A feasibility study of a rotary planar electrode array for electrical impedance mammography using a digital breast phantom

A feasibility study of an electrical impedance mammography (EIM) system with a rotary planar electrode array, named RPEIM, is presented. The RPEIM system is an evolution of the Sussex MK4 system, which is a prototype instrument for breast cancer detection. Comparing it with the other planar electrode EIM systems, the rotation feature enables a dramatic increase in the number of independent measurements. To assist impedance evaluation exploiting electrode array rotation, a synchronous mesh method is proposed. Using the synchronous mesh method, the RPEIM system is shown to have superior performance in image accuracy, spatial resolution and noise tolerance over the MK4 system. To validate the study, we report simulations based on a close-to-realistic 3D digital breast phantom, which comprises of: skin, nipple, ducts, acinus, fat and tumor. A digital breast phantom of a real patient is constructed, whose tumor was detected using the MK4 system. The reconstructed conductivity image of the breast phantom indicates that the breast phantom is a close replica of the patient’s real breast as assessed by the MK4 system in a clinical trial. A comparison between the RPEIM system and the MK4 system is made based on this phantom to assess the advantages of the RPEIM system

Biocomposite films based on κ-carrageenan/locust bean gum blends and clays : physical and antimicrobial properties

The aims of this work were to evaluate the physical and antimicrobial properties of biodegradable films composed of mixtures of κ-carrageenan (κ-car) and locust bean gum (LBG) when organically modified clay Cloisite 30B (C30B) was dispersed in the biopolymer matrix. Film-forming solutions were prepared by adding C30B (ranging from 0 to 16 wt.%) into the κ-car/LBG solution (40/60 wt.%) with 0.3 % (w/v) of glycerol. Barrier properties (water vapour permeability, P vapour; CO2 and O2 permeabilities), mechanical properties (tensile strength, TS, and elongation-at-break, EB) and thermal stability of the resulting films were determined and related with the incorporation of C30B. Also, X-ray diffraction (XRD) was done in order to investigate the effect of C30B in film structure. Antimicrobial effects of these films against Listeria monocytogenes, Escherichia coli and Salmonella enterica were also evaluated. The increase of clay concentration causes a decrease of P vapour (from 5.34 × 10−11 to 3.19 × 10−11 g (m s Pa)−1) and an increase of the CO2 permeability (from 2.26 × 10−14 to 2.91 × 10−14 g (m s Pa)−1) and did not changed significantly the O2 permeability for films with 0 and 16 wt.% C30B, respectively. Films with 16 wt.% clay exhibited the highest values of TS (33.82 MPa) and EB (29.82 %). XRD patterns of the films indicated that a degree of exfoliation is attained depending on clay concentration. κ-car/LBG–C30B films exhibited an inhibitory effect only against L. monocytogenes. κ-car/LBG–C30B composite films are a promising alternative to synthetic films in order to improve the shelf life and safety of food products.J. T. Martins, A. I. Bourbon, A. C. Pinheiro and M. A. Cerqueira gratefully acknowledge the Fundacao para a Ciencia e Tecnologia (FCT, Portugal) for their fellowships (SFRH/BD/32566/2006, SFRH/BD/73178/2010, SFRH/BD/48120/2008 and SFRH/BPD/72753/2010, respectively), and B. W. S. Souza acknowledges the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES, Brazil)

Multigigabit programmable comb decimator implemented in GaAs/AlGaAs HEMT technology

The architecture and design of a GaAs multi-GHz two-stage programmable decimator are presented. A transistor-level realisation of the first stage( the comb decimator) and the cell count of the second stage decimator in a 0.3 um GaAs/AlGaAs HEMT E/D process are considered. The performance has been calculated through measurements made on two 12-bit adders using SDCFL and DCFL gates. An alternating carry state technique allows a speed of 2GHz to be obtained with 2.2W power dissipation from the comb decimator; the transistor count is 4525

A High Optical Transmittance and Low Cost Touch Screen without Patterning

Transparent Conducting Oxide (TCO) materials such as Fluorine Tin Oxide (FTO) and Indium Tin Oxide (ITO) due to their optical and electrical properties are used in touch screens as electrodes and wires. This paper proposes a novel technique of using Electrical Resistivity Tomography (ERT) method in order to produce touch screens without pattering. Unlike existing techniques, the proposed methodology employs a uniform TCO coated screen with a maximum optical transmittance to convert the touch point coordinates into side electrodes voltages. The performance of the proposed method is tested experimentally on a FTO coated glass with a sheet resistance of 20 ohms/sq. The proposed methodology is found to be less complicated and low cost, since no pattern or electrodes are implemented in the display area

Scalable and energy efficient seizure detection based on direct use of compressively-sensed EEG data on an ultra low power multi-core architecture

