Volume 28, Issue 1

Due to its wide range of applications, the impact of multimedia in the real world has shown stupendous growth. Texts, images, audio, and video are the different forms of multimedia which are utilized by humans in various applications such as education and surveillance applications. A wide range of research has been carried out, and here in this paper, we propose an object racking with the aid of rough set theory in combination with the eminent soft computing technique evolutionary programming. Initially, the input video is segregated into frames, then the frames that belong to particular shots are identified through the shot segmentation process, and after that the object to be tracked is identified manually. Subsequently, the shape and texture feature is extracted, and then the rough set theory is applied. This is done to identify the presence of object in the frames. Consequently, genetic algorithm (GA) is utilized for the object monitoring process to mark the object with variant color. As a result, the selected object is tracked in an effective manner.

In this paper, we present a novel word clustering technique to capture contextual similarity among the words. Related word clustering techniques in the literature rely on the statistics of the words collected from a fixed and small word window. For example, the Brown clustering algorithm is based on bigram statistics of the words. However, in the sequential labeling tasks such as named entity recognition (NER), longer context words also carry valuable information. To capture this longer context information, we propose a new word clustering algorithm, which uses parse information of the sentences and a nonfixed word window. This proposed clustering algorithm, named as variable window clustering, performs better than Brown clustering in our experiments. Additionally, to use two different clustering techniques simultaneously in a classifier, we propose a cluster merging technique that performs an output level merging of two sets of clusters. To test the effectiveness of the approaches, we use two different NER data sets, namely, Hindi and BioCreative II Gene Mention Recognition. A baseline NER system is developed using conditional random fields classifier, and then the clusters using individual techniques as well as the merged technique are incorporated to improve the classifier. Experimental results demonstrate that the cluster merging technique is quite promising.

Production systems are composed of increasingly complex components with unique specifications. Therefore, since holding safety stocks of each component would be prohibitive, maintenance activities rely on the proper delivery of spare parts, making it available at the right time and place. Equipments monitored by sensors as well as the transmission of sensors data to the spare part supply chain represent an interesting venue for dealing with this contemporaneous industrial challenge. In this direction, this paper applies a simulation model derived from a real world scenario to analyze the performance of the collaboration between condition-based maintenance – also known as intelligent maintenance systems – and spare parts supply chains, in comparison with existing maintenance approaches. Obtained results substantiate the potential of monitoring, treating and transmitting equipment condition data to ensure cost-effective maintenance and production systems availability.

Glaucoma is one of the severe visual diseases that lead to damage the eyes irreversibly by affecting the optic nerve fibers and astrocytes. Consequently, the early detection of glaucoma plays a virtual role in the medical field. The literature presents various techniques for the early detection of glaucoma. Among the various techniques, retinal image-based detection plays a major role as it comes under noninvasive methods of detection. While detecting glaucoma disorder using retinal images, various medical features of the eyes, such as retinal nerve fiber layer, cup-to-disc ratio, apex point, optic disc, and optic nerve head, and image features, such as Haralick texture, higher-order spectra, and wavelet energy, are used. In this paper, a review and study were conducted for the different techniques of glaucoma detection using retinal fundus images. Accordingly, 45 research papers were reviewed and the analysis was provided based on the extracted features, classification accuracy, and the usage of different data sets, such as DIARETDB1 data set, MESSIDOR data set, IPN data set, ZEISS data set, local data set, and real data set. Finally, we present the various research issues and solutions that can be useful for the researchers to accomplish further research on glaucoma detection.

In the real-world complex product design (CPD) process, task allocating is an ongoing reactive process where the presence of unexpected design change is usually inevitable. Therefore, reallocating is necessary to respond to design change positively as a procedure to repair the affected task plan. General reallocating literature addressed the reallocating versions with fixed executing time. In this paper, a multi-objective reallocation model is developed with a feasible assumption that the task executing time is controllable. To illustrate this idea, a compressing executing time strategy (CETS) is proposed in CPD process, where the executing time can be controlled with a non-linear compression cost. When design change occurs during the executing, task-resource reallocating is required to absorb the interference effects. Reallocating implies an increase in design cost and system instability; the proposed method CETS can address this issue effectively. CETS considers three objectives: completing time, stability, and change-adaptation cost. An adaptive multi-objective hybrid genetic algorithm and tabu search (AMOGATS) is developed to solve this mathematical method. The computational results of specific simulation examples verify the superiority. It shows that CETS is sensitive to design change, and the proposed algorithm AMOGATS can be effective to achieve the allocating by coordinating the objective consistency.

The traditional ray casting algorithm has the capability to render three-dimensional volume data in the viewable two-dimensional form by sampling the color data along the rays. The speed of the technique relies on the computation incurred by the huge volume of rays. The objective of the paper is to reduce the computations made over the rays by eventually reducing the number of samples being processed throughout the volume data. The proposed algorithm incorporates the grouping strategy based on fuzzy mutual information (FMI) over a group of voxels in the conventional ray casting to achieve the reduction. For the data group, with FMI in a desirable range, a single primary ray is cast into the group as a whole. As data are grouped before casting rays, the proposed algorithm reduces the interpolation calculation and thereby runs with lesser complexity, preserving the image quality.

