Algorithm for Gesture Recognition Using an IR-UWB Radar Sensor

This paper introduces a human gesture recognition algorithm using an impulse radio ultra-wide- band (IR-UWB) radar sensor. Human gesture recognition has been one of the hottest research topics for quite a long time. Many gesture recognition algorithms or systems using other sensors have been proposed such as using cameras, RFID tags and so on. Among which gesture recognition systems using cameras have been extensively studied in past years and widely used in practical. While it might show some deficiencies in some cases. For example, the users might not like to be filmed by cameras considering their privacies. Besides, it might not work well in very dark environments. While RFID tags could be inconvenient to many people and are likely to be lost. Our gesture recognition algorithm uses IR-UWB radar sensor which has pretty high resolution in ranging and adjustable gesture recognition range, meanwhile, does not have problems in privacy issues or darkness. In this paper, the gesture recognition algorithm is based on the moving direction and distance change of the human hand and the change of the frontal surface area of hand towards radar sensor. By combining these changes while doing gestures, the algorithm may recognize basically 6 kinds of hand gestures. The experimental results show that these gestures are of quite good performance. The performance analysis from experiments is also given.

Effect of copper precursors on the catalytic performance of Cu-ZSM-5 catalysts in N2O decomposition

Five Cu-ZSM-5 catalysts were obtained by treating Na-ZSM-5(Si/Al ratio = 15) with aqueous solutions of different Cu precursors(CuCl_2, Cu(NO_3)_2, CuSO_4, Cu(CH_3COO)_2, and ammoniacal copper(II) complex ion). After being pretreated in flowing He at 500 ℃ to form active Cu+, these catalysts exhibited quite different activities in catalytic decomposition of N_2O. CZM-AC(II)(prepared by ammoniacal copper(Ⅱ) complex ion) with 9.4 wt% Cu content was the most active among these Cu-ZSM-5 catalysts, achieving almost complete N_2O conversion at 400 ℃.CZM-CA(prepared using Cu(CH_3COO)_2 as the Cu precursor) with 2.8 wt% Cu content was the second most active catalyst among these Cu-ZSM-5 catalysts, achieving almost complete N_2O conversion at 425 ℃. CZM-CC, CZMCN, and CZM-CS prepared by using Cu Cl_2, Cu(NO_3)_2, or CuSO_4 as the Cu precursor with similar Cu contents(≈ 1.7 wt%) were the least active among these Cu-ZSM-5 catalysts, achieving ca. 90% N_2O conversion at 500 ℃.XRD, ICP, SEM, TEM, EDX-mapping, and CO-IR experiments were conducted to characterize relevant samples.The superior activity of CZM-AC(Ⅱ) can be attributed to the high contents of total Cu+and dimeric Cu+among these samples. The influence of co-fed O_2 or H_2O on the catalytic performance of typical samples was also studied.

Establishment of Agrobacterium tumefaciens-mediated Genetic Transformation System for Aspergillus awamori

[ Objective ] This study aimed to establish Agrobacterium tumefaciens-mediated genetic transformation system for Aspergillus awamori and investigate the feasibility of expressing heterologous proteins in A. awamori. [ Method] Appropriate A. awamori host strains were determined according to the secretory protein profile. Selectable marker was selected for genetic transformation by drug sensitivity analysis. The established A. awamori genetic transformation system was used for transformation and expression analysis of Rhizomucor miehei lipase （RML）. The feasibility of using A. awamori to express heterologous proteins was investigated by identification of transformants and property analysis. [ Result~ Based on the analysis of secretory protein profile, A. awamori strains CBS115.52 and CICC2257 were determined as the host strains for heterologous protein expression ; drug sensitivity analysis shows that hygromycin B resistance gene （ HygBr ） is an effective ge- netic seleetable marker; by using Agrobacterium tumefaciens-mediatcd transformation （ATMT） method, the plasmid pHGW-amdS containing HygBr was successfully transformed into A. awamori strain CBS115.52 to establish the genetic transformation system ofA. awamorl with HygBr as selectable marker. RML was transformed into A. awamori and its expression was validated by substrate hydrolysis test, SDS-PAGE and Western blot. [ Conclusion] This study demonstrates that the genetic transformation system of A. awamori mediated by Agrobacterium tumefaciens has potential feasibility for expression of heterologous proteins.

Research on the Trust Model Based on the Groups’ Internal Recommendation in E-Commerce Environment

The trust plays an extremely important role in online shopping. In order to make online shopping trusty, this paper puts foreword a new trust model in e-commerce environment GIR-TM (Groups’ Internal Recommendation Trust Model). First, it regarded the network as a combination of groups, and then did the internal recommendation based on these groups. The GIR-TM, in the process of recommendation, distinguished clearly between the trust degrees of recommen-dation node and the trust degrees of recommended node, and then calculated the integrated credibility value of the recommended node according to the weight of recommendation node in the group, the partial trust degree and the de-gree of recommendation when the recommendation node recommends the recommended node, and the overall credibil-ity value of recommended node as well. Lastly through listing the experimental data and comparing with the HHRB-TM (History and Honest Recommendation Based Trust Model) on the same condition, it is verified that GIR-TM is feasible and effective.

Robust visual tracking based on scale invariance and deep learning

Visual tracking is a popular research area in com- puter vision, which is very difficult to actualize because of challenges such as changes in scale and illumination, rota- tion, fast motion, and occlusion. Consequently, the focus in this research area is to make tracking algorithms adapt to these changes, so as to implement stable and accurate vi- sual tracking. This paper proposes a visual tracking algorithm that integrates the scale invariance of SURF feature with deep learning to enhance the tracking robustness when the size of the object to be tracked changes significantly. Particle filter is used for motion estimation. The co^fidence of each parti- cle is computed via a deep neural network, and the result of particle filter is verified and corrected by mean shift because of its computational efficiency and insensitivity to external interference. Both qualitative and quantitative evaluations on challenging benchmark sequences demonstrate that the pro- posed tracking algorithm performs favorably against several state-of-the-art methods throughout the challenging factors in visual tracking, especially for scale variation.