Mohammad I. Daoud

A hybrid heuristic-genetic algorithm for task scheduling in heterogeneous processor networks

Efficient task scheduling on heterogeneous distributed computing systems (HeDCSs) requires the consideration of the heterogeneity of processors and the inter-processor communication. This paper presents a two-phase algorithm, called H2GS, for task scheduling on HeDCSs. The first phase implements a heuristic list-based algorithm, called LDCP, to generate a high quality schedule. In the second phase, the LDCP-generated schedule is injected into the initial population of a customized genetic algorithm, called GAS, which proceeds to evolve shorter schedules. GAS employs a simple genome composed of a two-dimensional chromosome. A mapping procedure is developed which maps every possible genome to a valid schedule. Moreover, GAS uses customized operators that are designed for the scheduling problem to enable an efficient stochastic search. The performance of each phase of H2GS is compared to two leading scheduling algorithms, and H2GS outperforms both algorithms. The improvement in performance obtained by H2GS increases as the inter-task communication cost increases.

Tissue Classification Using Ultrasound-Induced Variations in Acoustic Backscattering Features

Ultrasound (US) radio-frequency (RF) time series is an effective tissue classification method that enables accurate cancer diagnosis, but the mechanisms underlying this method are not completely understood. This paper presents a model to describe the variations in tissue temperature and sound speed that take place during the RF time series scanning procedures and relate these variations to US backscattering. The model was used to derive four novel characterization features. These features were used to classify three animal tissues, and they obtained accuracies as high as 88.01%. The performance of the proposed features was compared with RF time series features proposed in a previous study. The results indicated that the US-induced variations in tissue temperature and sound speed, which were used to derive the proposed features, were important contributors to the tissue typing capabilities of the RF time series. Simulations carried out to estimate the heating induced during the scanning procedure employed in this study showed temperature rises lower than 2 °C. The model and results presented in this paper can be used to improve the RF time series.

Needle detection in curvilinear ultrasound images based on the reflection pattern of circular ultrasound waves

PURPOSE Ultrasound imaging provides a low-cost, real-time modality to guide needle insertion procedures, but localizing the needle using conventional ultrasound images is often challenging. Estimating the needle trajectory can increase the success rate of ultrasound-guided needle interventions and improve patient comfort. In this study, a novel method is introduced to localize the needle trajectory in curvilinear ultrasound images based on the needle reflection pattern of circular ultrasound waves. METHODS A circular ultrasound wave was synthesized by sequentially firing the elements of a curvilinear transducer and recording the radio-frequency signals received by each element. Two features, namely, the large amplitude and repetitive reflection pattern, were used to identify the needle echoes in the received signals. The trajectory of the needle was estimated by fitting the arrival times of needle echoes to an equation that describes needle reflection of circular waves. The method was employed to estimate the trajectories of needles inserted in agar phantom, beef muscle, and porcine tissue specimens. RESULTS The maximum error rates of estimating the needle trajectories were on the order of 1 mm and 3° for the radial and azimuth coordinates, respectively. CONCLUSIONS These results suggest that the proposed method can improve the robustness and accuracy of needle segmentation methods by adding signature-based detection of the needle trajectory in curvilinear ultrasound images. The method can be implemented on conventional ultrasound imaging systems.

Computer-aided tissue characterization using ultrasound-induced thermal effects: analytical formulation and in-vitro animal study

Ultrasound radio-frequency (RF) time series analysis provides an effective tissue characterization method to differentiate between healthy and cancerous prostate tissues. In this paper, an analytical model is presented that partially describes the variations in tissue acoustic properties that accompany ultrasound RF time series acquisition procedures. These ultrasound-induced effects, which depend on tissue mechanical and thermophysical properties, are hypothesized to be among the major contributors to the tissue typing capabilities of the RF time series analysis. The model is used to derive two tissue characterization features. The two features are used with a support vector machine classifier to characterize three animal tissue types: chicken breast, bovine liver, and bovine steak. Accuracy values as high as 90% are achieved when the proposed features are employed to differentiate these tissue types. The proposed model may provide a framework to optimize the ultrasound RF time series analysis for future clinical procedures.

Tissue classification using depth-dependent ultrasound time series analysis: in-vitro animal study.

Time series analysis of ultrasound radio-frequency (RF) signals has been shown to be an effective tissue classification method. Previous studies of this method for tissue differentiation at high and clinical-frequencies have been reported. In this paper, analysis of RF time series is extended to improve tissue classification at the clinical frequencies by including novel features extracted from the time series spectrum. The primary feature examined is the Mean Central Frequency (MCF) computed for regions of interest (ROIs) in the tissue extending along the axial axis of the transducer. In addition, the intercept and slope of a line fitted to the MCF-values of the RF time series as a function of depth have been included. To evaluate the accuracy of the new features, an in vitro animal study is performed using three tissue types: bovine muscle, bovine liver, and chicken breast, where perfect two-way classification is achieved. The results show statistically significant improvements over the classification accuracies with previously reported features.

