IEEE Bio medical engineering 2016 Title and Abstract

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2016 TOPICS
Characterization of an HEM-mode dielectric resonator for 7 T human phosphorous
magnetic resonance imaging
ABSTRACT - To design and characterize a new set-up for dual nuclei MRI combining an
annular dielectric resonator filled with high permittivity material for phosphorous (31P) and a
traveling wave antenna for proton imaging. Methods: Recent studies have shown that an annular
cylinder filled with water can serve as dielectric resonator for proton MRI of the extremities at 7
T. Using a very high permittivity material such as BaTiO3, this type of dielectric resonator can
potentially be designed for lower gyromagnetic ratio nuclei. Combining this with a remote
antenna for proton imaging, an alternative method for dual frequency imaging at ultra-high field
has been implemented. Results: 3D electromagnetic simulations were performed to examine the
efficiency of the dielectric resonator. The new dielectric resonator was constructed for 31P
acquisition at 121 MHz on a human 7 T MRI system. Phantom and in-vivo scans demonstrated
the feasibility of the setup, although the current sensitivity of the dielectric resonator is only-half
that of an equivalently-sized birdcage. Conclusion: The new approach offers a simple
implementation for dual nuclei imaging at ultra-high field, with several possibilities for further
increases in sensitivity. Significance: Utilizing high permittivity materials enables very simple
designs for high field RF coils: in the current configuration the interactions between the proton
and phosphorous resonators are very low.
IEEE Transactions on Biomedical Engineering (February 2016)
Automated Detection of Engagement using Video-Based Estimation of Facial Expressions
and Heart Rate
We explored how computer vision techniques can be used to detect engagement while students
(N = 22) completed a structured writing activity (draft-feedback-review) similar to activities
encountered in educational settings. Students provided engagement annotations both
concurrently during the writing activity and retrospectively from videos of their faces after the
activity. We used computer vision techniques to extract three sets of features from videos, heart
rate, Animation Units (from Microsoft Kinect Face Tracker), and local binary patterns in three
orthogonal planes (LBP-TOP). These features were used in supervised learning for detection of

concurrent and retrospective self-reported engagement. Area Under the ROC Curve (AUC) was
used to evaluate classifier accuracy using leave-several-students-out cross validation. We
achieved an AUC = .758 for concurrent annotations and AUC = .733 for retrospective
annotations. The Kinect Face Tracker features produced the best results among the individual
channels, but the overall best results were found using a fusion of channels.
IEEE Transactions on Affective Computing (January 2016)
Human-Guided Robotic Comanipulation: Two Illustrative Scenarios
Emerging applications of robot systems that involve physical interaction with humans have
opened up new challenges in robot control. While various control techniques have been
developed for human-robot interaction, existing methods do not take advantages of both human
knowledge and the robot's ability. In this paper, a human-guided manipulation problem is
formulated and solved. The workspace is divided into a human region, where the human plays a
more active role in the manipulation task, and a robot region, where the robot is more dominant
in the manipulation. The proposed formulation allows the human to take control actions to deal
with unforeseen changes or uncertainty in the environment, and also allows the robot to take the
lead where the environment is exactly known. We mainly consider two basic scenarios, i.e.,
robot control first and human control later (R-H) or human control first and robot control later
(H-R), to illustrate the concept of human-guided comanipulation. Based on a smooth transition
between the human region and the robot region, an adaptive tracking controller is developed to
take advantages of both human knowledge and the robot's ability. The experimental results are
presented to illustrate the performance of the proposed control method.
IEEE Transactions on Control Systems Technology (January 2016)
A CNN Regression Approach for Real-Time 2D/3D Registration
In this paper, we present a Convolutional Neural Network (CNN) regression approach to address
the two major limitations of existing intensity-based 2-D/3-D registration technology: 1) slow
computation and 2) small capture range. Different from optimization-based methods, which
iteratively optimize the transformation parameters over a scalar-valued metric function
representing the quality of the registration, the proposed method exploits the information
embedded in the appearances of the digitally reconstructed radiograph and X-ray images, and

employs CNN regressors to directly estimate the transformation parameters. An automatic
feature extraction step is introduced to calculate 3-D pose-indexed features that are sensitive to
the variables to be regressed while robust to other factors. The CNN regressors are then trained
for local zones and applied in a hierarchical manner to break down the complex regression task
into multiple simpler sub-tasks that can be learned separately. Weight sharing is furthermore
employed in the CNN regression model to reduce the memory footprint. The proposed approach
has been quantitatively evaluated on 3 potential clinical applications, demonstrating its
significant advantage in providing highly accurate real-time 2-D/3-D registration with a
significantly enlarged capture range when compared to intensity-based methods.
IEEE Transactions on Medical Imaging (May 2016)
Low-Power, 8-Channel EEG Recorder and Seizure Detector ASIC for a Subdermal
Implantable System
EEG remains the mainstay test for the diagnosis and treatment of patients with epilepsy.
Unfortunately, ambulatory EEG systems are far from ideal for patients who have infrequent
seizures. These systems only last up to 3 days and if a seizure is not captured during the
recordings, a definite diagnosis of the patient‘s condition cannot be given. This work aims to
address this need by proposing a subdermal implantable, eight-channel EEG recorder and seizure
detector that has two modes of operation: diagnosis and seizure counting. In the diagnosis mode,
EEG is continuously recorded until a number of seizures are recorded. In the seizure counting
mode, the system uses a low-power algorithm to track the number of seizures a patient has,
providing doctors with a reliable count to help determine medication efficacy or other clinical
endpoint. An ASIC that implements the EEG recording and seizure detection algorithm was
designed and fabricated in a 0.18 CMOS process. The ASIC includes eight EEG channels and is
designed to minimize the system‘s power and size. The result is a power-efficient analog front
end that requires 2.75 per channel in diagnosis mode and 0.84 per channel in seizure counting
mode. Both modes have an input referred noise of approximately 1.1
IEEE Transactions on Biomedical Circuits and Systems (April 2016)
ConceFT for time-varying heart rate variability analysis as a measure of noxious
stimulation during general anesthesia

Heart rate variability (HRV) offers a noninvasive way to peek into the physiological status of the
human body. When this physiological status is dynamic, traditional HRV indices calculated from
power spectrum do not resolve the dynamic situation due to the issue of non-stationarity. Clinical
anesthesia is a typically dynamic situation that calls for time-varying HRV analysis.
Concentration of frequency and time (ConceFT) is a nonlinear time-frequency (TF) analysis
generalizing the multitaper technique and the synchrosqueezing transform. The result is a sharp
TF representation capturing the dynamics inside HRV. Companion indices of the commonly
applied HRV indices, including time-varying low frequency power (tvLF), time-varying high
frequency power and timevarying low-high ratio, are considered as measures of noxious
stimulation. Methods: To evaluate the feasibility of the proposed indices, we apply these indices
to study two different types of noxious stimulation, the endotracheal intubation and surgical skin
incision, under general anesthesia. The performance was compared with traditional HRV indices,
the heart rate reading and indices from electroencephalography. Results: The results indicate that
the tvLF index performs best, and outperforms not only the traditional HRV index, but also the
commonly used heart rate reading. Conclusion: With the help of ConceFT, the proposed HRV
indices is potential to provide a better quantification of the dynamic change of the autonomic
nerve system. Significance: Our proposed scheme of time-varying HRV analysis could
contribute to the clinical assessment of analgesia under general anesthesia.
IEEE Transactions on Biomedical Engineering (March 2016)
A Proximity Coupling RF Sensor for Wrist Pulse Detection Based on Injection-Locked
PLL
In this paper, a proximity coupling RF sensor based on injection-locked phase-locked loop (PLL)
for wrist pulse detection is proposed. The sensor is composed of two main parts: a free-running
oscillator and a PLL synthesizer containing a voltage-controlled oscillator. The free-running
oscillator is built with a two-port microstrip line resonator (inter-digital electrodes), which acts as
part of a transducer that can transform the expansion or contraction of the radial artery into an
impedance variation. Measurements show that the impedance variation of the resonator due to
changes in the radial artery causes a frequency change of up to 0.74 MHz in the free-running
oscillator. For the PLL part, the frequency change can be transformed to a variation in dc voltage

