A SOM Based Approach to Skin Detection with Application in Real Time Systems

Artificial neural network (ANN) is a simplified model of the biological nervous system consisting of nerve cells or neurons. The application of ANN to food process engineering is relatively novel. ANN had been employed in diverse applications like food safety and quality analyses, food image analysis, and modeling of various thermal and non-thermal food-processing operations. ANN has the ability to map nonlinear relationships without any prior knowledge and predicts responses even with incomplete information. Every neural network possesses data in the form of connection weights interconnecting lines between the input to hidden layer neurons and weights of hidden to output layer neurons, which has a significant role in predicting the output data. The applications of ANN in different unit operations in food processing were described that includes theoretical developments using intelligent characteristics for adaptability, automatic learning, classification, and prediction. The parallel architecture of ANN resulted in a fast response and low computational time making it suitable for application in real-time systems of different food process operations. The predicted responses obtained by the ANN model exhibited high accuracy due to lower relative deviation and root mean squared error and higher correlation coefficient. This paper presented the various applications of ANN for modeling nonlinear food engineering problems. The application of ANN in the modeling of the processes such as extraction, extrusion, drying, filtration, canning, fermentation, baking, dairy processing, and quality evaluation was reviewed. HIGHLIGHTS 1. This paper discusses application of ANN in different emerging trends in food process. 2. Application of ANN to develop non-linear multivariate modeling is illustrated. 3. ANNs have been shown to be useful tool for prediction of outcomes with high accuracy. 4. ANN resulted in fast response making it suitable for application in real time systems.

Fast object detection in digital grayscale images The problem of specific object detection in digital grayscale images is considered under the following conditions: relatively small image fragments can be analysed (a priori information about the size of objects is available); images contain a varying undefined background (clutter) of larger objects; processing time should be minimised and must be independent from the image contents; proposed methods should provide for efficient implementation in application-specific electronic circuits. The last two conditions reflect the aim to propose approaches suitable for application in real time systems where known sophisticated methods would be inapplicable. The research is motivated by potential applications in the food industry (detection of contaminants in products from their X-ray images), medicine (detection of anomalies in fragments of computer tomography images etc.). Possible objects to be detected may include compact small objects, curved lines in different directions, and small regions of pixels with brightness different from the background. The paper describes proposed image processing approaches to detection of such objects and the results obtained from processing of sample food images.

This paper presents a novel application of a hybrid learning approach to the optimisation of membership and nonmembership functions of a newly developed interval type-2 intuitionistic fuzzy logic system (IT2 IFLS) of a Takagi–Sugeno–Kang (TSK) fuzzy inference system with neural network learning capability. The hybrid algorithms consisting of decoupled extended Kalman filter (DEKF) and gradient descent (GD) are used to tune the parameters of the IT2 IFLS for the first time. The DEKF is used to tune the consequent parameters in the forward pass while the GD method is used to tune the antecedents parts during the backward pass of the hybrid learning. The hybrid algorithm is described and evaluated, prediction and identification results together with the runtime are compared with similar existing studies in the literature. Performance comparison is made among the proposed hybrid learning model of IT2 IFLS, a TSK-type-1 intuitionistic fuzzy logic system (IFLS-TSK), and a TSK-type interval type-2 fuzzy logic system (IT2 FLS-TSK) on two instances of the datasets under investigation. The empirical comparison is made on the designed systems using three artificially generated datasets and three real world datasets. Analysis of results reveal that IT2 IFLS outperforms its type-1 variants, IT2 FLS and most of the existing models in the literature. Moreover, the minimal run time of the proposed hybrid learning model for IT2 IFLS also puts this model forward as a good candidate for application in real time systems.

research-article Free Access Share on Embedded systems in real time applications, design & architecture Authors: A. L. Suseela View Profile , V. Lalith Kumar View Profile Authors Info & Claims UbiquityVolume 2005Issue AugustAugust 2005 pp 2https://doi.org/10.1145/1088431.1086450Online:01 August 2005Publication History 0citation170,093DownloadsMetricsTotal Citations0Total Downloads170,093Last 12 Months1,290Last 6 weeks56 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF

A real-time operating system (RTOS) provides a platform for the design and implementation of a wide range of applications in real-time systems, embedded systems, and mission-critical systems. This paper presents a formal design model for a general RTOS known as RTOS+ that enables a specific target RTOS to be rigorously and efficiently derived in real-world applications. The methodology of a denotational mathematics, Real-Time Process Algebra (RTPA), is described for formally modeling and refining architectures, static behaviors, and dynamic behaviors of RTOS+. The conceptual model of the RTOS+ system is introduced as the initial requirements for the system. The architectural model of RTOS+ is created using RTPA architectural modeling methodologies and refined by a set of Unified Data Models (UDMs). The static behaviors of RTOS+ are specified and refined by a set of Unified Process Models (UPMs). The dynamic behaviors of the RTOS+ system are specified and refined by the real-time process scheduler and system dispatcher. This work is presented in two papers; the conceptual and architectural models of RTOS+ is described in this paper, while the static and dynamic behavioral models of RTOS+ will be elaborated in a forthcoming paper.

