Optimization unit for real-time applications in unbalanced smart distribution networks.

This paper presents a new generic approach for developing a Jacobian matrix for use with the optimization unit in real-time energy management systems (EMS) for unbalanced smart distribution systems. The proposed formulation can replace approximated calculations for real-time optimal power flow in an optimization unit while providing greater accuracy and requiring less computational time, which is critical for real-time EMS. The effectiveness and robustness of the proposed approach have been tested through simulations with different distribution networks. The simulation results demonstrate a significant reduction in the computational time with the new proposed formulation. Moreover, the results demonstrate the scalability of the proposed approach as the reduction in the computational time is more significant for large practical systems. The proposed approach is characterized by evaluating the scalability and low computational time; thus, it can be used by grid operators in real-time energy management applications for large-scale practical distribution systems.

3-D flexible nano-textured high-density microelectrode arrays for high-performance neuro-monitoring and neuro-stimulation.

We introduce a new 3-D flexible microelectrode array for high performance electrographic neural signal recording and stimulation. The microelectrode architecture maximizes the number of channels on each shank and minimizes its footprint. The electrode was implemented on flexible polyimide substrate using microfabrication and thin-film processing. The electrode has a planar layout and comprises multiple shanks. Each shank is three mm in length and carries six gold pads representing the neuro-interfacing channels. The channels are used in recording important precursors with potential clinical relevance and consequent electrical stimulation to perturb the clinical condition. The polyimide structure satisfied the mechanical characteristics required for the proper electrode implantation and operation. Pad postprocessing technique was developed to improve the electrode electrical performance. The planar electrodes were used for creating 3-D "Waterloo Array" microelectrode with controlled gaps using custom designed stackers. Electrode characterization and benchmarking against commercial equivalents demonstrated the superiority of the Flex electrodes. The Flex and commercial electrodes were associated with low-power implantable responsive neuro-stimulation system. The electrodes performance in recording and stimulation application was quantified through in vitro and in vivo acute and chronic experiments on human brain slices and freely-moving rodents. The Flex electrodes exhibited remarkable drop in the electric impedance (100 times at 100 Hz), improved electrode-electrolyte interface noise (dropped by four times) and higher signal-to-noise ratio (3.3 times).

High-density intracortical microelectrode arrays with multiple metallization layers for fine-resolution neuromonitoring and neurostimulation.

Intracortical microelectrodes play a prominent role in the operation of neural interfacing systems. They provide an interface for recording neural activities and modulating their behavior through electric stimulation. The performance of such systems is thus directly meliorated by advances in electrode technology. We present a new architecture for intracortical electrodes designed to increase the number of recording/stimulation channels for a given set of shank dimensions. The architecture was implemented on silicon using microfabrication process and fabricated 3-mm-long electrode shanks with six relatively large (110 µm ×110 µm) pads in each shank for electrographic signal recording to detect important precursors with potential clinical relevance and electrical stimulation to correct neural behavior with low-power dissipation in an implantable device. Moreover, an electrode mechanical design was developed to increase its stiffness and reduce shank deflection to improve spatial accuracy during an electrode implantation. Furthermore, the pads were post-processed using pulsated low current electroplating and reduced their impedances by ≈ 30 times compared to the traditionally fabricated pads. The paper also presents microfabrication process, electrodes characterization, comparison to the commercial equivalents, and in vitro and in vivo validations.

Massively-parallel neuromonitoring and neurostimulation rodent headset with nanotextured flexible microelectrodes.

We present a compact wireless headset for simultaneous multi-site neuromonitoring and neurostimulation in the rodent brain. The system comprises flexible-shaft microelectrodes, neural amplifiers, neurostimulators, a digital time-division multiplexer (TDM), a micro-controller and a ZigBee wireless transceiver. The system is built by parallelizing up to four 0.35 µm CMOS integrated circuits (each having 256 neural amplifiers and 64 neurostimulators) to provide a total maximum of 1024 neural amplifiers and 256 neurostimulators. Each bipolar neural amplifier features 54 dB-72 dB adjustable gain, 1 Hz-5 kHz adjustable bandwidth with an input-referred noise of 7.99 µVrms and dissipates 12.9 µW. Each current-mode bipolar neurostimulator generates programmable arbitrary-waveform biphasic current in the range of 20-250 µA and dissipates 2.6 µW in the stand-by mode. Reconfigurability is provided by stacking a set of dedicated mini-PCBs that share a common signaling bus within as small as 22 × 30 × 15 mm³ volume. The system features flexible polyimide-based microelectrode array design that is not brittle and increases pad packing density. Pad nanotexturing by electrodeposition reduces the electrode-tissue interface impedance from an average of 2 MΩ to 30 kΩ at 100 Hz. The rodent headset and the microelectrode array have been experimentally validated in vivo in freely moving rats for two months. We demonstrate 92.8 percent seizure rate reduction by responsive neurostimulation in an acute epilepsy rat model.

An automated neural-fuzzy approach to malignant tumor localization in 2D ultrasonic images of the prostate.

In this paper, a new neural-fuzzy approach is proposed for automated region segmentation in transrectal ultrasound images of the prostate. The goal of region segmentation is to identify suspicious regions in the prostate in order to provide decision support for the diagnosis of prostate cancer. The new automated region segmentation system uses expert knowledge as well as both textural and spatial features in the image to accomplish the segmentation. The textural information is extracted by two recurrent random pulsed neural networks trained by two sets of data (a suspicious tissues' data set and a normal tissues' data set). Spatial information is captured by the atlas-based reference approach and is represented as fuzzy membership functions. The textural and spatial features are synthesized by a fuzzy inference system, which provides a binary classification of the region to be evaluated.

