Deep Learning Based Vehicle Make-Model Classification

This paper studies the problems of vehicle make & model classification. Some of the main challenges are reaching high classification accuracy and reducing the annotation time of the images. To address these problems, we have created a fine-grained database using online vehicle marketplaces of Turkey. A pipeline is proposed to combine an SSD (Single Shot Multibox Detector) model with a CNN (Convolutional Neural Network) model to train on the database. In the pipeline, we first detect the vehicles by following an algorithm which reduces the time for annotation. Then, we feed them into the CNN model. It is reached approximately 4% better classification accuracy result than using a conventional CNN model. Next, we propose to use the detected vehicles as ground truth bounding box (GTBB) of the images and feed them into an SSD model in another pipeline. At this stage, it is reached reasonable classification accuracy result without using perfectly shaped GTBB. Lastly, an application is implemented in a use case by using our proposed pipelines. It detects the unauthorized vehicles by comparing their license plate numbers and make & models. It is assumed that license plates are readable.Comment: 10 pages, ICANN 2018: Artificial Neural Networks and Machine Learnin

New iterative method for three-dimensional eddy-current problems

Author name used in this publication: Eric Ka-Wai Cheng2001-2002 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Reconfigurable, Wideband, Low-Profile, Circularly Polarized Antenna and Array Enabled by an Artificial Magnetic Conductor Ground

© 1963-2012 IEEE. A reconfigurable, wideband, and low-profile circular polarization (CP) antenna is presented. Its wideband CP reconfigurability is realized by incorporating RF switches into a cross-bowtie radiator. A compact, wide bandwidth, and polarization-independent artificial magnetic conductor ground plane is developed to minimize the overall profile of the antenna while maintaining its wide bandwidth. The simplicity of this single-element design facilitates the realization of a reconfigurable, wide bandwidth CP array that achieves higher directivity without changing its overall profile. Prototypes of the single element and of a 1 × 4 array of these elements were fabricated and tested. The measured results for both prototypes are in good agreement with their simulated values, validating their design principles. They are low profile with a height ∼ 0.05 λ0. The array exhibits a wide fractional operational bandwidth: 1.65 GHz (21.7%), and a high realized gain: 13 dBic. Since they would enhance their channel capacity and avoid polarization mismatch issues, these reconfigurable CP antenna systems are very suitable for modern wireless systems

A 3-D study of eddy current field and temperature rises in a compact bus duct system

Author name used in this publication: S. L. HoAuthor name used in this publication: H. C. WongAuthor name used in this publication: K. W. E. Cheng2005-2006 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Fuzzy Integral with Particle Swarm Optimization for a Motor-Imagery-Based Brain-Computer Interface

© 2016 IEEE. A brain-computer interface (BCI) system using electroencephalography signals provides a convenient means of communication between the human brain and a computer. Motor imagery (MI), in which motor actions are mentally rehearsed without engaging in actual physical execution, has been widely used as a major BCI approach. One robust algorithm that can successfully cope with the individual differences in MI-related rhythmic patterns is to create diverse ensemble classifiers using the subband common spatial pattern (SBCSP) method. To aggregate outputs of ensemble members, this study uses fuzzy integral with particle swarm optimization (PSO), which can regulate subject-specific parameters for the assignment of optimal confidence levels for classifiers. The proposed system combining SBCSP, fuzzy integral, and PSO exhibits robust performance for offline single-trial classification of MI and real-time control of a robotic arm using MI. This paper represents the first attempt to utilize fuzzy fusion technique to attack the individual differences problem of MI applications in real-world noisy environments. The results of this study demonstrate the practical feasibility of implementing the proposed method for real-world applications

A motor imagery based brain-computer interface system via swarm-optimized fuzzy integral and its application