Speaker identification identifies the speaker among a set of users by matching against a set of voiceprints. In speaker identification, the identification time depends on the number of feature vectors, their dimensionality and the number of speakers. In this paper, text independent speaker identification model is developed by taking in MFCCs with VQ to obtain pressed feature vectors without losing much information, and the numbers of speakers are reduced in the test by gender detection algorithm. Gaussian Mixture Model (GMM) is used a modeling technique. Results show that proposed approach always yields better improvements in accuracy and brings almost 50% reduces in time processing.

A wide application of real time systems is safety critical systems. These systems should be highly reliable. Fault tolerance is one of the approaches to achieve reliability. In this paper, a fault tolerance model for real time systems is proposed. This model incorporates the concept of time stamped fault tolerance. This model is based on distributed computing along with feed forward artificial neural network methodology. The proposed technique is based on execution of design diverse variants on replicated hardware, and assigning weights to the results produced by variants. Thus the proposed method encompasses both the forward and backward recovery mechanism, but main focus is on forward recovery. The main essence of the proposed technique is the inclusion of time for decision mechanism

BackgroundOur study aimed to compare the reference distributions of serum creatinine and urea obtained by direct sampling technique and two indirect sampling techniques including the Gaussian Mixture Model (GMM) and the Self-Organizing Map (SOM) clustering based on clinical laboratory records, so that the feasibility as well as the potential limitations of indirect sampling techniques could be clarified.MethodsThe direct sampling technique was used in the Pediatric Reference Interval in China (PRINCE) study, in which 15,150 healthy volunteers aged 0 to 19 years were recruited from 11 provinces across China from January 2017 to December 2018. The indirect sampling techniques were used in the Laboratory Information System (LIS) database of Beijing Children’s Hospital, in which 164,710 outpatients were included for partitioning of potential healthy individuals by GMM or SOM from January to December 2016. The reference distributions of creatinine and urea that were established by the PRINCE study and the LIS database were compared.ResultsThe density curves of creatinine and urea based on the PRINCE data and the GMM and SOM partitioned LIS data showed a large overlap. However, deviations were found in reference intervals among the three populations.ConclusionsBoth GMM and SOM can identify potential healthy individuals from the LIS data. The performance of GMM is consistent and stable. However, GMM relies on Gaussian fitting, and thus is not suitable for skewed data. SOM is applicable for high-dimensional data, and is adaptable to data distribution. But it is susceptible to sample size and outlier detection strategy.

Improved likelihood ratio test based voice activity detector applied to speech recognition

Now a day’s security and identification of a person are crucial in real time applications and solving the problems in biometric identification. Objectives: To address the issues in real time, the face and its organ’s identification are the main parts of the human body. The proposed work involves extraction of the face of the real time captured image, after extraction of the face, the organs like eyes, mouth and nose is extracted for identification of exact person. Methods/ Analysis: The present work captures the real time image from cameras or moving video devices for facial recognition using Principal Component Analysis (PCA), the facial features are extracted from both Hidden Markov Model (HMM), Gaussian mixture model (GMM) methods and classified into different organs using Artificial Neural Network (ANN). The features of organs are extracted in different stages, in first stage Eigen values with the help of PCA, second and third stage feature extraction with help GMM and HMM since these three techniques are the most powerful tools for statistical natural image processing. After extraction of organs of the face, the ANN is applied for classifications of eyes, mouth and nose separately. Findings: In the proposed work the facialorgans are separated into threeslight scale images and these are recombined to acquire the appreciationfacial image results like mouth, eyes and nose. The final obtained results shows that the proposed method has been achieved 95.8% recognition accuracy, Fault Rejection Ratio (FRR) is about 93.1% and Fault Acceptance Ratio (FAR) is 1.7 % which are implemented in Matlab2013A.

