Personal driving diary: Automated recognition of driving events from first-person videos

In this paper, we introduce the concept of personal driving diary. A personal driving diary is a multimedia archive of a person’s daily driving experience, describing important driving events of the user with annotated videos. This paper presents an automated system that constructs such multimedia diary by analyzing videos obtained from a vehicle-mounted camera. The proposed system recognizes important interactions between the driving vehicle and the other actors in videos (e.g., accident, overtaking, etc.), and labels them together with its contextual knowledge on the vehicle (e.g., mean velocity) to construct an event log. A decision tree based activity recognizer is designed, detecting driving events of vehicles and pedestrians from the first-person view videos by analyzing their trajectories and spatio-temporal relationships. The constructed diary enables efficient searching and event-based browsing of video clips, which helps the users when retrieving videos of dangerous situations. Our experiment confirms that the proposed system reliably generates driving diaries by annotating the vehicle events learned from training examples.     

Novel slotted stepped‐impedance resonator and its application to compact and high performance bandpass filter and diplexer design

By etching slots in the low‐impedance section of the conventional stepped‐impedance resonator, a novel slotted stepped‐impedance resonator (SSIR) is proposed. As two examples, a fourth‐order bandpass filter (BPF) operating at 1 GHz with a size of 0.078 λ_g × 0.062 λ_g and a miniaturized diplexer operating at 0.9/1.57 GHz with a size of 0.054 λ₀ × 0.086 λ₀ are designed based on the proposed SSIR. The fabricated BPF exhibits a high selectivity and a wide ?30 dB rejection upper stopband from 1.13 f₀ to 6.52 f₀, while the fabricated diplexer has up to ?60 dB output isolation. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2013.     

Input-to-state-stability modular command filtered back-stepping attitude control of a generic reentry vehicle

A command filtered back-stepping attitude controller is exploited and analyzed to design a dynamic state-feedback controller for a generic Reentry Vehicle. A novel back-stepping control that obviates the need to compute analytic derivatives in the traditional back-stepping design is presented by combining command filtered back-stepping method, sliding-mode-based integral filters and inputto-state stability (ISS) analysis. The ISS-modular approach provides a simple and effective way for controlling non-linear Reentry Vehicle satisfying the strict-feedback form, simultaneously solving the problem of “explosion of complexity” in traditional back-stepping approach. The stability analysis of the closed-loop system and the convergence of the aerodynamic angles are verified based on the smallgain theorem. Numerical simulations are included to demonstrate the effectiveness of the proposed control scheme.     

Probability hypothesis density filter based on strong tracking MIE for multiple maneuvering target tracking

Taking into account the difficulties of multiple maneuvering target tracking due to the unknown target number and the uncertain acceleration, a novel multiple maneuvering target tracking algorithm based on the Probability Hypothesis Density (PHD) filter and Modified Input Estimation (MIE) technique is proposed in this paper. First, the unknown acceleration vector is added to the target state to form a new augmented state vector. Then, strong tracking filter multiple fading factors are introduced to the MIE method which can adjust the prediction covariance and the corresponding filter gain at different rates in real time, so that the MIE method can adaptively track high maneuvering targets well. Finally, we combine this adaptive MIE method with the PHD filter, which can effectively track multiple maneuvering targets without much prior information. Simulation results show that the proposed algorithm has a higher tracking precision and a better real-time performance than the conventional maneuvering target tracking algorithms.     

数据结构中二叉树的生成及遍历非递归算法浅析

本文主要介绍数据结构中二叉树的生成,以及二叉树的先序、中序和后序的非递归算法。     

浅谈coreldraw教学以中学生为中心的具体体现

Coreldraw的教学比较注重实际操作能力,与传统科目的教学不同,它更应该以学生为教学的中心。所以在教学过程中要注意教学方式的改变。本文就coreldraw教学中如何做到以学生为中心做简单论述。     

任务驱动教学法在计算机语言教学中的探索

任务驱动教学法是建立在建构主义学习理论基础上的教学模式,将知识的学习放置于具体问题解决上来,通过学生自身或小组协作,来完成任务.在学习计算机编程的课程时,使用任务驱动法教学,可以大大提高教学效果,同时又能培养一种良好的团队精神,体现以学生的全面发展为中心的教育思想.     

On Controllability of Switched Linear Systems

This paper investigates the number of switchings and design of switching sequences for controllability of switched linear systems. Two related results are established. One is a new constructive approach to designing switching sequences. The controllable state set of each switching sequence designed via the approach coincides with the controllable subspace of switched linear systems. The other is a well-estimated value for the minimum number of switchings required for controllability. Each state in the controllable subspace can be steered to origin within this value of switching times.     

An application of MODIS data to snow cover monitoring in a pastoral area: A case study in Northern Xinjiang, China

Snow is an important land cover on the earth's surface. It is characterized by its changing nature. Monitoring snow cover extent plays a significant role in dynamic studies and prevention of snow-caused disasters in pastoral areas. Using NASA EOS Terra/MODIS snow cover products and in situ observation data during the four snow seasons from November 1 to March 31 of year 2001 to 2005 in northern Xinjiang area, the accuracy of MODIS snow cover mapping algorithm under varied snow depth and land cover types was analyzed. The overall accuracy of MODIS daily snow cover mapping algorithm in clear sky condition is high at 98.5%; snow agreement reaches 98.2%, and ranges from 77.8% to 100% over the 4-year period for individual sites. Snow depth (SD) is one of the major factors affecting the accuracy of MODIS snow cover maps. MODIS does not identify any snow for SD less than 0.5 cm. The overall accuracy increases with snow depth if SD is equal to or greater than 3 cm, and decreases for SD below 3 cm. Land cover has an important influence in the accuracy of MODIS snow cover maps. The use of MOD10A1 snow cover products is severely affected by cloud cover. The 8-day composite products of MOD10A2 can effectively minimize the effect of cloud cover in most cases. Cloud cover in excess of 10% occurs on 99% of the MOD10A1 products and 14.7% of the MOD10A2 products analyzed during the four snow seasons. User-defined multiple day composite images based on MOD10A1, with flexibilities of selecting composite period, starting and ending date and composite sequence of MOD10A1 products, have an advantage in effectively monitoring snow cover extent for regional snow-caused disasters in pastoral areas.