Interferometer for Co-operating Target Motion Detection

A correlation interferometer working with a cooperation target operating at 2.2GHz center frequency is presented. This simplified interferometer presented here uses a
lock-in amplifier to significantly increase system sensitivity when used in conjunction with a co-operating target signaling using amplitude modulation. The system is verified by detecting the angular velocity of passing tagged target. Experimental results show detectable range up over 110 meters in a multipath environment using a 10dBm EIRP tag.

Adaptive Fusion Of Particle Filtering And Spatio-Temporal Motion Energy For Human Tracking

Object tracking is an active research area nowadays due to its importance in human computer interface, teleconferencing and video surveillance. However, reliable tracking of objects in the presence of occlusions, pose and illumination changes is still a challenging topic. In this paper, we introduce a novel tracking approach that fuses two cues namely colour and spatio-temporal motion energy within a particle filter based framework. We conduct a measure of coherent motion over two image frames, which reveals the spatio-temporal dynamics of the target. At the same time, the importance of both colour and motion energy cues is determined in the stage of reliability evaluation. This determination helps maintain the performance of the tracking system against abrupt appearance changes. Experimental results demonstrate that the proposed method outperforms the other state of the art techniques in the used test datasets.

Offenders' Risk-Taking Attitude Inside and Outside the Prison Walls

It has long been assumed that risk taking is closely associated with criminal behavior. One reason for placing criminals behind bars-aside from punishment and protecting the public-is to prevent them from engaging in further risky criminal activities. Limited attention has been paid to whether being inside or outside prison affects offenders' risk-taking behaviors and attitudes. We compared risk-taking behaviors and attitudes in five risk domains (ethical, financial, health/safety, recreational, social) among 75 incarcerated offenders (i.e., offenders who are currently in prison) and 45 ex-offenders (i.e., offenders who have just been released from prison). Ex-offenders reported higher likelihood of engaging in risky behavior, driven largely by a willingness to take more risks in the recreational and ethical domains. Benefits attributed to risk taking as well as risk perception did not differ between incarcerated and ex-offenders, indicating that the opportunity to take risks might underlie behavioral risk intentions. Our results also indicate that risk-taking activities are better predicted by the expected benefits rather than by risk perception, aside from the health/safety domain. These results highlight the importance of studying the person and the environment and examining risk taking in a number of content domains.

Self-Feedback Motion Control for Cable-Driven Parallel Manipulators

This article proposes a closed-loop control scheme based on joint-angle feedback for cable-driven parallel manipulators (CDPMs), which is able to overcome various difficulties resulting from the flexible nature of the driven cables to achieve higher control accuracy. By introducing a unique structure design that accommodates built-in encoders in passive joints, the seven degrees of freedom (7-DOF) CDPM can obtain joint angle values without external sensing devices, and it is used for feedback control together with a proper closed-loop control algorithm. The control algorithm has been derived from the time differential of the kinematic formulation, which relates the joint angular velocities to the time derivative of cable lengths. In addition, the Lyapunov stability theory and Monte Carlo method have been used to mathematically verify the self-feedback control law that has tolerance for parameter errors. With the aid of co-simulation technique, the self-feedback closed-loop control is applied on a 7-DOF CDPM and it shows higher motion accuracy than the one with an open-loop control. The trajectory tracking experiment on the motion control of the 7-DOF CDPM demonstrated a good performance of the self-feedback control method.

Improved APF strategies for dual-arm local motion planning

Manipulator motion planning is a classic problem in robotics, with a number of complete solutions available for their motion in controlled (industrial) environments. Owing to recent technological advances in the field of robotics, there has been a significant development of more complex robots with high-fidelity sensors and more computational power. One such example has been a rise in the production of humanoid robots equipped with dual-arm manipulators which require complex motion planning algorithms. Also, the technological advances have resulted in a shift from using manipulators in strictly controlled environments, to investigating the deployment of manipulators in dynamic or unknown environments. As a result, a greater emphasis has been put on the development of local motion planners, which can provide real-time solutions to these problems. Artificial Potential Fields (APFs) is one such popular local motion planning technique, which can be applied to manipulator motion planning, however, the basic algorithm is severely prone to local minima problems. Here, two modified APF-based strategies for solving the dual-arm motion planning task in unknown environments are proposed. Both techniques make use of configuration sampling and subgoal selection to assist the APFs in avoiding these local minima scenarios. Extensive simulation results are presented to validate the efficacy of the proposed methodology.

Significance Driven Computation: A Voltage-Scalable, Variation-Aware, Quality-Tuning Motion Estimator

In this paper we present a design methodology for algorithm/architecture co-design of a voltage-scalable, process variation aware motion estimator based on significance driven computation. The fundamental premise of our approach lies in the fact that all computations are not equally significant in shaping the output response of video systems. We use a statistical technique to intelligently identify these significant/not-so-significant computations at the algorithmic level and subsequently change the underlying architecture such that the significant computations are computed in an error free manner under voltage over-scaling. Furthermore, our design includes an adaptive quality compensation (AQC) block which "tunes" the algorithm and architecture depending on the magnitude of voltage over-scaling and severity of process variations. Simulation results show average power savings of similar to 33% for the proposed architecture when compared to conventional implementation in the 90 nm CMOS technology. The maximum output quality loss in terms of Peak Signal to Noise Ratio (PSNR) was similar to 1 dB without incurring any throughput penalty.