Applications of real-time systems have grown significantly in both diversity and popularity, and the appetite for real-time software has never been higher. In contrast, the choice of programming languages used to develop such systems has stagnated, mostly limited to decades-old languages, specifically Ada and C/C++, and more recently real-time Java. We posit that the high cost and difficulty of developing new programming languages for real-time systems is the main reason for this mono-culture. To tackle the lack of diversity, we propose the design of a micro virtual machine on which managed programming languages for real-time systems can be developed. Our design facilitates bringing the advantages of correct managed languages to the real-time domain. We build on a previously published micro virtual machine specification, named Mu, and propose a set of modifications to its abstractions over concurrency and memory management to make it suitable for real-time systems. The resulting design is a basis for a new micro virtual machine specification we call RTMu, designed as a reliable and efficient foundation for the development of managed languages for real-time systems.

Dual-layer iterative algorithms are generally required when solving resource allocation problems in wireless communication systems. Specifically, the spectrum efficiency maximization problem for hybrid precoding architecture is hard to solve by the single-layer iterative algorithm. The dual-layer penalty dual decomposition (PDD) algorithm has been proposed to address the problem. Although the PDD algorithm achieves significant performance, it requires high computational complexity, which hinders its practical applications in real-time systems. To address this issue, we first propose a novel framework for deep-unfolding, where a dual-layer deep-unfolding neural network (DLDUNN) is formulated. We then apply the proposed frame-work to solve the spectrum efficiency maximization problem for hybrid precoding architecture. An efficient DLDUNN is designed based on unfolding the iterative PDD algorithm into a layer-wise structure. We also introduce some trainable parameters in place of the high-complexity operations. Simulation results show that the DLDUNN presents the performance of the PDD algorithm with remarkably reduced complexity.

Priority-based Functional Reactive Programming (P-FRP) is a new variant of FRP to model reactive applications in real-time systems. In P-FRP, when the currently running task is preempted by an arriving higher-priority task, the lower-priority running task is aborted and the higher-priority task will execute. The lower-priority task restarts when the higher-priority one completes. However, unlike the preemptive model, when a task aborts, all the changes made by this task are discarded. That is to say, when an aborted ask restarts, it should execute from the beginning. In order to provide a realistic Worst-Case Response Time (WCRT) of the tasks in P-FRP, it is therefore mandatory to derive a realistic Worst Case Execution Time (WCET) of each task. Previous studies have ignored memory latency in the derivation of the WCRT, making the resulting estimate inaccurate and unrealistic. Furthermore, these studies have also assumed that the WCET of each task is known a priori. In this paper, we introduce a scratchpad memory (SPM)-based platform for executing P-FRP tasks and an approach to determine the WCET of the tasks by considering the memory cost of the aborted tasks. We first compute the WCET of a task in a P-FRP system, and then calculate the memory penalty caused by preemption. In the next step, we derive the WCRT of the task sets in P-FRP by considering memory latency in the proposed platform. Experimental results from the derivations of the WCET and WCRT using task sets from the SNU real-time benchmarks and randomly generated tasks are presented to validate this approach.

Background/Objectives: This paper offers a clear traffic control system to pass emergency vehicles easily. In this paper to accomplishment the raise of current innovation called Automated Efficient Traffic Control System: waiting time of emergency vehicles and also used to control the traffic lights. Methods/Statistical Analysis: This makes the utilization of Radio Frequency Identifier (RFID). This makes it very tough to take away or get broken. We use RFID readerTK-5530, and PIC16F877A. A device Micro-chip to take a gander at the RFID Tag related to the vehicle. Findings: If RFID tag takes a look at fits in with the stolen automobile the Information is transmits the usage of GSM SIM300 to the police manager further, whilst ambulance is coming near to the intersection, it will touch to activity controller inside of the intersection switch on the unpractised light. Conclusion: The module makes utilization of Zigbee detachable on CC2500 and PIC16F877A. Device Micro-chip for remote correspondences among the rescue vehicle or visitor controller. It is used to sense ambulance in multidirectional and also sense the ambulance to reach the junction to junction communication with the priority of the vehicle.Keywords: Ambulance Detection, Data Collection of Vehicles, Micro Controller based System, Traffic Management

This paper presents an investigation for analysing the load carrying capacity of journal bearing in a variety of conditions using a proposed neural network (NN). The NN structure is very suitable for this kind of system. The network is capable of predicting the pressures of the experimental system. The network has parallel structure and fast learning capacity. It can be outlined from the results for both approaches, NN could be used to model journal bearing systems in real time applications.