IEEE Sensor journal 2016 Title and Abstract

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IEEE SENSORS JOURNAL TOPICS 2016
Capacitance Sensing of Moisture Content in Fuel Wood Chips
Abstract - Moisture content of wood chips is an important factor to be known in their utilization
as a biomass material. Several moisture measuring instruments are available in the market, but
for most of these instruments, some sort of sample preparation is needed that involves sizing,
grinding, and weighing. The samples in this process are usually destroyed, and the measurement
involves considerable time and labor. The standard methods of oven drying and Karl Fisher also
fall in the destructive and laborious category. In this paper, estimation of moisture content of
hardwood chips, from the measurement of certain electrical properties of a parallel-plate
capacitor, holding the samples of these materials between them at two radio frequencies, is
presented. The wood chip samples tested were in the moisture range of 3%-50%. The standard
error of prediction was 2.0, and the prediction accuracies were better for the samples with
moisture contents below 25%. The construction and performance of a prototype moisture sensing
instrument working on these principles is briefly described. This method being nondestructive
and rapid would be useful in the biofuel industry.
IEEE Sensors Journal (June 2016)
Tomographic Reconstruction of Soil Gas Distribution From Multiple Gas Sources Based
on Sparse Sampling
Abstract - A monitoring method is introduced that creates 2-D maps of the soil gas distribution.
The method combines linear gas sensing technology for in situ monitoring of gases in soil with
the mapping capabilities of computed tomography to reconstruct spatial and temporal resolved
gas distribution maps. A weighted iterative algebraic reconstruction method based on maximum
likelihood with expectation maximization in combination with a source-by-source reconstruction
approach is introduced that works with a sparse setup of orthogonally aligned linear gas sensors.
The reconstruction method successfully reduces artifact production, especially when multiple gas
sources are present, allowing the discrimination between true and non-existing the so-called
ghost source locations. Experimental validation by controlled field experiments indicates the

high potential of the proposed method for rapid gas leak localization and quantification with
respect to pipeline or underground gas storage issues.
An Electrochemical Sensor System with Renewable Copper Nano-clusters Modified
Electrode for Continuous Nitrate Determination
Abstract - An automatic electrochemical sensor system based on sequential injection analysis
(SIA) technique for continuous nitrate determination is demonstrated. A threeelectrode- system
(Pt-Pt-Ag/AgCl), in which the Pt workingelectrode was modified with renewable copper nano-
clusters, was designed and fabricated to implement sensitive and reproducible nitrate detection
under a programmed operation protocol. Based on an optimal potential protocol, the continuous
renewal of copper sensing material was achieved on the working-electrode surface to ensure
each measurement could be performed on a fresh and highly active copper surface, which
guaranteed that automotive and continuous nitrate determination could be performed in a long
period of time. The experiment results revealed that the response of the sensor system towards
standard nitrate samples was linear and reproducible with an average sensitivity of 1.093
μA/mg·L-1 over the concentration range from 0 mg/L to 12.1 mg/L in a continuous long-term
operation. The standard deviation (STD) for the fluctuation of the sensitivity was only 0.036
during the continuous repeated measurements. The developed system was used to detect nitrate
in real freshwater samples. The results were in good agreement with the data obtained by the
standard ultraviolet spectrophotometric method for nitrate detection with the maximal difference
of 16.64%.
Sense-Node Capacitance Reduction for SOI Pixel Sensor with Small Detection P-well
Abstract - A SOI pixel sensor with small detection p-well is researched and simulated to reduce
the sense-node capacitance while maintaining a widespread collection area and uniform field
within the sensing volume. In this pixel structure, two p-doped wells are adopted. A transfer-gate
is employed to form a transport channel for charge carriers from one p-doped well to another
when transfer-gate is on, or to isolate the two p-doped wells when transfer-gate is off. A fully-
depleted buried p-well is formed for charge collection and storage, which is not connected to the

