Computationally Efficient DOA Tracking Algorithm in Monostatic MIMO Radar with Automatic Association

We consider the problem of tracking the direction of arrivals (DOA) of multiple moving targets in monostatic multiple-input multiple-output (MIMO) radar. A low-complexity DOA tracking algorithm in monostatic MIMO radar is proposed. The proposed algorithm obtains DOA estimation via the difference between previous and current covariance matrix of the reduced-dimension transformation signal, and it reduces the computational complexity and realizes automatic association in DOA tracking. Error analysis and Cramér-Rao lower bound (CRLB) of DOA tracking are derived in the paper. The proposed algorithm not only can be regarded as an extension of array-signal-processing DOA tracking algorithm in (Zhang et al. (2008)), but also is an improved version of the DOA tracking algorithm in (Zhang et al. (2008)). Furthermore, the proposed algorithm has better DOA tracking performance than the DOA tracking algorithm in (Zhang et al. (2008)). The simulation results demonstrate effectiveness of the proposed algorithm. Our work provides the technical support for the practical application of MIMO radar

Specialized Re-Ranking: A Novel Retrieval-Verification Framework for Cloth Changing Person Re-Identification

Cloth changing person re-identification(Re-ID) can work under more complicated scenarios with higher security than normal Re-ID and biometric techniques and is therefore extremely valuable in applications. Meanwhile, higher flexibility in appearance always leads to more similar-looking confusing images, which is the weakness of the widely used retrieval methods. In this work, we shed light on how to handle these similar images. Specifically, we propose a novel retrieval-verification framework. Given an image, the retrieval module can search for similar images quickly. Our proposed verification network will then compare the input image and the candidate images by contrasting those local details and give a similarity score. An innovative ranking strategy is also introduced to take a good balance between retrieval and verification results. Comprehensive experiments are conducted to show the effectiveness of our framework and its capability in improving the state-of-the-art methods remarkably on both synthetic and realistic datasets.Comment: Accepted by Pattern Recognitio

Crucial Breakthrough of Functional Persistent Luminescence Materials for Biomedical and Information Technological Applications

Persistent luminescence is a phenomenon in which luminescence is maintained for minutes to hours without an excitation source. Owing to their unique optical properties, various kinds of persistent luminescence materials (PLMs) have been developed and widely employed in numerous areas, such as bioimaging, phototherapy, data-storage, and security technologies. Due to the complete separation of two processes, —excitation and emission—, minimal tissue absorption, and negligible autofluorescence can be obtained during biomedical fluorescence imaging using PLMs. Rechargeable PLMs with super long afterglow life provide novel approaches for long-term phototherapy. Moreover, owing to the exclusion of external excitation and the optical rechargeable features, multicolor PLMs, which have higher decoding signal-to-noise ratios and high storage capability, exhibited an enormous application potential in information technology. Therefore, PLMs have significantly promoted the application of optics in the fields of multimodal bioimaging, theranostics, and information technology. In this review, we focus on the recently developed PLMs, including inorganic, organic and inorganic-organic hybrid PLMs to demonstrate their superior applications potential in biomedicine and information technology

Compositional and predicted functional analysis of the gut microbiota of Radix auricularia (Linnaeus) via high-throughput Illumina sequencing

Due to its wide distribution across the world, the snail Radix auricularia plays a central role in the transferal of energy and biomass by consuming plant biomass in freshwater systems. The gut microbiota are involved in the nutrition, digestion, immunity, and development of snails, particularly for cellulolytic bacteria, which greatly contribute to the digestion of plant fiber. For the first time, this study characterized the gut bacterial communities of R. auricularia, as well as predicted functions, using the Illumina Miseq platform to sequence 16S rRNA amplicons. Both juvenile snails (JS) and adult snails (AS) were sampled. The obtained 251,072 sequences were rarefied to 214,584 sequences and clustered into 1,196 operational taxonomic units (OTUs) with 97% sequence identity. The predominant phyla were Proteobacteria (JS: 36.0%, AS: 31.6%) and Cyanobacteria (JS: 16.3%, AS: 19.5%), followed by Chloroflexi (JS: 9.7%, AS: 13.1%), Firmicutes (JS: 14.4%, AS: 6.7%), Actinobacteria (JS: 8.2%, AS: 12.6%), and Tenericutes (JS: 7.3%, AS: 6.2%). The phylum Cyanobacteria may have originated from the plant diet instead of the gut microbiome. A total of 52 bacterial families and 55 genera were found with >1% abundance in at least one sample. A large number of species could not be successfully identified, which could indicate the detection of novel ribotypes or result from insufficient availability of snail microbiome data. The core microbiome consisted of 469 OTUs, representing 88.4% of all sequences. Furthermore, the predicted function of bacterial community of R. auricularia performed by Phylogenetic Investigation of Communities by Reconstruction of Unobserved States suggests that functions related to metabolism and environmental information processing were enriched. The abundance of carbohydrate suggests a strong capability of the gut microbiome to digest lignin. Our results indicate an abundance of bacteria in both JS and AS, and thus the bacteria in R. auricularia gut form a promising source for novel enzymes, such as cellulolytic enzymes, that may be useful for biofuel production. Furthermore, searching for xenobiotic biodegradation bacteria may be a further important application of these snails

