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In this article for the sequence to catch the concept of ocular affinity, we suggest a deep convolutional neural network to know the embedding of images. We show the deep architecture of Siamese that learns embedding which correctly resembles objects' classification in visual similarity while trained on positive and negative picture combinations. We often introduce a novel system of loss calculation employing angular loss metrics based on the problem's requirements. The combined description of the low or top-level embeddings was its final embedding of the image. We also used the fractional distance matrix to calculate the distance in the n-dimensional space between the studied embeddings. Finally, we compare our architecture with many other deep current architectures and continue to prove our approach's superiority in terms of image recovery by image recovery. Architecture research on four datasets. We often illustrate how our proposed network is stronger than other conventional deep CNNs used by learning optimal embedding to capture fine-grained picture comparisons.

This article reviews the approaches used in data mining to perform a geographical study of regional datasets coupled with Geographic Information Systems (GIS). Firstly, we can look at the functions of data mining used by such data and then illustrate their precision compared to their classic data use. We will further explain the research conducted in this sector and point out that two separate methods exist: one is focused on space database learning while the other is based on space statistics. Finally, we will address the key distinctions between these two methods and their similar features.

VANET is a critical and demanding mission. Numerous methods exist, but none profits in a distributed fashion from physical layer parameters. This paper describes a method that enables individual nodes to estimate node density, independent of beacon messages, and other infrastructure-based information, of their surrounding network. In this paper, a discrete simulator of events was proposed to estimate the average number of simultaneously transmitting nodes, a functional channel model for the VANETs system, and a method of estimating node density. Proposed based on some equations to allow individual nodes to estimate their surrounding node density in real-time Optimized Node Cluster Algorithm with Network Density in which the composition of a cluster is triggered adjacent, these traffic signals is the same and has been predicated mostly on the position a vehicle might well take after crossing. Additional Ordered Tracking with Particle -Filter Routing in which receives simultaneous signal intensity versus node transmission and node density transmission. Conduct multiple location-related analyzes to test the plausibility of the neighboring single-hop nodes on mobility data. The system is designed to operate in the most complex situations where nodes have little knowledge of network topology and the results, therefore, indicate that the system is fairly robust and accurate.

Dimensionality reduction means obtaining additional training data by supplementing this study with supervised learning to match input variables. A training dataset is used due to a lack of training examples in some domains and avoiding the fitting problem. A Convolution Neural Network (GAN) to augment a classification model for computer vision has shown to improve prediction performance. Using the augmented neural network, this study analyses whether transfer processing within the same GAN will improve prediction performance even further. This proposed a method for assessing the effect of data augmentation in the processing of microscopic charcoal images. Morphological transformations and sub-images were various steps to data augmentation that have been investigated. A post-Inception-v3 framework is also applied to educate a barbecue species classifier using the data available. The best value was observed using the micro methodology, which had an overall precision of 99.36%

In the finance sector, time series forecasting has been widely used in situations such as stock prices forecasting and equity market forecasting. In recent years, techniques of computer vision include become increasingly popular in the monetary assumption of periodicity. A hot subject is how to mark information from macroeconomic variables to assess the accuracy rate of computer vision techniques and, as a result, final investment returns. Existing information from macroeconomic variables labelling methods primarily mark data and comparing current data to data from a limited period in the future. Information from a macroeconomic variable, on the other hand, are often quasi, with apparent unpredictability in the brief period. As a result, such tagging methods struggled to identify the time series analysis information’s persistent trend functionality, resulting in a misalignment among their pigeonhole results and actual market inclination. A new pigeonhole approach called “continuous trend labeling” is proposed in this paper to fix the issue. This article presents a novel feature preprocessing tack that avoids taking a gander distortion that plagues continuous data consistency and preprocessing approaches. The principle of constant trend marking and an advanced tagging framework for removing consistent trend characteristics from time-series data were then presented, along with a detailed logical explanation. Experiments on the ensembled model yielded a scoring accuracy of 52 percent, which was close to the Cagliari teams. The results were determined by combining all of the networks' ratings. Even though it appears to be a low percentage, being right on a trade position more than 50% of the time is considered good, particularly when looking at the data in isolation. Our labelling system is significantly superior to classifiers and other identification evaluation measures; province tagging approaches are used. According to the observations of the study, word embeddings like LSTM and GRU are suitable to finance related time series data prediction.