readout electrode. Some key parameters have been studied. Numerical simulation results show
that the proposed device can work effectively, but charge transfer efficiency is low for a small
amount of charge, which needs a further improvement.
Measurement of Gas Thermal Properties Using the Parametric Reduced-Order Modeling
Approach
Abstract - This paper deals with a thermal gas property micro-sensor. The proposed modeling
approach of the sensor was based on reduced order modeling, in contrast to the traditional
analytical modelling approach, which is the standard for this kind of sensors. This sensor was
deployed for the measurement of the thermal conductivity (k) and the volumetric heat capacity
(ρcp) of gases and works according to the temperature oscillation technique. A proper model is
crucial for the measurement accuracy. The scope of this paper was to investigate the applicability
of a sensor model based on a reduced-order modeling approach, intending to improve the
performance of this sensor, as the behavior of the sensor can be modeled much more accurately
than using an analytical model. For this reason, a parametric model-order reduction technique
using proper orthogonal decomposition was applied. The main advantage of the reduced-order
model is the high accuracy in the modeling of the conductive heat transfer problem, while it
requires low computation effort. The approach was tested experimentally, where the model was
calibrated in two pure gases and evaluated in 21 gases and gas mixtures. The sensor achieved an
accuracy in the thermal conductivity of 6.5% and in the volumetric heat capacity of 3.2%.
Haltere-Like Optoelectromechanical Gyroscope
Abstract - We report an optoelectromechanical vibratory gyroscope inspired by halteres of
dipteran flies. The gyroscope utilizes optical displacement sensing to achieve a Brownian-
motion-limited displacement sensitivity without mechanical resonant amplification in the sense
mode. This yields a threefold difference between the resonance frequencies of the drive and
sense modes, corresponding to a theoretical bandwidth of over 200 Hz, without compromising

the sensitivity. Our measurements show a noise-equivalent rotation rate of 3°/h/Hz$^{mathrm
{1/2}}$ under atmospheric-pressure conditions.
Sensitivity-Enhanced Pressure Sensor for Wireless Bladder Pressure Monitoring
Abstract - This paper presents a sensitivity-enhanced wireless passive LC pressure sensor for
monitoring bladder pressure, which can be minimally invasive implanted in the bladder or
integrated to a commercial urinary catheter and avoid battery lifetime problem. The proposed
sensor design and the theoretical model are useful to increase the pressure sensitivity of a typical
air-sealed capacitive pressure sensor, particularly having a flexible diaphragm. The test results of
an in vitro evaluation of the prototype LC pressure sensor show a pressure sensitivity of 6385
ppm/kPa (a pressure responsivity of 1.215 MHz/kPa) before encapsulation coating in air at room
temperature, whereas 4175 ppm/kPa (0.627 MHz/kPa) and 2679 ppm/kPa (0.304 MHz/kPa) after
coating with silicone rubber in air and in water operation, respectively. Finally, the feasibility of
the developed sensor is investigated through an animal test and confirmed by comparing with the
data of conventional cystometry.
A Comparative Study on Human Activity Recognition Using Inertial Sensors in a
Smartphone
Abstract - Activity recognition plays an essential role in bridging the gap between the low-level
sensor data and the high-level applications in ambient-assisted living systems. With the aim to
obtain satisfactory recognition rate and adapt to various application scenarios, a variety of
sensors have been exploited, among which, smartphone-embedded inertial sensors are widely
applied due to its convenience, low cost, and intrusiveness. In this paper, we explore the power
of triaxial accelerometer and gyroscope built-in a smartphone in recognizing human physical
activities in situations, where they are used simultaneously or separately. A novel feature
selection approach is then proposed in order to select a subset of discriminant features, construct
an online activity recognizer with better generalization ability, and reduce the smartphone power
consumption. Experimental results on a publicly available data set show that the fusion of both
accelerometer and gyroscope data contributes to obtain better recognition performance than that

