DFF-EDR:An Indoor Fingerprint Location Technology Using Dynamic Fusion Features of Channel State Information and Improved Edit Distance on Real Sequence

Positioning technology based on wireless network signals in indoor environments has developed rapidly in recent years as the demand for locationbased services continues to increase.Channel state information(CSI)can be used as location feature information in fingerprint-based positioning systems because it can reflect the characteristics of the signal on multiple subcarriers.However,the random noise contained in the raw CSI information increases the likelihood of confusion when matching fingerprint data.In this paper,the Dynamic Fusion Feature(DFF)is proposed as a new fingerprint formation method to remove the noise and improve the feature resolution of the system,which combines the pre-processed amplitude and phase data.Then,the improved edit distance on real sequence(IEDR)is used as a similarity metric for fingerprint matching.Based on the above studies,we propose a new indoor fingerprint positioning method,named DFF-EDR,for improving positioning performance.During the experimental stage,data were collected and analyzed in two typical indoor environments.The results show that the proposed localization method in this paper effectively improves the feature resolution of the system in terms of both fingerprint features and similarity measures,has good anti-noise capability,and effectively reduces the localization errors.

A performance evaluation of remotely sensed sea surface salinity products in combination with other surface measurements in reconstructing three-dimensional salinity fields

Several remotely sensed sea surface salinity（SSS） retrievals with various resolutions from the soil moisture and ocean salinity（SMOS） and Aquarius/SAC-D missions are applied as inputs for retrieving salinity profiles（S） using multilinear regressions. The performance is evaluated using a total root mean square（RMS） error, different error sources, and the feature resolutions of the retrieved S fields. In the mixed layer of the salinity, the SSS-S regression coefficients are uniformly large. The SSS inputs yield smaller RMS errors in the retrieved S with respect to Argo profiles as their spatial or temporal resolution decreases. The projected SSS errors are dominant, and the retrieved S values are more accurate than those of climatology in the tropics except for the tropical Atlantic, where the regression errors are abnormally large. Below that level, because of the influence of a sea level anomaly, the areas of high-accuracy S values shift to higher latitudes except in the high-latitude southern oceans, where the projected SSS errors are abnormally large. A spectral analysis suggests that the CATDS-0.25° results are much noisier and that the BEC-L4-0.25° results are much smoother than those of the other retrievals. Aquarius-CAP-1° generates the smallest RMS errors, and Aquarius-V2-1° performs well in depicting large-scale phenomena. BEC-L3-0.25°,which has small RMS errors and remarkable mesoscale energy, is the best fit for portraying mesoscale features in the SSS and retrieved S fields. The current priority for retrieving S is to improve the reliability of satellite SSS especially at middle and high latitudes, by developing advanced algorithms, combining both sensors, or weighing between accuracy and resolutions.

Improved Network for Face Recognition Based on Feature Super Resolution Method

Low-resolution face images can be found in many practical applications. For example, faces captured from surveillance videos are typically in small sizes. Existing face recognition deep networks, trained on high-resolution images, perform poorly in recognizing low-resolution faces. In this work, an improved multi-branch network is proposed by combining ResNet and feature super-resolution modules. ResNet is for recognizing high-resolution facial images and extracting features from both high-and low-resolution images.Feature super-resolution modules are inserted before the classifier of ResNet for low-resolution facial images. They are used to increase feature resolution. The proposed method is effective and simple. Experimental results show that the recognition accuracy for high-resolution face images is high, and the recognition accuracy for low-resolution face images is improved.