Covalently immobilized lipase on a thermoresponsive polymer with an upper critical solution temperature as an efficient and recyclable asymmetric catalyst in aqueous media

This work was financially supported by the National Natural Science Foundation of China (Grant No. 21203102), the Tianjin Municipal Natural Science Foundation (Grant No. 14JCQNJC06000), China Scholarship Council (Grant No. 201606200087), MOE (IRT13R30) and 111 Project (B12015).A thermoresponsive lipase catalyst with an upper critical solution temperature (UCST) of about 26 °C was exploited by covalent immobilization of an enzyme, Pseudomonas cepacia lipase (PSL), onto poly(acrylamide-co-acrylonitrile) via glutaraldehyde coupling. The experimental conditions for the PSL immobilization were optimized. The immobilized PSL was much more stable for wide ranges of temperature and pH than the free PSL. The material was also evaluated as an asymmetric catalyst in the kinetic resolution of racemic α-methylbenzyl butyrate at 55 °C in an aqueous medium and exhibited high catalytic performance and stability. Up to 50% conversion and 99.5% product enantiomeric excess were achieved, thus providing highly pure enantiomers. More importantly, this biocatalyst could be easily recovered by simple decantation for reuse based on temperature-induced precipitation. It showed good reusability and retained 80.5% of its original activity with a well reserved enantioselectivity in the 6th cycle. This work would shed light on the future development of new UCST-type enzyme catalysts.PostprintPeer reviewe

High precision recognition and adjustment of complicated shape details in fine cold rolling process of ultra-thin wide strip

With development of the ultra-thin wide strip, more complicated shape problems begin to happen in the high-speed rolling process. A new shape method with high precision has to be proposed for recognition and adjustment of local composite shape defects, which is not only difficult to be detected exactly, but also easily affected by the various inevitable process factors in the practical rolling process. So, it is necessary to comprehensively take advantage of the deformation mechanism and the intelligent algorithm according to massive practical effective shape signals via focusing on the accurate shape signals and the exact local shape pattern as well as the co-regulation features. Just counting on combining the shape evolution mechanism and the intelligent property, more complicated local shape details can be revealed and controlled. Finally, many practical cases were calculated by the new model to verify the fine control effect of various shape defects, which is pretty helpful in recognition and adjustment of the ultra-thin wide strip

Analysis of transient heat source and coupling temperature field during cold strip rolling

In the process of cold thin strip rolling, the effect of the roll gap heat source on the transient temperature of cold rolled strip is very significant, and especially for the lateral temperature difference fluctuation which easily leads to the additional shape deviation of the rolled strip. In this study, according to the new heat resource model, the coupled temperature field model with high precision can be established, and the influence of the heat resource on the transient temperature of the cold rolled strip can be obtained by comprehensively considering the emulsion heat transfer coefficient, the air cooling, and the heat conduction boundary conditions. Based on the above models and the actual working parameters, several cases were performed to show the detailed analyses of the transient temperature distributions under various rolling conditions. The results at different strip positions show that the lateral distributions of the roll gap heat source and the strip transient temperature at every stand or pass can be simulated well. This study can improve the calculation precision of the transient lateral temperature difference for the complex online shape deviations during cold thin strip rolling

Design of a Felid-like Humanoid Foot for Stability Enhancement

The foot is an important part of humanoid robot locomotion that can help with shock absorption while making contact with the ground. The mechanism of the foot directly affects walking stability. A novel foot mechanism inspired by the toes of felids is proposed. The foot has four bionic modules with soft pads and sharp claws installed at the four corners of a flat foot. This foot can reduce the impact experienced during foot landing and increase the time that the foot is in contact with the ground, which can improve the adaptability of the robot to different ground surface conditions with different levels of stiffness. The main structure of the bionic module is a four-bar linkage consisting of a slide way and a spring. Furthermore, the length of the four-bar linkage and the posture of the claw during insertion into soft ground are optimized to improve the stability and buffering performance. The validity of the proposed foot mechanism has been proved in simulations

MoNet : deep motion exploitation for video object segmentation

In this paper, we propose a novel MoNet model to deeply exploit motion cues for boosting video object segmentation performance from two aspects, i.e., frame representation learning and segmentation refinement. Concretely, MoNet exploits computed motion cue (i.e., optical flow) to reinforce the representation of the target frame by aligning and integrating representations from its neighbors. The new representation provides valuable temporal contexts for segmentation and improves robustness to various common contaminating factors, e.g., motion blur, appearance variation and deformation of video objects. Moreover, MoNet exploits motion inconsistency and transforms such motion cue into foreground/background prior to eliminate distraction from confusing instances and noisy regions. By introducing a distance transform layer, MoNet can effectively separate motion-inconstant instances/regions and thoroughly refine segmentation results. Integrating the proposed two motion exploitation components with a standard segmentation network, MoNet provides new state-of-the-art performance on three competitive benchmark datasets.Ministry of Education (MOE)Accepted versionHuaxin Xiao was supported by the China Scholarship Council under Grant 201603170287. Jiashi Feng was partially supported by NUS startup R-263-000-C08-133, MOE Tier-I R-263-000-C21-112, NUS IDS R-263-000-C67-646 and ECRA R-263-000-C87-133