New technologies to investigate the brain-gut axis

Functional gastrointestinal disorders are commonly encountered in clinical practice, and pain is their commonest presenting symptom. In addition, patients with these disorders often demonstrate a heightened sensitivity to experimental visceral stimulation, termed visceral pain hypersensitivity that is likely to be important in their pathophysiology. Knowledge of how the brain processes sensory information from visceral structures is still in its infancy. However, our understanding has been propelled by technological imaging advances such as functional Magnetic Resonance Imaging, Positron Emission Tomography, Magnetoencephalography, and Electroencephalography （EEG）. Numerous human studies have non-invasively demonstrated the complexity involved in functional pain processing, and highlighted a number of subcortical and cortical regions involved. This review will focus on the neurophysiological pathways （primary afferents, spinal and supraspinal transmission）, brainimaging techniques and the influence of endogenous and psychological processes in healthy controls and patients suffering from functional gastrointestinal disorders. Special attention will be paid to the newer EEG source analysis techniques. Understanding the phenotypic differences that determine an individual＇s response to injurious stimuli could be the key to understanding why some patients develop pain and hyperalgesia in response to inflammation/injury while others do not. For future studies, an integrated approach is required incorporating an individual＇s psychological, autonomic, neuroendocrine, neurophysiological, and genetic profile to define phenotypic traits that may be at greater risk of developing sensitised states in response to gut inflammation or injury.

基于负载均衡的CPU-GPU异构计算平台任务调度策略

针对中央处理单元-图形处理单元(central processing unit-graphics processing unit,CPU-GPU)异构计算系统中,CPU和GPU负载不均导致系统性能降低的问题,提出了一种基于队列的混合调度策略.该策略通过探测获得CPU和GPU处理指定任务的计算能力,将计算任务按照探测比例分配给CPU和GPU;将并行任务存入双向队列,以降低调度带来的额外开销.结果表明,使用该策略的基准测试程序系统性能平均提升了28.07%.总体而言,该调度策略能够缩短CPU与GPU完成各自计算任务后的等待时间,有效平衡系统CPU与GPU之间的负载,提升系统性能.

A new approach for real time object detection and tracking on high resolution and multi-camera surveillance videos using GPU

High resolution cameras and multi camera systems are being used in areas of video surveillance like security of public places, traffic monitoring, and military and satellite imaging. This leads to a demand for computational algorithms for real time processing of high resolution videos. Motion detection and background separation play a vital role in capturing the object of interest in surveillance videos, but as we move towards high resolution cameras, the time-complexity of the algorithm increases and thus fails to be a part of real time systems. Parallel architecture provides a surpass platform to work efficiently with complex algorithmic solutions. In this work, a method was proposed for identifying the moving objects perfectly in the videos using adaptive background making, motion detection and object estimation. The pre-processing part includes an adaptive block background making model and a dynamically adaptive thresholding technique to estimate the moving objects. The post processing includes a competent parallel connected component labelling algorithm to estimate perfectly the objects of interest. New parallel processing strategies are developed on each stage of the algorithm to reduce the time-complexity of the system. This algorithm has achieved a average speedup of 12.26 times for lower resolution video frames(320×240, 720×480, 1024×768) and 7.30 times for higher resolution video frames(1360×768, 1920×1080, 2560×1440) on GPU, which is superior to CPU processing. Also, this algorithm was tested by changing the number of threads in a thread block and the minimum execution time has been achieved for 16×16 thread block. And this algorithm was tested on a night sequence where the amount of light in the scene is very less and still the algorithm has given a significant speedup and accuracy in determining the object.

Implementing Delay Multiply and Sum Beamformer on a Hybrid CPU-GPU Platform for Medical Ultrasound Imaging Using Open MP and CUDA

Anovel beamforming algorithmnamed Delay Multiply and Sum(DMAS),which excels at enhancing the resolution and contrast of ultrasonic image,has recently been proposed.However,there are nested loops in this algorithm,so the calculation complexity is higher compared to the Delay and Sum(DAS)beamformer which is widely used in industry.Thus,we proposed a simple vector-based method to lower its complexity.The key point is to transform the nested loops into several vector operations,which can be efficiently implemented on many parallel platforms,such as Graphics Processing Units(GPUs),and multi-core Central Processing Units(CPUs).Consequently,we considered to implement this algorithm on such a platform.In order to maximize the use of computing power,we use the GPUs andmulti-core CPUs inmixture.The platform used in our test is a low cost Personal Computer(PC),where a GPU and a multi-core CPU are installed.The results show that the hybrid use of a CPU and a GPU can get a significant performance improvement in comparison with using a GPU or using amulti-core CPU alone.The performance of the hybrid system is increased by about 47%–63%compared to a single GPU.When 32 elements are used in receiving,the fame rate basically can reach 30 fps.In the best case,the frame rate can be increased to 40 fps.