Reduction in file size leads to reduction in the number of bits required to store it. When data is compressed, it must be decompressed into its original form bit for bit. Compound images are defined as images that contain a combination of text, natural (photo) images and graphic images. Here, compression is the process of reducing the amount of data required to represent information. Image compression is done on the basis of various loss and lossless compression algorithms. This research work deals with the preprocessing and transformations used to compress a compound image to produce a high compression ratio (CR), less compression time and so on. In the compression process the images are considered for preprocessing and discrete wavelet transform with adaptive particle swarm optimization process. The purpose of this optimization technique is to optimize the wavelet coefficient in Harr wavelet for improving the CR value. In the image compression process, run length coding is used to compress the compound images. Based on this technique, it produces minimum CR and less computation time of compound images.

Research on internal waves in the coastal ocean is one of the most important tasks both in physical oceanography and ocean monitoring network. Currently, how to quickly and accurately detect the ocean internal waves from the huge ocean surface is still a challenging issue. In this paper, we model the ocean internal wave detection as a task of region classification for texture images and then propose a rapid internal waves detection method based on a deep learning framework (PCANet). In the proposed method, two models have been trained: one is the deep feature representation model, which combines principal component analysis (PCA), binary hashing, and block-wise histograms and can extract more distinguishing features than handcraft feature. Moreover, because the filter learning in PCANet does not require regularized parameters and numerical optimization solver, the training process of the representation model is very fast. The other one is a classification model based on a linear support vector machine. The object proposal method has been applied to get the possible candidates when analyzing a captured image, which dramatically decreases the searching time. Experiment results on the data set captured by unmanned aerial vehicles verify the speed ability and effectiveness of the proposed method.

Recent research studies showed that brain-controlled systems/devices are breakthrough technology. Such devices can provide disabled people with the power to control the movement of the wheelchair using different signals (e.g. EEG signals, head movements, and facial expressions). With this technology, disabled people can remotely steer a wheelchair, a computer, or a tablet. This paper introduces a simple, low-cost human-machine interface system to help chaired people to control their wheelchair using several control sources. To achieve this paper’s aim, a laptop was installed on a wheelchair in front of the sitting person, and the 14-electrode Emotiv EPOC headset was used to collect the person’s head impressions from the skull surface. The superficially picked-up signals, containing the brain thoughts, head gestures, and facial emotions, were electrically encoded and then wirelessly sent to a personal computer to be interpreted and then translated into useful control instructions. Using these signals, two wheelchair control modes were proposed: automatic (using single-modal and multimodal approaches) and manual control. The automatic mode controller was accomplished using a software controller (Arduino), whereas a simple hardware controller was used for the manual mode. The proposed solution was designed using wheelchair, Emotiv EPOC EEG headset, Arduino microcontroller, and Processing language. It was then tested by totally chaired volunteers under different levels of trajectories. The results showed that the person’s thoughts can be used to seamlessly control his/her wheelchair and the proposed system can be configured to suit many levels and degrees of disability.

This paper presents a novel analytical method to develop the multiobjective function including energy and stability functions. The energy function has been developed by unique approach of orbital energy concept and the stability function obtained by modifying the pre-existing zero moment point (ZMP) trajectory. These functions are optimized using real coded genetic algorithm to produce an optimum set of walk parameters. The analytical results show that, when the energy function is optimized, the stability of the robot decreases. Similarly, if the stability function is optimized, the energy consumed by the robot increases. Thus, there is a clear trade-off between the stability and energy functions. Thus, we propose the multiobjective evolutionary algorithm to yield the optimum value of the walk parameters. The results are verified by Nao robot. This approach increases the energy efficiency of Nao robot by 67.05%, and stability increases by 75%. Furthermore, this method can be utilized on all ZMP classed bipeds.

Cloud computing is an emerging technology in distributed computing, which facilitates pay per model as per user demand and requirement. Cloud consists of a collection of virtual machines (VMs), which includes both computational and storage facility. In this paper, a task scheduling scheme on diverse computing systems using a hybridization of genetic and group search optimization (GGSO) algorithm is proposed. The basic idea of our approach is to exploit the advantages of both genetic algorithm (GA) and group search optimization algorithms (GSO) while avoiding their drawbacks. In GGSO, each dimension of a solution symbolizes a task, and a solution, as a whole, signifies all task priorities. The important issue is how to assign user tasks to maximize the income of infrastructure as a service (Iaas) provider while promising quality of service (QoS). The generated solution is competent to assure user-level (QoS) and improve Iaas providers’ credibility and economic benefit. The GGSO method also designs the producer, scrounger ranger, crossover operator, and suitable fitness function of the corresponding task. According to the evolved results, it has been found that our algorithm always outperforms the traditional algorithms.

Musical pitch estimation is used to recognize the musical note pitch or the fundamental frequency ( F 0 ) of an audio signal, which can be applied to a preprocessing part of many applications, such as sound separation and musical note transcription. In this work, a method for musical note recognition based on the classification framework has been designed using an optimization-based neural network (OBNN). A broad range of survey and research was reviewed, and all revealed the methods to recognize the musical notes. An OBNN is used here in recognizing musical notes. Similarly, we can progress the effectiveness of musical note recognition using different methodologies. In this document, the most modern investigations related to musical note recognition are effectively analyzed and put in a nutshell to effectively furnish the traits and classifications.