Alzheimer’s disease (AD) is one of the deadliest neurodegenerative diseases ailing the elderly population all over the world. An ensemble of Deep learning (DL) models can learn highly complicated patterns from MRI scans for the detection of AD by utilizing diverse solutions. In this work, we propose a computationally efficient, DL-architecture agnostic, ensemble of deep neural networks, named ‘Deep Transfer Ensemble (DTE)’ trained using transfer learning for the classification of AD. DTE leverages the complementary feature views and diversity introduced by many different locally optimum solutions reached by individual networks through the randomization of hyper-parameters. DTE achieves an accuracy of 99.05% and 85.27% on two independent splits of the large dataset for cognitively normal (NC) vs AD classification task. For the task of mild cognitive impairment (MCI) vs AD classification, DTE achieves 98.71% and 83.11% respectively on the two independent splits. It also performs reasonable on a small dataset consisting of only 50 samples per class. It achieved a maximum accuracy of 85% for NC vs AD on the small dataset. It also outperformed snapshot ensembles along with several other existing deep models from similar kind of previous works by other researchers.

Convolutional neural networks are currently the state-of-the-art solution for a wide range of image processing tasks. Their deep architecture extracts low- and high-level features from images, thus improving the model's performance. In this paper, we propose a method for image demosaicking based on deep convolutional neural networks. Demosaicking is the task of reproducing full color images from incomplete images formed from overlaid color filter arrays on image sensors found in digital cameras. Instead of producing the output image directly, the proposed method divides the demosaicking task into an initial demosaicking step and a refinement step. The initial step produces a rough demosaicked image containing unwanted color artifacts. The refinement step then reduces these color artifacts using deep residual estimation and multi-model fusion producing a higher quality image. Experimental results show that the proposed method outperforms several existing and state-of-the-art methods in terms of both the subjective and objective evaluations.

C-V2X (Cellular Vehicle-to-Everything) is a state-of-the-art wireless technology used in autonomous driving and intelligent transportation systems (ITS). This technology has extended the coverage and blind-spot detection of autonomous driving vehicles. Economically, C-V2X is much more cost-effective than the traditional sensors that are commonly used by autonomous driving vehicles. This cost-benefit makes it more practical in a large scale deployment. PC5-based C-V2X uses an RF (Radio Frequency) sidelink direct communication for low latency mission-critical vehicle sensor connectivity. Over the C-V2X radio communications, the autonomous driving vehicle’s sensor ability can now be largely enhanced to the distances as far as the network covers. In 2020, 5G is commercialized worldwide, and Taiwan is at the forefront. Operators and governments are keen to see its implications in people’s daily life brought by its low latency, high reliability, and high throughput. Autonomous driving class L3 (Conditional Automation) or L4 (Highly Automation) are good examples of 5G’s advanced applications. In these applications, the mobile networks with URLLC (Ultra-Reliable Low-Latency Communication) are perfectly demonstrated. Therefore, C-V2X evolution and 5G NR (New Radio) deployment coincide and form a new ecosystem. This ecosystem will change how people will drive and how transportation will be managed in the future. In this paper, the following topics are covered. Firstly, the benefits of C-V2X communication technology. Secondly, the standards of C-V2X and C-V2X applications for automotive road safety system which includes V2P/V2I/V2V/V2N, and artificial intelligence in VRU (Vulnerable Road User) detection, object recognition and movement prediction for collision warning and prevention. Thirdly, PC5-based C-V2X deployment status in global, especially in Taiwan. Lastly, current challenges and conclusions of C-V2X development.

With the irruption of inexpensive depth sensor devices, hand gesture tracking has become a topic of great interest. Two main problems to face respect other tracking algorithms are the high complexity of the hand structure, which translate in a very large amount of possible gestures, and the rapidness of the movements we are able to make when moving the hand or just the fingers. Recent approaches try to fit a 3D hand model to the observed RGB-D data by an optimization function that minimizes the error between the model and the data. However, these algorithms are very dependent on the initialization point, which are impractical to run in a natural environment. To solve these kinds of problems, it is common to use an offline data set with prelearned gestures that will serve as a first rough estimate. In concrete, we present an algorithm that uses an articulated ICP minimization function that is initialized by the parameters obtained from a data set of hand gestures trained through a deep learning framework. This setup has two strong points. First, deep learning provides a very fast and accurate estimate of performed hand gestures. Second, the articulated ICP algorithm allows capturing the possible variability of a gesture performed by different persons or slightly different gestures. Our proposed algorithm is evaluated and validated in several ways. Independent evaluations for the deep learning framework and articulated ICP are performed. Moreover, different real sequences are recorded to validate our approach and, finally, quantitative and qualitative comparisons are conducted with state-of-the-art algorithms.

Body shape is about proportion, and fashion style is all about dressing those proportions to look their very best. Figuring out the styles to suit a body shape can be a daunting task for many people. It is, therefore, essential to develop a framework for learning the compatibility of body shapes and clothing styles. Though fashion designers and fashion stylists have analyzed the correlation between human body shapes and fashion styles for a long time, this issue did not receive much attention in multimedia science. In this paper, we present a novel style recommender, on the basis of the user's body attributes. The rich amount of fashion styling knowledge from social big data is exploited for this purpose. We first construct a joint embedding of clothing styles and human body measurements with deep multimodal representation learning on a reference dataset that has been sorted to meet the fashion rules. We then discover the relevant semantic features by propagation and selection in clothing style and body shape graphs. Experiments demonstrate the effectiveness of the proposed framework when compared with several baseline methods.

Convolutional Neural Networks (CNNs) based approaches are popular for various image/video related tasks due to their state-of-the-art performance. However, for problems like object detection and segmentation, CNNs still suffer from objects with arbitrary shapes, sizes, occlusions, and varying viewpoints. This problem makes it mostly unsuitable for fire detection and segmentation since flames can have an unpredictable scale and shape. In this paper, we propose a method that detects and segments fire-regions with special considerations of their arbitrary sizes and shapes. Specifically, our approach uses a self-attention mechanism to augment spatial characteristics with temporal features, allowing the network to reduce its reliance on spatial factors like shape or size and take advantage of robust spatial-temporal dependencies. As a whole, our pipeline has two stages: In the first stage, we take out region proposals using Spatial-Temporal features, and in the second stage, we classify whether each region proposal is flame or not. Due to the scarcity of generous fire datasets, we adopt a transfer learning strategy to pre-train our classifier with the ImageNet dataset. Additionally, our Spatial-Temporal Network only requires semi-supervision, where it only needs one ground-truth segmentation mask per frame-sequence input. The experimental results of our proposed method significantly outperform the state-of-the-art fire detection with a 2 ~ 4% relative enhancement in F1-score for large scale fires and a nearly ~ 60% relative improvement for small fires at a very early stage.