ALBERT

Description: Recent advances in smart devices have sustained them as a better alternative for the design of human–machine interaction (HMI), because they are equipped with accelerometer sensor, gyroscope sensor, and an advanced operating system. This paper presents a continuous hand gestures recognition technique that is capable of continuous recognition of hand gestures using three-axis accelerometer and gyroscope sensors in a smart device. To reduce the influence of unstableness of a hand making the gesture and compress the data, a gesture coding algorithm is developed. An automatic gesture spotting algorithm is developed to detect the start and end points of meaningful gesture segments. Finally, a gesture is recognized by comparing the gesture code with gesture database using dynamic time warping algorithm. In addition, a prototype system is developed to recognize the continuous hand gestures-based HMI. With the smartphone, the user is able to perform the predefined gestures and control smart appliances using the Samsung AllShare protocol.

Description: As nanometer technology advances, conventional optical proximity correction (OPC) that minimizes the edge placement error (EPE) at the nominal process condition alone often leads to poor process windows. To improve the mask printability across various process corners, process-window OPC optimizes EPE for multiple process corners, but often suffers long runtime, due to repeated lithographic simulations. This paper presents an efficient process variation (PV)-aware mask optimization framework, namely PVOPC, to simultaneously minimize EPE and PV band with fast convergence. The PVOPC framework includes EPE-sensitivity-driven dynamic fragmentation, PV-aware EPE modeling, and correction with three new EPE-converging techniques and a systematic subresolution-assisted feature insertion algorithm. Experimental results show that our approach efficiently achieves high-quality EPE and PV band results.

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Description: Binning after volume production is a widely accepted technique to classify fabricated integrated circuits (ICs) into different clusters depending on different degrees of specification compliance. This allows the manufacturer to sell nonoptimal devices at lower rates, so adapting to customer’s quality-price requirements. The binning procedure can be carried out by measuring every single circuit performances, but this approach is costly and time-consuming. On the contrary, if alternate measurements are used to characterize the bins, the procedure is considerably enhanced. In such a case, the specification bin boundaries become arbitrary shape regions due to the highly nonlinear mappings between the specifications space and the alternate measurements space. The binning strategy proposed in this paper functions with the same efficiency regardless of these shapes. The digital encoding of the bins in the alternate measurements space using octrees is the key idea of the proposal. The strategy has two phases: 1) the training phase and 2) the binning phase. In the training phase, the specification bins are encoded using octrees. This first phase requires sufficient samples of each class to generate the octree under realistic variations, but it only needs to be performed once. The binning phase corresponds to the actual production binning of the fabricated ICs. This is achieved by evaluating the alternate measurements in the previously generated octree. The binning phase is fast due to the inherent sparsity of the octree data structure. In order to illustrate the proposal, the method has been applied to a band-pass Butterworth filter considering three specification bins as a proof of concept. Successful simulation results are reported showing considerable advantages as compared to a support vector machine (SVM)-based classifier. Similar bin misclassifications are obtained with both methods, 1.68% using octrees and 1.83% using SVM, while binning time is $5times $ times faster using octrees than using the SVM-based classifier.

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Description: The small-signal vibration model of Tb x Dy 1– x Fe y plate was developed by mechanical-electrical analogy method. Based on the model, we demonstrated the reasonableness of measuring the small-signal magnetomechanical behaviors by a laser Doppler vibrometer. The strain coefficient of the Tb x Dy 1– x Fe y plates was measured as a function of frequency and bias field ( $H_{mathrm{ dc}})$ , and Young’s modulus, mechanical quality factor ( $Q_{m})$ , relative permeability, and magnetomechanical coupling coefficient were investigated as a function of $H_{mathrm{ dc}}$ . Many novel characteristics were observed under the drive of a small-signal field (7.96 A/m). The change tendency of the strain coefficient at resonance differs from that at low frequency, and the resonant strain coefficients are significantly high (>85 nm/A) in a wide range of bias field from 4.78 to 55.72 kA/m. Both the negative- $Delta E$ and positive- $Delta E$ effects are observed, and the negative- $Delta E$ effect in the low field range is also remarkable. In particular, $Q_{m}$ sharply decreases from the initial value of 104 to a minimum value of 11.4 and, then, increases slowly, and the ratio of the maximum variation of $Q_{m}$ over $H_{mathrm{ dc}}$ to the minimum value of $Q_{m}$ exceeds $sim 812.3$ %. This is an important systematic investigation on the small-signal dynamic magnetomechanical behavior of Tb x Dy 1– x Fe y , and the results are highly beneficial to the designing of magnetostrictive devices.

