54,155 research outputs found
Traffic sign classification using transfer learning: An investigation of feature-combining model
The traffic sign classification system is a technology to help drivers to recognise the traffic sign hence reducing the accident. Many types of learning models have been applied to this technology recently. However, the deployment of learning models is unknown and shown to be non-trivial towards image classification and object detection. The implementation of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features as well as can save lots of training time. Besides, the feature-combining model exhibited great performance in the TL method in many applications. Nonetheless, the utilisation of such methods towards traffic sign classification applications are not yet being evaluated. The present study aims to exploit and investigate the effectiveness of transfer learning feature-combining models, particularly to classify traffic signs. The images were gathered from GTSRB dataset which consists of 10 different types of traffic signs i.e. warning, stop, repair, not enter, traffic light, turn right, speed limit (80km/s), speed limit (50km/s), speed limit (60km/s), and turn left sign board. A total of 7000 images were then split to 70:30 for train and test ratio using a stratified method. The VGG16 and VGG19 TL-features models were used to combine with two classifiers, Random Forest (RF) and Neural Network. In summary, six different pipelines were trained and tested. From the results obtained, the best pipeline was VGG16+VGG19 with RF classifier, which was able to yield an average classification accuracy of 0.9838. The findings showed that the feature-combining model successfully classifies the traffic signs much better than the single TL-feature model. The investigation would be useful for traffic signs classification applications i.e. for ADAS system
High-Resolution Road Vehicle Collision Prediction for the City of Montreal
Road accidents are an important issue of our modern societies, responsible
for millions of deaths and injuries every year in the world. In Quebec only, in
2018, road accidents are responsible for 359 deaths and 33 thousands of
injuries. In this paper, we show how one can leverage open datasets of a city
like Montreal, Canada, to create high-resolution accident prediction models,
using big data analytics. Compared to other studies in road accident
prediction, we have a much higher prediction resolution, i.e., our models
predict the occurrence of an accident within an hour, on road segments defined
by intersections. Such models could be used in the context of road accident
prevention, but also to identify key factors that can lead to a road accident,
and consequently, help elaborate new policies.
We tested various machine learning methods to deal with the severe class
imbalance inherent to accident prediction problems. In particular, we
implemented the Balanced Random Forest algorithm, a variant of the Random
Forest machine learning algorithm in Apache Spark. Interestingly, we found that
in our case, Balanced Random Forest does not perform significantly better than
Random Forest.
Experimental results show that 85% of road vehicle collisions are detected by
our model with a false positive rate of 13%. The examples identified as
positive are likely to correspond to high-risk situations. In addition, we
identify the most important predictors of vehicle collisions for the area of
Montreal: the count of accidents on the same road segment during previous
years, the temperature, the day of the year, the hour and the visibility
Modelling of advanced submicron gate InGaAs/InAIAs pHEMTS and RTD devices for very high frequency applications
InP based InAlAs/InGaAs pseudomorphic High Electron Mobility Transistors
(pHEMTs) have shown outstanding performances, which makes them prominent in high
frequency mm-wave and submillimeter-wave applications. However, conventional
InGaAs/InAlAs pHEMTs have major drawbacks, i.e., very low breakdown voltage and high
gate leakage current. These disadvantages degrade device performance, especially in
Monolithic Microwave Integrated Circuit (MMIC) low noise amplifiers (LNAs). The
optimisation of InAlAs/InGaAs epilayer structures through advanced bandgap engineering
together with gate length reduction from 1 m into deep sub-μm regime is the key solution
to enabled high breakdown and ultra-high speed, low noise pHEMT devices to be fabricated.
Concurrently, device modelling plays a vital role in the design and analysis of pHEMT
device and circuit performance. Physical modeling becomes essential to fully characterise
and understand the underlying physical phenomenon of the device, while empirical
modelling is significant in circuit design and predicts device’s characteristic performance.
In this research, the main objectives to accurately model the DC and RF
characteristics of the two-dimensional (2D) physical modelling for sub-μm gate length for
strained channel InAlAs/InGaAs/InP pHEMT has been accomplished and developed in
ATLAS Silvaco. All modelled devices were optimised and validated by experimental
devices which were fabricated at the University of Manchester; the sub-micrometer devices
were developed with T-gate using I-line optical lithography. The underlying device physics
insight are gained, i.e, the effects of changes to the device’s physical structure, theoretical
concepts and its general operation, hence a reliable pHEMT model is obtained. The kink
anomalies in I-V characteristics was reproduced and the 2D simulation results demonstrate
an outstanding agreement with measured DC and RF characteristics.
The aims to develop linear and nonlinear models for sub-μm transistors and their
implementation in MMIC LNA design is achieved with the 0.25 m
In0.7Ga0.3As/In0.52Al0.48As/InP pHEMT. An accurate technique for the extraction of empirical
models for the fabricated active devices has been developed and optimised using Advance
Design System (ADS) software which demonstrate excellent agreement between
experimental and modelled DC and RF data. A precise models for MMIC passive devices
have also been obtained and incorporated in the proposed design for a single and double
stage MMIC LNAs in C- and X-band frequency. The single stage LNA is designed to
achieve maximum gain ranging from 9 to 13 dB over the band of operation while the gain is
increased between 20 dB and 26 dB for the double stage LNA designs. A noise figure of
less than 1.2 dB and 2 dB is expected respectively, for the C- and X-band LNA designed
while retaining stability across the entire frequency bands.
Although the RF performance of pHEMT is being vigorously pushed towards
terahertz region, novel devices such as Resonant Tunnelling Diode (RTD) are needed to
support future ultra-high speed, high frequency applications especially when it comes to
THz frequencies. Hence, the study of physical modelling is extended to quantum modelling
of an advanced In0.8Ga0.2As/AlAs RTD device to effectively model both large size and
submicron RTD using Silvaco’s ATLAS software to reproduce the peak current density,
peak-to-valley-current ratio (PVCR), and negative differential resistance (NDR) voltage
range. The simple one-dimensional physical modelling for the RTD devices is optimised to
achieve an excellent match with the fabricated RTD devices with variations in the spacer
thickness, barrier thickness, quantum well thickness and doping concentration
DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion
Non-recurring traffic congestion is caused by temporary disruptions, such as
accidents, sports games, adverse weather, etc. We use data related to real-time
traffic speed, jam factors (a traffic congestion indicator), and events
collected over a year from Nashville, TN to train a multi-layered deep neural
network. The traffic dataset contains over 900 million data records. The
network is thereafter used to classify the real-time data and identify
anomalous operations. Compared with traditional approaches of using statistical
or machine learning techniques, our model reaches an accuracy of 98.73 percent
when identifying traffic congestion caused by football games. Our approach
first encodes the traffic across a region as a scaled image. After that the
image data from different timestamps is fused with event- and time-related
data. Then a crossover operator is used as a data augmentation method to
generate training datasets with more balanced classes. Finally, we use the
receiver operating characteristic (ROC) analysis to tune the sensitivity of the
classifier. We present the analysis of the training time and the inference time
separately
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