In recent years, deep neural networks have made substantial progress in object recognition. However, one issue with deep learning is that it is currently unclear which proposed framework is exaggerated for a specific hitch. As a result, distinct dispositions are attempt before one that produces satisfactory results is discovered. This paper described a distributed supervised learning method for finding the best network architecture by modifying specifications for a perceived task dynamically. In the case of the MNIST information gathering, it is shown that asynchronous supervised learning can agree on a solution space. Setting several hyperparameters can be time-consuming when constructing neural networks. In this post, we'll provide you with some tips and instructions for better organizing your hyperparameter tuning process, which should help you find a good setting for the hyperparameters much faster.

Metamaterial based one dimensional photonic grating is thoroughly investigated in this research to realize an anti reflection element pertaining to solar wavelength which can be incorporated with solar cell in lieu of existing antireflection material. To envisage the same, we manipulated with plane wave expansion technique to affirm the negligible reflection of the said grating structure. Furthermore, the simulation outcomes confirm that the proposed nanoscale device reflects a maximum of 10−13 % of solar energy at visible signals which could be an apt candidate for antireflection coating.

The present communication deals with a metamaterial based single photonic crystal fiber (PCF) to realize absorber, reflector, and transmitter application. The simulation studies which is made with the help of plane wave expansion method infers that the configuration including lattice spacing and diameter of air holes of the photonic crystal fiber along with the nature of the material play an important role to realize the aforementioned applications. Furthermore, the novelty of this work is that single PCF structure bestows absorber, reflector, and transmitter.

In recent development research, brain tumour examination is generally essential for diagnosing brain disorders. To distinguish the cancer cell, different strategies are utilized. The proposed novel strategy called a Deep Learning Convolution Neural Systems (DCNN) calculation is done that’s utilized to discover the participation work of dental. The metric is defined for assurance of shape by utilizing properties, which is irregular in the brain. By and large, the exactness of 96% is obtained utilizing the proposed DCNN strategy. The proposed division strategy’s affectability, specificity, and accuracy values are found to be 96.7%, 95.6% and 98.4% separately. The closeness of the proposed strategy relative to Genuine Positive values at the ROC bend demonstrates the execution as higher. Exploratory comes about demonstrate proposed framework viability compared with other location strategies.

Developing successful embedded vision applications necessitates a detailed review of various algorithmic optimization trade-offs and a wide variety of hardware design options. This makes it difficult for developers to navigate the solution space and find design points with the best performance trade-offs. In neural machine translation, large Transformer frameworks have produced state-of-the-art results and have become the industry standard. We will clarify the mathematical mechanisms behind the paper's efficient interpretation for neural machine translations and look for the best combination of known techniques to improve interpretation speed without compromising recognition accuracy in this paper. We perform an empirical study that compares various approaches and shows that using a hybrid of decoder self-attention replacement with simplified recurrent units, a deep encoder and shallow decoder architecture, and multiple head attention reseeding can achieve up to higher accuracy. By replacing heavy functions with lighter ones and enhancing the autoencoder's layers structure, excellent results can be achieved in a harmonious mix of time series, network architecture, and probabilistic solutions.

A basic understanding of probability, of its key mathematical features as well as the characteristics it presents within specific circumstances, is the aim of this paper. The action of probability is related to the characteristics of the phenomena that we can forecast. This relation can be described as a distribution of probability. Identify the nature of phenomena (which can also be described by variables), the distribution of probability is defined. The likelihood can be represented by a binomial or Poisson distribution for categorical (or discrete) variables in the majority of cases. For their potential use, distributions of probability are briefly defined along with some examples. The Poisson distribution is a discrete distribution of probabilities that is mostly utilized within a given time span for a model distribution of count data, such as the number of traffic accidents and the number of phone calls received. Each entry starts with a definition and explanation of the Poisson distribution properties, that is followed by a discussion of how to obtain or estimate the Poisson distribution. Finally, every entry provides a discussion of applications that use python libraries for delivery.

Wireless sensor networks are infrastructure-less network with numerous sensors to detect environmental conditions. It passes the data to the base station or sinks through relay nodes. Wireless data communication and cryptographic techniques. It also deals with the classification of cryptographic techniques like symmetric key cryptography and asymmetric key cryptography. It explains the efficient Selective Encryption algorithm to minimize the processing time and improve the security against different attack. Energy optimization technique to reduce transmission power. Using the Modified Cuckoo Search Resource allocation (MCS-RA) approach, energy-saving gains of up to 10dB are obtained to the EEP method of network resource allocation and 1.5 dB more than the traditional resource allocation method UEP (GA).

Music Information Retrieval is a vast area of study and research that deals with understanding the different features of soundtracks and being able to classify, group and modify them based on it. It has multiple sub fields like Music Classification, Generation, Recommendation, Processing, Recognition etc. In this project, I focused only on Music Classification. This paper’s main objective is to compare two different neural network architectures used in examining the genre of music. It employs the use of different parameters and types of optimization algorithms which helps us analyze which will help achieve higher accuracy.

Details persistence axioms suggest that almost any based method conducts a normal quest without substitution, as well as an evolutionary algorithm unless it makes fun of concern search goal or lookup burdened. The protection of salt marshes below rapid sea-level rise (SLR) usually includes the survival of underdeveloped moorlands of marsh betrayal zones with which wetlands can transition. Optimal conservation planning of this kind includes details on forest protection's potential benefits and the cost of worthy sites for migrating marshes in specific areas. Although available content is known within the literature on marsh benefits, the prior study offers little visibility into the related costs of land protection. Discrete mathematics shows that a largish task requires the success of issue data. Computers are ineffective to overcome even moderately sized hiccups without reliable information to direct them, considering their pace in performing inquiries. Three tests are proposed to classify the information needed for an effective search: (1) ligneous particulars that measure the difficulty of finding a goal using a random search; (2) physiologic particulars that estimate the challenge of obtaining a goal once a search has some of the problem-relevant information; and (3) active particulars that distinguish the differences among specific particulars. The results show the conservation planning perspective that models can provide and help review simplistic proxies to estimate the cost of conservation of land desirable for marsh migration. This paper establishes a technique focused on these knowledge measures to gauge the efficiency with which efficient search is enabled by dilemma information. This technique is then extended to numerous search instruments commonly used in the evolutionary quest.

Wireless Communication Network (WSN) applications are growing drastically daily, and their extensive nature attracts researchers and development authorities. The critical issues in wireless communication have been answering by advancements in cognitive radio (CR) techniques. The dynamic spectrum analysis allows cognitive radio at the receiver section to increase the spectrum efficiency and power consumption. The next-generation wireless networks, namely 5G technologies, accept advanced cognitive radios in their functionality. In this work, teaching and Learning based optimization are proposed for future communications purpose. The main aim of the study is to increase the spectral efficiency as well as energy consumption. The earlier methods can only targeting transmission rate and network complexity. The proposed adaptive teaching and learning based WCN can improve 78% energy efficiency and 47% data rate.

The sinking of RMS Titanic is presumably one of the most infamous and disastrous shipwrecks in history. During her maiden voyage and early morning hours of April 15, 1912, the Titanic regrettably sank after colliding with an iceberg, killing an approximate of 1502 passengers and crew out of 2224 making it one of many of the deadliest commercial maritime in history till date. The entire international community was deeply shocked and saddened after hearing the news of this sensational disaster which resulted in improved ship safety legislation. Her architect, Thomas Andrews died in the disaster. An eye-opening observation that came forth from the sinking of Titanic is the fact that some individuals had a better chance at surviving than the others. Kids and women had been given foremost priority. Social classes were heavily stratified in the early twentieth century, this was especially implemented on the Titanic Firstly, the aim is use and apply exploratory data analytics (EDA) to uncover previously unknown or hidden facts in the data set available. Then the task is to later anoint various machine learning models to conclude the study of which types of individuals are more likely to live. The outcomes of application of the different machine learning models were then set side by side and analyzed based upon precision.