The influence of water motion on the growth rate of the kelp Laminaria hyperborea

The kelp Laminaria hyperborea is a dominant component of the subtidal nearshore ecosystem and is subjected to a heterogeneous wave and current climate. Water motion is known to influence physiological processes in macroalgae such as photosynthesis and nutrient uptake attributed to mass-transfer limitation. The study attempts to establish the effect of water motion on the growth rates of blades and elongation rates of the stipes of L. hyperborea at adjacent wave-exposed and wave-sheltered locations over a 12month period from field observations. The observations were supported by detailed physical and chemical measurements (light, temperature, seawater nutrient concentrations and hydrodynamics) and of tissue carbon and nitrogen concentrations together with δ¹³carbon. Despite a 30% difference in the root mean square of the velocity (Vel_rms) between the two survey locations, there was no evidence to suggest that water motion had any direct influence on the growth rates of either the blades or elongation of stipes of L. hyperborea. No significant differences were observed between either environmental or plant physiological variables between the sheltered and exposed locations. Using an integral velocity parameter (Vel_rms) the present study also highlighted the importance of the tidally induced current component of water flow in the subtidal zone. 

Laughter Type Recognition from Whole Body Motion

Despite the importance of laughter in social interactions it remains little studied in affective computing. Respiratory, auditory, and facial laughter signals have been investigated but laughter-related body movements have received almost no attention. The aim of this study is twofold: first an investigation into observers' perception of laughter states (hilarious, social, awkward, fake, and non-laughter) based on body movements alone, through their categorization of avatars animated with natural and acted motion capture data. Significant differences in torso and limb movements were found between animations perceived as containing laughter and those perceived as nonlaughter. Hilarious laughter also differed from social laughter in the amount of bending of the spine, the amount of shoulder rotation and the amount of hand movement. The body movement features indicative of laughter differed between sitting and standing avatar postures. Based on the positive findings in this perceptual study, the second aim is to investigate the possibility of automatically predicting the distributions of observer's ratings for the laughter states. The findings show that the automated laughter recognition rates approach human rating levels, with the Random Forest method yielding the best performance.

Development of a Bridge Weigh-in-Motion Sensor: Performance comparison using fibre optic and electric resistance strain sensor systems

This paper addresses the problems of effective in situ measurement of the real-time strain for bridge weigh in motion in reinforced concrete bridge structures through the use of optical fiber sensor systems. By undertaking a series of tests, coupled with dynamic loading, the performance of fiber Bragg grating-based sensor systems with various amplification techniques were investigated. In recent years, structural health monitoring (SHM) systems have been developed to monitor bridge deterioration, to assess load levels and hence extend bridge life and safety. Conventional SHM systems, based on measuring strain, can be used to improve knowledge of the bridge's capacity to resist loads but generally give no information on the causes of any increase in stresses. Therefore, it is necessary to find accurate sensors capable of capturing peak strains under dynamic load and suitable methods for attaching these strain sensors to existing and new bridge structures. Additionally, it is important to ensure accurate strain transfer between concrete and steel, adhesives layer, and strain sensor. The results show the benefits in the use of optical fiber networks under these circumstances and their ability to deliver data when conventional sensors cannot capture accurate strains and/or peak strains.

Balls to the wall: How acoustic information from a ball in motion guides interceptive movement in people with Parkinson’s disease

Previous research has shown that Parkinson's disease (PD) patients can increase the speed of their movement when catching a moving ball compared to when reaching for a static ball (Majsak et al., 1998). A recent model proposed by Redgrave et al. (2010) explains this phenomenon with regard to the dichotomic organization of motor loops in the basal ganglia circuitry and the role of sensory micro-circuitries in the control of goal-directed actions. According to this model, external visual information that is relevant to the required movement can induce a switch from a habitual control of movement toward an externally-paced, goal-directed form of guidance, resulting in augmented motor performance (Bienkiewicz et al., 2013). In the current study, we investigated whether continuous acoustic information generated by an object in motion can enhance motor performance in an arm reaching task in a similar way to that observed in the studies of Majsak et al. (1998, 2008). In addition, we explored whether the kinematic aspects of the movement are regulated in accordance with time to arrival information generated by the ball's motion as it reaches the catching zone. A group of 7 idiopathic PD (6 male, 1 female) patients performed a ball-catching task where the acceleration (and hence ball velocity) was manipulated by adjusting the angle of the ramp. The type of sensory information (visual and/or auditory) specifying the ball's arrival at the catching zone was also manipulated. Our results showed that patients with PD demonstrate improved motor performance when reaching for a ball in motion, compared to when stationary. We observed how PD patients can adjust their movement kinematics in accordance with the speed of a moving target, even if vision of the target is occluded and patients have to rely solely on auditory information. We demonstrate that the availability of dynamic temporal information is crucial for eliciting motor improvements in PD. Furthermore, these effects appear independent from the sensory modality through-which the information is conveyed. 

Perception and Automatic Recognition of Laughter from Whole-Body Motion: Continuous and Categorical Perspectives

Despite its importance in social interactions, laughter remains little studied in affective computing. Intelligent virtual agents are often blind to users’ laughter and unable to produce convincing laughter themselves. Respiratory, auditory, and facial laughter signals have been investigated but laughter-related body movements have received less attention. The aim of this study is threefold. First, to probe human laughter perception by analyzing patterns of categorisations of natural laughter animated on a minimal avatar. Results reveal that a low dimensional space can describe perception of laughter “types”. Second, to investigate observers’ perception of laughter (hilarious, social, awkward, fake, and non-laughter) based on animated avatars generated from natural and acted motion-capture data. Significant differences in torso and limb movements are found between animations perceived as laughter and those perceived as non-laughter. Hilarious laughter also differs from social laughter. Different body movement features were indicative of laughter in sitting and standing avatar postures. Third, to investigate automatic recognition of laughter to the same level of certainty as observers’ perceptions. Results show recognition rates of the Random Forest model approach human rating levels. Classification comparisons and feature importance analyses indicate an improvement in recognition of social laughter when localized features and nonlinear models are used.