© 2016 IEEE. A brain-computer interface (BCI) system provides a convenient means of communication between the human brain and a computer, which is applied not only to healthy people but also for people that suffer from motor neuron diseases (MNDs). Motor imagery (MI) is one well-known basis for designing Electroencephalography (EEG)-based real-life BCI systems. However, EEG signals are often contaminated with severe noise and various uncertainties, imprecise and incomplete information streams. Therefore, this study proposes spectrum ensemble based on swam-optimized fuzzy integral for integrating decisions from sub-band classifiers that are established by a sub-band common spatial pattern (SBCSP) method. Firstly, the SBCSP effectively extracts features from EEG signals, and thereby the multiple linear discriminant analysis (MLDA) is employed during a MI classification task. Subsequently, particle swarm optimization (PSO) is used to regulate the subject-specific parameters for assigning optimal confidence levels for classifiers used in the fuzzy integral during the fuzzy fusion stage of the proposed system. Moreover, BCI systems usually tend to have complex architectures, be bulky in size, and require time-consuming processing. To overcome this drawback, a wireless and wearable EEG measurement system is investigated in this study. Finally, in our experimental result, the proposed system is found to produce significant improvement in terms of the receiver operating characteristic (ROC) curve. Furthermore, we demonstrate that a robotic arm can be reliably controlled using the proposed BCI system. This paper presents novel insights regarding the possibility of using the proposed MI-based BCI system in real-life applications

Advances in Reconfigurable Antenna Systems Facilitated by Innovative Technologies

© 2013 IEEE. Future fifth generation (5G) wireless platforms will require reconfigurable antenna systems to meet their performance requirements in compact, light-weight, and cost-effective packages. Recent advances in reconfigurable radiating and receiving structures have been enabled by a variety of innovative technology solutions. Examples of reconfigurable partially reflective surface antennas, reconfigurable filtennas, reconfigurable Huygens dipole antennas, and reconfigurable feeding network-enabled antennas are presented and discussed. They represent novel classes of frequency, pattern, polarization, and beam-direction reconfigurable systems realized by the innovative combinations of radiating structures and circuit components

Adaptive subspace sampling for class imbalance processing

© 2016 IEEE. This paper presents a novel oversampling technique that addresses highly imbalanced data distribution. At present, the imbalanced data that have anomalous class distribution and underrepresented data are difficult to deal with through a variety of conventional machine learning technologies. In order to balance class distributions, an adaptive subspace self-organizing map (ASSOM) that combines the local mapping scheme and globally competitive rule is proposed to artificially generate synthetic samples focusing on minority class samples. The ASSOM is conformed with feature-invariant characteristics, including translation, scaling and rotation, and it retains the independence of basis vectors in each module. Specifically, basis vectors generated via each ASSOM module can avoid generating repeated representative features that offer nothing but heavy computational load. Several experimental results demonstrate that the proposed ASSOM method with supervised learning manner is superior to other existing oversampling techniques

A novel low-profile wideband reconfigurable CP antenna array

© Institution of Engineering and Technology.All Rights Reserved. For future wireless communications, cost-effective, low-profile circular polarization (CP) antennas with wide bandwidth and high directivity are highly desirable to increase system capacity and suppress polarization mismatch. In this paper, a wideband circular polarization antenna array integrated with a polarization-independent artificial magnetic conductor (AMC) is reported that meets the demands. First, a wideband CP reconfigurable antenna with a pair of cross-bowtie radiators and a metal ground is presented to achieve a fractional bandwidth of 35.9%. By replacing the metal ground with a polarization-independent AMC ground, the antenna profile is reduced from 0.25λ0 to 0.05λ0 with only a slight bandwidth decrease. A wideband CP reconfigurable 4-element linear array is achieved using four of those elements. It is low profile (0.05 λ0), and has a wide operating bandwidth (21.7%), and a high realized gain (13 dBic)

Calculations of eddy current, fluid and thermal fields in an air insulated bus duct system

Author name used in this publication: S.L. HoAuthor name used in this publication: Edward W.C. LoAuthor name used in this publication: K. W. E. ChengRefereed conference paper2005-2006 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe