In recent years, single face image super-resolution (SR) using deep neural networks have been well developed. However, most of the face images captured by the camera in a real scene are from different views of the same person, and the existing traditional multi-frame image SR requires alignment between images. Due to multi-view face images contain texture information from different views, which can be used as effective prior information, how to use this prior information from multi-views to reconstruct frontal face images is challenging. In order to effectively solve the above problems, we propose a novel face SR network based on multi-view face images, which focus on obtaining more texture information from multi-view face images to help the reconstruction of frontal face images. And in this network, we also propose a texture attention mechanism to transfer high-precision texture compensation information to the frontal face image to obtain better visual effects. We conduct subjective and objective evaluations, and the experimental results show the great potential of using multi-view face images SR. The comparison with other state-of-the-art deep learning SR methods proves that the proposed method has excellent performance.

To solve the problem of the self-deployment of heterogeneous directional wireless sensor networks in 3D space, this paper proposes a weighted Voronoi diagram-based self-deployment algorithm (3DV-HDDA) in 3D space. To improve the network coverage ratio of the monitoring area, the 3DV-HDDA algorithm uses the weighted Voronoi diagram to move the sensor nodes and introduces virtual boundary torque to rotate the sensor nodes, so that the sensor nodes can reach the optimal position. This work also includes an improvement algorithm (3DV-HDDA-I) based on the positions of the centralized sensor nodes. The difference between the 3DV-HDDA and the 3DV-HDDA-I algorithms is that in the latter the movement of the node is determined by both the weighted Voronoi graph and virtual force. Simulations show that compared to the virtual force algorithm and the unweighted Voronoi graph-based algorithm, the 3DV-HDDA and 3DV-HDDA-I algorithms effectively improve the network coverage ratio of the monitoring area. Compared to the virtual force algorithm, the 3DV-HDDA algorithm increases the coverage from 75.93% to 91.46% while the 3DV-HDDA-I algorithm increases coverage from 76.27% to 91.31%. When compared to the unweighted Voronoi graph-based algorithm, the 3DV-HDDA algorithm improves the coverage from 80.19% to 91.46% while the 3DV-HDDA-I algorithm improves the coverage from 72.25% to 91.31%. Further, the energy consumption of the proposed algorithms after 60 iterations is smaller than the energy consumption using a virtual force algorithm. Experimental results demonstrate the accuracy and effectiveness of the 3DV-HDDA and the 3DV-HDDA-I algorithms.

For high definition (HD) videos, the 3D-High Efficiency Video Coding (3D-HEVC) reference algorithm incurs dramatically highly computation loads. Therefore, with the demands for the real-time processing of HD video, a hardware implementation is necessary. In this paper, a reconfigurable architecture is proposed that can support both median filtering preprocessing and mean filtering preprocessing to satisfy different scene depth maps. The architecture sends different instructions to the corresponding processing elements according to different scenarios. Mean filter is used to process near-range images, and median filter is used to process long-range images. The simulation results show that the designed architecture achieves an averaged PSNR of 34.55dB for the tested images. The hardware design for the proposed virtual view synthesis system operates at a maximum clock frequency of 160MHz on the BEE4 platform which is equipped with four Virtex-6 FF1759 LX550T Field-Programmable Gate Array (FPGA) for outputting 720p (1024×768) video at 124fps.

Pedestrian detection is a significant task in computer vision. In recent years, it is widely used in applications such as intelligent surveillance systems and automated driving systems. Although it has been exhaustively studied in the last decade, the occlusion handling issue still remains unsolved. One convincing idea is to first detect human body parts, and then utilize the parts information to estimate the pedestrians' existence. Many parts-based pedestrian detection approaches have been proposed based on this idea. However, in most of these approaches, the low-quality parts mining and the clumsy part detector combination is a bottleneck that limits the detection performance. To eliminate the bottleneck, we propose Discriminative Part CNN (DP-CNN). Our approach has two main contributions: (1) We propose a high-quality body parts mining method based on both convolutional layer features and body part subclasses. The mined part clusters are not only discriminative but also representative, and can help to construct powerful pedestrian detectors. (2) We propose a novel method to combine multiple part detectors. We convert the part detectors to a middle layer of a CNN and optimize the whole detection pipeline by fine-tuning that CNN. In experiments, it shows astonishing effectiveness of optimization and robustness of occlusion handling.