of using single source data, and that the proposed feature selector outperforms three other
comparative approaches in terms of four performance measures. In addition, great improvement
in time performance can be achieved with an effective feature selector, indicating the way of
power saving and its applicability to real-world activity recognition.
A Two-Dimensional (2D) Distributed-Deflection Sensor for Tissue Palpation With
Correction Mechanism for Its Performance Variation
Abstract - This paper presents a proof-of-concept study on a two-dimensional distributed-
deflection sensor for tissue palpation and the associated correction mechanism for its
performance variation. The core of the sensor is one whole polydimethylsiloxane microstructure
embedded with a 3 × 3 sensing-plate/transducer array. Upon pressing the sensor against a tissue,
the sensing-plate array translates its elasticity distribution to the deflection distribution, which
registers as resistance changes by the transducer array and is further converted into the stiffness
distribution. The connection of the sensing-plate/transducer array into one piece allows the
sensor interacting with a tissue in a continuous manner and, thus, unifies misalignment errors
from non-ideal normal contact between the sensor and the tissue. A correction mechanism is
developed to remove the effect of performance variation among the sensing-plate/transducer
array on the measured stiffness distribution of a tissue. The stiffness distribution normalized to
the minimum stiffness across a tissue is utilized to identify the existence and location of a tumor.
The related measurement errors are both theoretically and experimentally examined.
Simultaneous Refractive Index and Temperature Measurement Based on Mach–Zehnder
Interferometer Concatenating Two Bi-Tapers and a Long-Period Grating
Abstract - An all-fiber sensor for simultaneous refractive index (RI) and temperature
measurement based on Mach-Zehnder interferometer (MZI) is proposed and demonstrated
experimentally. The MZI consists of two bi-tapers and a long-period grating. Two dips in the
interference spectrum exhibit different responses to the variations of RI and temperature, and
simultaneous measurement of RI and temperature can be realized by means of sensitivity

coefficient matrix. Experimental results show that the RI and temperature sensitivity for the
proposed sensor are -108.16 nm/RI and 0.12 nm/°C, respectively.
Fabrication of Optical Fiber Sensors for Measuring Ageing Transformer Oil in
Wavelength
Abstract - In this paper, the fabrication and characterization of an optical fiber refractometer for
detecting ageing transformer oil is presented. Tin-dioxide thin films have been used for the
fabrication of optical fiber interferometers. Indium-tin-oxide thin films deposited on the optical
fibers are used as the lossy mode resonance supporting coatings. This optical fiber sensor is
based on the variation of an oil refractive index. This is detected by a wavelength shift in the
optical fiber sensor's spectral response. The fabricated sensors enable an accurate determination
of ageing transformer oil, which reveals their potential in real-life applications.
A Bernoulli Filter for Extended Target Tracking Using Random Matrices in a UWB
Sensor Network
Abstract - In this paper, we propose a new tractable Bernoulli filter based on the random matrix
framework to track an extended target in an ultra-wideband (UWB) sensor network. The
resulting filter jointly tracks the kinematic and shape parameters of the target and is called the
extended target Gaussian inverse Wishart Bernoulli (ET-GIW-Ber) filter. Closed form
expressions for the ET-GIW-Ber filter recursions are presented. A clustering step is inserted into
the measurement update stage in order to have a computationally tractable filter. In addition, a
new method that is consistent with the applied clustering method is embedded into the filter
recursions in order to adaptively estimate the time-varying number of measurements of the
extended target. The simulation results demonstrate the robust and effective performance of the
proposed filter. Furthermore, real data collected from a UWB sensor network are used to assess
the performance of the proposed filter. It is shown that the proposed filter yields a very
promising performance in estimation of the kinematic and shape parameters of the target.

