Virtual reality simulation of robotic-assisted min-imal invasive procedures reveals interesting issues related to the perception, control and manipulation of laparoscopic tools with emphasis given to the pivot trajectories and the Remote-Center-of-Motion (RCM) constrained motion planning. In this paper, the Gazebo simulator under Robot Operating System (ROS) allows the inclusion of hardware-in-the-loop for Minimally Invasive Surgery (MIS) procedures. The RCM constraint is addressed through the transformation of the surgical task space into the robot's taskspace, while addressing the robot's manipulability. Emphasis is given in calculating various geometric paths to be followed by the robot during surgery. Simulations were conducted using the ROS framework and the MoveIt kinematic planner using the RRTConnect path planning algorithm to evaluate the efficacy proposed scheme.

Mammography is still the most effective procedure for early diagnosis of breast cancer. Computer-aided Diagnosis (CAD) systems can be very helpful in this direction and an assistance tool for radiologists to recognize abnormal and normal regions of interest in digital mammograms faster than traditional screening program. In this paper, we propose a new method for breast cancer recognition of all types of lesions in digital mammograms using multimodal features in a quaternionic representation. The proposed method consists of two steps: First, a feature extraction module utilizes two dimensional discrete transforms based on ART, Shapelets and Gabor filters to extract low-level visual descriptors of ROS. In addition, semantic mammogram information provided by radiologists, is encoded into a 16-bit length word used as high-level feature vector. All multimodal features are fused in a compact quaternionic representation prior to their presentation to the classification module in the second step. For the classification task, ROSs are diagnosed using trained Support-Vector-Machines (SVMs). The proposed method is evaluated on regions taken from the DDSM database. The proposed feature set achieves a mean accuracy of 87.22% (±2.678) and AUC = 0.98, that is 34% higher than using only visual descriptors, proving the significant information that semantic descriptors contain.

This article presents the development of a custom designed motion capture system for assisting physicians in upper limb rehabilitation. The scope of the implementation is the reduction in costs and hospitalization time related with existing systems. The system architecture revolves around motion sensing nodes, each utilizing an Inertial Measurement Unit (IMU), mounted on the upper limb of a patient. A physical working prototype is implemented and evaluated using vision-based passive marker detection techniques. Additionally, a generic upper limb kinematic model, derived from robotic kinematic theory is developed. The parameterization of the model allows for modularity, since a variety of sensing modalities can be rapidly integrated. The combination of the theoretical and physical implementation are evaluated in a preliminary motion sensing scenario for the upper limb of a patient, to depict the feasibility of this approach.

In this paper, a novel methodology for estimating the shape of human biconcave Red Blood Cells (RBCs), using color scattering images, is presented. The information retrieval process includes, image normalization, features extraction using both Histogram of Oriented Gradients (HoG) and region covariance features (RCoV); and features dimensionality reduction using the Independent Component Analysis (ICA). The points of interest (PoIs) are detected using the Harriscorner detector in order to extract the image features. A scheme using adjustable algorithms, i.e. support vectors machine (SVM) is adopted in order to fuse the multimodal features. A Radial Basis Function Neural Network (RBF-NN) estimates the RBC geometrical properties. The proposed method is evaluated in both regression and identification tasks by processing images of a simulated device used to acquire scattering phenomena of moving RBCs. The evaluation database includes 23625 scattering images, obtained by means of the Boundary Element Method. The regression and identification accuracy of the actual RBC shape is estimated using three feature sets in the presence of additive white Gaussian noise from 60 to 10 dB SNR, giving a mean error rate less than 1 percent of the actual RBC shape, and more than 99 percent mean identification rate in a set of valid RBCs size.

A key module of modern advanced driver-assistance systems (ADASs) is the road detector, which has to be robust, even under adverse conditions. The ultimate goal of such a system, which uses only visual information acquired from a color video camera, is to classify each frame pixel as belonging to the road or not. In this direction, this paper proposes a new fully automatic algorithm that combines both time and spatial information using the efficient random-walker algorithm (RWA) as a segmentation tool. A novel technique for automatic seed selection is proposed, utilizing features derived from a shadow-resistant optical flow estimator using thec1channel of thec1c2c3color space, along with a priori information and previous frame segmentation results. The proposed system is qualitatively assessed using video sequences in both typical and adverse conditions, including heavy traffic, shadows, tunnels, rain, night, etc. It is also quantitatively compared with previous efforts on a publicly available manually annotated onboard video database, providing superior results.

Acoustic emission (AE) is one of the most important non-destructive testing (NDT) methods for materials and constructions. Using AE testing, the location of a single event (crack) can be classified efficiently into three typical areas in a ship hull. The problem is a typical classification problem based on the use of features extracted from piezo-sensors' signal. As in most classification problems, the extraction and selection of the most appropriate set of features plays a major role in the overall performance of the system. In this research work we investigate the use of an evolutionary algorithm to extract new features from a set of primitive features in a lower dimensional space through a linear transformation. These features are subsequently fed into a probabilistic neural network (PNN) that performs the classification. In simulation experiments, where a stiffened plate model (SPM) is partially sank into water, the localization rate in noisy environments outperforms a work, where a feature selection phase alone was used before the classification phase. The proposed hybrid computational intelligent approach shows the potential merit of using it in real life situations where the signal is distorted by noise.

