan is generated if the number plate is in wrong format. Also in case of theft or crime, the required number plate is fed to the database system and if it is matched with the detected number plate, an alert is generated. The captured image is in RGB format which is converted into grey scale. Here theories and techniques used to identify the area and Edge detector operators is used to measure threshold values to get the dilated image. After that flood fill operation can be used to stuff the wholes to get a fine binary picture. The fundamental steps to identify the vehicle license plate and plate size, then the partitioned picture is amplified with gray scale only to get the license plate of vehicle. Afterwards the template matching process done to match the image from license plate and template’s picture, thus the number plate detected is then matched with one stored in the database system to get complete information of the vehicle owner. The first section in your paper.

ENCRYPTION AND DECRYPTION USING IMAGE PROCESSING TECHNIQUES

Images consists various kinds of information which can be used for encryption and decryption of messages through them. This paper proposes a photograph encryption and decryption method to encrypt a covert snapshot with the aid of combining the Arnold transform system within the area and decrypting the duvet photograph through combining inverse Arnold become. First, grow to be a cover snapshot into subparts which consists of eight binary pictures with the aid of decimal value to eight-digit binary operation. Then, change into eight binary snap shots into sub-blocks of eight binary scrambled pixel by means of the Arnold change into, respectively. And then, recombine the sequence of the eight binary scrambled matrices right into a scrambled matrix with 256 grey phases in keeping with the special membership. Discrete Wavelet Transform (DWT) is used to perform picture compression on the input picture and secretly hidden photograph which is finished making use of alpha mixing. Sooner or later, derive an encrypted image from the scrambled photograph with the aid of the Hartley turn out to be. Second, decode the encrypted picture making use of inverse Arnold develop. Inverse DWT is performed to regain the compressed pictures. Simulations indicate that the proposed approach has a bigger photograph scrambling measure, more protection and has the robustness against occlusion and speckle noise attacks.

Flow visualization and skin friction determination in transitional channel flow

The present study experimentally determines the transitional Reynolds number range for plane channel flow and characterizes its transitional state. The pressure along the channel is measured to determine the skin friction coefficient as function of Reynolds number from the laminar state, through the transitional region into the fully turbulent state. The flow structure was studied through flow visualisation which shows that as the Reynolds number increases from the laminar state the transitional region starts showing randomly occurring turbulent spots. With increasing Reynolds number the spots shift into oblique patches and bands of small scale turbulence that form across the channel width, together with large-scale streaky structures found in areas between the turbulent regions. An image analysing technique was used to determine the intermittency factor, i.e. the turbulence fraction in the flow, as function of Reynolds number. It is found that the skin friction coefficient reaches its turbulent value before the flow is fully turbulent (the intermittency factor is still below one). This suggests that the observed streaky structures in non-turbulent regions contribute to the enhancement of the wall-normal transfer of momentum. Also above the Reynolds numbers where the turbulent skin friction coefficient has been established large-scale features consisting of irregular streaky structures are found. They have an oblique shape similar to the non-turbulent and turbulent patches in the transitional flow indicating that the transition process is not fully complete even above the Reynolds number where the skin friction reaches its turbulent level.Graphic abstractProcessing of flow visualization images to determine the turbulenceintermittency in the transitional region. Flow is from left to right, (a)unprocessed, (b) filtered (c) standard deviation of (b), (d) binary picture

Random Forest for video Text Amazigh

In this paper; we introduce a system of automatic recognition of Video Text Amazigh based on the Random Forest. After doing some pretreatments on the video and picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives. These characteristics are linked to pixels’ densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the convolutional neural network (CNN) and the Random Forest method. We carried out the experiments with a database containing 3300 samples collected from different writers. The experimental results show that our proposed OCR system is very efficient and provides good recognition accuracy rate of handwriting characters images acquired via Video camera phone.

Algebraic Properties of Parikh Matrices of Binary Picture Arrays

A word is a finite sequence of symbols. Parikh matrix of a word is an upper triangular matrix with ones in the main diagonal and nonnegative integers above the main diagonal which are counts of certain scattered subwords in the word. On the other hand, a picture array, which is a rectangular arrangement of symbols, is an extension of the notion of a word to two dimensions. Parikh matrices associated with a picture array have been introduced, and their properties have been studied. Here, we obtain certain algebraic properties of Parikh matrices of binary picture arrays based on the notions of power, fairness, and a restricted shuffle operator extending the corresponding notions studied in the case of words. We also obtain properties of Parikh matrices of arrays formed by certain geometric operations.

An Assistive and Assessment Smart Eye for Visually Impaired People using CAFFE Model

Those who are visionless or visually compromised and rely heavily on others, like their family or friends. Whereas in acquainted settings, they're going to face several obstacles whereas conducting their everyday activities. A bit like we tend to love democracy and knowledge it, so that they can expertise it too. They ought to not be set apart merely as a result of capable otherwise and particularly in today's world wherever we tend to are technologically thus advance. The planned work object discovery system identifies, calculates the gap to our camera from an illustrious entity in a picture and scans the important world objects from binary pictures or film, wherever the entity might belong to any category or cluster, like folks, cars, vehicles, etc. to finish this duty of detection an object in a picture or video, I used OpenCV packages, Caffe model, Python, and NumPy. This investigation work discovers however deep learning techniques are wont to notice a live object, find an item, reason an item, extract options and show information and lots of additional, in footage and videos use OpenCV and the way to use the Caffe model, and conjointly why select the restaurant prototype over alternative frames. To form our deep learning-based period factor detector with OpenCV, we did like to access webcams and apply factor discovery to every frame effectively.