In 3D IC design, thermal issue is a critical challenge. To eliminate hotspots, physical layouts are always adjusted by some incremental changes, such as shifting or duplicating hot blocks. In this paper, we distinguish the thermal-aware incremental changes in three different categories: migrating computation, growing unit and moving hotspot blocks. However, these modifications may degrade the packing area as well as interconnect distribution greatly. In this paper, mixed integer linear programming (MILP) models are devised according to these different incremental changes so that multiple objectives can be optimized simultaneously. Furthermore, to avoid random incremental modification, which may be inefficient and need long runtime to converge, here potential gain is modeled for each candidate incremental change. Based on the potential gain, a novel thermal optimization flow to intelligently choose the best incremental operation is presented. Experimental results show that migrating computation, growing unit and moving hotspot can reduce max on-chip temperature by 7%, 13% and 15% respectively on MCNC/GSRC benchmarks. Still, experimental results also show that the thermal optimization flow can reduce max on-chip temperature by 14% to the initial packings generated by an existing 3D floorplanning tool CBA, and achieve better area and total wirelength improvement than individual operations do. The results with the initial packings from CBA_T (Thermal-aware CBA floorplanner) show that 13.5% temperature reduction can be obtained by our incremental optimization flow.

In this paper, we propose a patch-wise learning based approach to deal with the multiple-shot people re-identification task. In the proposed approach, re-identification is formulated as a patch-wise set-to-set matching problem, with each patch set being matched using a specifically learned Mahalanobis distance metric. The proposed approach has two advantages: (1) a patch-wise representation that moderates the ambiguousness of a non-rigid matching problem (of human body) to an approximate rigid one (of body parts); (2) a patch-wise learning algorithm that enables more constraints to be included in the learning process and results in distance metrics of high quality. We evaluate the proposed approach on popular benchmark datasets and confirm its competitive performance compared to the state-of-the-art methods.

As the nodes of AWSN (Aerial Wireless Sensor Networks) fly around, the network topology changes frequently with high energy consumption and high cluster head mortality, and some sensor nodes may fly away from the original cluster and interrupt network communication. To ensure the normal communication of the network, this paper proposes an improved LEACH-M protocol for aerial wireless sensor networks. The protocol is improved based on the traditional LEACH-M protocol and MCR protocol. A Cluster head selection method based on maximum energy and an efficient solution for outlier nodes is proposed to ensure that cluster heads can be replaced prior to their death and ensure outlier nodes re-home quickly and efficiently. The experiments show that, compared with the LEACH-M protocol and MCR protocol, the improved LEACH-M protocol performance is significantly optimized, increasing network data transmission efficiency, improving energy utilization, and extending network lifetime.

In this paper, the effective Long Range (LoRa) based wireless sensor network is designed and implemented to provide the remote data sensing functions for the planned smart agricultural recycling rapid processing factory. The proposed wireless sensor network transmits the sensing data from various sensors, which measure the values of moisture, viscosity, pH, and electrical conductivity of agricultural organic wastes for the production and circulation of organic fertilizers. In the proposed wireless sensor network design, the LoRa transceiver module is used to provide data transmission functions at the sensor node, and the embedded platform by Raspberry Pi module is applied to support the gateway function. To design the cloud data server, the MySQL methodology is applied for the database management system with Apache software. The proposed wireless sensor network for data communication between the sensor node and the gateway supports a simple one-way data transmission scheme and three half-duplex two-way data communication schemes. By experiments, for the one-way data transmission scheme under the condition of sending one packet data every five seconds, the packet data loss rate approaches 0% when 1000 packet data is transmitted. For the proposed two-way data communication schemes, under the condition of sending one packet data every thirty seconds, the average packet data loss rates without and with the data-received confirmation at the gateway side can be 3.7% and 0%, respectively.

The aim of this paper is to propose effective attentional regions for fine-grained visual recognition. Based on the Spatial Transformers' capability of spatial manipulation within networks, we propose an extension model, the Attention-Guided Spatial Transformer Networks (AG-STNs). This model can guide the Spatial Transformers with hard-coded attentional regions at first. Then such guidance can be turned off, and the network model will adjust the region learning in terms of the location and scale. Such adjustment is conditioned to the classification loss so that it is actually optimized for better recognition results. With this model, we are able to successfully capture detailed attentional information. Also, the AG-STNs are able to capture attentional information in multiple levels, and different levels of attentional information are complementary to each other in our experiments. A fusion of them brings better results.