by injection of the modulated signal from the wrist pulse into a phase-locked oscillator. The
variation of the loop-control voltage, in one cycle of the pulse, is approximately 10-15 mV peak-
to-peak. Our sensor is demonstrated to be an effective noncontact and noninvasive scheme for
wrist pulse detection.
IEEE Transactions on Microwave Theory and Techniques (May 2016)
Measuring Blood Glucose Concentrations in Photometric Glucometers Requiring Very
Small Sample Volumes
Glucometers present an important self-monitoring tool for diabetes patients and therefore must
exhibit high accuracy as well as good usability features. Based on an invasive, photometric
measurement principle that drastically reduces the volume of the blood sample needed from the
patient, we present a framework that is capable of dealing with small blood samples, while
maintaining the required accuracy. The framework consists of two major parts: 1) image
segmentation; and 2) convergence detection. Step 1) is based on iterative mode-seeking methods
to estimate the intensity value of the region of interest. We present several variations of these
methods and give theoretical proofs of their convergence. Our approach is able to deal with
changes in the number and position of clusters without any prior knowledge. Furthermore, we
propose a method based on sparse approximation to decrease the computational load, while
maintaining accuracy. Step 2) is achieved by employing temporal tracking and prediction,
herewith decreasing the measurement time, and, thus, improving usability. Our framework is
tested on several real data sets with different characteristics. We show that we are able to
estimate the underlying glucose concentration from much smaller blood samples than is currently
state-of-theart with sufficient accuracy according to the most recent ISO standards and reduce
measurement time significantly compared to state-of-the-art methods.
A tensor decomposition based approach for detecting dynamic network states from EEG
Functional connectivity (FC), defined as the statistical dependency between distinct brain
regions, has been an important tool in understanding cognitive brain processes. Most of the
current work in FC has focused on the assumption of temporally stationary networks. However,
recent empirical work indicates that FC is dynamic due to cognitive functions. Goal: The

purpose of this paper is to understand the dynamics of FC for understanding the formation and
dissolution of networks of the brain. Method: In this paper, we introduce a two-step approach to
characterize the dynamics of functional connectivity networks (FCNs) by first identifying change
points at which the network connectivity across subjects shows significant changes and then
summarizing the FCNs between consecutive change points. The proposed approach is based on a
tensor representation of FCNs across time and subjects yielding a 4-mode tensor. The change
points are identified using a subspace distance measure on low rank approximations to the tensor
at each time point. The network summarization is then obtained through tensormatrix projections
across the subject and time modes. Results: The proposed framework is applied to
electroencephalogram (EEG) data collected during a cognitive control task. The detected change-
points are consistent with a priori known ERN interval. The results show significant
connectivities in medial-frontal regions which is consistent with widely observed ERN amplitude
measures. Conclusion: The tensor-based method outperforms conventional matrix-based
methods such as SVD in terms of both change-point detection and state summarization.
Significance: The proposed tensor-based method captures the topological structure of FCNs
which provides more accurate change-point-detection and state summarization.
IEEE Transactions on Biomedical Engineering (April 2016)
Adaptive Event-Triggered Control Based on Heuristic Dynamic Programming for
Nonlinear Discrete-Time Systems
This paper presents the design of a novel adaptive event-triggered control method based on the
heuristic dynamic programming (HDP) technique for nonlinear discrete-time systems with
unknown system dynamics. In the proposed method, the control law is only updated when the
event-triggered condition is violated. Compared with the periodic updates in the traditional
adaptive dynamic programming (ADP) control, the proposed method can reduce the computation
and transmission cost. An actor-critic framework is used to learn the optimal event-triggered
control law and the value function. Furthermore, a model network is designed to estimate the
system state vector. The main contribution of this paper is to design a new trigger threshold for
discrete-time systems. A detailed Lyapunov stability analysis shows that our proposed event-

triggered controller can asymptotically stabilize the discrete-time systems. Finally, we test our
method on two different discrete-time systems, and the simulation results are included.
IEEE Transactions on Neural Networks and Learning Systems (April 2016)
A Hybrid Approach for Segmentation and Tracking of Myxococcus xanthus Swarms
Cell segmentation and motion tracking in timelapse images are fundamental problems in
computer vision, and are also crucial for various biomedical studies. Myxococcus xanthus is a
type of rod-like cells with highly coordinated motion. The segmentation and tracking of M.
xanthus are challenging, because cells may touch tightly and form dense swarms that are difficult
to identify individually in an accurate manner. The known cell tracking approaches mainly fall
into two frameworks, detection association and model evolution, each having its own advantages
and disadvantages. In this paper, we propose a new hybrid framework combining these two
frameworks into one and leveraging their complementary advantages. Also, we propose an active
contour model based on the Ribbon Snake, which is seamlessly integrated with our hybrid
framework. Evaluated by 10 different datasets, our approach achieves considerable improvement
over the state-of-the-art cell tracking algorithms on identifying complete cell trajectories, and
higher segmentation accuracy than performing segmentation in individual 2D images.
IEEE Transactions on Medical Imaging (March 2016)
Accelerated High-Dimensional MR Imaging With Sparse Sampling Using Low-Rank
Tensors
High-dimensional MR imaging often requires long data acquisition time, thereby limiting its
practical applications. This paper presents a low-rank tensor based method for accelerated high-
dimensional MR imaging using sparse sampling. This method represents high-dimensional
images as low-rank tensors (or partially separable functions) and uses this mathematical structure
for sparse sampling of the data space and for image reconstruction from highly undersampled
data. More specifically, the proposed method acquires two datasets with complementary
sampling patterns, one for subspace estimation and the other for image reconstruction; image
reconstruction from highly undersampled data is accomplished by fitting the measured data with
a sparsity constraint on the core tensor and a group sparsity constraint on the spatial coecients

jointly using the alternating direction method of multipliers. The usefulness of the proposed
method is demonstrated in MRI applications; it may also have applications beyond MRI.
IEEE Transactions on Medical Imaging (April 2016)
Apnea-Hypopnea Index Prediction using Electrocardiogram Acquired during Sleep-Onset
Period
Objective: The most widely used methods for predicting obstructive sleep apnea are based on
clinical or anatomico-functional features. To improve exactitude in obstructive sleep apnea
screening, this study aimed to devise a new predictor of apnea-hypopnea index. Methods: We
hypothesized that less irregular respiration cycles would be observed in the patients with more
severe obstructive sleep apnea during sleep-onset period. From each of the 156 and 70 single-
lead electrocardiograms collected from the internal polysomnographic database and from the
Physionet Apnea-ECG database, respectively, the 150-sec sleep-onset period was determined
and the respiration cycles during this period were detected. Using the coefficient of variation of
the respiration cycles, obtained from the internal dataset, as a predictor, the apnea-hypopnea
index predictive model was developed through regression analyses and k-fold cross-validations.
The apnea-hypopnea index predictability of the regression model was tested with the Physionet
Apnea-ECG database. Results: The regression model trained and validated from the 143 and 13
data, respectively, produced an absolute error (mean ± SD) of 3.65 ± 2.98 events/h and a
Pearson‘s correlation coefficient of 0.97 (P < 0.01) between the apnea-hypopnea index predictive
values and the reference values for the 70 test data. Conclusion: The new predictor of apnea-
hypopnea index has the potential to be utilized in making more reasoned clinical decisions on the
need for formal diagnosis and treatment of obstructive sleep apnea. Significance: Our study is
the first study that presented the strategy for providing a reliable apnea-hypopnea index without
overnight recording.
Contrast-enhanced Ultrasound Angiogenesis Imaging by Mutual Information Analysis for
Prostate Cancer Localization
Objective: The role of angiogenesis in cancer growth has stimulated research aimed at non-
invasive cancer detection by blood perfusion imaging. Recently, contrast ultrasound dispersion

imaging was proposed as an alternative method for angiogenesis imaging. After the intravenous
injection of an ultrasoundcontrast- agent bolus, dispersion can be indirectly estimated from the
local similarity between neighboring time-intensity curves (TICs) measured by ultrasound
imaging. Up until now, only linear similarity measures have been investigated. Motivated by the
promising results of this approach in prostate cancer (PCa), we developed a novel dispersion
estimation method based on mutual information, thus including nonlinear similarity, to further
improve its ability to localize PCa. Methods: First, a simulation study was performed to establish
the theoretical link between dispersion and mutual information. Next, the method‘s ability to
localize PCa was validated in vivo in 23 patients (58 datasets) referred for radical prostatectomy
by comparison with histology. Results: A monotonic relationship between dispersion and mutual
information was demonstrated. The in-vivo study resulted in a receiver operating characteristic
(ROC) curve area equal to 0.77, which was superior (p=0.21-0.24) to that obtained by linear
similarity measures (0.74-0.75) and (p<0.05) to that by conventional perfusion parameters (0.70).
Conclusion: Mutual information between neighboring TICs can be used to indirectly estimate
contrast dispersion and can lead to more accurate PCa localization. Significance: An improved
PCa localization method can possibly lead to better grading and staging of tumors, and support
focal-treatment guidance. Moreover, future employment of the method in other types of
angiogenic cancer can be considered.
IEEE Transactions on Biomedical Engineering (May 2016)
A Pseudobipolar Junction Transistor for a Sensitive Optical Detection of Biomolecules
A new optical sensor system, called the pseudo-bipolar junction transistor (BJT) optical
measurement system (PBOS), based on a pseudo-BVceo of the BJT is proposed by adding a
back-to-back connection of a laser diode (LD) (or an LED) and a p-i-n photodiode (PD) in the
conventional optical measurement system operated in the photoconductive mode. A back-to-
back connection of two optoelectronic devices and illumination of the light from the LD to the
PD generates an optical current gain in the PD. It is similar to the current flowing mechanism in
the BJT under the base open condition, in which the forward emitter-base junction current
generates an electrical current gain in the base-collector junction. Similar to the negative
differential resistance (NDR) after BVceo of the BJT, the NDR is observed in the PBOS.

