BURA Collection:http://bura.brunel.ac.uk/handle/2438/1862024-03-15T07:45:28Z2024-03-15T07:45:28ZSafe training of traffic assistants for detection of dangerous accidentsLi, Yifanhttp://bura.brunel.ac.uk/handle/2438/281202024-02-05T10:56:24Z2023-01-01T00:00:00ZTitle: Safe training of traffic assistants for detection of dangerous accidents
Authors: Li, Yifan
Abstract: As the automotive industry continues to develop, the car's function is no longer limited to a simple means of transportation. Instead, it is more of a technological product that combines safe mobility, entertainment and safe driving technology. Furthermore, ensuring the safety of passengers is a critical element in the development of cars. Thus, safety-based autonomous driving and assisted driving systems are essential in
developing cars and future development strategies. However, the limitations of the current radar systems used in automobiles are
widespread. A single radar detection makes it difficult to carry out accurate object detection and identification and can only provide vague conceptual feedback. The radar feedback needs to be more accurate, especially when the distance is too close or too far. Any object that reflects radar waves is being used as a hazard warning. Due to the continuous development of information technology and artificial intelligence technology, integrating artificial intelligence into traditional industries to achieve automation and intellectual development is the main direction of current technological development and industry progress. For example, the application of AI
technology to the automotive industry enables comprehensive and immediate environmental awareness, comprehensive and accurate planning and decision-making, and precise and efficient vehicle control to ensure the safety of passengers. In this thesis we propose the use of the YOLO algorithm in Virtual Worlds to safely train the car's recognition to detect dangerous traffic accident situations in different environments without damage to property and danger to human well-being through
real-time video detection by obtaining more accurate information about obstacles or hazards. The YOLO series of Artificial Intelligent (AI) detection algorithms are used to detect objects through video or pictures. Unlike radar detection, YOLO can accurately analyse obstacles. Assisted driving and autonomous driving will be an essential part of the future of transportation, but training them for object detection and recognition of
dangerous traffic situations, which is a key aspect of its operation, is difficult because of the damage to property and human well-being. Therefore performing this training in virtual world is essential. First, we designed and built a virtual 3D city platform using the Unity 3D engine,
recreated as much realistic road information as possible in the 3D city. Then we used the YOLOV5 algorithm for detection of objects to obtain accurate virtual identification information successfully. After training, YOLOV5 can detect all vehicles and obstacles on the virtual road. From there, it can alert the driver of dangerous traffic situations accordingly instead of alerting for all objects to avoid unnecessary danger warnings.
On the other hand, environmental perception is essential to safe driving. Nevertheless, current research has seen various technologies applied to environmental perception, such as Microsoft's AIRSIM autonomous driving simulator, LIDAR technology and millimetre wave radar technology, which are currently heavily used. However, technology is constantly evolving, and LIDAR and millimetre wave radar are now at the forefront of environmental awareness. Accurate one-stage algorithms and databases are an important direction for the future. This is because such algorithms not only indicate the presence of an object in front of them but also identify exactly what type of object the output is(people, pets, ground obstacles, etc.). We have built
on the Yolo algorithm and applied it to assisted driving with a focus on safely training the AI to detect the driver's blind spot, by analysing the environment out of the driver's view and giving timely feedback. This thesis explores in depth the application of how to safely train YOLO for assisted
driving, building a 3D virtual city and testing it in different stages in a virtual
environment. The usefulness of the YOLO algorithm for driving car safety is verified. Through the continuous training of the YOLO algorithm, an extensive database can give the driver more results in terms of environmental perception. As a result, the occurrence of traffic accidents due to insufficient environmental perception for training YOLO can be increased by constructing virtual accidents without damage to property
and human well-being.
Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London2023-01-01T00:00:00ZIncorporating machine learning algorithms for channel prediction and power optimization scheme in power domain non-orthogonal multiple access systemGaballa, Mohamedhttp://bura.brunel.ac.uk/handle/2438/280732024-01-30T03:01:00Z2023-01-01T00:00:00ZTitle: Incorporating machine learning algorithms for channel prediction and power optimization scheme in power domain non-orthogonal multiple access system
Authors: Gaballa, Mohamed
Abstract: With the fast increase in the publicity of the Internet of Things (IoT) and cloud computing, the requirement for massive connectivity and highly reliable data rates is increasing day by day for communication networks. IoT can establish the connections among many types of smart devices, such as smart sensors, robots, and mobile devices. To satisfy these demands and such massive connectivity, three main services have been presented to the communication networks. These key services consist of massive machine type communication (mMTC) that permits massive connections between IoT terminals, enhanced mobile broadband (eMBB) that delivers a high data rate for mobile devices, and ultra-reliable and low-latency communication (URLLC) that confirms reliability and minimum latency for critical and sensible applications. These services are characterized by their quality of service (QoS), where URLLC has a stringent QoS policy for high reliability and low latency application, eMBB service is categorized by a moderate QoS policy, while mMTC has no precise QoS policy. These types of QoS are usually difficult to realize with the traditional orthogonal multiple access (OMA) due to limited spectrum resources, and delays. To satisfy and enhance these diverse QoS requirements, many potential multiple access schemes have been introduced into communication network. Among them, is the non-orthogonal multiple access (NOMA) scheme that has a achieved a popularity because it can support massive connectivity with limited resources, tolerable transmission delays, and high spectral efficiency. The key feature of NOMA is that multiple user devices can be served from the same radio resource block, such as time, frequency, and code. NOMA scheme applies superposition coding to combine signals related to multiple users at the transmitter side and implements successive interference cancellation procedure to differentiate and recover the signals of multiple devices at the receiver side.
