Implementation of Depth Map Filtering on GPU

The thesis work was part of the Mobile 3DTV project which studied the capture, coding and transmission of 3D video representation formats in mobile delivery scenarios. The main focus of study was to determine if it was practical to transmit and view 3D videos on mobile devices. The chosen approach for virtual view synthesis was Depth Image Based Rendering (DIBR). The depth computed is often inaccurate, noisy, low in resolution or even inconsistent over a video sequence. Therefore, the sensed depth map has to be post-processed and refined through proper filtering. Bilateral filter was used for the iterative refinement process, using the information from one of the associated high quality texture (color) image (left or right view). The primary objective of this thesis was to perform the filtering operation in real-time. Therefore, we ported the algorithm to a GPU. As for the programming platform we chose OpenCL from the Khronos Group. The reason was that the platform is capable of programming on heterogeneous parallel computing environments, which means it is platform, vendor, or hardware independent. It was observed that the filtering algorithm was suitable for GPU implementation. This was because, even though every pixel used the information from its neighborhood window, processing for one pixel has no dependency on the results from its surrounding pixels. Thus, once the data for the neighborhood was loaded into the local memory of the multiprocessor, simultaneous processing for several pixels could be carried out by the device. The results obtained from our experiments were quite encouraging. We executed the MEX implementation on a Core2Duo CPU with 2 GB of RAM. On the other hand we used NVIDIA GeForce 240 as the GPU device, which comes with 96 cores, graphics clock of 550 MHz, processor clock of 1340 MHz and 512 MB memory. The processing speed improved significantly and the quality of the depth maps was at par with the same algorithm running on a CPU. In order to test the effect of our filtering algorithm on degraded depth map, we introduced artifacts by compressing it using H.264 encoder. The level of degradation was controlled by varying the quantization parameter. The blocky depth map was filtered separately using our implementation on GPU and then on CPU. The results showed improvement in speed up to 30 times, while obtaining refined depth maps with similar quality measure as the ones processed using the CPU implementation

Role of early CT scan in diagnosis of occult scaphoid fractures- a prospective study

Background: Wrists injuries are one of the common presentations to emergency departments and orthopaedic clinics. The scaphoid bone is the most commonly injured of the carpal bones accounting for 50-80% of carpal injuries and predominantly occurs in young healthy individuals. Scaphoid fractures are the most problematic to diagnose in a clinical setting because it can take up to 6 weeks for scaphoid fractures to become conclusive on plain X-ray films. Aim of the study was to retrospective study was carried out to study the role of early CT scan in diagnosis of occult scaphoid fractures.Methods: A total of 123 patients presented with an acute wrist injury with subsequent signs of scaphoid injury in the absence of a diagnostic fracture on plain X-ray within the time period from June 2014 to May 2016 in a tertiary care centre.Results: This study shows that 31% of normal X-rays were pathological on CT scan and out of these; scaphoid fractures (74% of pathologies) represent a large number of patients with fractures that were missed by initial plain films.Conclusions: This study shows an extremely high false-negative rate for plain X-rays and advocate CT at the first attendance to fracture clinic if there is suspicion of scaphoid injury. An earlier diagnosis leads to appropriate management and reduces restrictions to the patient in terms of prolonged immobilization and repeated clinical reviews

Implementation of Depth Map Filtering on GPU

The thesis work was part of the Mobile 3DTV project which studied the capture, coding and transmission of 3D video representation formats in mobile delivery scenarios. The main focus of study was to determine if it was practical to transmit and view 3D videos on mobile devices. The chosen approach for virtual view synthesis was Depth Image Based Rendering (DIBR). The depth computed is often inaccurate, noisy, low in resolution or even inconsistent over a video sequence. Therefore, the sensed depth map has to be post-processed and refined through proper filtering. Bilateral filter was used for the iterative refinement process, using the information from one of the associated high quality texture (color) image (left or right view). The primary objective of this thesis was to perform the filtering operation in real-time. Therefore, we ported the algorithm to a GPU. As for the programming platform we chose OpenCL from the Khronos Group. The reason was that the platform is capable of programming on heterogeneous parallel computing environments, which means it is platform, vendor, or hardware independent. It was observed that the filtering algorithm was suitable for GPU implementation. This was because, even though every pixel used the information from its neighborhood window, processing for one pixel has no dependency on the results from its surrounding pixels. Thus, once the data for the neighborhood was loaded into the local memory of the multiprocessor, simultaneous processing for several pixels could be carried out by the device. The results obtained from our experiments were quite encouraging. We executed the MEX implementation on a Core2Duo CPU with 2 GB of RAM. On the other hand we used NVIDIA GeForce 240 as the GPU device, which comes with 96 cores, graphics clock of 550 MHz, processor clock of 1340 MHz and 512 MB memory. The processing speed improved significantly and the quality of the depth maps was at par with the same algorithm running on a CPU. In order to test the effect of our filtering algorithm on degraded depth map, we introduced artifacts by compressing it using H.264 encoder. The level of degradation was controlled by varying the quantization parameter. The blocky depth map was filtered separately using our implementation on GPU and then on CPU. The results showed improvement in speed up to 30 times, while obtaining refined depth maps with similar quality measure as the ones processed using the CPU implementation