Operating the PBOS in the NDR region, the system can provide much higher sensitivity and
lower limit of detection compared with the conventional optical measurement system in the
photoconductive mode with a p-i-n PD. We show a mathematical model of the sensitivity of the
PBOS to the transmittance of the optical path and our initial data for glucose detection as a
potential application of the system.
IEEE Transactions on Electron Devices (May 2016)
An Automatic User-adapted Physical Activity Classification Method Using Smartphones
In recent years, an increasing number of people have become concerned about their health. Most
chronic diseases are related to lifestyle, and daily activity records can be used as an important
indicator of health. Specifically, using advanced technology to automatically monitor actual
activities can effectively prevent and manage chronic diseases. The data used in this paper were
obtained from acceleration sensors and gyroscopes integrated in smartphones. We designed an
efficient Adaboost-Stump running on a smartphone to classify five common activities cycling,
running, sitting, standing and walking and achieved a satisfactory classification accuracy of 98%.
We designed an online learning method, and the classification model requires continuous
training with actual data. The parameters in the model then become increasingly fitted to the
specific user, which allows the classification accuracy to reach 95% under different use
environments. In addition, study paper also utilized the OpenCL framework to design the
program in parallel. This process can enhance the computing efficiency approximately 9-fold.
IEEE Transactions on Biomedical Engineering (May 2016)
Design and Validation of a Novel MR-Compatible Sensor for Respiratory Motion
Modelling and Correction
Goal: A novel magnetic resonance (MR) compatible accelerometer for respiratory motion
sensing (MARMOT) is developed as a surrogate of the vendors‘ pneumatic belts. We aim to
model and correct respiratory motion for free-breathing thoracic-abdominal MR imaging and to
simplify patient installation. Methods: MR-compatibility of MARMOT sensors was assessed in
phantoms and its motion modelling/correction efficacy was demonstrated on 21 subjects at 3T.
Respiration was modelled and predicted from MARMOT sensors and pneumatic belts, based on
real-time images and a regression method. The sensor accuracy was validated by comparing

motion errors in the liver/kidney. Sensor data were also exploited as inputs for motion-
compensated reconstruction of free-breathing cardiac cine MR images. Multiple and single
sensor placement strategies were compared. Results: The new sensor is compatible with the MR
environment. The average motion modelling and prediction errors with MARMOT sensors and
with pneumatic belts were comparable (liver and kidney) and were below 2 mm with all tested
configurations (belts, multiple/single MARMOT sensor). Motion corrected cardiac cine images
were of improved image quality, as assessed by an entropy metric (p<10-6), with all tested
configurations. Expert readings revealed multiple MARMOT sensors were the best (p<0.03) and
the single MARMOT sensor was similar to the belts (non-significant in 2 of the 3 readers).
Conclusion: The proposed sensor can model and predict respiratory motion with sufficient
accuracy to allow free-breathing MR imaging strategy. Significance: It provides an alternative
sensor solution for the respiratory motion problem during MR imaging and may improve the
convenience of patient set-up.
Comparison of the Impedance-Source Networks for Two and Multilevel Buck-Boost
Inverter Applications
Impedance-source networks are an increasingly popular solution in power converter applications,
especially in single stage buck-boost power conversion to avoid additional front end dc-dc power
converters. In the survey papers published, no analytical comparisons of different topologies
have been described, which makes it difficult to choose the best option. Thus, the aim of this
paper is to present a comprehensive analytical comparison of the impedance-source based buck-
boost inverters in terms of passive component count and semiconductor stress. Based on the
waveform of the input current, i.e. with or without a transformer, and with or without inductor
coupling, the impedancesource converters are classified. The main criterion in our
comprehensive comparison is the energy stored in the passive elements, which is considered both
under constant and predefined high frequency current ripple in the inductors and the voltage
ripple across the capacitors. Two-level and multilevel solutions are described. The conclusions
provide a ―one-stop‖ information source and a selection guide of impedancesource based buck-
boost inverters for different applications.

IEEE Transactions on Power Electronics (May 2016)
Robust Facial Expression Recognition for MuCI: A Comprehensive Neuromuscular Signal
Analysis
This paper presents a comprehensive study on the analysis of neuromuscular signal activities to
recognize eleven facial expressions for Muscle Computer Interfacing applications. A robust
denoising protocol comprised of Wavelet transform and Kalman filtering is proposed to enhance
the electromyogram (EMG) signal-to-noise ratio and improve classification performance. The
effectiveness of eight different time-domain facial EMG features on system performance is
examined and compared in order to identify the most discriminative one. Fourteen pattern
recognition-based algorithms are employed to classify the extracted features. These classifiers
are evaluated in terms of classification accuracy and processing time. Finally, the best methods
that obtain almost identical system performance are compared through the Normalized Mutual
Information (NMI) criterion and a repeated measure analysis of variance (ANOVA) for a
statistical significant test.To clarify the impact of signal denoising, all considered EMG features
and classifiers are assessed with and without this stage. Results show that: (1) the proposed
denosing step significantly improves the system performance; (2) Root Mean Square is the most
discriminative facial EMG feature; (3) discriminant analysis when the parameters are estimated
by the Maximum Likelihood algorithm achieves the highest classification accuracy and NMI;
however, ANOVA reveals no significant difference among the best methods with almost similar
performance.
Published in:
IEEE Transactions on Affective Computing (May 2016)
On the Use of Knitted Antennas and Inductively Coupled RFID Tags for Wearable
Applications
Recent advancements in conductive yarns and fabrication technologies offer exciting
opportunities to design and knit seamless garments equipped with sensors for biomedical
applications. In this paper, we discuss the design and application of a wearable strain sensor,
which can be used for biomedical monitoring such as contraction, respiration, or limb
movements. The system takes advantage of the intensity variations of the backscattered power

(RSSI) from an inductively-coupled RFID tag under physical stretching. First, we describe the
antenna design along with the modeling of the sheet impedance, which characterizes the
conductive textile. Experimental results with custom fabricated prototypes showed good
agreement with the numerical simulation of input impedance and radiation pattern. Finally, the
wearable sensor has been applied for infant breathing monitoring using a medical programmable
mannequin. A machine learning technique has been developed and applied to post-process the
RSSI data, and the results show that breathing and non-breathing patterns can be successfully
classified.
Published in:
IEEE Transactions on Biomedical Circuits and Systems (April 2016)
Real-time Tele-monitoring of Patients with Chronic Heart-Failure Using a Smartphone:
Lessons Learned
We present a smartphone-based system for remote real-time tele-monitoring of physical activity
in patients with chronic heart-failure (CHF). We recently completed a pilot study with 15
subjects to evaluate the feasibility of the proposed monitoring in the real world and examine its
requirements, privacy implications, usability, and other challenges encountered by the
participants and healthcare providers. Our tele-monitoring system was designed to assess patient
activity via minute-byminute energy expenditure (EE) estimated from accelerometry. In addition,
we tracked relative user location via global positioning system (GPS) to track outdoors activity
and measure walking distance. The system also administered daily-surveys to inquire about vital
signs and general cardiovascular symptoms. The collected data were securely transmitted to a
central server where they were analyzed in real time and were accessible to the study medical
staff to monitor patient health status and provide medical intervention if needed. Although the
system was designed for tele-monitoring individuals with CHF, the challenges, privacy
considerations, and lessons learned from this pilot study apply to other chronic health conditions,
such as diabetes and hypertension, that would benefit from continuous monitoring through
mobile-health (mHealth) technologies.
IEEE Transactions on Affective Computing (April 2016)
Data-Centered Runtime Verification of Wireless Medical Cyber-Physical System

