Mappadendang: Ekspresi Rasa Syukur Oleh Masyarakat Petani di Atakka Kabupaten Soppeng

This study aims to determine the ritual process and the meaning and values ​​contained in the traditional mappadendang party. In this study using descriptive qualitative research methods, namely research that describes situations directly in the research place. Meanwhile, the data collection techniques used were observation, interviews, and documentation. The results of the study indicate that the process of the traditional mappadendang party ritual in Soppeng Regency is very structured and neatly arranged. Starting from determining the day of implementation and the duration or length of time for implementation. After the process of determining the time is complete, the family who will carry out the traditional party begins to arrange the arrangement of the mappadendang traditional party. For example, entertainment events and players who will pound the pestle on the mortar and the attributes used by players at the traditional party. The traditional party ended with a marked meal together by all the people who attended the traditional party. In the traditional mappadendang party there are meanings and values ​​contained in it, these meanings are applied in the form of actions such as gratitude to God and respecting and preserving the heritage of ancestors or ancestors. Likewise with the values ​​contained in the traditional mappadendang party, the values ​​of togetherness, kinship, entertainment, and religion are merged into one in a traditional party that continues to be preserved and becomes a way of life for the community

Just-in-time adaptive similarity component analysis in nonstationary environments

This article introduces a just-in-time adaptive nonparametric multiclass component analysis technique for application in nonstationary environments. This generative model enables adaptive similarity-based classifiers to classify time-labeled inquiry patterns with superior accuracy in low-dimensional feature space. While there are adaptive forms of feature extraction methods, which transform training patterns to low-dimensional space and/or improve classifier accuracy, they are vulnerable to nonparametric changes in data and must continuously update their parameters. In the proposed method, an optimal transformation matrix transforms time-labeled instances from the original space to a new feature space to maximize the probability of selecting the correct class label for incoming instances using similarity-based classifiers. To this end, for a given time-labeled instance, nonparametric intra-class and extra-class distributions are proposed. The proposed method is also furnished to a temporal detector to provide the most convenient time for the adaptation phase. Experimental results on real and synthesized datasets that include real and artificial changes demonstrate the performance of the proposed method in terms of accuracy and dimension reduction in dynamic environments

Preserving brightness in histogram equalization based contrast enhancement techniques

Histogram equalization (HE) has been a simple yet effective image enhancement technique. However, it tends to change the brightness of an image significantly, causing annoying artifacts and unnatural contrast enhancement. Brightness preserving bi-histogram equalization (BBHE) and dualistic sub-image histogram equalization (DSIHE) have been proposed to overcome these problems but they may still fail under certain conditions. This paper proposes a novel extension of BBHE referred to as minimum mean brightness error bi-histogram equalization (MMBEBHE). MMBEBHE has the feature of minimizing the difference between input and output image's mean. Simulation results showed that MMBEBHE can preserve brightness better than BBHE and DSIHE. Furthermore, this paper also formulated an efficient, integer-based implementation of MMBEBHE. Nevertheless, MMBEBHE also has its limitation. Hence, this paper further proposes a generalization of BBHE referred to as recursive mean-separate histogram equalization (RMSHE). RMSHE is featured with scalable brightness preservation. Simulation results showed that RMSHE is the best compared to HE, BBHE, DSIHE, and MMBEBHE

Development of a miniature robot for swarm robotic application

Biological swarm is a fascinating behavior of nature that has been successfully applied to solve human problem especially for robotics application. The high economical cost and large area required to execute swarm robotics scenarios does not permit experimentation with real robot. Model and simulation of the mass number of these robots are extremely complex and often inaccurate. This paper describes the design decision and presents the development of an autonomous miniature mobile-robot (AMiR) for swarm robotics research and education. The large number of robot in these systems allows designing an individual AMiR unit with simple perception and mobile abilities. Hence a large number of robots can be easily and economically feasible to be replicated. AMiR has been designed as a complete platform with supporting software development tools for robotics education and researches in the Department of Computer and Communication Systems Engineering, UPM. The experimental results demonstrate the feasibility of using this robot to implement swarm robotic applications

Unsupervised place recognition for assistive mobile robots based on local feature descriptions.