There are some challenges related to resource allocation in NOMA system, such as power allocation and channel estimation. Machine learning has obtained publicity over the past several years, and many machine learning models and algorithms have been industrialized. Deep learning is a subset of machine learning, and it has distinct advantages over traditional machine learning methods, such as being capable of working on huge volumes of data in complex networks. Furthermore, reinforcement learning also is a type of machine learning, and the main aim of reinforcement learning is to train an agent to carry out a certain task within an uncertain environment. Deep learning and reinforcement learning approaches can be investigated and inspected to be one of the candidate’s algorithms for resource allocation and channel estimation in NOMA system. In this thesis, simulation results clearly indicates that deep learning and reinforcement learning algorithms can provide a superior improvement in terms of diverse performance metrics when Rayleigh and Rician fading channels are considered. Also, in this thesis, a benchmark schemes are also simulated to highlight how much enhancement has been achieved in the system performance when our proposed machine learning models are applied compared to the results obtained by the benchmark schemes.
Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London2023-01-01T00:00:00ZTowards a muon collider studies from KURNS and MICEBrown, Craighttp://bura.brunel.ac.uk/handle/2438/279652024-01-06T03:00:54Z2023-01-01T00:00:00ZTitle: Towards a muon collider studies from KURNS and MICE
Authors: Brown, Craig
Abstract: The proposed Muon Collider offers two distinct advantages in comparison to lepton
and hadron colliders. The larger mass of the muon in comparison to the electron
means synchrotron radiation is less of a concern and higher centre of mass collision
energies can be reached. Secondly, the muon collider would collide point-like particles
in comparison to hadron colliders, resulting in cleaner collision processes.
Challenges faced by the Muon Collider are investigated in this thesis. A charge
exchange experiment was performed at the Kyoto University Institute for Integrated
Radiation and Nuclear Science to investigate electron detachment cross-sections as
a function of projectile energy. Results are presented for 11 MeV hydrogen anions
striking a carbon foil.
Ionisation cooling, an increase in the position and momentum phase-space density
of a beam, was investigated by the Muon Ionisation Cooling Experiment (MICE)
for various absorber materials. The systematic uncertainties associated with the
liquid hydrogen absorber are presented. The ionisation cooling result can be affected
by various biases. The MICE momentum reconstruction was biased by the nonuniformity
of the magnetic field in the tracker regions of the MICE experiment. It
was also biased by misalignments of the solenoid, tracker and magnetic axes.
The ionisation cooling result can also be biased by transmission losses. Normalizing
the phase space densities by their sample sizes as MICE had done was
found to be incorrect. When transmission losses are missing not at random, the
changing covariance matrix of the remaining distribution needs to be accounted for
as well. Using a transfer matrix approach, a correction procedure was outlined to
impute missing data points for the downstream distribution affected by transmission
losses. The correct downstream covariance matrix could then be found, meaning
the remaining downstream sample could be compared to the full upstream sample
unaffected by any biases due to transmission losses.
Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London2023-01-01T00:00:00ZImproved scintillator design for thermal neutron detectionGunaratna Mudiyanselage, Nadeera Hemamalihttp://bura.brunel.ac.uk/handle/2438/277032023-11-23T19:13:50Z2022-01-01T00:00:00ZTitle: Improved scintillator design for thermal neutron detection
Authors: Gunaratna Mudiyanselage, Nadeera Hemamali
Abstract: Neutron detectors are used in various applications in nuclear security and nuclear safety. The most efficient neutron detection systems used in these applications are based on 3He technology. The growing demand for 3He already exceeds production in the next few years leading to an exponential increase of the price. The last decade has been driven by the quest for finding competitive alternative technologies to replace 3He based detectors.
Thus, intense research and development continues to explore new phosphor materials as scintillators or the optimization of existing scintillators taking advantage of new technological methods for their preparation. The development of phosphors with rare earth elements such as gadolinium show a high potential for use as efficient and cost-effective inorganic scintillators for neutron detection.
The appealing feature of gadolinium, which has one of the highest neutrons capture cross sections, and the production of electrons instead of heavy charged particles, has pushed several research programs to study possible alternatives that use gadolinium. The work presented in this thesis is mainly focussed on the investigation of the development of scintillator layers based on natural gadolinium, mainly Gd2O3:Eu3+, GdBO3:Eu3+, and Li6Gd(BO3)3:Eu3+ for thermal neutron detection. Scintillators were prepared, using the natGd based phosphor prepared using simple urea precipitation method followed by K-bar technique. Hexagonal boron nitride (h-BN) based thin films have also been developed using the RF sputtering technique. Performance of those thin film scintillators were tested for thermal neutrons. The key goals of the presented research work being the identification of improvements in the different parts of neutron detector design based on both experimental measurements and simulation activities.
Silicon Photomultipliers (SiPMs) represent a well-consolidated and cost-effective technology for a large range of applications requiring the detection of low light levels. In recent years, research efforts have been devoted to improving the basic performance of this kind of detector. In the presented research, a SiPM based readout system is used.
The front-end readout system for the SiPM has been developed and tested. The presented measurement results demonstrate that the implemented circuit has features that are attributable to photon detection. Here the research work mainly focused on reducing the power consumption of the required electronics and reducing the PCB size, to implement a lightweight portable handheld detector for ease of use in field activities.
Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London2022-01-01T00:00:00Z