Signature-based algorithm for improved needle localization in ultrasound images: A feasibility study

Ultrasound imaging is commonly used to guide needle insertion procedures, such as biopsies and anesthesia. In such procedures, the needle can be poorly visualized, making it difficult to determine the needle trajectory. A novel needle detection algorithm is introduced based on the unique needle reflection pattern of circular ultrasound waves. The proposed algorithm was evaluated by locating a needle placed in an agar phantom. Circular waves were created using a 5 MHz, curvilinear transducer array. The coordinates of the needle axis were estimated when the angle between the needle and the transducer central axis was varied between 50° and 90°. The differences between the radial and azimuth coordinates of the needle estimated using the proposed algorithm and those calculated using computed tomography scan of the phantom were within 0.1 to 0.5 mm and 0.0° to 3.1°, respectively.

In-house alert sounds detection and direction of arrival estimation to assist people with hearing difficulties

A prototype of a low-cost, wearable system is presented to assist persons with hearing impairments by detecting in-house alert sounds and estimating their direction of arrival (DOA). The prototype is composed of a circular microphone array, a small microcomputer, and motors for vibration alerting. The array includes five microphones uniformly distributed on the outer surface of a ring with circumference comparable to human waist. Each microphone is connected to one of the analog inputs of the microcomputer. The power of the microphone signals is analyzed to detect alert sounds. Upon the detection of an alert sound, a low-complexity time-difference of arrival method is used to determine the sound DOA with mean error of 14.1°. The user is notified about the detection of alert sound and its DOA by vibrating the closest motor to the sounds source. The time required to detect and localize an alert sound is around 35 msec.

An Interactive Rehabilitation Framework for Assisting People with Cerebral Palsy

Active participation in physical rehabilitation exercises is crucial for cerebral palsy (CP) patients to maintain and improve their muscles. However, limited percentage of CP patients participates regularly in rehabilitation exercises, mainly due to the lack of motivation. In this study, a comprehensive framework is proposed to actively engage CP patients in upper-limb rehabilitation exercises. The framework includes a set of Kinect-based computer games that cover a wide spectrum of physical exercises. The physical therapist can prescribe a customized set of these games to each CP patient based on his requirements and capabilities. Moreover, the framework enables the physical therapist to track the progress of the CP patient. The framework supports several features that improve the effectiveness of rehabilitation, motivate the patient to participate actively in game-based rehabilitation, and improve the social interaction of the patient by interacting with other individuals. Preliminary results are reported that describe the implementation of three Kinect-based games for CP rehabilitation. The games have been employed in the rehabilitation of three CP patients, and the results confirm the feasibility of the framework to complement conventional rehabilitation exercises.

GPU Accelerated Implementation of Ultrasound Radio-Frequency Time Series Analysis

The ultrasound radio-frequency (RF) time series method has been shown to be an effective approach for accurate tissue classification and cancer detection. Previous studies of the RF time series method were based on a serial MATLAB implementation of feature calculation that involved long running times. Clinical applications of the RF time series method require a fast and efficient implementation that enables realistic imaging studies within a short time frame. In this paper, a parallel implementation of the RF time series method is developed to support clinical ultrasound imaging studies. The parallel implementation uses a Graphics Processing Unit (GPU) to compute the tissue classification features of the RF time series method. Moreover, efficient graphical representations of the RF times series features are obtained using the Qt framework. Tread computing is used to concurrently compute and visualize the RF time series features. The parallel implementation of the RF time series is evaluated for various configurations of number of frames and number of scan lines per frame acquired in an imaging study. Results demonstrate that the parallel implementation enables imaging of tissue classification at interactive time. A typical RF time series of 128 frames and 128 scan lines per frame, the parallel implementation be processed in 0.8128 ± 0.0420 sec.

Monitoring of tissue ablation using time series of ultrasound RF data

PURPOSE This paper is the first report on the monitoring of tissue ablation using ultrasound RF echo time series. METHODS We calcuate frequency and time domain features of time series of RF echoes from stationary tissue and transducer, and correlate them with ablated and non-ablated tissue properties. RESULTS We combine these features in a nonlinear classification framework and demonstrate up to 99% classification accuracy in distinguishing ablated and non-ablated regions of tissue, in areas as small as 12mm2 in size. We also demonstrate significant improvement of ablated tissue classification using RF time series compared to the conventional approach of using single RF scan lines. CONCLUSIONS The results of this study suggest RF echo time series as a promising approach for monitoring ablation, and capturing the changes in the tissue microstructure as a result of heat-induced necrosis.