Marco Pulimeno

An Augmented Reality Application for Minimally Invasive Surgery

Augmented Reality (AR) technology can be used in minimally invasive surgery to provide direct visualisation, thus providing the advantage given by open surgery. It can be used to increase the physician’s view by using and showing information gathered from patient medical images. The developed AR system can improve the performances of a surgeon involved in a minimally invasive surgery procedure and provide visual information, located in the correct position of the patient’s body, not visible by means of normal senses. The virtual environment is built using the real patient’s CT images of the abdominal area; information about shapes and dimensions of the human organs is obtained by means of segmentation and classification algorithms. The developed application is based on the AR Toolkit and it is possible to visualize all the organs in a given abdominal area or, using specific markers, only some of them. The system can be used in pre-operative planning of a laparoscopic surgical procedure in order to find both the right points to introduce the surgical instruments and the best path to reach the organ concerned in the surgical operation.

A parallel space saving algorithm for frequent items and the Hurwitz zeta distribution

We design a parallel algorithm for frequent items based on the sequential Space Saving algorithm.We show that the algorithm is cost-optimal for k = O ( 1 ) and therefore extremely fast and useful in a wide range of applications.We experimentally validate the algorithm on synthetic data distributed using a Hurwitz zeta function and a Zipf one, and also on real datasets, confirming the theoretical cost-optimality and that the error committed is very low and close to zero.We compare the performances and the error committed by our algorithm against a parallel algorithm recently proposed by Agarwal et?al. for merging datasets derived by the Space Saving or Frequent algorithms. We present a message-passing based parallel version of the Space Saving algorithm designed to solve the k-majority problem. The algorithm determines in parallel frequent items, i.e., those whose frequency is greater than a given threshold, and is therefore useful for iceberg queries and many other different contexts. We apply our algorithm to the detection of frequent items in both real and synthetic datasets whose probability distribution functions are a Hurwitz and a Zipf distribution respectively. Also, we compare its parallel performances and accuracy against a parallel algorithm recently proposed for merging summaries derived by the Space Saving or Frequent algorithms.

A Simulation of a Billiards Game Based on Marker Detection

Performance improvements in graphics hardware have made it possible to visualize complex virtual environments and provided opportunities to interact with these in a more realistic way. In this paper a Virtual Reality application for simulating a billiards game is presented. A real billiard cue is used and its movements are reproduced in the virtual environment using a visual marker detection system. No force feedback is provided to the player. In the game simulation the virtual environment has been built using the development environment OpenSceneGraph and rigid body dynamics have been simulated utilizing the PhysX physics engine. ARToolkit was the visual marker-based detection system utilized to replicate the movements of the real cue used by the player in the virtual environment.

The Simulation of a Billiard Game Using a Haptic Interface

Recently the computer entertainment technology has generated a deal of interest among researchers and developers as it is recognized as showing high promise in creating exciting new forms of human computer interaction. Performance improvements in graphics hardware and the diffusion of the low cost haptic interfaces have made it possible to visualize complex virtual environments and provided opportunities to interact with these in a more realistic way. In this paper a virtual reality application of a game of billiard game is presented; to allow to the user an interactive and realistic interaction is provided a force feedback by means of a commercial haptic interface. To build an immersive virtual environment has been used the development environment XIR and to simulate the rigid body dynamics has been utilized the ODE library.

Parallel space saving on multi- and many-core processors

Given an array A of n elements and a value 2≤k≤n, a frequent item or k‐majority element is an element occurring in A more than n/k times. The k‐majority problem requires finding all of the k‐majority elements. In this paper, we deal with parallel shared‐memory algorithms for frequent items; we present a shared‐memory version of the Space Saving algorithm, and we study its behavior with regard to accuracy and performance on many and multi‐core processors, including the Intel Phi accelerator. We also investigate a hybrid MPI/OpenMP version against a pure MPI‐based version. Through extensive experimental results, we prove that the MPI/OpenMP parallel version of the algorithm significantly enhances the performance of the earlier pure MPI version of the same algorithm. Results also prove that for this algorithm the Intel Phi accelerator does not introduce any improvement with respect to the Xeon octa–core processor.