Description: The presence of particles, which can intrude into the air bearing, is one of the most common factors in the failure of hard disk drives (HDDs). Previous studies have investigated particles trajectories with the assumption of ideal trapping or reflecting boundary conditions in air-filled drives. However, only the colliding particle with insufficient energy to escape the potential well will be trapped by the surface. In this paper, considering the particle-surface energy during the collision, the trapping criterion of the incident normal critical velocity ( $V_{text {ni}}^{mathrm {ast }})$ for Al 2 O 3 particles is developed as the boundary conditions for different colliding surfaces inside a 2.5 in drive. Then, trapping status for Al 2 O 3 particles and particles trajectories inside the drive are simulated by using the commercial computational fluid dynamics solver FLUENT with user-defined functions. The results reveal that the particles will travel longer distances until trapped by HDD components when considering the trapping criterion. In addition, smaller particles will more likely degrade the head–disk interface reliability, since they easily stick on the disk surface.

Description: Benefiting from its simple switching scheme (only a bidirectional current source), high-speed and low-power spin-transfer torque (STT) has been regarded as one of the most promising switching mechanisms for a magnetic tunnel junction (MTJ)-based non-volatile memory and logic circuits. However, it suffers from a number of reliability issues like write error induced by its intrinsic stochasticity, process variation, and so on. In order to reduce the write error rate, the mainstream solution is to enlarge the write pulse duration at the expense of write energy dissipation. Some self-terminated write circuits have been proposed to avoid the wasted write energy. But the hardware cost of these write circuits is especially large. In this paper, a novel cost-efficient self-terminated write circuit is proposed using two simple built-in sensing circuits. The proposed write circuit is simulated with a physics-based STT-MTJ compact model and a commercial CMOS 40 nm design kit. The simulation result shows about 35% reduction of circuit area and 10% lower energy consumption in comparison with that in prior work. In addition, the Error-Free write operation under process variation of both the CMOS transistor and the STT-MTJ is achieved due to its large sense margin ( $sim 320$ mV).

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Description: Domain wall nanomagnet (DWNM)-based devices have been extensively studied as a promising alternative to the conventional CMOS technology in both the memory and logic implementations due to their non-volatility, near-zero standby power, and high integration density characteristics. In this paper, we leverage a physics-based model of a DWNM device to design a highly scalable current-mode majority gate to achieve a novel one bit full-adder (FA) circuit. The modeled DWNM specifications are calibrated with the experimentally measured data. The functionality of the proposed DWNM-based FA (DWNM-FA) is verified using a SPICE circuit simulator. The detailed analysis and the calculations have been performed to realize the proposed DWNM-FA delay and power consumption corresponding to the various induced input currents at different operating temperatures. The power-delay product of DWNM-FA is examined to tune the operation within the optimum induced input current region to obtain desired power-delay requirements over a range of 200 $mu text{A}$ to 1 mA at temperatures from 298 to 378 K. Finally, the comparison results exhibit 52% and 49% area improvement as well as 41% and 31% improvement in device count complexity over CMOS-based and magnetic tunnel junction-based FA designs, respectively.

Description: Bulk CoCr 2 O 4 undergoes a transition from paramagnetic to long-range ferrimagnetic phase at $T_{c}$ (94 K) to a long-range and/or short-range spiral order at $T_{s}$ ( $sim 24$ K), and finally shows a lock-in-transition below 15 K. The spiral component induces an electric polarization and also a spontaneous magnetization for which it is said to be multiferroic. Reducing the size of a CoCr 2 O 4 multiferroic material to $sim 50$ nm by a coprecipitation method, we obtain a pure cubic phase with space group, Fd3m and lattice parameter (8.334 ± 0.003 °A). A rich sequence of magnetic transitions are examined by measuring temperature and field-dependent magnetization and diffused neutron scattering (DNS) using polarized neutron at different temperatures. While paramagnetic to ferrimagnetic transition is enhanced from 97 K in bulk to 99 K at 0.5 kOe field, followed by a decrease in lock-in-transition ( $T_{L}$ ) from 15 K in bulk to 8 K, spiral ordering temperature does not show a significant change. A strong disagreement between paramagnetic moment obtained from the fitting of $chi ^{-1}=({T}/{C})+({1}/{chi _{o}})-({b}/{T-theta })$ and ferrimagnetic moment obtained from the $M$ versus $H$ loop taken at 2 K, nonsaturated magnetization at 50–100 kOe field, two order of magnitude higher coercivity ( $H_{c}$ ), and splitting of ac susceptibly confirm the core–shell structure of the particles. Furthermore, a magnetic scattering analysis clearly shows that while the paramagnetic to ferrimagnetic transition is continuous, the spiral ordering is sharp, short range, and commensurate in contrast to incommensurate spiral order observed single crystal of CoCr 2 O 4 .

