Adaptive human motion analysis and prediction

Human motion analysis and prediction is an active research area where predicting human motion is often performed for a single time step based on historical motion. In recent years, longer term human motion prediction has been attempted over a number of future time steps. Most current methods learn motion patterns (MPs) from observed trajectories and then use them for prediction. However, these learned MPs may not be indicative due to inadequate observation, which naturally affects the reliability of motion prediction. In this paper, we present an adaptive human motion analysis and prediction method. It adaptively predicts motion based on the classified MPs in terms of their credibility, which refers to how indicative the learned MPs are for the specific environment. The main contributions of the proposed method are as follows: First, it provides a comprehensive description of MPs including not only the learned MPs but also their evaluated credibility. Second, it predicts long-term future motion with reasonable accuracy. A number of experiments have been conducted in simulated scenes and real-world scenes and the prediction results have been quantitatively evaluated. The results show that the proposed method is effective and superior in its performance when compared with a recursively applied Auto-Regressive (AR) model, which is called the Recursive Short-term Predictor (RSP) for long-term prediction. The proposed method has 17.73% of improvement over the RSP in prediction accuracy in the experiment with the best performance. On average, the proposed method has 5% improvement over the RSP in prediction accuracy over 10 experiments. © 2011 Elsevier Ltd. All rights reserved.postprin

Gender Determination using Fingerprint Features

Several previous studies have investigated the gender difference of the fingerprint features. However, regarding to the statistical significance of such differences, inconsistent results have been obtained. To resolve this problem and to develop a method for gender determination, this work proposes and tests three fingertip features for gender determination. Fingerprints were obtained from 115 normal healthy adults comprised of 57 male and 58 female volunteers. All persons were born in Taiwan and were of Han nationality. The age range was18-35 years. The features of this study are ridge count, ridge density, and finger size, all three of which can easily be determined by counting and calculation. Experimental results show that the tested ridge density features alone are not very effective for gender determination. However, the proposed ridge count and finger size features of left little fingers are useful, achieving a classification accuracy of 75% (P-valu

Power Allocation and Time-Domain Artificial Noise Design for Wiretap OFDM with Discrete Inputs

Optimal power allocation for orthogonal frequency division multiplexing (OFDM) wiretap channels with Gaussian channel inputs has already been studied in some previous works from an information theoretical viewpoint. However, these results are not sufficient for practical system design. One reason is that discrete channel inputs, such as quadrature amplitude modulation (QAM) signals, instead of Gaussian channel inputs, are deployed in current practical wireless systems to maintain moderate peak transmission power and receiver complexity. In this paper, we investigate the power allocation and artificial noise design for OFDM wiretap channels with discrete channel inputs. We first prove that the secrecy rate function for discrete channel inputs is nonconcave with respect to the transmission power. To resolve the corresponding nonconvex secrecy rate maximization problem, we develop a low-complexity power allocation algorithm, which yields a duality gap diminishing in the order of O(1/\sqrt{N}), where N is the number of subcarriers of OFDM. We then show that independent frequency-domain artificial noise cannot improve the secrecy rate of single-antenna wiretap channels. Towards this end, we propose a novel time-domain artificial noise design which exploits temporal degrees of freedom provided by the cyclic prefix of OFDM systems {to jam the eavesdropper and boost the secrecy rate even with a single antenna at the transmitter}. Numerical results are provided to illustrate the performance of the proposed design schemes.Comment: 12 pages, 7 figures, accepted by IEEE Transactions on Wireless Communications, Jan. 201

An Adaptive Load Balance and Handoff Management Strategy for Hierarchical Infrastructure Networks

Hierarchical cellular networks that employ microcells with overlaying macrocells have been proposed to increase the traffic-carrying capacity and circuit quality. Variations in the traffic loads among cells will lessen the traffic-carrying capacity. Moreover, the handoff procedure usually takes place when the call crosses the cell boundary. An ineffective management will increase the system overheads, such as channel switch, data switch, and even network switch. The invetigation proposes an effective load balance and handoff management strategy. This strategy are implemented to solve traffic-adaption problem that can enhance the traffic-carrying capacity for variations in traffic. For the management of handoff procedure, our strategy considers the mobility of mobile hosts and the bandwidth utilization in macrocells. It can descrease the number of handoffs and, accordingly, lessen the system overhead. Furthermore, the simulation results are presented to confirm the efficiency of the proposed strategy