Wireless medical cyber-physical systems are widely adopted in the daily practices of medicine,
where huge amount of data are sampled by the wireless medical devices and sensors and passed
to the decision support systems (DSS). Many text-based guidelines have been encoded for work-
flow simulation of DSS to automate health care based on those collected data. But for some
complex and life-critical diseases, it is highly desirable to automatically rigorously verify some
complex temporal properties encoded in those data, which brings new challenges to current
simulation based DSS with limited support of automatical formal verification and realtime data
analysis. In this paper, we conduct the first study on applying runtime verification to cooperate
with current DSS based on real-time data. Within the proposed technique, a user-friendly domain
specific language, named DRTV, is designed to specify vital real-time data sampled by medical
devices and temporal properties originated from clinical guidelines. Some interfaces are
developed for data acquisition and communication. Then, for medical practice scenarios
described in DRTV model, we will automatically generate event sequences and runtime property
verifier automata. If a temporal property violates, real-time warnings will be produced by the
formal verifier and passed to medical DSS. We have used DRTV to specify different kinds of
medical care scenarios, and applied the proposed technique to assist existing wireless medical
cyber-physical system. As presented in experiment results, in terms of warning detection, it
outperforms the only use of DSS or human inspection, and improves the quality of clinical health
care of hospital.
IEEE Transactions on Industrial Informatics (May 2016)
Simultaneous Hand–Eye, Tool–Flange, and Robot–Robot Calibration for Comanipulation
by Solving the Problem
Multirobot comanipulation shows great potential in surpassing the limitations of single-robot
manipulation in complicated tasks such as robotic surgeries. However, a dynamic multirobot
setup in unstructured environments poses great uncertainties in robot configurations. Therefore,
the coordination relationships between the end-effectors and other devices, such as cameras
(hand-eye calibration) and tools (tool-flange calibration), as well as the relationships among the
base frames (robot-robot calibration) have to be determined timely to enable accurate robotic
cooperation for the constantly changing configuration of the systems. We formulated the

problem of hand-eye, tool-flange, and robot-robot calibration to a matrix equation AXB=YCZ. A
series of generic geometric properties and lemmas were presented, leading to the derivation of
the final simultaneous algorithm. In addition to the accurate iterative solution, a closed-form
solution was also introduced based on quaternions to give an initial value. To show the feasibility
and superiority of the simultaneous method, two nonsimultaneous methods were compared
through thorough simulations under various robot movements and noise levels. Comprehensive
experiments on real robots were also performed to further validate the proposed methods. The
comparison results from both simulations and experiments demonstrated the superior accuracy
and efficiency of the proposed simultaneous calibration method.
IEEE Transactions on Robotics (April 2016)
A Muscle Fibre Conduction Velocity Tracking ASIC for Local Fatigue Monitoring
Electromyography analysis can provide information about a muscle‘s fatigue state by estimating
Muscle Fibre Conduction Velocity (MFCV), a measure of the travelling speed of Motor Unit
Action Potentials (MUAPs) in muscle tissue. MFCV better represents the physical
manifestations of muscle fatigue, compared to the progressive compression of the myoelectic
Power Spectral Density, hence it is more suitable for a muscle fatigue tracking system. This
paper presents a novel algorithm for the estimation of MFCV using single threshold bit-stream
conversion and a dedicated application-specified integrated circuit (ASIC) for its
implementation, suitable for a compact, wearable and easy to use muscle fatigue monitor. The
presented ASIC is implemented in a commercially available AMS 0.35 CMOS technology
and utilizes a bit-stream cross-correlator that estimates the conduction velocity of the myoelectric
signal in real time. A test group of 20 subjects was used to evaluate the performance of the
developed ASIC, achieving good accuracy with an error of only 3.2% compared to Matlab.
IEEE Transactions on Biomedical Circuits and Systems (May 2016)
Adaptive Kinematic Control of a Robotic Venipuncture Device Based on Stereo Vision,
Ultrasound, and Force Guidance
Robotic systems have slowly entered the realm of modern medicine; however, outside the
operating room, medical robotics has yet to be translated to more routine interventions such as
blood sampling or intravenous fluid delivery. In this paper, we present a medical robot that

safely and rapidly cannulates peripheral blood vessels—a procedure commonly known as
venipuncture. The device uses near-infrared and ultrasound imaging to scan and select suitable
injection sites, and a 9-DOF robot to insert the needle into the center of the vessel based on
image and force guidance. We first present the system design and visual servoing scheme of the
latest generation robot, and then evaluate the performance of the device through workspace
simulations and free-space positioning tests. Finally, we perform a series of motion tracking
experiments using stereo vision, ultrasound, and force sensing to guide the position and
orientation of the needle tip. Positioning experiments indicate sub-millimeter accuracy and
repeatability over the operating workspace of the system, while tracking studies demonstrate
real-time needle servoing in response to moving targets. Lastly, robotic phantom cannulations
demonstrate the use of multiple system states to confirm that the needle has reached the center of
the vessel.
IEEE Transactions on Industrial Electronics (April 2016)
Phase Aberration and Attenuation Effects on Acoustic Radiation Force-Based Shear Wave
Generation
Elasticity is measured by shear wave elasticity imaging (SWEI) methods using acoustic radiation
force to create the shear waves. Phase aberration and tissue attenuation can hamper the
generation of shear waves for in vivo applications. In this study, the effects of phase aberration
and attenuation in ultrasound focusing for creating shear waves were explored. This includes the
effects of phase shifts and amplitude attenuation on shear wave characteristics such as shear
wave amplitude, shear wave speed, shear wave center frequency, and bandwidth. Two samples
of swine belly tissue were used to create phase aberration and attenuation experimentally. To
explore the phase aberration and attenuation effects individually, tissue experiments were
complemented with ultrasound beam simulations using fast object-oriented C++ ultrasound
simulator (FOCUS) and shear wave simulations using finite-element-model (FEM) analysis. The
ultrasound frequency used to generate shear waves was varied from 3.0 to 4.5 MHz. Results: The
measured acoustic pressure and resulting shear wave amplitude decreased approximately 40%-
90% with the introduction of the tissue samples. Acoustic intensity and shear wave displacement
were correlated for both tissue samples, and the resulting Pearson's correlation coefficients were

0.99 and 0.97. Analysis of shear wave generation with tissue samples (phase aberration and
attenuation case), measured phase screen, (only phase aberration case), and FOCUS/FEM model
(only attenuation case) showed that tissue attenuation affected the shear wave generation more
than tissue aberration. Decreasing the ultrasound frequency helped maintain a focused beam for
creation of shear waves in the presence of both phase aberration and attenuation.
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (Feb. 2016)
A Novel Grading Biomarker for the Prediction of Conversion from Mild Cognitive
Impairment to Alzheimer's Disease
Objective: Identifying mild cognitive impairment (MCI) subjects who will progress to
Alzheimer‘s disease is not only crucial in clinical practice, but also has a significant potential to
enrich clinical trials. The purpose of this study is to develop an effective biomarker for an
accurate prediction of MCI-to-AD conversion from magnetic resonance (MR) images. Methods:
We propose a novel grading biomarker for the prediction of MCI-to-AD conversion. First, we
comprehensively study the effects of several important factors on the performance in the
prediction task including registration accuracy, age correction, feature selection and the selection
of training data. Based on the studies of these factors, a grading biomarker is then calculated for
each MCI subject using sparse representation techniques. Finally, the grading biomarker is
combined with age and cognitive measures to provide a more accurate prediction of MCI-to-AD
conversion. Results: Using the ADNI dataset, the proposed global grading biomarker achieved
an area under the receiver operating characteristic curve (AUC) in the range of 79%-81% for the
prediction of MCI-to-AD conversion within 3 years in 10-fold cross validations. The
classification AUC further increases to 84%-92% when age and cognitive measures are
combined with the proposed grading biomarker. Conclusion: The obtained accuracy of the
proposed biomarker benefits from the contributions of different factors: a tradeoff registration
level to align images to the template space; the removal of the normal aging effect; selection of
discriminative voxels; the calculation of the grading biomarker using AD and normal control
groups; the integration of sparse representation technique and the combination of cognitive
measures. Significance: The evaluation on the ADNI dataset shows the efficacy of the proposed

biomarker and demonstrates a significant contribution in accurate prediction of MCI-to-AD
conversion.
Biomimetic Hybrid Feedback Feedforward Neural-Network Learning Control
This brief presents a biomimetic hybrid feedback feedforward neural-network learning control
(NNLC) strategy inspired by the human motor learning control mechanism for a class of
uncertain nonlinear systems. The control structure includes a proportional-derivative controller
acting as a feedback servo machine and a radial-basis-function (RBF) NN acting as a
feedforward predictive machine. Under the sufficient constraints on control parameters, the
closed-loop system achieves semiglobal practical exponential stability, such that an accurate NN
approximation is guaranteed in a local region along recurrent reference trajectories. Compared
with the existing NNLC methods, the novelties of the proposed method include: 1) the
implementation of an adaptive NN control to guarantee plant states being recurrent is not needed,
since recurrent reference signals rather than plant states are utilized as NN inputs, which greatly
simplifies the analysis and synthesis of the NNLC and 2) the domain of NN approximation can
be determined a priori by the given reference signals, which leads to an easy construction of the
RBF-NNs. Simulation results have verified the effectiveness of this approach.
IEEE Transactions on Neural Networks and Learning Systems (March 2016)
Reproducibility in Computational Neuroscience Models and Simulations
Objective: Like all scientific research, computational neuroscience research must be
reproducible. Big data science, including simulation research, cannot depend exclusively on
journal articles as the method to provide the sharing and transparency required for
reproducibility. Methods: Ensuring model reproducibility requires the use of multiple standard
software practices and tools, including version control, strong commenting and documentation,
and code modularity. Results: Building on these standard practices, model sharing sites and tools
have been developed that fit into several categories: 1. standardized neural simulators, 2. shared
computational resources, 3. declarative model descriptors, ontologies and standardized
annotations; 4. model sharing repositories and sharing standards. Conclusion: A number of
complementary innovations have been proposed to enhance sharing, transparency and