A Comprehensive Review and Meta-Analysis on Applications of Machine Learning Techniques in Intrusion Detection

Securing a machine from various cyber-attacks has been of serious concern for researchers, statutory bodies such as governments, business organizations and users in both wired and wireless media. However, during the last decade, the amount of data handling by any device, particularly servers, has increased exponentially and hence the security of these devices has become a matter of utmost concern. This paper attempts to examine the challenges in the application of machine learning techniques to intrusion detection. We review different inherent issues in defining and applying the machine learning techniques to intrusion detection. We also attempt to identify the best technological solution for the changing usage pattern by comparing the different machine learning techniques on different datasets and summarizing their performance using various performance metrics. This paper highlights the research challenges and future trends of intrusion detection in dynamic scenarios of intrusion detection problems in diverse network technologies

Implementation of Depth Map Filtering Algorithms on Mobile-Specific Platforms

The paper addresses the problem of implementing depth map filtering algorithms optimized for mobile platforms. Main algorithm being targeted is the bilateral filter and its implementation on a mobile platform1 has been studied. Furthermore, an alternative approach of using OpenCL to control a graphics accelerator is explored. Experimental results of the latter look quite positive.Peer reviewe

Online Identification of Hierarchical Heavy Hitters: Algorithms, Evaluation, and Applications

In traffic monitoring, accounting, and network anomaly detection, it is often important to be able to detect high-volume traffic clusters in near real-time. Such heavy-hitter traffic clusters are often hierarchical (i.e., they may occur at different aggregation levels like ranges of IP addresses) and possibly multidimensional (i.e., they may involve the combination of different IP header fields like IP addresses, port numbers, and protocol). Without prior knowledge about the precise structures of such traffic clusters, a naive approach would require the monitoring system to examine all possible combinations of aggregates in order to detect the heavy hitters, which can be prohibitive in terms of computation resources

Responsive Neurostimulation System (RNS) in setting of cranioplasty and history of multiple craniotomies

Introduction: Stereoelectroencephalography (SEEG) and subdural grids (SDG) are both effective options for localizing the ictal onset zone in patients with frequent seizures. The choice of intracranial monitoring technique utilized depends upon several factors, including the patient's clinical presentation and history. This article addresses a rare instance in which SEEG was not an option due to patient's morphology. Case report A 36-year-old man with history of medically intractable epilepsy and multiple craniotomies complicated by infection and subsequent cranioplasty was presented for possible surgical evaluation. Initially, SEEG was attempted but ultimately terminated because of difficulty related to prior cranioplasty and scarring to the brain. Eventually, a subdural grid system was placed to establish the patient's ictal onset zones after which RNS implantation was performed. Discussion: The SDG placement was successful and localized the patient's ictal onset to the hand-motor region of the left hemisphere. RNS was then implanted and postoperatively the patient had a significant decrease in his seizure burden. Conclusion: The case illustrates a possible limitation of SEEG placement, particularly in patients with a history of cranioplasty and multiple prior craniotomies. We also describe the first placement of an RNS generator and system in the setting of prior cranioplasty

Robot-assisted placement of depth electrodes along the long Axis of the amygdalohippocampal complex

AbstractBackgroundClassically, transoccipital hippocampal depth electrode implantation requires a stereotactic headframe and arc and the patient to be placed in a seated or prone position, which can be cumbersome to position and uncomfortable for the surgeon. Robotic intracranial devices are increasingly being utilized for stereotactic procedures such as stereolectroencephalography (SEEG) but commonly require patients be placed in head-neutral position to perform facial registration.ObjectiveHere we describe a novel robotic implantation technique where a stereotactic intracranial robot is used to place bilateral hippocampal depth electrodes in the lateral position.MethodsFour patients underwent SEEG depth electrode placement, which included placement of bilateral hippocampal depth electrodes. Each patient was positioned in the lateral position and registered to the robot with laser facial registration. Trajectories were planned with the robotic navigation software, which then identified the appropriate entry points and trajectories needed to reach the targets. After electrode implantation, target localization was confirmed using computed tomography (CT).ResultsElectrodes targeting the amygdalohippocampal complex were accurate and there were no complications in this group. An average of seven electrodes were placed per patient. Ictal onset was localized for each patient. All patients subsequently underwent temporal lobectomy and 75% have been seizure free since surgery.ConclusionsWe have developed the Robot-Assisted Lateral Transoccipital Approach (RALTA), which is an advantageous technique for placing bilateral amygdalohippocampal depth electrodes using robotic guidance. Benefits of this technique include fewer electrodes required per patient and ease of positioning compared with seated or prone positioning