Jui-Jiun Jian

Intelligent feature extraction and classification of anuran vocalizations

Abstract An intelligent identification system for mixed anuran vocalizations is developed in this work to provide the public to easily consult online. The raw mixed anuran vocalization samples are first filtered by noise removal, high frequency compensation, and discrete wavelet transform techniques in order. An adaptive end-point detection segmentation algorithm is proposed to effectively separate the individual syllables from the noise. Six features, including spectral centroid, signal bandwidth, spectral roll-off, threshold-crossing rate, spectral flatness, and average energy, are extracted and served as the input parameters of the classifier. Meanwhile, a decision tree is constructed based on several parameters obtained during sample collection in order to narrow the scope of identification targets. Then fast learning neural-networks are employed to classify the anuran species based on feature set chosen by wrapper feature selection method. A series of experiments were conducted to measure the outcome performance of the proposed work. Experimental results exhibit that the recognition rate of the proposed identification system can achieve up to 93.4%. The effectiveness of the proposed identification system for anuran vocalizations is thus verified.

An adaptive multimedia streaming dissemination system for vehicular networks

An adaptive seamless streaming dissemination system for vehicular networks is presented in this work. An adaptive streaming system is established at each local server to prefetch and buffer stream data. The adaptive streaming system computes the parts of prefetched stream data for each user and stores them temporarily at the local server, based on current situation of the users and the environments where they are located. Thus, users can download the prefetched stream data from the local servers instead of from the Internet directly, meaning that the video playing problem caused by network congestion can be avoided. Several techniques such as stream data prefetching, stream data forwarding, and adaptive dynamic decoding were utilized for enhancing the adaptability of different users and environments and achieving the best transmission efficiency. Fuzzy logic inference systems are utilized to determine if a roadside base station or a vehicle can be chosen to transfer stream data for users. Considering the uneven deployment of BSs and vehicles, a bandwidth reservation mechanism for premium users was proposed to ensure the QoS of the stream data premium users received. A series of simulations were conducted, with the experimental results verifying the effectiveness and feasibility of the proposed work.

Application of cellular automata and type-2 fuzzy logic to dynamic vehicle path planning

Abstract Nowadays, most road navigation systems’ planning of optimal routes is conducted by the On Board Unit (OBU). If drivers want to obtain information about the real-time road conditions, a Traffic Message Channel (TMC) module is also needed. However, this module can only provide the current road conditions, as opposed to actually planning appropriate routes for users. In this work, the concept of cellular automata is used to collect real-time road conditions and derive the appropriate paths for users. Notably, type-2 fuzzy logic is adopted for path analysis for each cell established in the cellular automata algorithm. Besides establishing the optimal routes, our model is expected to be able to automatically meet the personal demands of all drivers, achieve load balancing between all road sections to avoid the problem of traffic jams, and allow drivers to enjoy better driving experiences. A series of simulations were conducted to compare the proposed approach with the well-known A* Search algorithm and the latest state-of-the-art path planning algorithm found in the literature. The experimental results demonstrate that the proposed approach is scalable in terms of the turnaround times for individual users. The practicality and feasibility of applying the proposed approach in the real-time environment is thus justified.

A self-adaptive joint bandwidth allocation scheme for heterogeneous wireless networks

Our approach coordinately manages radio resources with multiple radio access technologies in an optimum way. A mobility prediction module and a remaining bandwidth estimation module are established at each BS as shown in the lower part of the above figure. The global radio resource manager located at the upper part of this figure consists of a bandwidth utilization optimization module. A mobile host (MH) collects required parameters and the latest MHs state is immediately updated at the BS the MH resides in when a MHs state changes. The mobility prediction module will check with a pre-built lookup table to determine whether the handoff will occur. If the handoff is predicted to occur, the MH sends out a bandwidth requirement to a nearest BS, and the remaining bandwidth estimation module in the target BS will be activated to determine the amount of remaining bandwidth that can be used by the handoffs. In case some BS finds that its bandwidth is going to be inadequate, a bandwidth adjustment coordinator located at the BS will request the common bandwidth utilization optimization module at its upper level to reallocate the bandwidth for the BSs/APs of different RATs. The results of the bandwidth reallocation will then be sent back to each BS to reassign the bandwidth to the MHs. A mobility prediction module is proposed to predict the mobility pattern of mobile host.A lookup table records the probability for the possible handoffs moving into the coverage of the BS.Bandwidth is estimated for the possible arriving MHs outside the coverage of the BS.The optimization of utilization and fairness for the bandwidth allocation are addressed.Hybrid Genetic Algorithm is employed to achieve real-time computation requirement. Recently, people have been able to connect with different types of networks anytime, anywhere using advanced network technologies. In order to properly distribute wireless network resources among different clients, this work proposed a user mobility prediction algorithm, which takes the coverage of different kinds of base stations, and the volatile mobility of pedestrians, vehicles, and mass transportation, into consideration. In addition, a novel bandwidth utilization optimization technique is proposed in the algorithm to allocate bandwidth more efficiently. The Hybrid Genetic Algorithm, which combines the Genetic Algorithm and local searches to improve the frequency of finding a Pareto set, is used to realize the optimization problem as well. Compared with our previous work and the other four methods in the literature, the simulation results show that our proposed work can achieve desirable performance by network utilization, throughput, and QoS quality in the heterogeneous wireless networks.

A Load-Balancing Based Charging Management Mechanism for Electric Vehicles

This work proposes an EV charging management mechanismand utilizes the scheduling systems for the charging stations to determine when to store electricity into batteries according to the real-time electricity price and charging requirement of EVs. A charging suggestion module is presented in this work to locate the most suitable charging station or battery exchange station for the EVs according to the available information in hand. When an EV cannot reach at any charging station due to the lack of electricity, a mobile charging vehicle management module is used to assisting the EV in finding a suitable mobile charging vehicle for recharging. The experimental results show that the proposed work can balance the loading of battery charging and exchange stations, and lower the load peak to make electricity cost down. Besides, the proposed charging suggestion module can decrease the driving distance of EVs for finding the charging stations and the waiting time wasted while charging. The mobile charging vehicle management module can effectively prevent EVs from halting on the road owing to running out of the electricity.

A Seamless Multimedia Streaming Dissemination System for Vehicular Networks

An adaptive streaming system for vehicular networks is established at each local server to prefetch and buffer stream data. The adaptive streaming system computes the parts of prefetched stream data for each user and stores them temporarily at the local server, based on current situation of the users and the environments that they are located at. Several techniques such as stream data prefetching, stream data forwarding, and adaptive dynamic decoding, were utilized for strengthening the adaptability of different users and environments and achieving the best transmission efficiency. Fuzzy logic inference systems are utilized to determine if a roadside base station (BS) or a vehicle can be chosen to transfer stream data for users. The bandwidth reservation mechanism for premium user was proposed to ensure the QoS of stream data premium user received. A series of simulations verified the effectiveness and feasibility of the proposed work.