Effects of Planting Density on Visually Graded Lumber and Mechanical Properties of Taiwania

The purpose of this study was to investigate the effects of planting density on the quality of visually graded lumber, and the strength properties of 35-year-old Taiwania (Taiwania cryptomerioides Hay). The results are summarized as follows.(1) Lumber obtained from the site with type S planting density (6940 trees/ha) were mostly of better grade (84.6% including first and second grades), followed by type Q (2500 trees/ha) (69.1%), type R (3300 trees/ha) (62.5%), whereas poorer lumber was found mostly from trees with type P planting density (1000 trees/ha) (41.6%).(2) Specimens cut from trees of type S planting density site had the largest average values of ultrasonic velocity (Vu), dynamic modulus of elasticity obtained from transversal vibration (Edt), dynamic modulus of elasticity obtained from ultrasonic velocity (Edu), modulus of elasticity at bending (MOE), and modulus of rupture at bending (MOR), followed in decreasing order by those of type P, type R, and type Q sites.(3) Interrelations between Vu, Edu, Edt, MOE, and MOR can be represented by positive linear regression formulas. The differences were highly significant

THE DESIGN MODEL OF MICRO END-MILLS MADE BY USING THE FINITE ELEMENT METHOD

The green processing technology has been brought to the focus of attention around the world. The development and application of green cutting depend on the machine and cutting tool technology progress, in so far as the development of cutting tool technology has quite a big influence. The study was focused on the simulation analysis model for micro milling SKD61 tool steel developed by the finite element method. First, because the impact of the effective rake angle on the oblique cutting model is equivalent to that of the rake angle on the orthogonal cut model, the complex tool geometry of an end-mill will be simplified to the orthogonal cutting model. Using the Taguchi method, the FEM simulation of orthogonal cutting operation was performed under different cutting speeds, cutting depths, effective rake angles and relief angles were modified. The cutting force, tool maximum temperature, tool maximum temperature and tip distance, and the contact length of tool and chip are the major performance indexes of micro milling process. Finally, the multiple cutting performance characteristic resulting from the grey relational analysis reveals that the influencing priority ranks for micro end-mills made of SKD61 tool steel are cutting speed, effective rake angle, relief angle, and cutting depth. The FEM model is suitable for simulating the cutting performance of micro cutting process, and can also be used as a design base for micro endmills

Modality-Independent Teachers Meet Weakly-Supervised Audio-Visual Event Parser

Audio-visual learning has been a major pillar of multi-modal machine learning, where the community mostly focused on its modality-aligned setting, i.e., the audio and visual modality are both assumed to signal the prediction target. With the Look, Listen, and Parse dataset (LLP), we investigate the under-explored unaligned setting, where the goal is to recognize audio and visual events in a video with only weak labels observed. Such weak video-level labels only tell what events happen without knowing the modality they are perceived (audio, visual, or both). To enhance learning in this challenging setting, we incorporate large-scale contrastively pre-trained models as the modality teachers. A simple, effective, and generic method, termed Visual-Audio Label Elaboration (VALOR), is innovated to harvest modality labels for the training events. Empirical studies show that the harvested labels significantly improve an attentional baseline by 8.0 in average F-score (Type@AV). Surprisingly, we found that modality-independent teachers outperform their modality-fused counterparts since they are noise-proof from the other potentially unaligned modality. Moreover, our best model achieves the new state-of-the-art on all metrics of LLP by a substantial margin (+5.4 F-score for Type@AV). VALOR is further generalized to Audio-Visual Event Localization and achieves the new state-of-the-art as well. Code is available at: https://github.com/Franklin905/VALOR