Low-cost Wake-up Receiver for RF Energy Harvesting Wireless Sensor Networks
Abstract - Wake-up receiver (WuRx) is a promising hardware solution that extends the life of a
sensor node by switching off its radio to reduce idle listening energy consumption. The existing
passive WuRxs are RFID tag based, which incur high cost and complexity. In this paper, we
study cost-effective and long range WuRx solutions for range-based wake-up (RW) as well as
directed wake-up (DW). In particular, we consider the case of an RF energy harvesting wireless
sensor node and investigate how a low-cost WuRx can be built using an RF energy harvester
available at the node without compromising the harvesting sensitivity and efficiency.
Experimental results show that our developed prototype can achieve a wake-up range of 1:16 m
with +13 dBm transmit power. Further, our empirical study shows that at +30 dBm transmit
power the wake-up distance of our developed RW module is > 9 m. High accuracy of DW is
demonstrated by sending a 5-bit ID from a transmitter at a bit rate up to 33:33 kbps. Finally, we
present optimized WuRx designs for RW and DW using Agilent advanced design system, which
offer up to 5:69 times higher wake-up range for RW and energy savings per bit of about 0:41 mJ
and 21:40 nJ, respectively, at the transmitter and the sensor node in DW.
On Energy Hole and Coverage Hole Avoidance in Underwater Wireless Sensor Networks
Abstract - Due to the limited battery capacity of sensor nodes, a minimization of energy
consumption is a potential research area in underwater wireless sensor networks (UWSNs).
However, energy hole and coverage hole creations lead performance degradation of UWSNs in
terms of network lifetime and throughput. In this paper, we address the energy hole creation
issue in depth-based routing techniques, and devise a technique to overcome the deficiencies in
existing techniques. Besides addressing the energy hole issue, the proposition of a coverage hole
repair technique is also part of this paper. In areas of the dense deployment, sensing ranges of
nodes redundantly overlap. Our proposed technique takes a benefit of redundant overlapping and
repairs a coverage hole during network operation. Simulation results show that our two
techniques cohesively conserve nodes' energy, which ultimately maximizes the network lifetime
and throughput at the cost of increased delay.

A Web-based Office Climate Control System using Wireless Sensors
Abstract - Conventional HVAC systems usually achieve the desired control level by means of
simple on-off control which can often result in high energy wastage. A potential solution to this
issue is intelligent self-regulating HVAC controllers which base their actions/decisions on sensor
data. In this paper, an office climate monitoring and control system is designed and
implemented. The system consists of various wireless sensor nodes and a control node. The
sensor nodes provide the sensor data necessary to determine occupancy and the control node
executes the algorithm which decides whether to activate cooling or heating based on the sensor
data. This system can serve as a controller and can be integrated into HVAC systems in smart
buildings. It is shown that the developed control algorithm executed on the control node results
in an improvement of up to 39% in energy efficiency over conventional on-off controllers for
HVAC systems.
Error of a temperature probe for cancer ablation monitoring caused by respiratory
movements: ex vivo and in vivo analysis
Abstract - Hyperthermal techniques are spreading as an alternative to conventional surgery for
cancer removal. A real-time temperature feedback can be used to adjust the treatment settings, in
order to improve the clinical outcomes. In the present work we experimentally assessed the
feasibility for distributed temperature monitoring of a custom probe which consists of a needle
embedding six fiber Bragg gratings (FBGs). Since FBGs are also sensitive to strain, we focused
on the analysis of the measurement error (artifact) caused by respiratory movements. We
assessed the artifact both on ex vivo pig liver and lung (by mimicking the movement of these
organs caused by respiration) and on in vivo trial on pig liver. Lastly, we proposed an algorithm
to detect and minimize the artifact during ex vivo liver laser ablation. During both ex vivo and in
vivo trials the probe insertion within the organ was easy and safe. The artifact was significant (up
to 3°C), but the correction algorithm allows minimizing the error. The main advantages of the
proposed probe are: i) spatially resolved temperature monitoring (in 6 points of the tissue by
inserting a single needle), ii) the needle is Magnetic Resonance (MR)-compatible, hence can be

