Palmprint gender classification by convolutional neural network

Palmprint gender classification can revolutionise the performance of authentication systems, reduce searching space and speed up matching rate. However, to the best of their knowledge, there is no literature addressing this issue. The authors design a new convolutional neural network (CNN) structure, fine‐tuning Visual Geometry Group Network, up to 19 layers to achieve a 20‐layer network, for palmprint gender classification. Experimental results show that the proposed structure could achieve good performance for gender classification. They also investigate palmprint images with 15 different kinds of spectra. They empirically find that a palmprint image acquired by the Blue spectrum could achieve 89.2% correct classification and could be considered as a suitable spectrum for gender classification. The neural network is able to classify a 224 × 224 × 3‐pixel palmprint image in <23 ms, verifying that the proposed CNN is an effective real‐time solution

A Lightweight Human Fall Detection Network

The rising issue of an aging population has intensified the focus on the health concerns of the elderly. Among these concerns, falls have emerged as a predominant health threat for this demographic. The YOLOv5 family represents the forefront of techniques for human fall detection. However, this algorithm, although advanced, grapples with issues such as computational demands, challenges in hardware integration, and vulnerability to occlusions in the designated target group. To address these limitations, we introduce a pioneering lightweight approach named CGNS-YOLO for human fall detection. Our method incorporates both the GSConv module and the GDCN module to reconfigure the neck network of YOLOv5s. The objective behind this modification is to diminish the model size, curtail floating-point computations during feature channel fusion, and bolster feature extraction efficacy, thereby enhancing hardware adaptability. We also integrate a normalization-based attention module (NAM) into the framework, which concentrates on salient fall-related data and deemphasizes less pertinent information. This strategic refinement augments the algorithm’s precision. By embedding the SCYLLA Intersection over Union (SIoU) loss function, our model benefits from faster convergence and heightened detection precision. We evaluated our model using the Multicam dataset and the Le2i Fall Detection dataset. Our findings indicate a 1.2% enhancement in detection accuracy compared with the conventional YOLOv5s framework. Notably, our model realized a 20.3% decrease in parameter tally and a 29.6% drop in floating-point operations. A comprehensive instance analysis and comparative assessments underscore the method’s superiority and efficacy

A Large-Scale Multi-Hop Localization Algorithm Based on Regularized Extreme Learning for Wireless Networks

A novel large-scale multi-hop localization algorithm based on regularized extreme learning is proposed in this paper. The large-scale multi-hop localization problem is formulated as a learning problem. Unlike other similar localization algorithms, the proposed algorithm overcomes the shortcoming of the traditional algorithms which are only applicable to an isotropic network, therefore has a strong adaptability to the complex deployment environment. The proposed algorithm is composed of three stages: data acquisition, modeling and location estimation. In data acquisition stage, the training information between nodes of the given network is collected. In modeling stage, the model among the hop-counts and the physical distances between nodes is constructed using regularized extreme learning. In location estimation stage, each node finds its specific location in a distributed manner. Theoretical analysis and several experiments show that the proposed algorithm can adapt to the different topological environments with low computational cost. Furthermore, high accuracy can be achieved by this method without setting complex parameters

Sedimentology, provenance and geochronology of the Miocene Qiuwu Formation: Implication for the uplift history of Southern Tibet

Located on the south of the Gangdese, the Qiuwu Formation has traditionally been considered as Eocene coal-bearing clastic sediments consisting of sandstone, mudstone and conglomerate, unconformably on top of Gangdese batholith. However, its precise age and depositional environment remain ambiguous. Here, we present a newly measured stratigraphic section near the Ngamring County, western Xigaze. Detrital zircon U–Pb ages were also applied to trace the provenance of sediments and to constrain the maximum depositional age of the Qiuwu Formation. Sedimentary facies analyses indicate subaqueous fan and alluvial fan depositional environments. Clast composition of the conglomerate is dominated by magmatic rocks at the lower part, while chert and mafic detritus occur in the upper part, suggesting a southern source. Sandstone modal analyses indicate that the compositions of quartz, feldspar and lithic grains changed from transitional arc to dissected arc, implying the unroofing of the Gangdese arc. Detrital zircon U–Pb ages of the Qiuwu Formation are compared with those from Gangdese magmatic rocks and Yarlung-Zangbo ophiolites, suggesting that the Gangdese arc is a main source of the Qiuwu detritus and that the southern source played a role during the later stage. The major peak of detrital zircon ages is at 45–55 Ma, which corresponds to Linzizong volcanic rocks in southern Gangdese arc. The weighted mean age of the five youngest zircons from the lower part of the section is 21.0 ± 2.2 Ma, suggesting that the Qiuwu Formation was deposited in early Miocene, coeval with other conglomerates exposed along the southern margin of Gangdese. Combining new observations with previously published data, we propose that the provenance of the Qiuwu Formation had shifted from a single northern source to double sources from both the north and the south. Activities of Great Counter Thrust were primarily responsible for the shift by making the south area a high elevation to provide sediments for the Qiuwu Formation

Controlling Factors for Organic Carbon Burial in the Late Cretaceous Nenjiang Formation of the Songliao Basin, NE China

The Songliao Basin (SLB) is a large terrestrial petroliferous basin located in northeastern China. The Nenjiang Formation represents excellent hydrocarbon source rocks for the Daqing oil field. Previous studies have indicated that the oil shale intervals from the first (K2n1) and second (K2n2) members of the Nenjiang Formation were formed in different depositional settings. In this study, we provide a new high-resolution (1 m interval) record from SK-1s core and compile three sets of published datasets from two drilling holes (Zk3389 and LY-1) and a composite outcrop section. According to the total organic carbon (TOC) chemostratigraphy, we have divided three variation cycles spanning from K2n1 to K2n2 and detected three potential oil shale intervals in the Nenjiang Formation. Combined with the productivity, salinity, and oxygenation proxies, we discuss the paleolimnological environmental changes during deposition of the Nenjiang Formation. Our new and compiled records support the model that excellent preservation conditions were associated with the formation of organic-rich sediments in the K2n1, while the productivity was the major controlling factor for organic matter enrichment in the K2n2