Biologically Plausible Sequence Learning with Spiking Neural Networks

Motivated by the celebrated discrete-time model of nervous activity outlined by McCulloch and Pitts in 1943, we propose a novel continuous-time model, the McCulloch-Pitts network (MPN), for sequence learning in spiking neural networks. Our model has a local learning rule, such that the synaptic weight updates depend only on the information directly accessible by the synapse. By exploiting asymmetry in the connections between binary neurons, we show that MPN can be trained to robustly memorize multiple spatiotemporal patterns of binary vectors, generalizing the ability of the symmetric Hopfield network to memorize static spatial patterns. In addition, we demonstrate that the model can efficiently learn sequences of binary pictures as well as generative models for experimental neural spike-train data. Our learning rule is consistent with spike-timing-dependent plasticity (STDP), thus providing a theoretical ground for the systematic design of biologically inspired networks with large and robust long-range sequence storage capacity.

Deep Learning-Based Earlier Detection of Esophageal Cancer Using Improved Empirical Wavelet Transform From Endoscopic Image

In the current scenario, the research perspective on esophageal cancer becomes severe, high-prognosis malignancy; poor prognosis is primarily attributed to the fact that most tumors remain asymptomatic and unrelated before it grows through the esophagus. Significant decreases in mortality from esophageal cancer may require effective approaches to detect and nurse more patients at early, curable stages. A new Improved Empirical Wavelet Transform (IEWT) dependent on feature extraction approach and a consistent homology for the diagnosis of early esophageal endoscopic cancer have been proposed in this article. The approach is to convert an input endoscope image into CIE colored spaces L * x * y, and the x* and y* components to create a fusion image for analysis. Further, the two kinds of wavelets are obtained by adding the two forms to the fusion signal. Another is the lower-frequency component provided by the improved empirical wavelet transformation of the wave, and the other is the high- components generated from the Deep Learning-based Complex Empirical Wavelet Transformation (DL-CEWT). The fractal sizes are determined using the box interpolation method for each small block, and the abnormal regions are defined for the basis of their fractal sizes. Binary pictures are obtained by the complex threshold in each frequency variable and then divided into small blocks in every binary image. Using the homology of every block to obtain the new features in the entry image. The extraction strategies for this application are comprehensive and preliminary findings indicate that the method is effective for the early detection of esophageal cancer in an image.

Reducing Contrastive Knowledge

According to one form of epistemic contrastivism, due to Jonathan Schaffer, knowledge is not a binary relation between an agent and a proposition, but a ternary relation between an agent, a proposition, and a context-basing question. In a slogan: to know is to know the answer to a question. I argue, first, that Schaffer-style epistemic contrastivism can be semantically represented in inquisitive dynamic epistemic logic, a recent implementation of inquisitive semantics in the framework of dynamic epistemic logic; second, that within inquisitive dynamic epistemic logic, the contrastive ternary knowledge operator is reducible to the standard binary one. The reduction shows, I argue, that Schaffer’s argument in favor of contrastivism is compatible with a binary picture of knowledge. This undercuts the force of the argument in favor of contrastivism.

Design of an Integrated Subretinal Implant using Cellular Neural Networks for Binary Image Generation in a 130 nm BiCMOS Process.

Blindness caused by the eye diseases Retinitis-Pigmentosa and Age-Related-Macular-Degeneration leads to a degeneration of the photoreceptor layer while postsynaptic cells mostly stay intact. In this Paper a new concept for retinal implants is proposed. Instead of converting the incident light to a gray-scale picture with corresponding continuous-value stimulation levels, we here suggest to produce a binary image picture that only highlight edges in order to stimulate the retina solely at points which belong to an edge. An integrated test circuit is designed with a 130 nm BiCMOS process by using cellular neural networks for binary image generation. The circuit yields a simulated maximum rated power consumption of 2.61 mW for a 1000 information processing cells.

Heterogeneous absorption of antimicrobial peptide LL37 in Escherichia coli cells enhances population survivability.

Antimicrobial peptides (AMPs) are broad spectrum antibiotics that selectively target bacteria. Here we investigate the activity of human AMP LL37 against Escherichia coli by integrating quantitative, population and single-cell level experiments with theoretical modeling. We observe an unexpected, rapid absorption and retention of a large number of LL37 peptides by E. coli cells upon the inhibition of their growth, which increases population survivability. This transition occurs more likely in the late stage of cell division cycles. Cultures with high cell density exhibit two distinct subpopulations: a non-growing population that absorb peptides and a growing population that survive owing to the sequestration of the AMPs by others. A mathematical model based on this binary picture reproduces the rather surprising observations, including the increase of the minimum inhibitory concentration with cell density (even in dilute cultures) and the extensive lag in growth introduced by sub-lethal dosages of LL37 peptides.

Determination of the particle content in laser melt injected tracks

Surfaces of machine parts or tools are subjected to corrosive stresses or wear. To increase the wear resistance of surfaces, metal matrix composite (MMC) can be applied. For higher wear resistance, it is important to achieve a high particle content and a homogenous particle distribution. In this work, aluminium bronze substrate was reinforced by spherical fused tungsten carbides via laser melt injection. The laser power, travel speed and powder mass flow were varied. The influence of these process parameters on the hard particle content was investigated in single tracks. Furthermore, laser melting and laser melt injecting were compared. Laser melt injecting resulted in a significant increase in the track volume. Analytical calculation showed that the volume increase was higher than the volume of the injected particles. Single tracks composed of MMC could be produced without cracks and pores. Depending on the energy input per unit length, undesired melting of the particles was observed. The particle content was investigated via binary pictures from metallographic cross-sections using two different software packages. An approach to calculate the particle content as a function of the width, depth and height of laser melt injected tracks via circle segments is presented.