used during MR-guided procedure. Even if the model is close to humans, further trials are
required to investigate the feasibility of the probe for clinical applications.
Simple Mathematical Operation-Based Calibration Method for Giant Magnetoresistive
Current Sensor Applying B-Spline Modeling
Abstract - Nowadays, giant magnetoresistance (GMR)-based sensor technology is widely used
in numerous industrial applications. However, an inexpensive and efficient method for
compensating the nonlinear output of GMR spin valve sensors is still under the industrial
prospect, which limits the application of the GMR sensors. By applying the B-Spline modeling,
excellent results can be achieved; nevertheless, the sensor has to be calibrated in the whole
temperature range. In this paper, a simple mathematical operation-based calibration method for
GMR spin valve-type current sensors applying B-Spline modeling is introduced. Instead to
measure dozens of temperatures and per temperature several hundreds of points, this method
allows a calibration for each further sensor applying B-Spline modeling with only two
temperatures and per temperature only 15 points. In comparison with the original calibration
measurement over temperature for B-Spline modeling, the new calibration with minimized
efforts shows outstanding measurement accuracy of ±0.6% at a nominal current of 50 A in a
temperature range from -20 °C to 80 °C.
High Detectivity Organic Photodetectors With Molybdenum (VI) Oxide and C60 Layers
Abstract - This paper proposes high-sensitive polymer/ fullerene bulk-heterojunction organic
photodetectors (OPDs). A typical polymeric hole transport layer, (poly(3,4-
ethylenedioxythophene):poly(styrenesulfonate), has substitutes with a molybdenum (VI) oxide
(MoO3) layer. A thin C60 hole blocking layer was also introduced to reduce the dark current
performance of the OPD. Optical interference simulation also accomplished along with the
generalized transfer matrix method to show the behavior of wavelengths in OPDs. As a result,
the lowest dark current density of 14 pA/cm2
, and 40% above EQE, has achieved in the visible

range. In regards to the photodetector characteristics, a reasonable responsivity of 0.21 A/W and
high detectivity of 2.4 × 1012
cmHz1/2
/W were also obtained.
Accelerometer-Based Hand Gesture Recognition by Neural Network and Similarity
Matching
Abstract - In this paper, we present an accelerometer-based pen-type sensing device and a user-
independent hand gesture recognition algorithm. Users can hold the device to perform hand
gestures with their preferred handheld styles. Gestures in our system are divided into two types:
the basic gesture and the complex gesture, which can be represented as a basic gesture sequence.
A dictionary of 24 gestures, including 8 basic gestures and 16 complex gestures, is defined. An
effective segmentation algorithm is developed to identify individual basic gesture motion
intervals automatically. Through segmentation, each complex gesture is segmented into several
basic gestures. Based on the kinematics characteristics of the basic gesture, 25 features are
extracted to train the feedforward neural network model. For basic gesture recognition, the input
gestures are classified directly by the feedforward neural network classifier. Nevertheless, the
input complex gestures go through an additional similarity matching procedure to identify the
most similar sequences. The proposed recognition algorithm achieves almost perfect user-
dependent and user-independent recognition accuracies for both basic and complex gestures.
Experimental results based on 5 subjects, totaling 1600 trajectories, have successfully validated
the effectiveness of the feedforward neural network and similarity matching-based gesture
recognition algorithm.
Tiled-Block Image Reconstruction by Wavelet- Based, Parallel-Filtered Back-Projection
Abstract - We demonstrate an algorithm, relevant to tomography sensor systems, to obtain
images from the parallel reconstruction of essentially localized elements at different scales. This
is achieved by combining methodology to reconstruct images from limited and/or truncated data,
with the time-frequency capabilities of the wavelet transform. Multiscale, as well as time-
frequency, localization properties of the separable two-dimensional wavelet transform are
exploited as an approach for faster reconstruction. The speedup is realized not only by reducing

the computation load on a single processor, but also by achieving the parallel reconstruction of
several tiled blocks. With tiled-block image reconstruction by wavelet-based, parallel filtered
back-projection (FBP), we measure more than 36 times gain in speed, compared with standard
FBP.
A Customized RFID-Based Sensor System for Intelligent Oilwell
Abstract - A customized radio frequency identification (RFID)-based sensor system is proposed
to achieve reliable data collection and transmission between downhole and ground for the
intelligent oilwell. In this paper, the overall system architecture is introduced first, and then, the
corresponding hardware systems are designed in detail. The fatal effects of the extreme
downhole environments are studied, which can change the RFID antenna inductance and result
in the communication failure of the RFID system. To compensate for the changed inductance
due to the metal tube and the inductance disturbance coming from the liquid, an optimal design
for the RFID antenna and reader is presented to improve the system’s robustness and decrease
the sensitivity in volatile application conditions. The system is tested both in laboratory and on
the site of the oilfield, and the results show that the system can achieve reliable data collection
and transmission in the extreme oilwell production environment.
IEEE Sensors Journal (July 2016)
Multi-Channel Surface Electromyography Electrodes: A Review
Abstract - This paper is a review of multi-channel surface electromyography (sEMG) electrode
used in the research for investigating the properties of muscles. Over 300 papers from five
recognized journals were examined from the year 2000 to 2013 with only 64 stating the use of
multi-channel electrodes in their research. The review determined that multi-channel electrodes
can be classified as linear array or 2D array electrodes. The 2D array is the basis for developing
the high-spatial-resolution sEMG (HSR-sEMG) or high density sEMG (HD-sEMG). The
important factors considered in this review of the electrodes are: 1) the material used; 2) the
inter-electrode distance; and 3) the configuration for the collection of the electromyography
signals. The basic configurations of the sEMG electrodes are monopolar, bipolar, and double
differential. It was found that the majority of the linear array electrodes were used to collect

