Wireless sensor networks (WSNs) are generally a group of spatially scattered and devoted sensors to record and monitor the physical environmental condition, and the collected data is grouped at a central location. In fact, the environmental conditions such as sound, humidity, temperature, wind, pollution levels, etc., can be clearly determined by WSNs. The principal objective of WSNs is to organize the whole sensor nodes in their related positions, thereby developing an effective network. In WSNs, target COVerage (TCOV) and Network CONnectivity (NCON) are the main concern of the sensor deployment problem. Many research works aspire the evolvement of smart context awareness algorithm for sensor deployment issues in WSN. Here the TCOV and NCON process are deployed as the minimization problem. This article makes an analysis of different GA variations in attaining the objective. The GA variations are as follows: self-adaptive genetic algorithm (SAGA), deterministic-adaptive genetic algorithm (DAGA), Individual- Adaptive Genetic Algorithm (IAGA). Finally, the methods are compared to one another in terms of connectivity and coverage performance.
When working with a high volume of information that follows an exponential pattern, the authors confront big data. This huge amount of information makes big data retrieval and analytics important issues. There have been many attempts to solve data analytic problems using distributed platforms, but the main problem with the proposed methods is not observing the data locality. In this article, a MapReduce-based method called Hengam is proposed. In this method, data format unification helps nodes to have data independence. The unified format leads to an increase in the information retrieval speed and prevents data exchange betoen nodes. The proposed method was evaluated using data items from an ICT company and the information retrieval time was much better than that of other open-source distributed data warehouse software.
This article describes the Bees Algorithm in standard formulation and presents two applications to real-world continuous optimisation engineering problems. In the first case, the Bees Algorithm is employed to train three artificial neural networks ANNs to model the inverse kinematics of the joints of a three-link manipulator. In the second case, the Bees Algorithm is used to optimise the parameters of a linear model used to approximate the torque output for an electro-hydraulic load system. In both cases, the Bees Algorithm outperformed the state-of-the-art in the literature, proving to be an effective optimisation technique for engineering systems.
This article addresses the issue regarding the exploitation of conventional fuel diesel. To overcome this issue, the Tamanu oil-diesel oil blend is introduced, where a new neural model is proposed, which is trained by renowned firefly algorithm, termed as FF-NM. In addition, different compression ratios such as 15, 16, 17, 17.5 and blend ratios like 5:95, 6:94, 7:93, 8:92, and 9:91and 10:90 is exploited. The emission analysis and the combustion characteristics of the TO-diesel oil blend are evaluated as well as the MSE analysis is carried out for the proposed FF-NM method. For all the predicted parameters, the MSE of the proposed method is low for varying blend as well as the compression ratios. Moreover, the emission characteristics of the HC, CO2, NOx, CO, as well as O2 at different CR concerning the actual, and FF-NM is computed with the chosen blend ratios. From analysis, it is recognized that the estimation errors are less for the FF-NM approach. Hence, the simulation outcomes demonstrate the better performance of the proposed FF-NM approach under various compression ratios of 15, 16, 17 and 17.5, respectively.
One of the infrastructureless networks built by various independent mobile nodes is mobile ad hoc network (MANET), which is an emerging technology, requiring a secure routing protocol for data transmission. Accordingly, literature presents various secure routing protocols for MANETs by utilizing trust and data encryption. In this article, a whale optimization algorithm (WOA) is utilized for selecting the optimal secured routing path in the MANET. The WOA algorithm utilizes the trust factor and the distance between the nodes for computing the fitness for the routing path. Overall, the steps involved in the proposed routing algorithm are as follows: i) Measuring the trust and the distance-based metrics for every node; ii) Discovering k-disjoint path; and iii) Determining the optimal path based on the trust and the distance-based metrics. The performance of the trust-based WOA (T-Whale) is analyzed using the metrics, energy, throughput, and packet delivery rate. From the simulation results, it is evident that the T-Whale algorithm has the improved energy, throughput, and PDR values of 27.4520, 0.4, and 0.4, at the simulation time of 10 sec over the conventional trust random search algorithm when the node is under attack.
