Pattern Recognition of Human Postures Using the Data Density Functional Method

Abstract

In this paper, we propose a new approach to recognize the motional patterns of human postures by introducing the data density functional method. Under the framework of the proposed method, sensed time signals will be mapped into specific physical spaces. The most probable cluster number within the specific physical space can be determined according to the principle of energy stability. Then, each corresponding cluster boundary can be measured by searching for the local lowest energy level. Finally, the configuration of the clusters in the space will characterize the most probable states of the motional patterns. The direction of state migration and the corresponding transition region between these states then constitute a significant motional feature in the specific space. Differing from conventional methods, only a single tri-axial gravitational sensor was employed for data acquirement in our hardware scheme. By combining the motional feature and the sensor architecture as prior information, experimental results verified that the most probable states of the motional patterns can be successfully classified into four common human postures of daily life. Furthermore, error motions and noise only offer insignificant influences. Eventually, the proposed approach was applied on a simulation of turning-over situations, and the results show its potential on the issue of elderly and infant turning-over monitoring.

Highly Selective Enamination of β‐ketoesters Catalyzed by Interlocked [Cu8] and [Cu18] Nanocages

“…Among many strategies to synthesize such compounds, the enamination of β‐ketoesters with amines is one of the important and extensively used methods . To promote this reaction, various homogeneous and heterogeneous catalysts have been used, such as inorganic acids, metal salts, carbon materials, and metal–organic frameworks (MOFs), amongst others . It should be noted that all the reported catalysts show high catalytic activity and excellent chemoselectivity, ensuring that amines react with the ketone part rather than ester part of β‐ketoesters.…”

“…The enamination of β‐ketoesters is one of the primary routes to synthesize β‐enaminoesters, which are very important precursors for biologically active compounds. Various catalysts have been explored to promote this reaction, including HCl, InBr 3 , Cu nanoparticles, and so on . All the reported catalysts display high catalytic activity and chemoselectivity.…”

Medical Image Segmentation with Adjustable Computational Complexity Using Data Density Functionals

“…Automatic identification and segmentation of medical imageries benefit the planning and guidance of modern surgery [1][2][3][4][5], clinical investigations [6][7][8][9][10], rehabilitation [11][12][13], and so forth. High-quality reconstructive anatomical morphology provides convenience on surgery planning and the understanding between organ functionalities and pathological diagnosis.…”

“…Meanwhile, to reduce extensive and tedious manual interventions and for the convenience of visualization, the applied method should execute artificial intelligence approaches for automatically achieving the goals of image segmentation and pattern recognition. Considering the preprocessing of the medical imageries on data compression and denoising would be valuable for large-scale medical images to pursue further execution efficiency [11,12,32].…”

A Simple, Efficient Green Protocol for the Synthesis of β‐Enaminone and Enamino Ester Derivatives by Using Onion Extract as Green Catalyst

“…The most well‐known and exploited route to β‐enaminones 3 involves the direct condensation of β‐dicarbonyl compounds 1 with amines 2 in refluxing aromatic hydrocarbons with azeotropic removal of water. Other improved procedures have also been subsequently reported which include the reaction of amines 2 with β‐dicarbonyl compounds 1 supported on AcOH under ultrasound, CoCl 2 .6H 2 O, CAN, microwave irradiation, TMSTf, Zn(OAc) 2 .2H 2 O, K 2 HPO 4 , InBr 3 , [(PPh 3 )AuCl]/AgOTf, silica gel, HClO 4 ⋅SiO 2 , Bi(TFA) 3 , NaAuCl 4 , ZrOCl 2 .8H 2 O, catalyst free, solvent free‐ grinding, NaHSO 4 /SiO 2 , LiHSO 4 /SiO 2 , Ni(OAc) 2 , copper catalyst, CuBr 2 /L 2 /pyrrolidine, CuI, mesoporous alumina sulphuric acid, water‐lactic acid, L‐proline, silica‐supported antimony(III) chloride, clays, LaCl 3 , MW/K‐10 or MW/KSF, DMF/DMA, Ag/HMMS, PPA‐SiO 2 , ionic liquids based, nano fibrous silica sulphuric acid (KCC‐1‐SA) . Unfortunately many of these processes are have major or minor limitations such as drastic reaction conditions, prolonged reaction time, high temperature, low yields and tedious work‐up procedures.…”