either bipolar or double differential signals. The 2D array electrodes were used to collect
monopolar signals. The HSR-sEMG electrodes used a normal double differentiating filter,
referred to as Laplacian configuration. The HD-sEMG has versatility being able to collect all
types of signals, such as monopolar, bipolar, double differential, or signals filtered by Laplacian
configuration.
An Event-Driven Ultra-Low-Power Smart Visual Sensor
Abstract - In this paper, we present an ultra-low-power smart visual sensor architecture. A 10.6-
low-resolution contrast-based imager featuring internal analog preprocessing is coupled with an
energy-efficient quad-core cluster processor that exploits near-threshold computing within a few
milliwatt power envelope. We demonstrate the capability of the smart camera on a moving
object detection framework. The computational load is distributed among mixed-signal pixel and
digital parallel processing. Such local processing reduces the amount of digital data to be sent out
of the node by 91%. Exploiting context aware analog circuits, the imager only dispatches
meaningful postprocessed data to the processing unit, lowering the sensor-to-processor
bandwidth by with respect to transmitting a full pixel frame. To extract high-level features, an
event-driven approach is applied to the sensor data and optimized for parallel runtime execution.
A system energy saving is reached through the event-driven approach with respect to frame-
based processing, on a low-power MCU node. The near-threshold parallel processor further
reduces the processing energy cost by, achieving an overall system energy cost of per frame,
which results to be and up to lower than, respectively, an event-based imaging system based on
an asynchronous visual sensor and a traditional frame-based smart- visual sensor.
MotionSynthesis Toolset (MoST): An Open Source Tool and Data Set for Human Motion
Data Synthesis and Validation
Abstract - Access to experimental data in the development of algorithms and techniques for
wearable computing devices and body sensor networks allows faster validation and refinement of
algorithms. The MotionSynthesis Toolset is an open source toolset and database built to assist in
data collection and data sharing, and allow collaboration in review and validation of data sets.

The tools can generate a sequence of movements and synthesize a data stream based on the data
stored in the database. New movements can be added to the database and the tools by the
community. The tools also allow visualization, and validation of the movements and data with
video and signal waveforms. The data set has more than 20 subjects and multiple repetitions of
the movements from each subject to increase data diversity.
Energy-Efficient Intelligent Street Lighting System Using Traffic-Adaptive Control
Abstract - Lighting, both indoor and outdoor, consumes a substantial amount of energy, making
improved efficiency a significant challenge. A promising approach to address outdoor lighting is
the smart control of public lighting. Smart lighting using electronically controlled light-emitting
diode (LED) lights for adaptable illumination and monitoring is being used to achieve an energy
efficient system. However, the traffic engineering integrated with smart control for energy
optimization has not been widely used. In this paper, a novel concept of traffic-flow-based smart
(LED) street lighting for energy optimization is proposed. The developed smart grid architecture-
based system uses low power ZigBee mesh network to provide maximum energy efficiency in
response to adaptive traffic on the road. Moreover, the scalable wireless network of smart LED
lights offers improved reliability, reduced cost, and more user satisfaction. In order to validate
the performance, the proposed system was implemented and tested in a real environment inside a
university campus. Experimental results show that in comparison with the replaced conventional
metal halide lighting, our system is capable of 68%–82% energy savings depending on the
variations in daylight hours between summer and winter. A significant reduction in greenhouse
gases, improved overall system reliability, and reduced maintenance due to smart control
suggests promising results for future wide-area deployment.
A Two-Filter Integration of MEMS Sensors and WiFi Fingerprinting for Indoor
Positioning
Abstract - Indoor positioning has become increasingly important in the past decade. Some
approaches for the integration of micro-electro-mechanical systems (MEMS) sensors and WiFi
fingerprinting (FP) have been proposed for indoor positioning. However, most of the existing