reproducibility. The individual user can be encouraged to make use of version control,
commenting, documentation and modularity in development of models. The community can help
by requiring model sharing as a condition of publication and funding. Significance: Model
management will become increasingly important as multiscale models become larger, more
detailed and correspondingly more difficult to manage by any single investigator or single
laboratory. Additional big data management complexity will come as the models become more
useful in interpreting experiments, thus increasing the need to ensure clear alignment between
modeling data, both parameters and results, and experiment.
Accelerating Convolutional Sparse Coding for Curvilinear Structures Segmentation by
Refining SCIRD-TS Filter Banks
Deep learning has shown great potential for curvilinear structure (e.g. retinal blood vessels and
neurites) segmentation as demonstrated by a recent auto-context regression architecture based on
filter banks learned by convolutional sparse coding. However, learning such filter banks is very
time-consuming, thus limiting the amount of filters employed and the adaptation to other data
sets (i.e. slow re-training). We address this limitation by proposing a novel acceleration strategy
to speedup convolutional sparse coding filter learning for curvilinear structure segmentation. Our
approach is based on a novel initialisation strategy (warm start), and therefore it is different from
recent methods improving the optimisation itself. Our warmstart strategy is based on carefully
designed hand-crafted filters (SCIRD-TS), modelling appearance properties of curvilinear
structures which are then refined by convolutional sparse coding. Experiments on four diverse
data sets, including retinal blood vessels and neurites, suggest that the proposed method reduces
significantly the time taken to learn convolutional filter banks (i.e. up to �82%) compared to
conventional initialisation strategies. Remarkably, this speed-up does not worsen performance; in
fact, filters learned with the proposed strategy often achieve a much lower reconstruction error
and match or exceed the segmentation performance of random and DCT-based initialisation,
when used as input to a random forest classifier.
Published in:

4-D Flow Control in Porous Scaffolds: Toward a Next Generation of Bioreactors
Tissue engineering (TE) approaches that involve seeding cells into pre-determined tissue
scaffolds ignore the complex environment where the material properties are spatially
inhomogeneous and evolve over time. We present a new approach for controlling mechanical
forces inside bioreactors, which enables spatiotemporal control of flow fields in real time. Our
adaptive approach offers the flexibility of dialing-in arbitrary shear stress distributions and
adjusting flow field patterns in a scaffold over time in response to cell growth without needing to
alter scaffold structure. This is achieved with a multi-inlet bioreactor and a control algorithm
with learning capabilities to dynamically solve the inverse problem of computing the inlet
pressure distribution required over the multiple inlets to obtain a target flow field. The new
method constitutes a new platform for studies of cellular responses to mechanical forces in
complex environments and opens potentially transformative possibilities for TE.
A Proximity Coupling RF Sensor for Wrist Pulse Detection Based on Injection-Locked
PLL
In this paper, a proximity coupling RF sensor based on injection-locked phase-locked loop (PLL)
for wrist pulse detection is proposed. The sensor is composed of two main parts: a free-running
oscillator and a PLL synthesizer containing a voltage-controlled oscillator. The free-running
oscillator is built with a two-port microstrip line resonator (inter-digital electrodes), which acts as
part of a transducer that can transform the expansion or contraction of the radial artery into an
impedance variation. Measurements show that the impedance variation of the resonator due to
changes in the radial artery causes a frequency change of up to 0.74 MHz in the free-running
oscillator. For the PLL part, the frequency change can be transformed to a variation in dc voltage
by injection of the modulated signal from the wrist pulse into a phase-locked oscillator. The
variation of the loop-control voltage, in one cycle of the pulse, is approximately 10-15 mV peak-
to-peak. Our sensor is demonstrated to be an effective noncontact and noninvasive scheme for
wrist pulse detection.
IEEE Transactions on Microwave Theory and Techniques (May 2016)

Accelerating Convolutional Sparse Coding for Curvilinear Structures Segmentation by
Refining SCIRD-TS Filter Banks
Deep learning has shown great potential for curvilinear structure (e.g. retinal blood vessels and
neurites) segmentation as demonstrated by a recent auto-context regression architecture based on
filter banks learned by convolutional sparse coding. However, learning such filter banks is very
time-consuming, thus limiting the amount of filters employed and the adaptation to other data
sets (i.e. slow re-training). We address this limitation by proposing a novel acceleration strategy
to speedup convolutional sparse coding filter learning for curvilinear structure segmentation. Our
approach is based on a novel initialisation strategy (warm start), and therefore it is different from
recent methods improving the optimisation itself. Our warmstart strategy is based on carefully
designed hand-crafted filters (SCIRD-TS), modelling appearance properties of curvilinear
structures which are then refined by convolutional sparse coding. Experiments on four diverse
data sets, including retinal blood vessels and neurites, suggest that the proposed method reduces
significantly the time taken to learn convolutional filter banks (i.e. up to �82%) compared to
conventional initialisation strategies. Remarkably, this speed-up does not worsen performance; in
fact, filters learned with the proposed strategy often achieve a much lower reconstruction error
and match or exceed the segmentation performance of random and DCT-based initialisation,
when used as input to a random forest classifier.
Toward Optimal Feature Selection in Naive Bayes for Text Categorization
Automated feature selection is important for text categorization to reduce the feature size and to
speed up the learning process of classifiers. In this paper, we present a novel and efficient feature
selection framework based on the Information Theory, which aims to rank the features with their
discriminative capacity for classification. We first revisit two information measures: Kullback-
Leibler divergence and Jeffreys divergence for binary hypothesis testing, and analyze their
asymptotic properties relating to type I and type II errors of a Bayesian classifier. We then
introduce a new divergence measure, called Jeffreys-Multi-Hypothesis (JMH) divergence, to
measure multi-distribution divergence for multi-class classification. Based on the JMH-
divergence, we develop two efficient feature selection methods, termed maximum discrimination

(MD) and MD �2 methods, for text categorization. The promising results of extensive
experiments demonstrate the effectiveness of the proposed approaches.
IEEE Transactions on Knowledge and Data Engineering (May 2016)
CEREBRE: A Novel Method for Very High Accuracy Event-Related Potential Biometric
Identification
The vast majority of existing work on brain biometrics has been conducted on the ongoing
electroencephalogram. Here, we argue that the averaged event-related potential (ERP) may
provide the potential for more accurate biometric identification, as its elicitation allows for some
control over the cognitive state of the user to be obtained through the design of the challenge
protocol. We describe the Cognitive Event-RElated Biometric REcognition (CEREBRE)
protocol, an ERP biometric protocol designed to elicit individually unique responses from
multiple functional brain systems (e.g., the primary visual, facial recognition, and
gustatory/appetitive systems). Results indicate that there are multiple configurations of data
collected with the CEREBRE protocol that all allow 100% identification accuracy in a pool of 50
users. We take this result as the evidence that ERP biometrics are a feasible method of user
identification and worthy of further research.
Published in:
IEEE Transactions on Information Forensics and Security (July 2016)
Real-time ‘eye-writing’ recognition using electrooculogram (EOG)
Eye movements can be used as alternative inputs for human-computer interface (HCI) systems
such as virtual or augmented reality systems as well as new communication ways for patients
with locked-in syndrome. In this study, we developed a real-time electrooculogram (EOG)-based
eye-writing recognition system, with which users can write predefined symbolic patterns with
their volitional eye movements. For the ‗eye-writing‘ recognition, the proposed system first
reconstructs the eye-written traces from EOG waveforms in real-time; then, the system
recognizes the intended symbolic inputs with a reliable recognition rate by matching the input
traces with the trained eye-written traces of diverse input patterns. Experiments with 20
participants showed an average recognition rate of 87.38 % (F1 score) for 29 different symbolic
patterns (26 lower case alphabet characters and three functional input patterns representing

