Moving Vehicle Recognition and Classification based on Time Domain Approach

AbstractDifferentially Hearing Ability Enabled (DHAE) community cannot discriminate the sound information from a moving vehicle approaching from their behind. This research work is mainly focused on recognition of different vehicles and its position using noise emanated from the vehicle A simple experimental protocol has been designed to record the sound signal emanated from the moving vehicle under different environment conditions and also at different vehicle speed Autoregressive modeling algorithm is used for the analysis to extract the features from the recorded vehicle noise signal. Probabilistic neural network (PNN) models are developed to classify the vehicle type and its distance. The effectiveness of the network is validated through stimulation

Semantic knowledge base in support of activity recognition in smart home environments

Activity recognition plays a major role in smart home technologies in providing services to users. One of the approaches to identify ac-tivity is through the use of knowledge-driven reasoning. This paper presents a framework of semantic activity recognition, which is used to support smart home systems to identify users’ activities based on the existing context. The framework consists of two main compo-nents: a semantic knowledge base and an activity recognition module. The knowledge base is represented using ontology and it is used to provide a semantic understanding of the environment in order to classify users’ patterns of activities. Experimental results show that the proposed approach can support the classification process and accurately infer users’ activities with the accuracy of 90.9%

Semantic knowledge base in support of activity recognition in smart home environments

Activity recognition plays a major role in smart home technologies in providing services to users. One of the approaches to identify ac-tivity is through the use of knowledge-driven reasoning. This paper presents a framework of semantic activity recognition, which is used to support smart home systems to identify users’ activities based on the existing context. The framework consists of two main compo-nents: a semantic knowledge base and an activity recognition module. The knowledge base is represented using ontology and it is used to provide a semantic understanding of the environment in order to classify users’ patterns of activities. Experimental results show that the proposed approach can support the classification process and accurately infer users’ activities with the accuracy of 90.9%

An Enhanced Random Linear Oracle Ensemble Method using Feature Selection Approach based on Naïve Bayes Classifier

Random Linear Oracle (RLO) ensemble replaced each classifier with two mini-ensembles, allowing base classifiers to be trained using different data set, improving the variety of trained classifiers. Naïve Bayes (NB) classifier was chosen as the base classifier for this research due to its simplicity and computational inexpensive. Different feature selection algorithms are applied to RLO ensemble to investigate the effect of different sized data towards its performance. Experiments were carried out using 30 data sets from UCI repository, as well as 6 learning algorithms, namely NB classifier, RLO ensemble, RLO ensemble trained with Genetic Algorithm (GA) feature selection using accuracy of NB classifier as fitness function, RLO ensemble trained with GA feature selection using accuracy of RLO ensemble as fitness function, RLO ensemble trained with t-test feature selection, and RLO ensemble trained with Kruskal-Wallis test feature selection. The results showed that RLO ensemble could significantly improve the diversity of NB classifier in dealing with distinctively selected feature sets through its fusionselection paradigm. Consequently, feature selection algorithms could greatly benefit RLO ensemble, with properly selected number of features from filter approach, or GA natural selection from wrapper approach, it received great classification accuracy improvement, as well as growth in diversity

A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes

Accurate activity recognition plays a major role in smart homes to provide assistance and support for users, especially elderly and cognitively impaired people. To realize this task, knowledge-driven approaches are one of the emerging research areas that have shown interesting advantages and features. However, several limitations have been associated with these approaches. The produced models are usually incomplete to capture all types of human activities. This resulted in the limited ability to accurately infer users’ activities. This paper presents an alternative approach by combining knowledge-driven with data-driven reasoning to allow activity models to evolve and adapt automatically based on users’ particularities. Firstly, a knowledge-driven reasoning is presented for inferring an initial activity model. The model is then trained using data-driven techniques to produce a dynamic activity model that learns users’ varying action. This approach has been evaluated using a publicly available dataset and the experimental results show the learned activity model yields significantly higher recognition rates compared to the initial activity model

3D Reconstruction of Fruit Shape based on Vision and Edge Sections

The fruit industry has been known as one of the largest businesses in Malaysia, where most of the fruits pass through the peeling process well in advance before the final product as juice in a bottle or slices in a can. The current industrial fruit peeling techniques are passive and inefficient by cutting parts of the pulp of the fruit with peels leading to losses. To avoid this issue, a multi-axis CNC fruit peeler can be used to precisely peel the outer layer with the guidance of a 3D virtual model of fruit. In this work, a new cost-effective method of 3D image reconstruction was developed to convert 36 fruit images captured by a normal RGB camera to a 3D model by capturing a single image every 10 degrees of fruit rotation along a fixed axis. The point cloud data extracted with edge detection were passed to Blender 3D software for meshing in different approaches. The vertical link frame meshing method developed in this research proved a qualitative similarity between the output result and the scanned fruit in a processing time of less than 50 seconds

Wireless Communication for Mobile Robots Using Commercial System

Commercial mobile robots provide good platform for the  study and development of algorithms for wireless mobile communication of devices. E-Puck is a good example, with wireless communication utilising Bluetooth among others. However, the limitations of Bluetooth communications lead to the investigation of using X-Bee module as an alternative. This is to allow the E-Puck to communicate with a computer and other mobile robots using specified Zigbee protocol. This paper presents X-Bee module as wireless communication method between computer and E-Pucks and the way they exchange data

Influence of Synchronized Dead Point Elimination Crank on Cyclist Muscle Fatigue

The aim of this study was to investigate the influence of newly proposed bicycle’s crank to crank angle setting on the Vastus Lateralis (VL) and Bicep Femoris (BF) muscle activity during cycling. Procedures of Conconi Test were used throughout the experiment for the data collection purpose. The muscles activities were recorded using surface electromyography and software LabChart7. The raw data were further processed in time (Root-Mean-Square, RMS) and frequency (Mean Power Frequency, MPF) domain. It was found that 0° crank to crank setting (similar to conventional crank to crank angle setting) caused the prime mover VL (Normalized RMS = 0.119) to fatigue more than BF (Normalized RMS = 0.102). This setting is expected to decrease the cycling performance. In addition, −5° is the best crank to crank angle setting that causes least fatigue to both VL and BF. In short, to increase the cycling performance by avoiding the fatigue to the main muscles, −5° is the suggested as setting angle for the proposed crank design

Influence of Synchronized Dead Point Elimination Crank on Cyclist Muscle Fatigue

The aim of this study was to investigate the influence of newly proposed bicycle’s crank to crank angle setting on the Vastus Lateralis (VL) and Bicep Femoris (BF) muscle activity during cycling. Procedures of Conconi Test were used throughout the experiment for the data collection purpose. The muscles activities were recorded using surface electromyography and software LabChart7. The raw data were further processed in time (Root-Mean-Square, RMS) and frequency (Mean Power Frequency, MPF) domain. It was found that 0° crank to crank setting (similar to conventional crank to crank angle setting) caused the prime mover VL (Normalized RMS = 0.119) to fatigue more than BF (Normalized RMS = 0.102). This setting is expected to decrease the cycling performance. In addition, −5° is the best crank to crank angle setting that causes least fatigue to both VL and BF. In short, to increase the cycling performance by avoiding the fatigue to the main muscles, −5° is the suggested as setting angle for the proposed crank design