integration approaches only focus on aiding MEMS sensors by WiFi FP. This letter proposes a
two-filter integration for MEMS sensors and WiFi FP. In the proposed approach, the integrated
positioning solution is used to constrain the search space of WiFi FP, and achieve a constrained
constrained FP (CFP) solution. Then, a Kalman filter serves for obtaining a smoothed CFP
solution (SCFP). Finally, an extended Kalman filter serves for the integration of SCFP and
MEMS sensors. Field tests show the proposed integration approach can improve both positioning
accuracy and computational efficiency.
M2M Encountering: Collaborative Localization via Instant Inter-Particle Filter Data
Fusion
Abstract - Much research has addressed indoor localization by integrating portable/wearable
sensing and communication technologies. While GPS has dominated the outdoor environments,
indoor localization schemes have to consider different observations, such as radio-frequency
signals, vision or motions data, and apply data fusion to combine various sensor inputs. In this
paper, we observe that when two devices meet up, which we call machine-to-machine (M2M)
encountering, they can collaboratively calibrate each other’s potential locations via M2M
communications. We apply this technique to particle filter (PF), a common fusion technique, and
show how to take the M2M encountering opportunities, which may happen frequently in
crowded areas, to allow user devices to collaboratively calibrate their locations. Hence, the PF
technique, which normally fuses observations from individual devices, is extended to an inter-
PF, cross-device domain. We validate our inter-PF solution by simulations as well as a prototype
system with smartphones and ZigBee infrastructure deployed in an office building. It is verified
that the inter-PF helps converge the positioning results more rapidly, and improves location
quality. Also, the proposed encountering mechanism has potential to be applied to other
localization algorithms to improve their accuracy.
An Empirical Study for PCA- and LDA-Based Feature Reduction for Gas Identification
Abstract - Increasing the number of sensors in a gas identification system generally improves its
performance as this will add extra features for analysis. However, this affects the computational

complexity, especially if the identification algorithm is to be implemented on a hardware
platform. Therefore, feature reduction is required to extract the most important information from
the sensors for processing. In this paper, linear discriminant analysis (LDA) and principal
component analysis (PCA)-based feature reduction algorithms have been analyzed using the data
obtained from two different types of gas sensors, i.e., seven commercial Figaro sensors and in-
house fabricated $4 times 4$ tin-oxide gas array sensor. A decision tree-based classifier is used
to examine the performance of both the PCA and LDA approaches. The software implementation
is carried out in MATLAB and the hardware implementation is performed using the Zynq
system-on-chip (SoC) platform. It has been found that with the $4 times 4$ array sensor, two
discriminant functions (DF) of LDA provide 3.3% better classification than five PCA
components, while for the seven Figaro sensors, two principal components and one DF show the
same performances. The hardware implementation results on the programmable logic of the
Zynq SoC shows that LDA outperforms PCA by using 50% less resources as well as by being
11% faster with a maximum running frequency of 122 MHz.
Magnetometer Calibration Using Inertial Sensors
Abstract - In this paper, we present a practical algorithm for calibrating a magnetometer for the
presence of magnetic disturbances and for magnetometer sensor errors. To allow for combining
the magnetometer measurements with inertial measurements for orientation estimation, the
algorithm also corrects for misalignment between the magnetometer and the inertial sensor axes.
The calibration algorithm is formulated as the solution to a maximum likelihood problem, and
the computations are performed offline. The algorithm is shown to give good results using data
from two different commercially available sensor units. Using the calibrated magnetometer
measurements in combination with the inertial sensors to determine the sensor’s orientation is
shown to lead to significantly improved heading estimates.

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