Color Enhancement of Low Illumination Garden Landscape Images

The unfavorable shooting environment severely hinders the acquisition of actual landscape information in garden landscape design. Low quality, low illumination garden landscape images (GLIs) can be enhanced through advanced digital image processing. However, the current color enhancement models have poor applicability. When the environment changes, these models are easy to lose image details, and perform with a low robustness. Therefore, this paper tries to enhance the color of low illumination GLIs. Specifically, the color restoration of GLIs was realized based on modified dynamic threshold. After color correction, the low illumination GLI were restored and enhanced by a self-designed convolutional neural network (CNN). In this way, the authors achieved ideal effects of color restoration and clarity enhancement, while solving the difficulty of manual feature design in landscape design renderings. Finally, experiments were carried out to verify the feasibility and effectiveness of the proposed image color enhancement approach.

The dynamic threshold model of bandwagon innovations: Role of organizational attention and legitimacy

Innovations are not always adopted due to their expected economic impact but often due to bandwagon pressure. Fueled by economic uncertainty, these “bandwagon innovations” are adopted once the bandwagon pressure reaches a certain threshold. Existing literature, however, has not examined this threshold’s sources nor considered the effect of a bandwagon adoption decision on threshold. Therefore, building on current knowledge about the bandwagon effect, organizational attention, and legitimacy, this paper develops a theoretical model to help understand the factors affecting threshold and making organizations more or less likely to adopt bandwagon innovations. The novel dynamic threshold model proposed here explains how attention to social or economic factors can affect an organization’s threshold. The model shows that the threshold may change such that an organization may be more likely to adopt a bandwagon innovation after prior resistance or resist one after prior adoption. Implications for organizational decision-makers and future research avenues are also discussed.

Music Segmentation Algorithm Based on Self-Adaptive Update of Confidence Measure

To improve the accuracy of music segmentation and enhance segmentation effect, an algorithm based on the adaptive update of confidence measure is proposed. According to the theory of compressed sensing, the music fragments are denoised, and thus the denoised signals are subjected to short-term correlation analysis. Then, the pitch frequency is extracted, and the music fragments are roughly classified by wavelet transform to realize the preprocessing of the music fragments. In order to calculate the confidence measure of the music segment, the SVM method is used, whereas the adaptive update of the confidence measure is studied using reliable data selection algorithm. The dynamic threshold notes are segmented according to the update result to realize music segmentation. Experimental results show that the recall and precision values of the algorithm reach 97.5% and 93.8%, respectively, the segmentation error rate is low, and it can achieve effective segmentation of music fragments, indicating that the algorithm is effective.

Causal Reasoning Model Based on Medical Knowledge Graph for Disease Diagnosis

In view of the non-interpretability of disease diagnosis models based on deep learning, a knowledge reasoning model based on medical knowledge graph for intelligent diagnosis is proposed. Given the patient symptom set, the co-occurrence of the patient and the disease is calculated, then the patient suffering from one disease is calculated. Based on the dynamic threshold value, the final disease diagnosis result of the patient is outputted. According to the symptoms of patients and the symptoms in the knowledge graph, the causal reasoning of the disease diagnosis is interpretable. Experiments on 145,712 pediatric electronic medical records in Chinese show that the proposed model can predict diseases with interpretability, and the accuracy reaches-82.12%.

A Dynamic Threshold Cancellation Technique for a High-Power Conversion Efficiency CMOS Rectifier

Power conversion efficiency (PCE) has been one of the key concerns for power management circuits (PMC) due to the low output power of the vibrational energy harvesters. This work reports a dynamic threshold cancellation technique for a high-power conversion efficiency CMOS rectifier. The proposed rectifier consists of two stages, one passive stage with a negative voltage converter, and another stage with an active diode controlled by a threshold cancellation circuit. The former stage conducts the signal full-wave rectification with a voltage drop of 1 mV, whereas the latter reduces the reverse leakage current, consequently enhancing the output power delivered to the ohmic load. As a result, the rectifier can achieve a voltage and power conversion efficiency of over 99% and 90%, respectively, for an input voltage of 0.45 V and for low ohmic loads. The proposed circuit is designed in a standard 130 nm CMOS process and works for an operating frequency range from 800 Hz to 51.2 kHz, which is promising for practical applications.

An adaptive scheme for degradation suppression in Lidar based SLAM

Purpose With the booming development of computer, optical and sensing technologies and cybernetics, the technical research in unmanned vehicle has been advanced to a new era. This trend arouses great interest in simultaneous localization and mapping (SLAM). Especially, light detection and ranging (Lidar)-based SLAM system has the characteristics of high measuring accuracy and insensitivity to illumination conditions, which has been widely used in industry. However, SLAM has some intractable problems, including degradation under less structured or uncontrived environment. To solve this problem, this paper aims to propose an adaptive scheme with dynamic threshold to mitigate degradation. Design/methodology/approach We propose an adaptive strategy with a dynamic module is proposed to overcome degradation of point cloud. Besides, a distortion correction process is presented in the local map to reduce the impact of noise in the iterative optimization process. Our solution ensures adaptability to environmental changes. Findings Experimental results on both public data set and field tests demonstrated that the algorithm is robust and self-adaptive, which achieved higher localization accuracy and lower mapping error compared with existing methods. Originality/value Unlike other popular algorithms, we do not rely on multi-sensor fusion to improve the localization accuracy. Instead, the pure Lidar-based method with dynamic threshold and distortion correction module indeed improved the accuracy and robustness in localization results.