Finite difference time domain (FDTD) modeling of implanted deep brain stimulation electrodes and brain tissue.

This paper demonstrates the electromagnetic modeling and simulation of an implanted Medtronic deep brain stimulation (DBS) electrode using finite difference time domain (FDTD). The model is developed using Empire XCcel and represents the electrode surrounded with brain tissue assuming homogenous and isotropic medium. The model is created to study the parameters influencing the electric field distribution within the tissue in order to provide reference and benchmarking data for DBS and intra-cortical electrode development.

Prostate tissue texture feature extraction for suspicious regions identification on TRUS images.

In this work, two different approaches are proposed for region of interest (ROI) segmentation using transrectal ultrasound (TRUS) images. The two methods aim to extract informative features that are able to characterize suspicious regions in the TRUS images. Both proposed methods are based on multi-resolution analysis that is characterized by its high localization in both the frequency and the spatial domains. Being highly localized in both domains, the proposed methods are expected to accurately identify the suspicious ROIs. On one hand, the first method depends on a Gabor filter that captures the high frequency changes in the image regions. On the other hand, the second method depends on classifying the wavelet coefficients of the image. It is shown in this paper that both methods reveal details in the ROIs which correlate with their pathological representations. It was found that there is a good match between the regions identified using the two methods, a result that supports the ability of each of the proposed methods to mimic the radiologist's decision in identifying suspicious regions. Studying two ROI segmentation methods is important since the only available dataset is the radiologist's suspicious regions, and there is a need to support the results obtained by either one of the proposed methods. This work is mainly a preliminary proof of concept study that will ultimately be expanded to a larger scale study whose aim will be introducing an assisting tool to help the radiologist identify the suspicious regions.

Real-time automated neural-network sleep classifier using single channel EEG recording for detection of narcolepsy episodes.

Conventional sleep staging and classification methods involve complicated settings to acquire multiple electrophysiological signals for extended recording durations, followed by specialists' analysis which is a time consuming exercise. These procedures need to be carried out in sleep clinics and are not suitable for applications based on real-time sleep monitoring and analysis. In this paper, a real-time sleep staging and classification technique is proposed using single EEG channel based on an artificial neural network classifier. This method is optimized to run on portable processing platforms with limited processing capabilities.

Design optimization methodology for dry EEG spikes array electrode.

Monitoring bio-electric events is a vital practice which provides medical data required in many clinical and research applications. Improving the performance of portable and ambulatory recording devices requires developing stable biomedical electrodes suitable for long term recording. This paper introduces an optimization design methodology to improve the electrical performance of dry electrodes used in electroencephalography through optimizing the geometrical design while abiding by design constraints which guarantee biocompatibility and mechanical stability.

Prostate cancer multi-feature analysis using trans-rectal ultrasound images.

This note focuses on extracting and analysing prostate texture features from trans-rectal ultrasound (TRUS) images for tissue characterization. One of the principal contributions of this investigation is the use of the information of the images' frequency domain features and spatial domain features to attain a more accurate diagnosis. Each image is divided into regions of interest (ROIs) by the Gabor multi-resolution analysis, a crucial stage, in which segmentation is achieved according to the frequency response of the image pixels. The pixels with a similar response to the same filter are grouped to form one ROI. Next, from each ROI two different statistical feature sets are constructed; the first set includes four grey level dependence matrix (GLDM) features and the second set consists of five grey level difference vector (GLDV) features. These constructed feature sets are then ranked by the mutual information feature selection (MIFS) algorithm. Here, the features that provide the maximum mutual information of each feature and class (cancerous and non-cancerous) and the minimum mutual information of the selected features are chosen, yielding a reduced feature subset. The two constructed feature sets, GLDM and GLDV, as well as the reduced feature subset, are examined in terms of three different classifiers: the condensed k-nearest neighbour (CNN), the decision tree (DT) and the support vector machine (SVM). The accuracy classification results range from 87.5% to 93.75%, where the performance of the SVM and that of the DT are significantly better than the performance of the CNN.

Prostate segmentation algorithm using dyadic wavelet transform and discrete dynamic contour.

Knowing the location and the volume of the prostate is important for ultrasound-guided prostate brachytherapy, a commonly used prostate cancer treatment method. The prostate boundary must be segmented before a dose plan can be obtained. However, manual segmentation is arduous and time consuming. This paper introduces a semi-automatic segmentation algorithm based on the dyadic wavelet transform (DWT) and the discrete dynamic contour (DDC). A spline interpolation method is used to determine the initial contour based on four user-defined initial points. The DDC model then refines the initial contour based on the approximate coefficients and the wavelet coefficients generated using the DWT. The DDC model is executed under two settings. The coefficients used in these two settings are derived using smoothing functions with different sizes. A selection rule is used to choose the best contour based on the contours produced in these two settings. The accuracy of the final contour produced by the proposed algorithm is evaluated by comparing it with the manual contour outlined by an expert observer. A total of 114 2D TRUS images taken for six different patients scheduled for brachytherapy were segmented using the proposed algorithm. The average difference between the contour segmented using the proposed algorithm and the manually outlined contour is less than 3 pixels.