Detection of the speaker position is a crucial task in hands-free speech recognition applications. In this paper we present a novel speech beam-former for noisy environments. Initially, the localization algorithm extracts a set of candidate directions of the signal sources using array signal processing methods in the frequency domain. Then, a minimum variance (MV) beam-former identifies the speech signal in the direction where the signal's spectrum entropy is minimized. The proposed method is evaluated by a phoneme recognition system using noise recordings from an air-condition fan and the TIMIT speech corpus. Extended experiments, carried out in the range of 25–0 dB, show almost perfect estimation of the speaker DOA in all cases. As a consequence, the recognition rate increases significantly compared to the rate obtained by a single microphone. The recognition improvement increases especially in very low SNRs.

A novel method for accurate detection of regions of interest (ROIs) that contain circumscribed lesions in mammograms is presented. The mammograms are segmented using a statistical threshold and a number of candidate regions are extracted. Then a set of qualification criteria is employed to filter these regions retaining the most suspicious for which a radial-basis function neural network makes the final decision marking them as ROIs that contain abnormal tissue. The proposed method detects the exact location of the circumscribed lesions with an accuracy of 90.9%, and a very low number of false positive regions per image (2.1 ROIs per image) in the MIAS database.

In this paper we present a novel solution to the Overdetermined Blind Speech Separation (OBSS) problem for improving speech recognition accuracy of N simultaneous speakers in real room environments using M, (M>N) microphones. The proposed OBSS system uses basic NxN Blind Speech Separation networks that process in parallel all different combinations of the available mixture signals in the frequency domain, resulting to lower computational complexity and faster convergence. Extensive experiments using an array of two to ten microphones and two simultaneous speakers in a simulated real room, showed that when the number of the microphones increases beyond two, the separation performance is improved and the phoneme recognition accuracy of an HMM based decoder increases drastically (more than 6%). Therefore, the introduction of more microphones than speakers is justified in order to improve speech recognition accuracy in multi simultaneous speaker environments.

In this paper we present a new on-line blind signal separation method capable to separate convolutive speech signals of moving speakers in highly reverberant rooms. The separation network used is a recurrent network which performs separation of convolutive speech mixtures in the time domain, without any prior knowledge of the propagation media, based on the maximum likelihood estimation (MLE) principle. The proposed method proved to be able to improve significantly (more than 10% in all adverse mixing situations) the performance of a continuous phoneme-based speech recognition system and therefore can be used as a front-end to separate simultaneous speech of speakers who are moving in arbitrary directions in reverberant rooms.

A novel method for fast detection of regions of suspicion (ROS) that contain circumscribed lesions in mammograms is presented. The position and the size of ROS are first recognized with the aid of a radial-basis-function neural network (RBFNN) by performing windowing analysis. Then a set of criteria is employed to these regions to make the final decision concerning the abnormal ones. Accelerated estimation of the high-order statistical features decreases the computational complexity 55 times in multiplication operations. The proposed method detects the exact location of the circumscribed lesions with accuracy of 72.7% (overlap between groundtruthed and detected regions greater than 50%) for mammograms containing masses, while the recognition rate for the normal ones reaches 77.7% in the MIAS database.

The aim of this paper is to study the use of a robust acquisition system based on a microphone array for speech related applications in real situations. A comparison is performed between two beamforming methods: the Delay and Sum beamforming (DS) and the Spatial Reference Optimal beamforming (SRO). Both of them are frequency domain designed, using harmonic spatial distributed microphones. The quality of the microphone array output signal for the different beamforming approaches is measured by using a continuous speech recognition system based on discrete HMMs. Results of the speech recognition system show the advantage of using a microphone array with the SRO beamforming as front-end.

In extensive experiments, the recognition score of a speaker independent isolated word speech recognition system based on a continuous density HMM (CDHMM) has been measured in the presence of real life noises in various SNRs. In all experiments the results show improvement in the mean recognition score when the subband adaptive filtering LMS method is used in comparison to the full-band LMS method. This improvement increases when changing types of noise distort the speech signal.

This paper presents a clustering algorithm producing multiple whole-word continuous density hidden Markov models (CDHMM) for isolated word recognition systems. The algorithm estimates a minimum number of CDHMM per word that approaches or satisfies a minimum predefined word-dependent recognition accuracy in the training set. Significantly lower memory requirements and a better and more uniformly distributed recognition accuracy among the words of the vocabulary are measured by comparing this algorithm with the modified K-means clustering method.

The authors present a simple and fast algorithm for accurately locating the endpoints of isolated words spoken in an office environment. This algorithm is based on energy measures and threshold logic and is adaptive to almost any low-noise acoustic environment. Experimental results from the algorithm's application were both acoustically checked and compared to hand-edited results by skilled personnel. The algorithm was tested on a vocabulary of 34 words, uttered by 25 speakers and recorded in different acoustic environments. The accuracy of the algorithm is quite acceptable.>