Examining the Effectiveness of Mindfulness Based Cognitive Therapy (MBCT) on Increasing Resilience of War Injured Veterans

The present study aimed to investigate the impression of Mindfulness based on cognitive therapy (MBCT) on raise resilience of war injured veterans. The study is quasi-experimental study, with pre-test and post-test and control group. The sample included 30 war injured veterans in Mashhad city and they were divided into two groups as 15 in Mindfulness based cognitive therapy and 15 people in control group. The members of MBCT received 8 sessions of MBCT but there was no intervention for control group. Before and after intervention, Conner-Davidson Resilience Scale (CD-RISC) was completed by sample group. The results of covariance analysis showed that in post-test resilience scores in MBCT group had significant increase compared to control group. The study findings released that MBCT increased resilience of war injured veterans

On Intelligent Surveillance Systems and Face Recognition for Mass Transport Security

We describe a project to trial and develop enhanced surveillance technologies for public safety. A key technology is robust recognition of faces from low-resolution CCTV footage where there may be as few as 12 pixels between the eyes. Current commercial face recognition systems require 60-90 pixels between the eyes as well as tightly controlled image capture conditions. Our group has thus concentrated on fundamental face recognition issues such as robustness to low resolution and image capture conditions as required for uncontrolled CCTV surveillance. In this paper, we propose a fast multi-class pattern classification approach to enhance PCA and FLD methods for 2D face recognition under changes in pose, illumination, and expression. The method first finds the optimal weights of features pairwise and constructs a feature chain in order to determine the weights for all features. Computational load of the proposed approach is extremely low by design, in order to facilitate usage in automated surveillance. The method is evaluated on PIE, FERET, and Asian Face databases, with the results showing that the method performs remarkably well compared to several benchmark appearance-based methods. Moreover, the method can reliably recognise faces with large pose angles from just one gallery image

Obstacle-free range determination for rail track maintenance vehicles

Maintenance trains travel in convoy. In Australia, only the first train of the convoy pays attention to the track sig- nalization (the other convoy vehicles simply follow the preceding vehicle). Because of human errors, collisions can happen between the maintenance vehicles. Although an anti-collision system based on a laser distance meter is already in operation, the existing system has a limited range due to the curvature of the tracks. In this paper, we introduce an anti-collision system based on vision. The two main ideas are, (1) to warp the camera image into an image where the rails are parallel through a projective transform, and (2) to track the two rail curves simultaneously by evaluating small parallel segments. The performance of the system is demonstrated on an image dataset

Recognizing Geochemical Anomalies Associated with Mineral Resources Using Singularity Analysis and Random Forest Models in the Torud-Chahshirin Belt, Northeast Iran

Identifying the local geochemical anomalies from stream sediment samples is challenging in regional-scale exploration programs. For this purpose, some robust and reliable techniques must be applied to distinguish the geochemical targets from the background values. In this research, a procedure of several tools, including singularity mapping (SM), random forests (RF), success-rate curves, and the t-Student method, were employed to analyze the geochemical anomalies within the intrusive-plutonic Torud-Chahshirin belt (TCB), northeast Iran. In this regard, the success-rate curves were initially applied to extract efficient geochemical signatures. Then, singularity analysis was used on the selected geochemical elements (Au, Cu, Pb, and Zn), which were transformed via centered log-ratio (clr) transformation. In the next step, due to the complexity of the ore-forming processes in the TCB, the structural factors (e.g., fault intersection and faults with different orientations) were determined. Based on the success-rate curves, NE-trending faults and fault density were distinguished as critical structural criteria. Afterward, the RF model as a robust machine learning algorithm was executed on the four efficient SM-based geochemical layers and two efficient structural factors. The anomaly map derived by the RF model (Accuracy = 98.85% and Error = 1.15%) illustrates a very high relationship with Cu ± Au mineral occurrences. Therefore, the RF algorithm assisted by the singularity method is more trustworthy for highlighting the weak geochemical prospectivity areas in the TCB

Obstacle-free range determination for rail track\ud maintenance vehicles

Maintenance trains travel in convoy. In Australia,\ud only the first train of the convoy pays attention to the track sig-\ud nalization (the other convoy vehicles simply follow the preceding\ud vehicle). Because of human errors, collisions can happen between\ud the maintenance vehicles. Although an anti-collision system based\ud on a laser distance meter is already in operation, the existing\ud system has a limited range due to the curvature of the tracks.\ud In this paper, we introduce an anti-collision system based on\ud vision. The two main ideas are, (1) to warp the camera image\ud into an image where the rails are parallel through a projective\ud transform, and (2) to track the two rail curves simultaneously\ud by evaluating small parallel segments. The performance of the\ud system is demonstrated on an image dataset

High throughput variable size non-square gabor engine with feature pooling based on GPU

Increasing application of Gabor feature space in various computer vision tasks and its high computational demand, encourages using parallel computing technologies. In this work we have designed a high throughput GPU based Gabor kernel that mimics the function of initial biological visual cortex layers namely 'Simple' and 'Complex' cells. The kernel is basically a Gabor filter bank with adjustable number of orientations and scales, supporting 'Non-Square' and 'Variable Size' filter masks on different channels. Consequently our GPU based Gabor kernel tends to be adjustably more accurate, more flexible for different applications, with optimum computational cost at lower resources. The second important task of our high throughput engine is 'Gabor Feature Pooling' with Max and Histogram methods, similar to biological visual 'Complex cells'. This part of our 'Gabor Engine' makes it very practical for computer vision applications, since in addition to massive Gabor features, it also provides more abstract spatial invariant orientational information based on image Gabor features. We have optimised the Engine design to take maximum advantage of all GPU parallel resources and maximum bandwidths of all memories. © 2010 IEEE

Demo: An automated face enrolment and recognition system across multiple cameras on CCTV networks

In this paper, we present an architecture for a video analytics framework, specifically designed for automatic enrollment and subsequent re-identification of faces on a network of cameras. The proposed system can be used as an assistive tool for applications such as passenger screening at airports as passengers walk through various sections of the airport