A novel superpixel segmentation approach driven by uniform mixture model with spatially constrained (UMMS) is proposed. Under this algorithm, each observation, i.e. pixel is first represented as a five-dimensional vector which consists of colour in CLELAB space and position information. And then, we define a new uniform distribution through adding pixel position, so that this distribution can describe each pixel in input image. Applied weighted 1-Norm to difference between pixels and mean to control the compactness of superpixel. In addition, an effective parameter estimation scheme is introduced to reduce computational complexity. Specifically, the invariant prior probability and parameter range restrict the locality of superpixels, and the robust mean optimization technique ensures the accuracy of superpixel boundaries. Finally, each defined uniform distribution is associated with a superpixel and the proposed UMMS successfully implements superpixel segmentation. The experiments on BSDS500 dataset verify that UMMS outperforms most of the state-of-the-art approaches in terms of segmentation accuracy, regularity, and rapidity.

Local spatio-temporal features are popular in the human action recognition task. In practice, they are usually coupled with a feature encoding approach, which helps to obtain the video-level vector representations that can be used in learning and recognition. In this paper, we present an efficient local feature encoding approach, which is called Approximate Sparse Coding (ASC). ASC computes the sparse codes for a large collection of prototype local feature descriptors in the off-line learning phase using Sparse Coding (SC) and look up the nearest prototype's precomputed sparse code for each to-be-encoded local feature in the encoding phase using Approximate Nearest Neighbour (ANN) search. It shares the low dimensionality of SC and the high speed of ANN, which are both desired properties for a local feature encoding approach. ASC has been excessively evaluated on the KTH dataset and the HMDB51 dataset. We confirmed that it is able to encode large quantity of local video features into discriminative low dimensional representations efficiently.

Fine-grained image classification is a difficult problem, and previous studies mainly overcome this problem by locating multiple discriminative regions in different scales and then aggregating complementary information explored from the located regions. However, locating discriminative regions introduces heavy overhead and is not suitable for real-world application. In this paper, we propose the recursive multi-scale channel-spatial attention module (RMCSAM) for addressing this problem. Following the experience of previous research on fine-grained image classification, RMCSAM explores multi-scale attentional information. However, the attentional information is explored by recursively refining the deep feature maps of a convolutional neural network (CNN) to better correspond to multi-scale channel-wise and spatial-wise attention, instead of localizing attention regions. In this way, RMCSAM provides a lightweight module that can be inserted into standard CNNs. Experimental results show that RMCSAM can improve the classification accuracy and attention capturing ability over baselines. Also, RMCSAM performs better than other state-of-the-art attention modules in fine-grained image classification, and is complementary to some state-of-the-art approaches for fine-grained image classification. Code is available at https://github.com/Dichao-Liu/Recursive-Multi-Scale-Channel-Spatial-Attention-Module.

In recent years, compared with the traditional face super-resolution (SR) algorithm, the face SR based on deep neural network has shown strong performance. Among these methods, attention mechanism has been widely used in face SR because of its strong feature expression ability. However, the existing attention-based face SR methods can not fully mine the missing pixel information of low-resolution (LR) face images (structural prior). And they only consider a single attention mechanism to take advantage of the structure of the face. The use of multi-attention could help to enhance feature representation. In order to solve this problem, we first propose a new pixel attention mechanism, which can recover the structural details of lost pixels. Then, we design an attention fusion module to better integrate the different characteristics of triple attention. Experimental results on FFHQ data sets show that this method is superior to the existing face SR methods based on deep neural network.

Finding the correspondence between two images of the same object or scene is an active research field in computer vision. This paper develops a rapid and effective Content-based Superpixel Image matching and Stitching (CSIS) scheme, which utilizes the content of superpixel through multi-features fusion technique. Unlike popular keypoint-based matching method, our approach proposes a superpixel internal feature-based scheme to implement image matching. In the beginning, we make use of a novel superpixel generation algorithm based on content-based feature representation, named Content-based Superpixel Segmentation (CSS) algorithm. Superpixels are generated in terms of a new distance metric using color, spatial, and gradient feature information. It is developed to balance the compactness and the boundary adherence of resulted superpixels. Then, we calculate the entropy of each superpixel for separating some superpixels with significant characteristics. Next, for each selected superpixel, its multi-features descriptor is generated by extracting and fusing local features of the selected superpixel itself. Finally, we compare the matching features of candidate superpixels and their own neighborhoods to estimate the correspondence between two images. We evaluated superpixel matching and image stitching on complex and deformable surfaces using our superpixel region descriptors, and the results show that new method is effective in matching accuracy and execution speed.