Place recognition is an important perceptual robotic problem, especially in the navigation process. Previous place-recognition approaches have been used for solving ‘global localization’ and ‘kidnapped robot’ problems. Such approaches are usually performed in a supervised mode. In this paper, a robust appearance-based unsupervised place clustering and recognition algorithm is introduced. This method fuses several image features using speed up robust features (SURF) by agglomerating them into a union form of features inside each place cluster. The number of place clusters can be extracted by investigating the SURF-based scene similarity diagram between adjacent images. During a human-guided learning step, the robot captures visual information acquired by an embedded camera and converts them into topological place clusters. Experimental results show the robustness, accuracy, and efficiency of the method, as well as its ability to create topological place clusters for solving global localization and kidnapped robot problems. The performance of the developed system is remarkable in terms of time, clustering error, and recognition precision

Interoperability Framework for Smart Home Systems.

Recent advancements in smart home systems have increased the utilization of consumer devices and appliances in home environment. However, many of these devices and appliances exhibit certain degree of heterogeneity and do not adapt towards joint execution of operation. Hence, it is rather difficult to perform interoperation especially to realize desired services preferred by home users. In this paper, we propose a new intelligent interoperability framework for smart home systems execution as well as coordinating them in a federated manner. The framework core is based on Simple Object Access Protocol (SOAP) technology that provides platform independent interoperation among heterogeneous systems. We have implemented the interoperability framework with several home devices to demonstrate their effectiveness for interoperation. The performance of the framework was tested in Local Area Network (LAN) environment and proves to be reliable in smart home settin

A multi-purpose watermarking scheme based on hybrid of lifting wavelet transform and Arnold transform

This paper introduces a new multi-purpose image watermarking algorithm which based on a hybrid of lifting wavelet transform (LWT) and Arnold transform for copyright protection and image authentication. In the proposed scheme, the original image is first decomposed by LWT into four subbands. Then the robust watermark which is a binary logo image is decomposed by Discrete Wavelet Transform (DWT) as such only the high frequency subband of the watermark are embedded in the low frequency subband of the original image. The fragile watermark is block wise self-generated from the original image and scrambled using Arnold transform which is later embedded in the spatial domain of the robust watermarked image. Self-generated fragile watermark supports self-authentication with high localization, whereas scrambling the fragile watermark increases the security of the algorithm. On the other hand, the lifting scheme approaches have almost one half the amounts of operations compared to the DWT based approaches. The overall system has been tested against various attacks and the results demonstrated that the robust watermark can be decoded successively under each attack. In addition, the proposed algorithm can detect any tampering attempts

A new method for MR grayscale inhomogeneity correction

Intensity inhomogeneity is a smooth intensity change inside originally homogeneous regions. Filter-based inhomogeneity correction methods have been commonly used in literatures. However, there are few literatures which compare effectiveness of these methods for inhomogeneity correction. In this paper, a new filter-based inhomogeneity correction method is proposed and the effectiveness of the proposed method and other filter-based inhomogeneity correction methods are compared. The methods with different kernel sizes are applied on MRI brain images and the quality of inhomogeneity correction of different methods are compared quantitatively. Experimental results show the proposed method in a kernel size of 20 * 20 performs almost better than or equal the performance of other methods in all kernel sizes

Optimizing of ANFIS for estimating INS error during GPS outages.

Global positioning system (GPS) has been extensively used for land vehicle navigation systems. However, GPS is incapable of providing permanent and reliable navigation solutions in the presence of signal evaporation or blockage. On the other hand, navigation systems, in particular, inertial navigation systems (INSs), have become important components in different military and civil applications due to the recent advent of micro-electro-mechanical systems (MEMS). Both INS and GPS systems are often paired together to provide a reliable navigation solution by integrating the long-term GPS accuracy with the short-term INS accuracy. This article presents an alternative method to integrate GPS and INS systems and provide a robust navigation solution. This alternative approach to Kalman filtering (KF) utilizes artificial intelligence based on adaptive neuro-fuzzy inference system (ANFIS) to fuse data from both systems and estimate position and velocity errors. The KF is usually criticized for working only under predefined models and for its observability problem of hidden state variables, sensor error models, immunity to noise, sensor dependency, and linearization dependency. The training and updating of ANFIS parameters is one of the main problems. Therefore, the challenges encountered implementing an ANFIS module in real time have been overcome using particle swarm optimization (PSO) to optimize the ANFIS learning parameters since PSO involves less complexity and has fast convergence. The proposed alternative method uses GPS with INS data and PSO to update the intelligent PANFIS navigator using GPS/INS error as a fitness function to be minimized. Three methods of optimization have been tested and compared to estimate the INS error. Finally, the performance of the proposed alternative method has been examined using real field test data of MEMS grade INS integrated with GPS for different GPS outage periods. The results obtained outperform KF, particularly during long GPS signal blockage