Tooth caries or cavities diagnosing are concerned as the most significant research work, as this is the common oral disease suffered by humans. Many approaches have been proposed under the topics including demineralization and decaying as well. However, the imaging modalities often suffer from various critical or complex aspects that struggles the methods to attain accurate diagnosis. This article turns to introduce a new cavity diagnosis model with three phases: (i) pre-processing (ii) feature extraction (iii) classification. In the first phase, a new bi-histogram equalization with adaptive sigmoid functions (BEASF) is introduced to enhance the image quality followed by other enhancements models like grey thresholding and active contour. Then, the features are extracted using multilinear principal component analysis (MPCA). Further, the classification is done via neural network (NN) classifier. After the implementation, the proposed model compares its performance over other conventional methods like principal component analysis (PCA), linear discriminant analysis (LDA) and independent component analysis (ICA) and the performance of the approach is analyzed in terms of measures such as accuracy, sensitivity, specificity, precision, false positive rate (FPR), false negative rate (FNR), negative predictive value (NPV), false discovery rate (FDR), F1Score and Mathews correlation coefficient (MCC), and proves the superiority of proposed work.
The main goal of this article is to present an approach that allows the automatic management of autistic communication patterns by processing audio and video from the therapy session of individuals suffering autistic spectrum disorders ASD. Such patients usually have social and communication alterations that make it difficult to evaluate the meaning of those expressions. As their communicational skills may have different degrees of variation, it is very hard to understand the semantics behind the verbal behavior. The current work is based on previous work on machine learning for individual performance evaluation. Statistics show that autistic verbal behavior are physically expressed by repetitive sounds and related movements that are evident and stereotyped. The works of Leo Kanner and Ángel Riviere are also considered here. Using machine learning and neural nets with certain set of parameters, it is possible to automatically detect patterns in audio and video recording of patient's performance, which is an interesting opportunity to communicate with ASD patients.
This paper discusses the direct perception of an unknown object and the action decision to grasp an unknown object using depth sensor for social robots. Conventional methods estimate the accurate physical parameters when a robot wants to grasp an unknown object. Therefore, we propose a perceptual system based on an invariant concept in ecological psychology, which perceives the information relevant to the action of the robot. Firstly, we proposed the plane detection based approach for perceiving an unknown object. In this paper, we propose the sensation of grasping which is expressed by using inertia tensor, and applied with fuzzy inference using the relation between principle moment of inertia. The sensation of grasping encourages the decision for the grasping action directly without inferring from physical value such as size, posture and shape. As experimental results, we show that the sensation of grasping expresses the relative position and posture between the robot and the object, and the embodiment of the robot arm by one parameter. And, we verify the validity of the action decision from the sensation of grasping.
This paper presents a health promotion system with robot partner for elderly care. Aging society in Japan has been a big serious problem. The number of caregivers is not enough in the current situation and is not expected to substantially increase in future. Hence, comprehensive care and health promotion should be provided to heighten awareness about health. In this study, we built a daily exercise support system with a robot partner utilized as an exercise instructor. Moreover, we propose a human-robot communication model based on self-serving bias. In the experiment, we conduct a demonstration experiment and interview survey to discuss the validity of the communication model.
Conversion of Bangla language to another native language and another language to Bangla language using Universal Networking Language (UNL) is highly demanding due to rapidly increasing the usage of Internet-based applications. UNL has been used by various researchers as an inter-lingual approach for an Automated Machine Translation (AMT) scheme. This article presents a novel work on construction of EnConverter for Bangla language with a special focus on generation of UNL attributes and resolving relations of Bangla text. The architecture of Bangla EnConverter, algorithms for understanding the Bangla input sentence; resolution of UNL relations; and attributes for Bangla text/language are also explained in this article. This article highlights the analysis rules for EnConverter and indicates its usage in generation of UNL expressions. This article presents the results of implementation of Bangla EnConverter and compares these with the system available at Russian and English Language Server.
