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The current method of Vcom adjustment in VR products is to start with the smallest Vcom and increase the voltage with a larger adjustment step while testing the change in flicker value, the flicker value will slowly decrease and when the flicker value starts to get bigger, reduce the adjustment step and adjust the Vcom in the opposite direction until the second flicker gets bigger and meets the specification, then the optimum Vcom will be found (quotes) [1].There are obvious drawbacks to this current approach: 1. Dueto the different optimal Vcom of different modules, the time to find the optimal Vcom is long and not fixed;2. Vcom will drift with time (Vcom Drift), the problem of irregular time will lead to increased risk of Flicker NG. In order to solve the shortcomings of the current Vcom adjustment methods, we introduced the mathematical theory and deduced the Vcom calculation process to establish the deterministic‐Vcom (D‐Vcom) algorithm. After experiments, the DVcom algorithm can effectively reduce the Vcom adjustment time and has the advantage of fixed adjustment time. This is very helpful for the actual production efficiency and optical stability of the products. [ABSTRACT FROM AUTHOR]

In recent years, the rapid development of mobile devices and embedded system raises a demand for intelligent models to address increasingly complicated problems. However, the complexity of the structure and extensive parameters press significantly on efficiency, storage space, and energy consumption. Additionally, the explosive growth of tasks with enormous model structures and parameters makes it impossible to compress models manually. Thus, a standardized and effective model compression solution achieving lightweight neural networks is established as an urgent demand by the industry. Accordingly, Dynamic Channel Ranking Strategy (DCRS) method is proposed to compress deep convolutional neural networks. DCRS selects channels with high contribution of each prunable layer according to compression ratio searched by reinforcement learning agent. Compared with current model compression methods, DCRS efficaciously applies various channel ranking strategies on prunable layers. Experiments indicate with a 50% compression ratio, compressed MobileNet achieved 70.62% top1 and 88.2% top5 accuracy on ImageNet, and compressed ResNet achieved 92.03% accuracy on CIFAR-10. DCRS reduces more FLOPS in these neural networks. The compressed model achieves the best Top-1 and Top-5 accuracy on ResNet50, the best Top-1 accuracy on MobilNetV1. [ABSTRACT FROM AUTHOR]

As the market for near‐eye display devices and other similar devices expands, these devices and their display performance are receiving more and more attention and challenges. Therefore, this paper reports a display module consisting of a high PPI fast LCD based on LTPS backplane paired with an ultra‐multi‐partition Mini‐LED backlight. The display adopts a new pixel structure and is able to achieve a transmission rate of 1.8% and a high contrast ratio of 500:1 on 1411 PPI. Besides, the multi‐partition Mini‐LED backlight unit is able to achieve 1728 partitions of local dimming and high dynamic contrast ratio of the display module, meanwhile the brightness of the backlight is increased by 4.9% with the same power consumption, making the overall display module brighter. [ABSTRACT FROM AUTHOR]

In autonomous driving and Intelligent transportation systems, pedestrian detection is vital in reducing traffic accidents. However, detecting small-scale and occluded pedestrians is challenging due to the ineffective utilization of the low-feature content of small-scale objects. The main reasons behind this are the stochastic nature of weight initialization and the greedy nature of nonmaximum suppression. To overcome the aforesaid issues, this work proposes a multifocus feature extractor module by fusing feature maps extracted from the Gaussian and Xavier mapping function to enhance the effective receptive field. We also employ a focused attention feature selection on a higher layer feature map of the single shot detector (SSD) region proposal module to blend with its low-layer feature to tackle the vanishing of the feature detail due to convolution and pooling operation. In addition, this work proposes a decaying nonmaximum suppression function considering score and Intersection Over Union (IOU) parameters to tackle high miss rates caused by greedy nonmaximum suppression used by SSD. Extensive experiments have been conducted on the Caltech pedestrian dataset with the original annotations and the improved annotations. Experimental results demonstrate the effectiveness of the proposed method, particularly for small and occluded pedestrians. [ABSTRACT FROM AUTHOR]

The estimation of crowd density is crucial for applications such as autonomous driving, visual surveillance, crowd control, public space planning, and warning visually distracted drivers prior to an accident. Having strong translational, reflective, and scale symmetry, models for estimating the density of a crowd yield an encouraging result. However, dynamic scenes with perspective distortions and rapidly changing spatial and temporal domains still present obstacles. The main reasons for this are the dynamic nature of a scene and the difficulty of representing and incorporating the feature space of objects of varying sizes into a prediction model. To overcome the aforementioned issues, this paper proposes a parallel multi-size receptive field units framework that leverages the majority of the CNN layer's features, allowing for the representation and participation in the model prediction of the features of objects of all sizes. The proposed method utilizes features generated from lower to higher layers. As a result, different object scales can be handled at different framework depths, and various environmental densities can be estimated. However, the inclusion of the vast majority of layer features in the prediction model has a number of negative effects on the prediction's outcome. Asymmetric non-local attention and the channel weighting module of a feature map are proposed to handle noise and background details and re-weight each channel to make it more sensitive to important features while ignoring irrelevant ones, respectively. While the output predictions of some layers have high bias and low variance, those of other layers have low bias and high variance. Using stack ensemble meta-learning, we combine individual predictions made with lower-layer features and higher-layer features to improve prediction while balancing the tradeoff between bias and variance. The UCF CC 50 dataset and the ShanghaiTech dataset have both been subjected to extensive testing. The results of the experiments indicate that the proposed method is effective for dense distributions and objects of various sizes. [ABSTRACT FROM AUTHOR]