Space, Backspace, and Enter keys), demonstrating the promise of our EOG-based eye-writing
recognition system in practical scenarios.
IEEE Transactions on Neural Systems and Rehabilitation Engineering (March 2016)
Design and Evaluation of a Soft and Wearable Robotic Glove for Hand Rehabilitation
In the modern world due to an increased aging population hand disability is becoming
increasingly common. The prevalence of conditions such as stroke is placing an ever growing
burden on the limited fiscal resources of health care providers and the capacity of their physical
therapy staff. As a solution, this paper presents a novel design for a wearable and adaptive glove
for patients so that they can practice rehabilitative activities at home, reducing the workload for
therapists and increasing the patient‘s independence. As an initial evaluation of the design‘s
feasibility the prototype was subjected to motion analysis to compare its performance with the
hand in an assessment of grasping patterns of a selection of blocks and spheres. The outcomes of
this paper suggest that the theory of design has validity and may lead to a system that could be
successful in the treatment of stroke patients to guide them through finger flexion and extension,
which could enable them to gain more control and confidence in interacting with the world
around them.
IEEE Transactions on Neural Systems and Rehabilitation Engineering (January 2016)
All Spin Artificial Neural Networks Based on Compound Spintronic Synapse and Neuron
Artificial synaptic devices implemented by emerging post-CMOS non-volatile memory
technologies such as Resistive RAM (RRAM) have made great progress recently. However, it is
still a big challenge to fabricate stable and controllable multilevel RRAM. Benefitting from the
control of electron spin instead of electron charge, spintronic devices, e.g., magnetic tunnel
junction (MTJ) as a binary device, have been explored for neuromorphic computing with low
power dissipation. In this paper, a compound spintronic device consisting of multiple vertically
stacked MTJs is proposed to jointly behave as a synaptic device, termed as compound spintronic
synapse (CSS). Based on our theoretical and experimental work, it has been demonstrated that
the proposed compound spintronic device can achieve designable and stable multiple resistance
states by interfacial and materials engineering of its components. Additionally, a compound
spintronic neuron (CSN) circuit based on the proposed compound spintronic device is presented,

enabling a multi-step transfer function. Then, an All Spin Artificial Neural Network (ASANN) is
constructed with the CSS and CSN circuit. By conducting system-level simulations on the
MNIST database for handwritten digital recognition, the performance of such ASANN has been
investigated. Moreover, the impact of the resolution of both the CSS and CSN and device
variation on the system performance are discussed in this work.
Respiratory Artefact Removal in Forced Oscillation Measurements: A Machine Learning
Approach
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Goal: Respiratory artefact removal for the forced oscillation technique can be treated as an
anomaly detection problem. Manual removal is currently considered the gold standard but this
approach is laborious and subjective. Most existing automated techniques used simple statistics
and/or rejected anomalous data points. Unfortunately, simple statistics are insensitive to
numerous artefacts, leading to low reproducibility of results. Furthermore, rejecting anomalous
data points causes an imbalance between the inspiratory and expiratory contributions. Methods:
From a machine learning perspective, such methods are unsupervised and can be considered
simple feature extraction. We hypothesize that supervised techniques can be used to find
improved features that are more discriminative and more highly correlated with the desired
output. Features thus found are then used for anomaly detection by applying quartile
thresholding which rejects complete breaths if one of its features is out of range. The thresholds
are determined by both saliency and performance metrics rather than qualitative assumptions as
in previous works. Results: Feature ranking indicates that our new landmark features are among
the highest scoring candidates regardless of age across saliency criteria. F1-scores, receiver
operating characteristic, and variability of the mean resistance metrics show that the proposed
scheme outperforms previous simple feature extraction approaches. Our subject-independent
detector, 1IQR-SU, demonstrated approval rates of 80:6% for adults and 98% for children,

higher than existing methods. Conclusion: Our new features are more relevant. Our removal is
objective and comparable to the manual method. Significance: This is a critical work to automate
FOT quality control.
On Quantitative Biomarkers of VNS Therapy using EEG and ECG Signals
Objective: The goal of this work is to objectively evaluate the effectiveness of neuromodulation
therapies, specifically, Vagus Nerve Stimulation (VNS) in reducing the severity of seizures in
patients with medically refractory epilepsy. Methods: Using novel quantitative features obtained
from combination of electroencephalographic (EEG) and electrocardiographic (ECG) signals
around seizure events in 16 patients who underwent implantation of closed-loop VNS Therapy
System, namely AspireSR®, we evaluated if automated delivery of VNS at the time of seizure
onset reduces the severity of seizures by reducing EEG spatial synchronization as well as the
duration and magnitude of heart rate increase. Unsupervised classification was subsequently
applied to test the discriminative ability and validity of these features to measure responsiveness
to VNS therapy. Results: Results of application of this methodology to compare 105 pre-VNS
treatment and 107 post-VNS treatment seizures revealed that seizures that were acutely
stimulated using VNS had a reduced ictal spread as well as reduced impact on cardiovascular
function compared to the ones that occurred prior to any treatment. Furthermore, application of
an unsupervised Fuzzy-C-Mean (FCM) classifier to evaluate the ability of the combined EEG-
ECG based features to classify pre- and post-treatment seizures achieved a classification
accuracy of 85.85%. Conclusion: These results indicate the importance of timely delivery of
VNS to reduce seizure severity and thus help achieve better seizure control for patients with
epilepsy. Significance: The proposed set of quantitative features could be used as potential
biomarkers for predicting long-term response to VNS therapy.
The Semi-Variogram and Spectral Distortion Measures for Image Texture Retrieval
Semi-variogram estimators and distortion measures of signal spectra are utilized in this paper for
image texture retrieval. On the use of the complete Brodatz database, most high retrieval rates
are reportedly based on multiple features and the combinations of multiple algorithms, while the

classification using single features is still a challenge to the retrieval of diverse texture images.
The semi-variogram, which is theoretically sound and the cornerstone of spatial statistics, has the
characteristics shared between true randomness and complete determinism and, therefore, can be
used as a useful tool for both the structural and statistical analysis of texture images. Meanwhile,
spectral distortion measures derived from the theory of linear predictive coding provide a
rigorously mathematical model for signal-based similarity matching and have been proven useful
for many practical pattern classification systems. Experimental results obtained from testing the
proposed approach using the complete Brodatz database, and the the University of Illinois at
Urbana-Champaign texture database suggests the effectiveness of the proposed approach as a
single-feature-based dissimilarity measure for real-time texture retrieval.
IEEE Transactions on Image Processing (April 2016)
Skull optical clearing solution for enhancing ultrasonic and photoacoustic imaging
The performance of photoacoustic microscopy (PAM) degrades due to the turbidity of the skull
that introduces attenuation and distortion of both laser and stimulated ultrasound. In this
manuscript, we demonstrated that a newly developed skull optical clearing solution (SOCS)
could enhance not only the transmittance of light, but also that of ultrasound in the skull in vitro.
Thus the photoacoustic signal was effectively elevated, and the relative strength of the artifacts
induced by the skull could be suppressed. Furthermore in vivo studies demonstrated that SOCS
could drastically enhance the performance of photoacoustic microscopy for cerebral
microvasculature imaging.
IEEE Transactions on Medical Imaging (February 2016)
An Insect Eye Inspired Miniaturized Multi-Camera System for Endoscopic Imaging
In this work, we present a miniaturized high definition vision system inspired by insect eyes,
with a distributed illumination method, which can work in dark environments for proximity
imaging applications such as endoscopy. Our approach is based on modeling biological systems
with off-the-shelf miniaturized cameras combined with digital circuit design for real time image
processing. We built a 5 mm radius hemispherical compound eye, imaging a $180^{circ}times
180^{circ}$ degrees field of view while providing more than 1.1 megapixels (emulated
ommatidias) as real-time video with an inter-ommatidial angle $Deltaphi=0.5^{circ}$ at 18 mm

radial distance. We made an FPGA implementation of the image processing system which is
capable of generating 25 fps video with 1080 $times$ 1080 pixel resolution at a 120 MHz
processing clock frequency. When compared to similar size insect eye mimicking systems in
literature, the system proposed in this paper features 1000$times$ resolution increase. To the
best of our knowledge, this is the first time that a compound eye with built-in illumination idea is
reported. We are offering our miniaturized imaging system for endoscopic applications like
colonoscopy or laparoscopic surgery where there is a need for large field of view high definition
imagery. For that purpose we tested our system inside a human colon model. We also present the
resulting images and videos from the human colon model in this paper.
Proposal for an All-Spin Artificial Neural Network: Emulating Neural and Synaptic
Functionalities Through Domain Wall Motion in Ferromagnets
Non-Boolean computing based on emerging post-CMOS technologies can potentially pave the
way for low-power neural computing platforms. However, existing work on such emerging
neuromorphic architectures have either focused on solely mimicking the neuron, or the synapse
functionality. While memristive devices have been proposed to emulate biological synapses,
spintronic devices have proved to be efficient at performing the thresholding operation of the
neuron at ultra-low currents. In this work, we propose an All-Spin Artificial Neural Network
where a single spintronic device acts as the basic building block of the system. The device offers
a direct mapping to synapse and neuron functionalities in the brain while inter-layer network
communication is accomplished via CMOS transistors. To the best of our knowledge, this is the
first demonstration of a neural architecture where a single nanoelectronic device is able to mimic
both neurons and synapses. The ultra-low voltage operation of low resistance magneto-metallic
neurons enables the low-voltage operation of the array of spintronic synapses, thereby leading to
ultra-low power neural architectures. Device-level simulations, calibrated to experimental
results, was used to drive the circuit and system level simulations of the neural network for a
standard pattern recognition problem. Simulation studies indicate energy savings by in
comparison to a corresponding digital/analog CMOS neuron implementation.

