Recently, convolutional neural network (CNN) has led to significant improvement in the field of computer vision, especially the improvement of the accuracy and speed of semantic segmentation tasks, ...which greatly improved robot scene perception. In this article, we propose a multilevel feature fusion dilated convolution network (Refine-DeepLab). By improving the space pyramid pooling structure, we propose a multiscale hybrid dilated convolution module, which captures the rich context information and effectively alleviates the contradiction between the receptive field size and the dilated convolution operation. At the same time, the high-level semantic information and low-level semantic information obtained through multi-level and multi-scale feature extraction can effectively improve the capture of global information and improve the performance of large-scale target segmentation. The encoder–decoder gradually recovers spatial information while capturing high-level semantic information, resulting in sharper object boundaries. Extensive experiments verify the effectiveness of our proposed Refine-DeepLab model, evaluate our approaches thoroughly on the PASCAL VOC 2012 data set without MS COCO data set pretraining, and achieve a state-of-art result of 81.73% mean interaction-over-union in the validate set.
Customer awareness and interest in mobile payments are increasing. However, security and privacy risks remain major barriers to their adoption, with customers worrying about their personal data being ...hacked or intercepted. In this paper, we present the design of a secure scheme for mobile payments that can guarantee mutual nonrepudiation between the customer, merchant, and banker. A customer can use the proposed scheme to make a payment with the same PayWord chains of a single account from multiple devices.
Instead of paying by cash, check, or credit cards, customers can now also use their mobile devices to pay for a wide range of services and both digital and physical goods. However, customers’ ...security concerns are a major barrier to the broad adoption and use of mobile payments. In this paper we present the design of a secure operational model for mobile payments in which access control is based on a service-oriented architecture. A customer uses his/her mobile device to get authorization from a remote server and generate a two-dimensional barcode as the payment certificate. This payment certificate has a time limit and can be used once only. The system also provides the ability to remotely lock and disable the mobile payment service.
Recently, image compression using adaptive block truncation coding based on edge quantization (ABTC-EQ) was proposed by Mathews and Nair. Their approach deals with an image for two types of blocks, ...edge blocks and non-edge blocks. Different from using the bi-clustering approach on all blocks in previous block truncation coding (BTC)-like schemes, ABTC-EQ adopts tri-clustering to tackle edge blocks. The compression ratio of ABTC-EQ is reduced, but the visual quality of the reconstructed image is significantly improved. However, it is observed that ABTC-EQ uses 2 bits to represent the index of three clusters in a block. We can only use an average of 5/3 bits by variable-length code to represent the index of each cluster. On the other hand, there are two observations on the quantization levels in a block. The first observation is that the difference between the two quantization values is often smaller than the quantization values themselves. The second observation is that more clusters may enhance the visual quality of the reconstructed image. Based on variable-length coding and the above observations, we design variants of ABTC-EQ to enhance the visual quality of the reconstructed image and compression ratio.
The PKI framework is a widely used network identity verification framework. Users will register their identity information with a certification authority to obtain a digital certificate and then show ...the digital certificate to others as an identity certificate. After others receive the certificate, they must check the revocation list from the CA to confirm whether the certificate is valid. Although this architecture has a long history of use on the Internet, significant doubt surrounds its security. Because the CA may be attacked by DDoS, the verifier may not obtain the revocation list to complete the verification process. At present, there are many new PKI architectures that can improve on the CA’s single point of failure, but since they still have some shortcomings, the original architecture is still used. In this paper, we proposed a semidecentralized PKI architecture that can easily prevent a single point of failure. Users can obtain cryptographic evidence through specific protocols to clarify the responsibility for the incorrect certificate and then submit the cryptographic evidence to the smart contract for automatic judgment and indemnification.
Semantic segmentation is a task that covers most of the perception needs of intelligent vehicles in an unified way. Recent studies witnessed that attention mechanisms achieve impressive performance ...in computer vision task. Current attention mechanisms based segmentation methods differ with each other in position and form of the attention mechanism, and perform differently in practice. This paper firstly introduces the effectiveness of multi-scale context features and attention mechanisms in segmentation tasks. We find that multi-scale and channel attention can play a vital role in constructing effective context features. Based on this analysis, this paper proposes an efficient attention pyramid network (EAPNet) for semantic segmentation. Specifically, to efficient handle the problem of segmenting objects at multiple scales, we design efficient channel attention pyramid (ECAP) which employ atrous convolution with channel attention in cascade or in parallel to capture multi-scale context by using multiple atrous rates. Furthermore, we propose a residual attention fusion block (RAFB), whose purpose is to simultaneously focus on meaningful low-level feature maps and spatial location information. At the same time, we will explore different channel attention modules and spatial attention modules, and describe their impact on network performance. We empirically evaluate our EAPNet on two semantic segmentation datasets, including PASCAL VOC 2012 and Cityscapes datasets. Experimental results show that without MS COCO pre-training and any post-processing, EAPNet achieved 81.7% mIoU on the PASCAL VOC 2012 validation set. With deeplabv3+ as the benchmark, EAPNet improve the model performance of more than 1.50% mIoU.
To solve the motion planning of the live working manipulator, this research proposes a hybrid data-model–driven algorithm called the P-SAC algorithm. In the model-driven part, to avoid obstacles and ...make the trajectory as smooth as possible, we designed the trajectory model of the sextic polynomial and used the PSO algorithm to optimize the parameters of the trajectory model. The data generated by the model-driven part are then passed into the replay buffer to pre-train the agent. Meanwhile, to guide the manipulator in reaching the target point, we propose a reward function design based on region guidance. The experimental results show that the P-SAC algorithm can reduce unnecessary exploration of reinforcement learning and can improve the learning ability of the model-driven algorithm for the environment.
The geometrical morphologies fabricated by continuous and interlacing printing modes on untreated multi-crystalline solar cells are quantitively explored for uniform and high aspect-ratio finger ...electrodes. The voltage waveform of printhead is well modulated by in-house developed inkjet prototype printer for optimizing the droplet volume and velocity. As the fingers are fabricated with continuous printing mode by varying printing parameters, the bulging and coffee ring morphologies could not be eliminated resulting from the rough and anisotropic features. The interlacing printing mode is first introduced for finger electrodes fabrication. Results show that the width of the printed finger is not influenced by the printing layers and maintains the stable values of ∼60 μ m with pre-heating temperature 80 °C. Moreover, the interlacing printing mode has a high tolerance of droplet spacing variance (30-45 μ m) and suppresses the coffee ring morphology which obtains uniform finger electrodes. The study not only offers good guidance of finger electrodes fabrication for multi-crystalline solar cell manufacturing, but also proposes in-depth insights for the three-dimensional circuit structures fabrication of the emerging printed electronics.
To realize efficient remote human-computer interaction of robots, a robot remote operating system based on virtual reality and digital twin is proposed. The system builds a digital twin model based ...on the Unity 3D engine to establish a connection with the robot entity, assisting the online remote programming and real-time manipulation of the robot unit. The system uses HTC VIVE to build a virtual reality framework. To actualize the mutual drive between the real space and the virtual space, a mathematical model of the robot is constructed through the forward and inverse kinematics of the robot. Through the combination of eye-tracking-based eye movement interaction and the unique controller interaction of virtual reality system, a multi-sensory multi-input collaborative interaction method is accomplished. The method realizes the robot joints driving of users using multiple interaction methods simultaneously, simplifies the robot programming and control procedure, and optimizes the operation experience. Tests demonstrate that the system is capable of effectively providing monitoring, teleoperation and programming services for remote interaction of robots.
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