Mining frequent items in the time fading model

We introduce FDCMSS, a novel sketch-based algorithm for frequent items working in the time fading model. The algorithm cleverly combines key ideas borrowed from forward decay, the Count-Min and the Space Saving algorithms.We formally prove the correctness of our algorithm.We experimentally validate the algorithm on synthetic data distributed using a Zipf distribution, and also on real datasets.We compare the performances and the error committed by our algorithm against λ-HCount, an algorithm recently proposed by Chen and Mei. Extensive experimental results show that FDCMSS outperforms λ-HCount with regard to speed, space used, precision attained and error committed on both synthetic and real datasets. We present FDCMSS, a new sketch-based algorithm for mining frequent items in data streams. The algorithm cleverly combines key ideas borrowed from forward decay, the Count-Min and the Space Saving algorithms. It works in the time fading model, mining data streams according to the cash register model. We formally prove its correctness and show, through extensive experimental results, that our algorithm outperforms λ-HCount, a recently developed algorithm, with regard to speed, space used, precision attained and error committed on both synthetic and real datasets.

An Interactive and Immersive 3D Game Simulation Provided with Force Feedback

Performance improvements in graphics hardware and the diffusion of the low cost haptic interfaces have made it possible to visualize complex virtual environments and provided opportunities to interact with these in a more realistic way. In this paper a Virtual Reality application of a game of billiards is presented. By means of a commercial haptic interface a force feedback is provided, thus rendering the interaction realistic and exciting to the user. The introduction of the force feedback makes it possible to obtain a realistic simulation as it is possible to strike the billiard ball and to feel the contact between cue and ball. The virtual environment has been built using the development environment XVR and rigid body dynamics have been simulated utilizing the ODE library. Since in the real game it is possible to use the left hand when aiming and striking the ball, in the play modality it is possible to fix the cue movement in the desired direction in order to allow a more careful aim and a more stable interaction in the virtual environment. In addition it is possible to choose the force with which the ball is hit.

On Frequency Estimation and Detection of Frequent Items in Time Faded Streams

We deal with the problem of detecting frequent items in a stream under the constraint that items are weighted, and recent items must be weighted more than older ones. This kind of problem naturally arises in a wide class of applications in which recent data is considered more useful and valuable with regard to older, stale data. The weight assigned to an item is, therefore, a function of its arrival timestamp. As a consequence, whilst in traditional frequent item mining applications we need to estimate frequency counts, we are instead required to estimate decayed counts. These applications are said to work in the time fading model. Two sketch-based algorithms for processing time-decayed streams have been recently published independently near the end of 2016. The Filtered Space Saving with Quasi-Heap (FSSQ) algorithm, besides a sketch, also uses an additional data structure called quasi-heap to maintain frequent items. Forward Decay Count-Min Space Saving (FDCMSS), our algorithm, cleverly combines key ideas borrowed from forward decay, the Count-Min sketch and the Space Saving algorithm. Therefore, it makes sense to compare and contrast the two algorithms in order to fully understand their strengths and weaknesses. We show, through extensive experimental results, that FSSQ is better for detecting frequent items than for frequency estimation. The use of the quasi-heap data structure slows down the algorithm owing to the huge number of maintenance operations. Therefore, FSSQ may not be able to cope with high-speed data streams. FDCMSS is better suitable for frequency estimation; moreover, it is extremely fast and can be used in the context of high-speed data streams and for the detection of frequent items as well, since its recall is always greater than 99%, even when using an extremely tiny amount of space. Therefore, FDCMSS proves to be an overall good choice when considering jointly the recall, precision, average relative error and the speed.

Fast and accurate mining of correlated heavy hitters

The problem of mining correlated heavy hitters (CHH) from a two-dimensional data stream has been introduced recently, and a deterministic algorithm based on the use of the Misra–Gries algorithm has been proposed by Lahiri et al. to solve it. In this paper we present a new counter-based algorithm for tracking CHHs, formally prove its error bounds and correctness and show, through extensive experimental results, that our algorithm outperforms the Misra–Gries based algorithm with regard to accuracy and speed whilst requiring asymptotically much less space.

CUDA Based Parallel Implementations of Space-Saving on a GPU

We present four CUDA based parallel implementations of the Space-Saving algorithm for determining frequent items on a GPU. The first variant exploits the open-source CUB library to simplify the implementation of a users defined reduction, whilst the second is based on our own implementation of the parallel reduction. The third and the fourth, built on the previous variants, are meant to improve the performance by taking advantage of hardware based atomic instructions. In particular, we implement a warp based ballot mechanism to accelerate the Space-Saving updates. We show that our implementation of the parallel reduction, coupled with the ballot based update mechanism, is the fastest, and provides extensive experimental results regarding its performance.