CNN-Based Spectral Super-Resolution of Panchromatic Night-Time Light Imagery: City-Size-Associated Neighborhood Effects

Data on artificial night-time light (NTL), emitted from the areas, and captured by satellites, are available at a global scale in panchromatic format. In the meantime, data on spectral properties of NTL give more information for further analysis. Such data, however, are available locally or on a commercial basis only. In our recent work, we examined several machine learning techniques, such as linear regression, kernel regression, random forest, and elastic map models, to convert the panchromatic NTL images into colored ones. We compared red, green, and blue light levels for eight geographical areas all over the world with panchromatic light intensities and characteristics of built-up extent from spatially corresponding pixels and their nearest neighbors. In the meantime, information from more distant neighboring pixels might improve the predictive power of models. In the present study, we explore this neighborhood effect using convolutional neural networks (CNN). The main outcome of our analysis is that the neighborhood effect goes in line with the geographical extent of metropolitan areas under analysis: For smaller areas, optimal input image size is smaller than for bigger ones. At that, for relatively large cities, the optimal input image size tends to differ for different colors, being on average higher for red and lower for blue lights. Compared to other machine learning techniques, CNN models emerged comparable in terms of Pearson’s correlation but showed performed better in terms of WMSE, especially for testing datasets.

Input Quantity Decision and Coordination Mechanism with Two Production Chances and Yield Uncertainty

Conducting a second production run can improve the company’s capability of meeting the market demand. Few works examine optimal input quantity decisions under the mode with two production chances considering demand and yield uncertainty. We propose a vendor-managed inventory (VMI) supply chain with one supplier and one retailer. The supplier has two production chances and faces yield uncertainty in each production run. It is necessary for the supplier to make trade-offs between the cost and benefit of the second production run, then decide whether to conduct the second production run. We investigate the supplier’s optimal input quantity decision in each production run and obtain the supply chain members’ expected profits. As a comparison, the mode with one production chance is also developed. We find that two production chances can help improve the performance of the supply chain under yield uncertainty. A revenue-sharing contract is introduced to coordinate the supply chain with two production chances, and efficient profit allocation is achieved through adjusting the revenue-sharing ratio and the wholesale price. An extension is conducted for a sensitivity analysis of unit punishment cost on the supplier’s input quantity decisions.

Optimal solutions of multiplier DEA models

Conventional models of data envelopment analysis (DEA) are based on the constant and variable returns-to-scale production technologies. Any optimal input and output weights of the multiplier DEA models based on these technologies are interpreted as being the most favorable for the decision making unit (DMU) under the assessment when the latter is benchmarked against the set of all observed DMUs. In this paper we consider a very large class of DEA models based on arbitrary polyhedral technologies, which includes almost all known convex DEA models. We highlight the fact that the conventional interpretation of the optimal input and output weights in such models is generally incorrect, which raises a question about the meaning of multiplier models. We address this question and prove that the optimal solutions of such models show the DMU under the assessment in the best light in comparison to the entire technology, but not necessarily in comparison to the set of observed DMUs. This result allows a clear and meaningful interpretation of the optimal solutions of multiplier models, including known models with a complex constraint structure whose interpretation has been problematic and left unaddressed in the existing literature.

Novel Stable Capacitive Electrocardiogram Measurement System

This study presents a noncontact electrocardiogram (ECG) measurement system to replace conventional ECG electrode pads during ECG measurement. The proposed noncontact electrode design comprises a surface guard ring, the optimal input resistance, a ground guard ring, and an optimal voltage divider feedback. The surface and ground guard rings are used to reduce environmental noise. The optimal input resistor mitigates distortion caused by the input bias current, and the optimal voltage divider feedback increases the gain. Simulated gain analysis was subsequently performed to determine the most suitable parameters for the design, and the system was combined with a capacitive driven right leg circuit to reduce common-mode interference. The present study simulated actual environments in which interference is present in capacitive ECG signal measurement. Both in the case of environmental interference and motion artifact interference, relative to capacitive ECG electrodes, the proposed electrodes measured ECG signals with greater stability. In terms of R–R intervals, the measured ECG signals exhibited a 98.6% similarity to ECGs measured using contact ECG systems. The proposed noncontact ECG measurement system based on capacitive sensing is applicable for use in everyday life.

Constructing LDPC Codes with Any Desired Girth

In wireless sensor networks, the reliability of communication can be greatly improved by applying low-density parity-check (LDPC) codes. Algorithms based on progressive-edge-growth (PEG) pattern and quasi-cyclic (QC) pattern are the mainstream approaches to constructing LDPC codes with good performance. However, these algorithms are not guaranteed to remove all short cycles to achieve the desired girth, and their excellent inputs are difficult to obtain. Herein, we propose an algorithm, which must be able to construct LDPC codes with the girth desired. In addition, the optimal input to the proposed algorithm is easy to find. Theoretical and experimental evidence of this study shows that the LDPC codes we construct have better decoding performance and less power consumption than the PEG-based and QC-based codes.