8 research outputs found
Addressing the programming challenges of practical interferometric mesh based optical processors
We demonstrate a novel mesh of Mach-Zehnder interferometers (MZIs) for
programmable optical processors. The proposed mesh, referred to as Bokun mesh,
is an architecture that merges the attributes of the prior topologies Diamond
and Clements. Similar to Diamond, Bokun provides diagonal paths passing through
every individual MZI enabling direct phase monitoring. However, unlike Diamond
and similar to Clements, Bokun maintains a minimum optical depth leading to
better scalability. Providing the monitoring option, Bokun's programming is
faster improving the total energy efficiency of the processor. The performance
of Bokun mesh enabled by an optimal optical depth is also more resilient to the
loss and fabrication imperfections compared to architectures with longer depth
such as Reck and Diamond. Employing an efficient programming scheme, the
proposed architecture improves energy efficiency by 83% maintaining the same
computation accuracy for weight matrix changes at 2 kHz
3D Medical Image Segmentation based on multi-scale MPU-Net
The high cure rate of cancer is inextricably linked to physicians' accuracy
in diagnosis and treatment, therefore a model that can accomplish
high-precision tumor segmentation has become a necessity in many applications
of the medical industry. It can effectively lower the rate of misdiagnosis
while considerably lessening the burden on clinicians. However, fully automated
target organ segmentation is problematic due to the irregular stereo structure
of 3D volume organs. As a basic model for this class of real applications,
U-Net excels. It can learn certain global and local features, but still lacks
the capacity to grasp spatial long-range relationships and contextual
information at multiple scales. This paper proposes a tumor segmentation model
MPU-Net for patient volume CT images, which is inspired by Transformer with a
global attention mechanism. By combining image serialization with the Position
Attention Module, the model attempts to comprehend deeper contextual
dependencies and accomplish precise positioning. Each layer of the decoder is
also equipped with a multi-scale module and a cross-attention mechanism. The
capability of feature extraction and integration at different levels has been
enhanced, and the hybrid loss function developed in this study can better
exploit high-resolution characteristic information. Moreover, the suggested
architecture is tested and evaluated on the Liver Tumor Segmentation Challenge
2017 (LiTS 2017) dataset. Compared with the benchmark model U-Net, MPU-Net
shows excellent segmentation results. The dice, accuracy, precision,
specificity, IOU, and MCC metrics for the best model segmentation results are
92.17%, 99.08%, 91.91%, 99.52%, 85.91%, and 91.74%, respectively. Outstanding
indicators in various aspects illustrate the exceptional performance of this
framework in automatic medical image segmentation.Comment: 37 page
Crowd Abnormal Behaviour Detection and Analysis
The analysis and understanding of abnormal behaviours in human crowds is a challenging task in pattern recognition and computer vision. First of all, the semantic definition of the term “crowd” is ambiguous. Secondly, the taxonomy of crowd behaviours is usually rudimentary and intrinsically complicated. How to identify and construct effective features for crowd behaviour classification is a prominent challenge. Thirdly, the acquisition of suitable video for crowd analysis is another critical problem.
In order to address those issues, a categorization model for abnormal behaviour types is defined according to the state-of-the-art. In the novel taxonomy of crowd behaviour, eight types of crowd behaviours are defined based on the key visual patterns. An enhanced social force-based model is proposed to achieve the visual realism in crowd simulation, hence to generate customizable videos for crowd analysis. The proposed model consists of a long-term behavior control model based on A-star path finding algorithm and a short-term interaction handling model based on the enhanced social force. The proposed simulation approach produced all the crowd behaviours in the new taxonomy for the training and testing of the detection procedure. On the aspect of feature engineering, an innovative signature is devised for assisting the segmentation of crowd in both low and high density. The signature is modelled with derived features from Grey-Level Co-occurrence Matrix. Another major breakthrough is an effective approach for efficiently extracting spatial temporal information based on the information entropy theory and Gabor background subtraction. The extraction approach is capable of obtaining the texture with most motion information, which could help the detection approach to achieve the real-time processing.
Overall, these contributions have supported the crucial components in a pipeline of abnormal crowd behaviour detecting process. This process is consisted of crowd behaviour taxonomy, crowd video generation, crowd segmentation and crowd abnormal behaviour detection. Experiments for each component show promising results, and proved the accessibility of the proposed approaches
Semi-automatic liquid filling system using NodeMCU as an integrated Iot Learning tool
Computer programming and IoT are the key skills required in Industrial
Revolution 4.0 (IR4.0). The industry demand is very high and therefore related
students in this field should grasp adequate knowledge and skill in college or university
prior to employment. However, learning technology related subject without
applying it to an actual hardware can pose difficulty to relate the theoretical knowledge
to problems in real application. It is proven that learning through hands-on
activities is more effective and promotes deeper understanding of the subject matter
(He et al. in Integrating Internet of Things (IoT) into STEM undergraduate education:
Case study of a modern technology infused courseware for embedded system
course. Erie, PA, USA, pp 1–9 (2016)). Thus, to fulfill the learning requirement, an
integrated learning tool that combines learning of computer programming and IoT
control for an industrial liquid filling system model is developed and tested. The
integrated learning tool uses NodeMCU, Blynk app and smartphone to enable the
IoT application. The system set-up is pre-designed for semi-automation liquid filling
process to enhance hands-on learning experience but can be easily programmed for
full automation. Overall, it is a user and cost friendly learning tool that can be developed
by academic staff to aid learning of IoT and computer programming in related
education levels and field