In a real-world environment, a social robot is constantly required to make many critical decisions in an ambiguous and demanding stressful environment. Hence, a biological stress response system model is a good gauge indicator to judge when the robot should react to such environment and adapt itself towards the environment changes. This work is to implement the Smerek's reflective processing model into human-robot communication application where reflective processing is triggered during such situations where the best action is not known. The authors want to investigate how to address better the human-robot communication problems with the focus on reflective processing model in the perspectives of working memory, Spiking Neural Network SNN and stress response system. The authors had applied their proposed Spiking Reflective Processing model for the human-robot communication application in a university population. The initial experimental results showed the positive attitude changes before and after the human-robot interaction experiment.
Recently, the importance of community-centric systems is increasing in the human society. Human-centric systems can enhance the accessibility and usability of systems and devices, and they can improve the quality of life in many areas such as supporting human activities, communication and interactions in healthcare, and welfare. However, there is a need to shift from human-centric systems to community-centric systems and improve the quality of community in social networks and communities. This paper provides a survey of human-centric and community-centric systems. In the case of human-centric systems the acquisition of human data including sensing, monitoring and gathering data for life log are discussed. Constructing user models and applying the models for health care support are also proposed. In the case of community-centric systems, the community detection on the Web is presented. Various visualization systems for community detection on the Web are introduced. Emergency support systems as an important application of community-centric systems are discussed as well.
When a robot considers an action-decision based on a future prediction, it is necessary to know the property of disturbance signals from the outside environment. On the other hand, the properties of disturbance signals cannot be described simply, such as non-periodic function, nonlinear time-varying function nor almost-periodic function. In case of a robot control, sampling rate for control will be affected description of disturbance signals such as frequency or amplitude. If the sampling rate for acquiring a disturbance signal is not correct, the action will be taken far from its actual property. In general, future prediction using machine learning is based on the tendency obtained through past training or learning. In this case, an optimal action will be determined uniquely based on a property of disturbance. However, in this type of situation, the learning time increases in proportional to the amount of training data, either, the tendency may not be found using prediction, in the worst case. In this paper, we focus on prediction for almost-periodic disturbance. In particular, we consider the situation where almost-periodic disturbance signals occur. From this perspective, we propose a method that identifies the frequency of an almost- periodic function based on the frequency of the disturbance using Fourier transform, nearest-neighbor one-step-ahead forecasts and Nyquist-Shannon sampling theorem.
Julia and Mandelbrot sets have been studied continuously attracting fractal scientists since their creation. As a result, Julia and Mandelbrot sets have been analyzed intensively. In this article, researchers have studied the effect of noise on these sets and analyzed perturbation. Continuing the trend in this article, they analyze perturbation and find the corresponding amount of dynamic noise in the Mandelbrot map. Further, in order to recover a distorted fractal image, a restoration algorithm is presented.
Automatic handwritten character recognition is one of the most critical and interesting research areas in domain of pattern recognition. The problem becomes more challenging if domain is handwritten Hindi character as Hindi characters are cursive in nature and demonstrate a lot of similar features. A number of feature extraction, classification and recognition techniques have been devised and being used in this area; still the efficiency and accuracy is awaited. In this article, performance of various feed-forward neural networks is evaluated for the generalized classification of handwritten Hindi characters using various feature extraction methods. To study and analyze the performance of the selected neural networks, training and test character patterns are presented to each model and their recognition accuracy is measured. It has been analyzed that the Radial basis function network and Exact Radial basis network give highest recognition accuracy while Elman backpropagation neural network gives lowest recognition rate for most of the selected feature extraction methods.
Nowadays, various robot partners have been developed to realize human-friendly interactions. In general, a robot system is composed of hardware modules, software modules, and application contents. It takes much time to design utterance contents and motion patterns as application contents simultaneously, but the design support systems mainly focus on the generation of robot motion patterns. Furthermore, a methodology is needed to easily change the specification of hardware and software according to diversified needs, and the developmental environment to design the application contents on verbal and nonverbal communication with people. In this paper, the authors propose robot partners with the modularized architecture of hardware and software by using smart devices, and propose a developmental environment to realize easy contents design of verbal and nonverbal communication. In order to solve the problem of difficulty in the content design, they develop a design support environment using design templates of communication application contents. Next, they apply the robot partner to navigate visitors to the robot contest of the system design forum held in Tokyo Metropolitan University. Finally, they show several examples of the interaction cases, and discuss the interaction design for smart device based robot partners.