Nanoscale Mo- Entrapped in Engineering Thermoplastic: Inorganic Pathway to
Bactericidal and Fungicidal Action
In our contemporary endeavor, metallic molybdenum (Mo) and semiconducting molybdenum
trioxide (MoO3) nanostructures have been simultaneously generated via solid state reaction
between molybdenum (III) chloride (MoCl3) and polyphenylene sulfide (PPS) at 285°C in
unimolar ratio for different time durations, namely, 6h, 24h and 48h. The resultant
nanocomposites (NCs) revealed formation of predominantly metallic Mo for all the samples.
However, MoO3 gradually gained prominent position as secondary phase with rise in reaction
time. The present study was intended to investigate the antibacterial potential of metal-metal
oxide-polymer NCs i.e. Mo-MoO3–PPS against microorganisms viz., Pseudomonas aeruginosa,
Staphylococcus aureus, Klebsiella pneumoniae, and Aspergillus fumigatus. The antibacterial
activity of the NCs was evaluated by agar well diffusion investigation. Maximum sensitivity
concentrations of NCs were determined by finding out minimum inhibitory concentration (MIC)
and minimum bactericidal/fungicidal concentration (MBC/MFC). Moreover, the NCs prepared at
reaction time of 48h exhibited best MBC values and were tested with time kill assay which
revealed that the growth of S. aureus was substantially inhibited by Mo-MoO3- PPS NCs. This
synchronized formation of Mo-MoO3 nanostructures in an engineering thermoplastic may have
potential antimicrobial applications in biomedical devices and components. Prima-facie results
on antifungal activity are indicative of the fact that these materials can show anti-cancer
behavior.
IEEE Transactions on NanoBioscience (May 2016)
Time Delay Mapping of High-Resolution Gastric Slow Wave Activity
Goal: Analytic monitoring of electrophysiological data has become an essential component of
efficient and accurate clinical care. In the gastrointestinal (GI) field, recent advances in high-
resolution (HR) mapping are now providing critical information about spatiotemporal profiles of
slow wave activity in normal and disease (dysrhythmic) states. The current approach to
analyzing GI HR electrophysiology data involves the identification of individual slow wave
events in the electrode array, followed by tracking and clustering of events to create a
spatiotemporal map. This method is labor and computationally intensive and is not well suited

for real-time clinical use or chronic monitoring. Methods: In this study, an automated novel
technique to assess propagation patterns was developed. The method utilized time-delays of the
slow wave signals which was computed through cross correlations, to calculate velocity.
Validation was performed with both synthetic and human and porcine experimental data.
Results: The slow wave profiles computed via the time delay method compared closely with
those computed using the traditional method (speed difference 7.2±2.6%; amplitude difference
8.6±3.5%, and negligible angle difference). Conclusion: This novel method provides rapid and
intuitive analysis and visualization of slow wave activity. Significance: This techniques will find
major applications in the clinical translation of acute and chronic HR electrical mapping for
motility disorders, and act as a screening tool for detailed detection and tracking of individual
propagating wavefronts, without the need for comprehensive standard event-detection analysis.
Development of Novel 3D Printed Scaffolds with Core-shell Nanoparticles for Nerve
Regeneration
A traumatic injury of peripheral nerves is serious clinical problem that may lead to major loss of
nerve function, affecting quality of patient‘s life. Currently nerve autograft is widely used to
reconstruct the nerve gap. However, such surgical procedure suffers from many disadvantages
including donor site morbidity and limited availability. In order to address these issues, neural
tissue engineering has focused on the development of synthetic nerve scaffolds to support
bridging a larger gap and improving nerve generation. For this purpose, we fabricated a novel 3D
biomimetic scaffold, which has tunable porous structure and embedded core-shell nanoparticles
with sustained neurogenic factor delivery system, using stereolithography based 3D printing and
co-axial electrospraying techniques. Our results showed that scaffolds with larger porosity
significantly improve PC-12 neural cell adhesion compared to ones with smaller porosity.
Furthermore, scaffolds embedded with bovine serum albumin containing nanoparticles showed
an enhancement in cell proliferation relative to bared control scaffolds. More importantly,
confocal microscopy images illustrated that the scaffold with nerve growth factor (NGF)
nanoparticles greatly increased the length of neurites and directed neurite extension of PC-12
cells along the fiber. In addition, the 3D printed nanocomposite scaffolds also improved the

average neurite length of primary cortical neurons. The results in this study demonstrate the
potential of this 3D printed scaffold in improving neural cell function and nerve growth.
Miniaturizing Floating Traps to Increase RF Safety of Magnetic Resonance Guided
Percutaneous Procedures
Objective: MRI in the area of cardiovascular catheter-based interventional procedures is an
active field. A common intervention – revascularization of chronic total occlusions, requires a
conductive guidewire for revascularization. The mechanical properties of guidewires are
paramount to the successful execution of such procedures. Furthermore to benefit from MRI
techniques, additional conductors are required to transmit signal from the tip of a catheter. Long
thin conductors in MRI systems pose a safety risk in the form of RF heating due to induced RF
currents on the conductors. Unfortunately many existing techniques to mitigate this risk require
physical modification of the conductors, inevitably resulting in detrimental mechanical trade-offs
in the guidewire. This manuscript proposes a novel application and miniaturization of an existing
device, the floating RF trap. The RF trap couples strongly to any thin conductor passing through
the trap lumen inducing significant series impedance. This results in reduction of induced RF
currents and thus heating. Methods & Results: This study shows theoretical and experimental
analysis of induced impedance as well as theoretical reduction in heating due to various
distributions of traps along the length of a catheter. Results of measuring induced current and
heating in phantom experiments are also presented. Through comparison with commercial
simulation packages and results of phantom experiments, it is shown that miniaturized RF traps
can be modelled accurately, including their induced series impedance and effect on induced RF
current. Conclusion & Significance: It was demonstrated that floating RF traps present a feasible
method to mitigate RF heating while maintaining important mechanical properties of guidewires.
Allocation of power quality monitors using the P-median to identify non-technical losses
This study sets out to develop a procedure that enables monitors to be allocated that are able to
estimate values of voltage and current of a circuit. Taking the importance of the load as a starting
point, this paper puts forward a model for allocating power quality monitors in distribution

systems based on the P-median model. As a first step, by using an improved model that already
exists, the least number of monitors which guarantee the observability of the system is obtained.
Then, the modified P-median model is applied which incorporates a constraint so as to allocate
monitors in accordance with the importance of the load, which is considered as the volume of the
load to tackle non-technical losses. A model such as this is required especially when dealing with
non-technical losses, which can be one of the main causes of undervoltage, thereby affecting the
quality of the service. The proposed model was applied in a real distribution network considering
the importance of the load throughout the distribution system.
IEEE Transactions on Power Delivery (April 2016)
The iterative reweighted Mixed-Norm Estimate for spatio-temporal MEG/EEG source
reconstruction
Source imaging based on magnetoencephalography (MEG) and electroencephalography (EEG)
allows for the noninvasive analysis of brain activity with high temporal and good spatial
resolution. As the bioelectromagnetic inverse problem is ill-posed, constraints are required. For
the analysis of evoked brain activity, spatial sparsity of the neuronal activation is a common
assumption. It is often taken into account using convex constraints based on the l1-norm. The
resulting source estimates are however biased in amplitude and often suboptimal in terms of
source selection due to high correlations in the forward model. In this work, we demonstrate that
an inverse solver based on a block-separable penalty with a Frobenius norm per block and a l0:5-
quasinorm over blocks addresses both of these issues. For solving the resulting non-convex
optimization problem, we propose the iterative reweighted Mixed Norm Estimate (irMxNE), an
optimization scheme based on iterative reweighted convex surrogate optimization problems,
which are solved efficiently using a block coordinate descent scheme and an active set strategy.
We compare the proposed sparse imaging method to the dSPM and the RAP-MUSIC approach
based on two MEG data sets. We provide empirical evidence based on simulations and analysis
of MEG data that the proposed method improves on the standard Mixed Norm Estimate (MxNE)
in terms of amplitude bias, support recovery, and stability.
IEEE Transactions on Medical Imaging (April 2016)
4D imaging of cardiac trabeculae contracting in vitro using gated OCT