we provided a framework for the acquisition of articulated electricity regulations for consistent states and actions, but it has only been attainable in summarised domains since then. Developers adapt our environment to learning maximum entropy policies, leading to a simple Q-learning service, which communicates the global optimum through a Boltzmann distribution. We could use previously approved amortized Stein perturbation theory logistic regression rather than estimated observations from that distribution form to obtain a stochastic diffusion network. In simulated studies with underwater and walking robots, we confirm that the entire algorithm's cost provides increased exploration or term frequency that allows the transfer of skills between tasks. We also draw a comparison to critical actor methods, which can represent on the accompanying energy-based model conducting approximate inference. Misleading multiplayer uses the recompense power to ensure that the user is further from either the evolutionary algorithms but has now evolved to become a massive task in developing intelligent exploration for deep reinforcement learning. In a misleading game, nearly all cutting-edge research techniques, including those qualify superstition yet, even with self-recompenses, which achieves enhanced outcomes in the sparse re-ward game, often easily collapse into global optimization traps. We are introducing another exploration tactic called Maximum Entropy Expand (MEE) to remedy this shortage (MEE). Based on entropy rewards but the off-actor-critical reinforced learning algorithm, we split the entity adventurer policy into two equal parts, namely, the target rule and the adventure policy. The explorer law is used to interact with the world, and the target rule is used to create trajectories, with the higher precision of the targets to be achieved as the goal of optimization. The optimization goal of the targeted approach is to maximize extrinsic rewards in order to achieve the global result. The ideal experience replay used to remove the catastrophic forgetting issue that leads to the operator's information becoming non-normalized during the off-exploitation period. To prevent the vulnerable, diverging, and generated by the dangerous triad, an on-policy form change is used specifically. Users analyse data likening our strategy with a region technique for deep learning, involving grid world experimentation techniques and deceptively recompense Dota 2 environments. The case illustrates that the MME strategy tends to be productive in escaping the current paper's coercive incentive trap and learning the correct strategic plan.

the authors suggest possible arrangements between different supermarket chains in this study, using a strategic methodology and game theory to develop a modern, with generations to come, effectively diets. The authors will specifically acknowledge service providers and sellers of vegetarian (vegetarian) but semi (or non-vegetarian) foods. The strategic approach being used by authors offers a model of mathematical game theory that could help vegan food manufacturers get easier entrants and free substantial ads. Besides that, the template could allow non-vegetarian food producers to turn to vegan and vegetarian production seamlessly. In particular, the authors suggest an arrangement between McDonald’s and Muscle of Wheat since they assume that without the aid of a major food supplier already on the market, Wheat’s strength will not reach a global market. The game theory model describes an irregular R&D partnership between McDonald’s and strength of Wheat. Demand for food items that are organic, nutritious, and environmentally friendly has been growing. In response, producers' market natural or other quality-differentiated foods, often claiming to have implemented good habits focused on environmental welfare. These goods have unfalsifiable consistency characteristics, as well. Because the seller may deceive the buyer with a baseless assertion, the manufacturer who maximizes profit can earn a better price with lower production costs. The research addressed in this paper indicates that the availability of high-quality certificates requires regular agreements and third-party oversight. Policy implications of this research are recorded for national organic food criteria.

The Binary classification is the most challenging problem in machine learning. One of the most promising technique to solve this problem is by implementing genetic programming (GP). GP is one of Evolutionary Algorithm (EA) that used to solve problems that humans do not know how to solve it directly. The objectives of this research is to demonstrate the use of genetic programming in this type of problems; that is, other types of techniques are typically used, e.g., regression, artificial neural networks. Genetic programming presents an advantage compared to those techniques, which is that it does not need an a priori definition of its structure. The algorithm evolves automatically until finding a model that best fits a set of training data. Feature engineering was considered to improve the accuracy. In this research, feature transformation and feature creation were implemented. Thus, genetic programming can be considered as an alternative option for the development of intelligent systems mainly in the pattern recognition field.

The concept of home automation is an on growing topic in this century and it plays a vital role in our daily lives as individuals, as innovators and also as a collective society. It reduces the human labor, time, and the amount of effort spent on daily essential and tiring tasks. The primary idea behind this research is to cover various challenges and possibilities regarding home automation. Today, the demand for better home security systems has increased widely. Not only do homes need to be secure but also need a simple and refined control system. These days, home automation is being exercised in every smart home and it considers all factors including the security of appliances too, especially when a person is not at their home, which eventually also helps to minimize the usage of electricity. It is a wide-ranging concept as it can have from one automated device to a number of automated devices and can have the whole house in one system’s control at any point of time. This huge task can produce a variety of challenges, but once they are solved, it can lead to a range of possibilities and thus be one of the most useful and advanced innovations of our tech-based generation and our fast-moving society as a whole. In this paper, a voice recognition-based model has been proposed, which enhances the user experience in smart home management. This topic was hence chosen due to its vast number of possibilities in the near future and its innumerable advancements day by day in this evolving society. The objective thus remains to list its challenges and also to provide solutions for them and come up with a system that is effective and affordable.