Extracting information from dense multi-channel neural sensors for accurate diagnosis of brain disorders necessitates computationally expensive and advanced signal processing approaches to analyze the massive volume of recorded data. Compressive Sensing (CS) is an efficient method for reducing the computational complexity and power consumption in the resource-constrained multi-site neural systems. However, reconstructing the signal from compressed measurements is computationally intensive, making it unsuitable for real-time applications such as seizure detection. In this paper, a seizure detection algorithm is proposed to overcome these limitations by circumventing the reconstruction phase and directly processing the compressively sampled EEG signals. The Lomb-Scargle Periodogram (LSP) is used to extract the spectral energy features of the compressed data. Performance of the seizure detector using non-linear support vector machine (SVM) classifier, tested on 24 patients of the CHB-MIT data-set for compression ratios (CR) of 1\u201364x, is 96\u201393%, 92-87%, 0.95\u20130.91, and <1 s for sensitivity, accuracy, the area under the curve, and latency, respectively. A power-efficient classification method based on the utilization of dual linear SVM classifiers is proposed. The proposed classification method based on the dual linear SVM classification achieved better classification performance compared to commonly used classifiers, such as K-nearest neighbor, random forest, artificial neural network, and linear SVM, while consuming low power in comparison to non-linear SVM kernels. The hardware-optimized implementation of this algorithm is proposed on a low-power multi-core SoC for near-sensor data analytics: Mr. Wolf. Optimized implementation of this algorithm on Mr. Wolf platform leads to detecting a seizure with an energy budget of 18.4 \u3bcJ and 3.9 \u3bcJ for a compression ratio of 24x using non-linear SVM classifier and the dual linear SVM based classification method, respectively

Scalable and energy efficient seizure detection based on direct use of compressively-sensed EEG data on an ultra low power multi-core architecture

Extracting information from dense multi-channel neural sensors for accurate diagnosis of brain disorders necessitates computationally expensive and advanced signal processing approaches to analyze the massive volume of recorded data. Compressive Sensing (CS) is an efficient method for reducing the computational complexity and power consumption in the resource-constrained multi-site neural systems. However, reconstructing the signal from compressed measurements is computationally intensive, making it unsuitable for real-time applications such as seizure detection. In this paper, a seizure detection algorithm is proposed to overcome these limitations by circumventing the reconstruction phase and directly processing the compressively sampled EEG signals. The Lomb-Scargle Periodogram (LSP) is used to extract the spectral energy features of the compressed data. Performance of the seizure detector using non-linear support vector machine (SVM) classifier, tested on 24 patients of the CHB-MIT data-set for compression ratios (CR) of 1–64x, is 96–93%, 92-87%, 0.95–0.91, and <1 s for sensitivity, accuracy, the area under the curve, and latency, respectively. A power-efficient classification method based on the utilization of dual linear SVM classifiers is proposed. The proposed classification method based on the dual linear SVM classification achieved better classification performance compared to commonly used classifiers, such as K-nearest neighbor, random forest, artificial neural network, and linear SVM, while consuming low power in comparison to non-linear SVM kernels. The hardware-optimized implementation of this algorithm is proposed on a low-power multi-core SoC for near-sensor data analytics: Mr. Wolf. Optimized implementation of this algorithm on Mr. Wolf platform leads to detecting a seizure with an energy budget of 18.4 μJ and 3.9 μJ for a compression ratio of 24x using non-linear SVM classifier and the dual linear SVM based classification method, respectively

A neural network system for diagnosis and assessment of tremor in Parkinson disease patients

Tremor is one of the most important symptom in Parkinson's disease, which has been assessed clinically by neurologists as part of UPDRS scale. In this paper, we have implemented a supervised learning pattern recognition system to assess UPDRS of each Parkinson patient tremor to fill the absence of a reliable diagnosis and monitoring system for Parkinson patients. In our system a simple noninvasive method based on the recorded acceleration through the smartphone have been used for data acquisition. The results show high accuracy in the classifier block and neural network. A tight correlation between UPDRS scale and acceleration values reveals 91 percent accuracy by neural network with two hidden layers. © 2015 IEEE

Compressed sensing based seizure detection for an ultra low power multi-core architecture

Extracting information from brain signals in advanced Brain Machine Interfaces (BMI) often requires computationally demanding processing. The complexity of the algorithms traditionally employed to process multi-channel neural data, such as Principal Component Analysis (PCA), dramatically increases while scaling-up the number of channels and requires more power-hungry computational platforms. This could hinder the development of low-cost and low-power interfaces which can be used in wearable or implantable real-Time systems. This work proposes a new algorithm for the detection of epileptic seizure based on compressively sensed EEG information, and its optimization on a low-power multi-core SoC for near-sensor data analytics: Mr. Wolf. With respect to traditional algorithms based on PCA, the proposed approach reduces the computational complexity by 4.4x in ARM Cortex M4-based MCU. Implementing this algorithm on Mr.Wolf platform allows to detect a seizure with 1 ms of latency after acquiring the EEG data for 1 s, within an energy budget of 18.4 μJ. A comparison with the same algorithm on a commercial MCU shows an improvement of 6.9x in performance and up to 18.4x in terms of energy efficiency

A neural network system for diagnosis and assessment of tremor in parkinson disease patients

Tremor is one of the most important symptom in Parkinson’s disease, which has been assessed clinically by neurologists as part of UPDRS scale. In this paper, we have implemented a supervised learning pattern recognition system to assess UPDRS of each Parkinson patient tremor to fill the absence of a reliable diagnosis and monitoring system for Parkinson patients. In our system a simple noninvasive method based on the recorded acceleration through the smartphone have been used for data acquisition. The results show high accuracy in the classifier block and neural network. A tight correlation between UPDRS scale and acceleration values reveals 91 percent accuracy by neural network with two hidden layers