With the expansion of the markets about near‐eye display devices and other similar consumer devices, the highly optical efficiency and display performance of these devices have also received more and more attention and challenge. So in this paper, a novel backlight unit for liquid crystal display (LCD) is reported. The angle of the radiation can change with the position of the active area (AA) in two directions. The change range of the chief radiation angle can vary more than ±20°. In the meanwhile, with the same power consumption, the brightness of the backlight can achieve or exceed the existing brightness level of the backlight. [ABSTRACT FROM AUTHOR]

This article studies the nonfragile sampled-data filtering problems for Takagi–Sugeno fuzzy systems with uncertainties and delays. By introducing two delay-product-type terms and other augmented terms, a novel Lyapunov–Krasovskii functional containing more detailed information of time delays is constructed. With the help of some new bounding inequalities, improved stability results for the addressed systems are established. Besides, a resilient sampled-data controller is devised with less cost. Furthermore, the efficiency of the obtained criteria is illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

Abstract Background Influenza A virus can infect a wide variety of animal species including humans, pigs, birds and other species. Viral ribonucleoprotein (vRNP) was involved in genome replication, transcription and host adaptation. Currently, firefly luciferase (Fluc) reporter system was used in vRNP functional assay. However, its limitation for the testing by virus infection resulted in an increased need for rapid, sensitive, and biosafe techniques. Here, an influenza A virus UTR-driven gene reporter for vRNP assay based on secreted Gaussia luciferase (Gluc) activity was evaluated. Results By measuring Gluc levels in supernatants, reporter gene activity could be detected and quantitated after either reconstitution of influenza A virus polymerase complex or viral infection of 293T and A549 cells, respectively. As compared with Fluc reporter, Gluc-based reporter was heat-tolerant (65°C for 30 min) and produced 50-fold higher bioluminescent activity at 24 h posttransfection. Signals generated by Gluc reporter gene could be detected as early as 6 h post-infection and accumulated with time. Testing by viral infection, stronger signals were detected by Gluc reporter at a MOI of 0.001 than that of 1 and the effects of PB2-627K/E or amantadine on influenza vRNP activity were elucidated more effectively by the Gluc reporter system. Conclusions This approach provided a rapid, sensitive, and biosafe assay of influenza vRNP function, particularly for the highly pathogenic avian influenza viruses.

Recently, a method called Meta-SR has solved the problem of super-resolution of arbitrary scale factor with only one single model. However, it has a limited reconstruction accuracy compared with RDN + and EDSR +. Inspired by Meta-SR, we noticed that by combining the core idea of Meta-SR and D-DBPN, we might construct a network that has as good image reconstruction accuracy as D-DBPN's, at the same time, keeps arbitrary scaling function. According to Meta-SR's Meta-Upscale Module, we designed a different structure called Meta-Downscale Module. By using these two different modules and back-projection structure, we construct an arbitrary back-projection network, which has the ability to enlarge images with arbitrary scale factor by using only one single model, meanwhile, obtains state-of-the-art reconstruction results. Through extensive experiments, our proposed method performs better reconstruction effect than Meta-SR and more efficient than D-DBPN. Besides that, we also evaluated the proposed method on widely used benchmark dataset on single image super-resolution. The experimental results show the superiority of our model compared to RDN+ and EDSR+. [ABSTRACT FROM AUTHOR]

Summary: As a typical representative of distributed computing framework, Spark has been continuously developed and popularized. It reduces the data transmission time through efficient memory‐based operations and solves the shortcomings of the traditional MapReduce computation model in iterative computation. In Spark, data skew is very prominent due to the uneven distribution of input data and the unbalanced allocation of default partitioning algorithm. When data skew occurs, the execution efficiency of the program will be reduced, especially in the reduce stage of Spark. Therefore, this paper proposes ReducePartition to solve data skew problem at reduce stage of Spark platform. First, the compute node samples the local data according to the sampling algorithm to predict the overall characteristics of data distribution. Then, to take full use of cluster resources, ReducePartition divides data into multiple partitions evenly. Next, taking into account the differences in computational capabilities among Executors, each task is assigned to Executor with the highest performance factor according to the greedy strategy. Finally, the results of the related algorithms and ReducePartition are compared by using WordCount benchmark and Sort benchmarks on heterogeneous Spark standalone cluster. The performance of the ReducePartition under different degree of data skew and different data size is analyzed. Experimental results show that the proposed algorithm can effectively reduce the impact of data skew on the total makespan of Spark big data applications, and the average total makespan is reduced by 30% to 50% while resource utilization is increased by 20%‐30% on average. [ABSTRACT FROM AUTHOR]