IoT has redefined the interaction of human life with smart objects and added a new dimension to it. Thus, we are able to connect to media and anything at any place and anywhere. The Internet of things (IoT) is often seen as a common problem area, despite options being proposed for a wide range of applications. Nonetheless, confidentiality and security requirements are very unique with reference to the requirements of a native smart home. This paper discusses the smart home protocols, which are used for short range communication with low consumption of power. We have also discussed the security issues related to smart home management. Paper also discusses about the smart lock management which we have explained in detail with its implementation protocol and other challenges. The adaption of the IoT is increasing day by day which benefits to all of us, but at the same time it also challenges us to keep addressing the issues with timely manner.

Digital twins, block chains, Internet of things (IoT) and artificial intelligence (AI) may re-define our imagination and future vision of globalization. Digital twins will likely affect most of the worldwide enterprises as it duplicates the physical models for remote monitoring, viewing and controlling based on numerous algorithms and its digital format. It is the digitalized living model of the physical system that continuously adapts to operational variations based on real-time data from numerous IoT sensors and devices and provides future forecasts corresponding to the physical counterparts with the help of machine learning and IoT. We have investigated the related research that is progressing in this field. In the context of IoT Cyber Physical Systems (CPSs), the major research areas such as machine learning and big data are examined. Machine learning facilitates the development of new models and technology systems in the domain of intelligent smart manufacturing.

In this work, we determined the electron dispersing structure factors, just as the vitality levels of certain cores. The computation of electron dispersing structure factors needs numerous issues to be remembered for request to make these figures attainable and quick in time in light of enormous measure of terms speak to arithmetic, quantum mechanical speculations, atomic shell model hypotheses and equations. In the current work, we examined the impacts of the higher setup outside the shell model space and the inactive center which included Tassie Model (TM) to discuss the Longitudinal C2 electron scattering form factors for the nuclei: 116Sn, 92Mo,90Zr ,39K and 32S, which calculated for nuclei under consideration, are compared with those of experimental data. The HO and SKX possibilities have been utilized to compute the wave elements of outspread single-molecule framework components. Some hypothetical vitality levels of the 52Cr, 32S and 181Ta nuclei are calculated compared with their experimental data. The shell model for windows code NuShellX@MSU has been used in this study.This article is being retracted because it contains substantial overlap with a manuscript submitted to Papers in Physics by Shamil Radhi and ultimately not published, under the supervision of Professor Dr. Khalid Salih Jassim at the University of Babylon, College of Education for Pure Sciences. In addition, the name and affiliation of K. Praghash was used without their knowledge or consent. AIP Publishing has a strong commitment to preserving the integrity of the scientific record, and given the extent of the overlap and the failure to properly credit the original source, this article has been retracted on August 16, 2021.

Simulation upshot of photonic crystal fiber (PCF) is lucidly divulged in this research to envisage photonic topological insulator which depicts as a new application of silicon photonics. The notion and proposition of present work manipulates with plane wave expansion method to realize the photonic current at surface of PCF rather than core region. Finally the current proposal asserts that photonic crystal fiber with apt structure and parameter can be a good candidate for photonic topological insulator application.

A single apt photonic device is adduced in this research for operating two signals concurrently In this case the proposed device deals with synthetic grating silicon on insulator (SOI) structure, where signals are realized with the wavelengths of 1310 nm and 1550 nm. To accomplish the same, reflectance and transmittance characteristics are investigated using plane wave expansion (PWE) technique pertaining wavelengths. To validate the PWE method, this report compares the output f some exsiting work with present simulating result which is carried out by PWE method. Finally, this work divulges that the thickness of odd and even layer including nature, position and configuration of grating SOI structure is a paramount parameter to yield maximum output from such aforementioned proposed structure.