Cardiac trabeculae are widely used as experimental muscle preparations for studying heart
muscle. However, their geometry (diameter, length, and shape) can vary not only amongst
samples, but also within a sample, leading to inaccuracies in estimating their stress production,
volumetric energy output, and/or oxygen consumption. Hence, it is desirable to have a system
that can accurately image each trabecula in vitro during an experiment. To this end, we
constructed an optical coherence tomography (OCT) system and implemented a gated imaging
procedure to image actively contracting trabeculae and reconstruct their time-varying geometry.
By imaging a single cross section while monitoring the developed force, we found that gated
stimulation of the muscle was sufficiently repeatable to allow us to reconstruct multiple
contractions to form a 4D representation of a single muscle contraction cycle. The complete
muscle was imaged at various lengths and the cross-sectional area along the muscle was
quantified during the contraction cycle. The variation of cross-sectional area along the length
during a contraction tended to increase as the muscle was contracting, and this increase was
greater at longer muscle lengths. To our knowledge, this is the first system that is able to
measure the geometric change of cardiac trabeculae in vitro during a contraction, allowing cross-
sectional stress and other volume-dependent parameters to be estimated with greater accuracy.
Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional
Neural Networks
Cerebral microbleeds (CMBs) are small haemorrhages nearby blood vessels. They have been
recognized as important diagnostic biomarkers for many cerebrovascular diseases and cognitive
dysfunctions. In current clinical routine, CMBs are manually labelled by radiologists but this
procedure is laborious, time-consuming, and error prone. In this paper, we propose a novel
automatic method to detect CMBs from magnetic resonance (MR) images by exploiting the 3D
convolutional neural network (CNN). Compared with previous methods that employed either
low-level hand-crafted descriptors or 2D CNNs, our method can take full advantage of spatial
contextual information in MR volumes to extract more representative high-level features for
CMBs, and hence achieve a much better detection accuracy. To further improve the detection
performance while reducing the computational cost, we propose a cascaded framework under 3D

CNNs for the task of CMB detection. We first exploit a 3D fully convolutional network (FCN)
strategy to retrieve the candidates with high probabilities of being CMBs, and then apply a well-
trained 3D CNN discrimination model to distinguish CMBs from hard mimics. Compared with
traditional sliding window strategy, the proposed 3D FCN strategy can remove massive
redundant computations and dramatically speed up the detection process. We constructed a large
dataset with 320 volumetric MR scans and performed extensive experiments to validate the
proposed method, which achieved a high sensitivity of 93.16% with an average number of 2.74
false positives per subject, outperforming previous methods using low-level descriptors or 2D
CNNs by a significant margin. The proposed method, in principle, can be adapted to other
biomarker detection tasks from volumetric medical data.
Steering of Magnetic Devices with a Magnetic Particle Imaging System
Small magnetic devices have been steered in arbitrary direction and with variable force using a
preclinical demonstrator system for magnetic particle imaging (MPI). Fast localization due to the
high imaging rate of over 40 volumes per second and strong forces due to the high field gradient
of more than 1 T/m render an MPI system a good platform for image-guided steering of
magnetic devices. In this paper, these capabilities are demonstrated in phantom experiments,
where a closed feedback loop has been realized to exert translational forces in horizontal and
vertical direction on a magnetic device moving in a viscous medium. The MPI system allows for
the controlled application of those forces by combining variable homogeneous fields with strong
field gradients.
Refined multiscale Hilbert-Huang spectral entropy and its application to central and
peripheral cardiovascular data
Objective: Spectral entropy has been applied in variety of fields. Multiscale spectral entropy
(MSSE) has also recently been proposed to take into account structures on several scales.
However, MSSE has some drawbacks, such as the coarsegraining procedure performed in the
time domain. In this study we propose a new framework to compute MSSE. This framework is
also adapted for non-stationary data. Methods: Our work relies on processing steps performed

directly in the frequency domain. For non-stationary signals, the evolution of entropy values with
scales is observed along time. Our algorithm is herein evaluated both on synthetic time series
(stationary and non-stationary signals) and on data from the cardiovascular system (CVS). For
this purpose, HRV (from the central CVS), laser Doppler flowmetry, and laser speckle contrast
data (both from the peripheral CVS) are analyzed. Results: The results show that our framework
has better performances than the existing algorithms to compute MSSE, both in terms of
reliability and computational cost. Moreover, it is able to reveal repetitive patterns on central and
peripheral CVS signals. These patterns may be linked to physiological activities. Furthermore,
from the processing of microvascular data, it is able to distinguish young from elderly subjects.
Conclusion: Our framework outperforms other algorithms to compute MSSE. It also has the
advantage of revealing physiological information. Significance: By showing better performances
than existing algorithms to compute MSSE, our work is a new and promising way to compute an
entropy measure from the spectral domain. It also has the advantage of stressing physiologically-
linked phenomena.
A Bayesian Classification Approach Using Class-Specific Features for Text Categorization
In this paper, we present a Bayesian classification approach for automatic text categorization
using class-specific features. Unlike conventional text categorization approaches, our proposed
method selects a specific feature subset for each class. To apply these class-specific features for
classification, we follow Baggenstoss's PDF Projection Theorem (PPT) to reconstruct the PDFs
in raw data space from the class-specific PDFs in low-dimensional feature subspace, and build a
Bayesian classification rule. One noticeable significance of our approach is that most feature
selection criteria, such as Information Gain (IG) and Maximum Discrimination (MD), can be
easily incorporated into our approach. We evaluate our method's classification performance on
several real-world benchmarks, compared with the state-of-the-art feature selection approaches.
The superior results demonstrate the effectiveness of the proposed approach and further indicate
its wide potential applications in data mining.
IEEE Transactions on Knowledge and Data Engineering (June 1 2016)
Feasibility of Swept Synthetic Aperture Ultrasound Imaging

Ultrasound image quality is often inherently limited by the physical dimensions of the imaging
transducer.We hypothesize that, by collecting synthetic aperture data sets over a range of
aperture positions while precisely tracking the position and orientation of the transducer, we can
synthesize large effective apertures to produce images with improved resolution and target
detectability. We analyze the two largest limiting factors for coherent signal summation:
aberration and mechanical uncertainty. Using an excised canine abdominal wall as a model phase
screen, we experimentally observed an effective arrival time error ranging from 18.3 ns to 58 ns
(root-mean-square error) across the swept positions. Through this clutter-generating tissue, we
observed a 72.9% improvement in resolution with only a 3.75 dB increase in side lobe amplitude
compared to the control case. We present a simulation model to study the effect of calibration
and mechanical jitter errors on the synthesized point spread function. The relative effects of these
errors in each imaging dimension are explored, showing the importance of orientation relative to
the point spread function. We present a prototype device for performing swept synthetic aperture
imaging using a conventional 1-D array transducer and ultrasound research scanner. Point target
reconstruction error for a 44.2 degree sweep shows a reconstruction precision of 82.8 μm and
17.8 μm in the lateral and axial dimensions respectively, within the acceptable performance
bounds of the simulation model. Improvements in resolution, contrast and contrast-to-noise ratio
are demonstrated in vivo and in a fetal phantom.
IEEE Transactions on Medical Imaging (February 2016)
Control of a Robotic Hand Using a Tongue Control System - a Prosthesis Application
Objective: The aim of this study was to investigate the feasibility of using an inductive tongue
control system (ITCS) for controlling robotic/prosthetic hands and arms. Methods: This work
presents a novel dual modal control scheme for multi-grasp robotic hands combining standard
EMG with the ITCS. The performance of the ITCS control scheme was evaluated in a
comparative study. Ten healthy subjects used both the ITCS control scheme and a conventional
EMG control scheme to complete grasping exercises with the IH1 Azzurra robotic hand
implementing five grasps. Time to activate a desired function or grasp was used as the
performance metric. Results: Statistically significant differences were found when comparing the
performance of the two control schemes. On average, the ITCS control scheme was 1.15 seconds

faster than the EMG control scheme, corresponding to a 35.4 % reduction in the activation time.
The largest difference was for grasp 5 with a mean AT reduction of 45.3 % (2.38 s). Conclusion:
The findings indicate that using the ITCS control scheme could allow for faster activation of
specific grasps or functions compared with a conventional EMG control scheme. Significance:
For transhumeral and especially bilateral amputees, the ITCS control scheme could have a
significant impact on the prosthesis control. In addition, the ITCS would provide bilateral
amputees with the additional advantage of environmental and computer control for which the
ITCS was originally developed.
IEEE Transactions on Biomedical Engineering (January 2016)
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