In this paper, we propose an inconsistency resolution method based on a new concept, insecure backtracking role mapping. By analyzing the role graph, we prove that the root cause of security inconsistency in distributed interoperation is the existence of insecure backtracking role mapping. We propose a novel and efficient algorithm to detect the inconsistency via finding all of the insecure backtracking role mappings. Our detection algorithm will not only report the existence of inconsistency, but also generate the inconsistency information for the resolution. We reduce the inconsistency resolution problem to the known Minimum-Cut problem, and based on the results generated by our detection algorithm we propose an inconsistency resolution algorithm which could guarantee the security of distributed interoperation. We demonstrate the effectiveness of our approach through simulated tests and a case study.

This paper proposes a security violation detection method for RBAC based interoperation to meet the requirements of secure interoperation among distributed systems. We use role mappings between RBAC systems to implement trans-system access control, analyze security violation of interoperation with role mappings, and formalize definitions of secure interoperation. A minimum detection method according to the feature of RBAC system in distributed environment is introduced in detail. This method reduces complexity by decreasing the amount of roles involved in detection. Finally, we analyze security violation further based on the minimum detection method to help administrators eliminate security violation.

In this paper, we describe a post-processing technique having high extraction efficiency (ExE) for de-biasing and de-correlating a random bitstream generated by true random number generators (TRNGs). This research is based on the N-bit von Neumann (VN_N) post-processing method. It improves the ExE of the original von Neumann method close to the Shannon entropy bound by a large N value. However, as the N value increases, the mapping table complexity increases exponentially (2N), which makes VN_N unsuitable for low-power TRNGs. To overcome this problem, at the algorithm level, we propose a waiting strategy to achieve high ExE with a small N value. At the architectural level, a Hamming weight mapping-based hierarchical structure is used to reconstruct the large mapping table using smaller tables. The hierarchical structure also decreases the correlation factor in the raw bitstream. To develop a technique with high ExE and low cost, we designed and fabricated an 8-bit von Neumann with waiting strategy (VN_8W) in a 130-nm CMOS. The maximum ExE of VN_8W is 62.21%, which is 2.49 times larger than the ExE of the original von Neumann. NIST SP 800-22 randomness test results proved the de-biasing and de-correlation abilities of VN_8W. As compared with the state-of-the-art optimized 7-element iterated von Neumann, VN_8W achieved more than 20% energy reduction with higher ExE. At 0.45V and 1MHz, VN_8W achieved the minimum energy of 0.18pJ/bit, which was suitable for sub-pJ low energy TRNGs.

Negative bias temperature instability (NBTI) has become a critical reliability phenomena in advanced CMOS technology. In this paper, we propose an analytical temperature-aware dynamic NBTI model, which can be used in two circuit operation cases: executing tasks with different temperatures, and switching between active and standby mode. A PMOS Vth degradation model and a digital circuits' temporal performance degradation estimation method are developed based on our NBTI model. The simulation results show that: 1) the execution of a low temperature task can decrease ΔVth due to NBTI by 24.5%; 2) switching to standby mode can decrease ΔVth by 52.3%; 3) for ISCAS85 benchmark circuits, the delay degradation can decrease significantly if the circuit execute low temperature task or switch to standby mode; 4) we have also observed the execution time's ratio of different tasks and the ratio of active to standby time both have a considerable impact on NBTI effect.

In this letter, we propose a semantic guided infrared and visible image fusion method, which can train a network to fuse different semantic objects with different fusion weights according to their own characteristics. First, we design the appropriate fusion weights for each semantic object instead of the whole image. Second, we employ the semantic segmentation technology to obtain the semantic region of each object, and generate special weight maps for the infrared and visible image via pre-designed fusion weights. Third, we feed the weight maps into the loss function to guide the image fusion process. The trained fusion network can generate fused images with better visual effect and more comprehensive scene representation. Moreover, we can enhance the modal features of various semantic objects, benefiting subsequent tasks and applications. Experiment results demonstrate that our method outperforms the state-of-the-art in terms of both visual effect and quantitative metrics.

The conical area evolutionary algorithm (CAEA) has a very high run-time efficiency for bi-objective optimization, but it can not tackle problems with more than two objectives. In this letter, a conical hypervolume evolutionary algorithm (CHEA) is proposed to extend the CAEA to a higher dimensional objective space. CHEA partitions objective spaces into a series of conical subregions and retains only one elitist individual for every subregion within a compact elitist archive. Additionally, each offspring needs to be compared only with the elitist individual in the same subregion in terms of the local hypervolume scalar indicator. Experimental results on 5-objective test problems have revealed that CHEA can obtain the satisfactory overall performance on both run-time efficiency and solution quality.