Based upon the experimental results of the two CFOA-based fully-uncoupled sinusoidal oscillators proposed earlier by Bhaskar-Gupta-Senani-Singh, recently Abuelma’atti and Khalifa issued a caution that the classification of this class of oscillators providing independent control of condition of oscillation (CO) and frequency of oscillation (FO) through separate elements, as fully-uncoupled, does not appear to be correct. In this communication, we present the results of a recheck of the experimental data of Abuelma’atti and Khalifa and show that their experimental results are not repeatable and in most of the cases, the oscillations do not even exist and hence, the measurement data as given by them is misleading. Furthermore, it is shown that the measured oscillation frequencies, as shown by Abuelma’atti and Khalifa in four tables, do not tally with those calculated from their own formula as well as the experimental results taken by us. This communication thus, refutes the claims made in the quoted paper of Abuelma’atti and Khalifa published in this journal earlier.

The percentage of individuals who need to move around with the help of external assistance, whether triggered by a disease or an accident, is steadily growing. The aids must be more advanced, taking advantage of technological developments to boost the people's quality of life and promote their integration into the community. This can help facilitate motion and grant independence and freedom to an individual to help them blend in with the society. The main goal of this project is to build a wheelchair that can be fully controlled by a person’s facial movement without any physical feedback from the user as a control input. With our system, we aim to enable quadriplegic patients who cannot move parts of their lower body to regain their independence to move around without the need for an individual to push them around. Using artificial intelligence and the Internet of Things, we have developed an extremely low-cost system that enables people of determination to move a wheelchair simply by using their facial expressions and their smartphone.

World population is expanding continuously; the current world population is more than seven billion. And approximately 65–70% of the total population by 2050 will be in urban cities. This has also given a significant rise in the concept of Smart cities. To keep a pace with this fast moving life, Smart city provides equilibrium. With the fast moving world the life of human also became very fast which comes with lots of daily problems too. People forget where they placed their things. Daily life becomes a mess to many people nowadays. Our paper is an attempt to provide a design of a smart device which will help user for day to day memory management. A device, which will coordinate the help of RFID and IOT to recognize every second in a day. The methodology of the same paper talks about RFID and sensor-based object and one central device coordination. Once you put it on and it tracks everything, analyses everything and tells you where your object was kept last. This device will be very helpful especially for patients suffering from memory loss problems. Memory is a problem to all and the device can help out in this busy world.

Since the introduction of differential difference amplifier (DDA) in 1987, several CMOS implementations of the DDA have been evolved, as well as, a number of applications for realizing linear and non-linear functions using DDAs have been reported. However, to the best of authors’ knowledge, any circuit for realizing single-resistance-controlled oscillator (SRCO) using DDAs has not been reported so far. The objective of this paper is to report four new DDA-based SRCOs using DDAs, as another new application of DDAs. The workability of the derived SRCOs has been verified by PSPICE simulation as well as realizing the DDAs by commercially available video differencing amplifier AD830.

The differential difference amplifier (DDA) was introduced more than two decades back and its several applications as well as bipolar/CMOS implementations have been reported so far. This paper presents a new second order multiple-input---single-output type universal filter configuration using only two DDAs along with only five passive components which can realize all the five generic filter functions from the same configuration under various conditions in terms of the four input signals. Furthermore, it is demonstrated that DDA-based circuits can be practically implemented using commercially available video differencing amplifier IC AD830.

The Detection of vehicles, whether wheeled or tracked can be achieved using seismic sensors as all vehicles when they move create vibrations which travel as waves through the earth and based on the type of vibrations generated by the movement of vehicles; classification of the vehicles can be carried out. This can also be achieved by using acoustic sensors but this approach inherits the problem of Doppler shift which is also associated with detection of signals through Radars. This paper is devoted to seismic detection of vehicles by using unpowered geophones sensors. The event triggering is captured in real time in different terrain scenarios like loose earth or concrete approaches. The geophones are connected through wires with the event detection and data analyzing system. However, the complexity of the seismic waves and its dependency on the underlying geological characteristics throws the challenges in the vehicle detection and recognition and extraction of robust feature vector which can explicitly correspond to a specific type of vehicle or target. This paper has emerged from the series of field experiments carried out and performance evaluation conducted to derive the optimal relationship of the different parameters. It proposes a new feature extraction algorithm- spectral statistics and wavelet coefficients characterization (SSWCC). SSWCC extracts a feature vector from both the frequency and the time frequency domain analysis of the seismic signals, including the spectrum, the power spectral density (PSD)and the wavelet coefficients. The SSWCC algorithm is designed for real-time applications, and a series of experiments shown its robustness and effectiveness