In recent years, autonomous robots become to be desired to treat multi-task. A robot must decide a concrete action for plural objectives. Major researches try to realize this by weighted rewards. Weighted rewards can represent a human's intention easily. But weight of each task must change dynamically by a change of surrounding situation or of a robot status. Authors consider an independent learning for each task and selection of one concrete action from candidates of each learning. Authors propose a priority function to calculate priority for each task corresponding to surrounding situation or a robot status and propose a system which do decision making by using the priority function. Authors confirmed the usefulness of proposed method with simulation.
A principled framework for general adaptive intelligent systems is described and applied to the domain of social robotics. Under the principled framework, the author develops computational methods to address an important aspect of a social robot, which is the ability to rapidly adapt to changes in the environment such as the introduction of novel objects and installations that serve novel purposes. Methods are also developed to address another important aspect of a social robot, which is the ability to understand the needs of humans that it interacts with by having a deep model of their needs, which enables the robot to assist humans in various tasks in a socially realistic manner. The author describes the methods of causal learning and script learning through computational visual observation that allow a robot to acquire the scripts and plans that enable it to understand the intentions of humans as well as solve problems to provide assistance to humans. The robot thus adapts rapidly to changing environmental factors as new observation provides new knowledge to guide its behavior. The assistance provided to humans is formulated as a script interaction problem and the optimal points at which assistance is provided are computed using a motivational strength model derived from psychological research and formulated computationally for robotic purposes. Also, a method is proposed to handle competition of needs which arises frequently in the course of robot-human interactions to generate socially realistic and appropriate behavior on the part of the robot. This paper uses primarily a home environment to demonstrate the methodology involved, but a robot that incorporates the methodology described could rapidly adapt to any environments such as the office and factory.
A NAO humanoid robot is programmed to act as an autonomous exercise instructor at a senior living community. In an on-site session, the robot does i a warm-up routine in which the robot directs participants to ask it to perform various tasks such as dancing and reciting poems and ii an exercise routine in which the robot guides participants through various physical exercises such as leg, hand, and neck exercises. The participants include six elderly residents, three nurses/caregivers, and two administrators. The elderly group is categorized with respect to cognitive awareness and physical capability. The session is videoed and then analyzed to measure several dimensions of human-robot interaction with these diverse participants, including affective reaction, effective reaction, and group responsiveness. Following the exercise session, a focus group session is conducted with the seniors and a separate focus group session conducted with the nurses and administrators to glean further data.
This paper proposes a cognitive architecture for building a topological map incrementally inspired by beta oscillations during place cell learning in hippocampus. The proposed architecture consists of two layer: the short-term memory layer and the long-term memory layer. The short-term memory layer emulates the entorhinal and the ? is the orientation system; the long-term memory layer emulates the hippocampus. Nodes in the topological map represent place cells (robot location), links connect nodes and store robot action (i.e. adjacent angle between connected nodes). The proposed method is formed by multiple Gaussian Adaptive Resonance Theory to receive data from various sensors for the map building. It consists of input layer and memory layer. The input layer obtains sensor data and incrementally categorizes the acquired information as topological nodes temporarily (short-term memory). In the long-term memory layer, the categorized information will be associated with robot actions to form the topological map (long-term memory). The advantages of the proposed method are: 1) it is a cognitive model that does not require human defined information and advanced knowledge to implement in a natural environment; 2) it can generate the map by processing various sensors data simultaneously in continuous space that is important for real world implementation; and 3) it is an incremental and unsupervised learning approach. Thus, the authors combine their Topological Gaussian ARTs method (TGARTs) with fuzzy motion planning to constitute a basis for mobile robot navigation in environment with slightly changes. Finally, the proposed approach was verified with several simulations using standardized benchmark datasets and real robot implementation.