Description: The finite-element analysis for the simulation of magnetic fields in electrical machines leads to an index-1 differential-algebraic equation (DAE) (as opposed to a conventional ordinary differential equation), because the electrical conductivity can be zero in certain regions. First, we construct a DAE-compatible time integration scheme which is energy-balanced, meaning that in a linear system, the input stored and lost powers sum exactly to zero. Second, we use a method based on the energy balance to compute torque. We show that the energy balance method approaches the virtual work principle applied at remeshing layer, as the time step is refined. A similar result also holds if the rotation of the rotor is implemented by Nitsche’s method, which is an instance of the so-called mortar methods.

Description: Investigations about induction sensors, electromagnetic launchers, shields, transformers, and power line-induced currents address increased number of low-frequency research and industrial applications. In general, a magneto-quasi-static (MQS) approximation is considered for the solutions of low-frequency problems in electromagnetics. This approximation leads to a diffusion process when displacement currents are neglected. However, keeping the displacement currents, Maxwell’s equations are valid at low frequencies. In this manner, the finite difference time domain (FDTD) method must be modified by the slowing down propagation velocity at low-frequency regime. In this paper, important and crucial points of the MQS approximation and its application in the FDTD method are clarified in the sense of analytical and numerical aspects. A material scaling technique of dielectric permittivity for the QS FDTD application is analyzed within comprehensive investigations. Furthermore, a criterion for choosing a proper value of scaling parameter will be revealed. Finally, effects of proper and improper values of the scaling parameter are presented with validated analytical and numerical results.

Description: The ability to manipulate the relative magnetization alignment between ferromagnetic source and drain electrodes attached to a molecule or small quantum dot is a prerequisite for a number of spintronic device applications. The influence of electrode shape and field orientation on pairwise magnetization reversal mechanisms in nanogap and point-contact structures is investigated here using micromagnetic simulations. A favorable device geometry and setup are identified for enabling planar, monodomain source and drain electrodes with a magnetization alignment that may be controllably switched between a parallel and antiparallel configuration.

Description: In typical inductive power transfer (IPT) applications, soft magnetic materials are employed in both transmitter and receiver coils as low reluctance paths in order to improve the inductive coupling and support the guidance of magnetic flux. Considering their critical role in the overall performance, this paper investigates the impact of magnetic material properties on the basic features of inductive powering systems. To this end, a group of Ni–Zn and Ni–Cu–Zn spinel ferrite samples with a wide value range of magnetic properties is prepared. The materials are characterized in terms of their structural and magnetic properties, and they are used to build various substrate assemblies for the IPT transmitter (Tx) coil. Conducting Al and Cu sheets and biasing permanent magnets are combined with ferrite disks to simulate common Tx coil designs. This allows the identification of the correlation between the material properties, especially power losses, and the measured power transfer efficiency and shielding effectiveness.

Description: This paper presents a method to estimate steady wind and a maneuvering strategy for loitering under a strong, steady wind. The wind estimation uses Global Positioning System velocity only through a novel filter design without airspeed measurement. The wind estimate is then used to guide the aircraft to crab in a direction perpendicular to the wind, thereby avoiding large changes in flight variables if an orbit-type maneuver is attempted. The proposed method is demonstrated through flight tests.

Description: We propose biased estimators to find the direction of arrival of emitters present in the mainlobe of a spinning antenna-based electronic intelligence system. The proposed estimators were constructed by using Bayesian techniques and by performing a linear transformation and an affine transformation on the maximum likelihood estimator. From a Monte Carlo simulation and experimental results, we demonstrate that the proposed estimators outperform the limit set by the popular performance benchmark, the Cramer-Rao lower bound.

Description: Objective: We present the framework for wearable joint rehabilitation assessment following musculoskeletal injury. We propose a multimodal sensing (i.e., contact based and airborne measurement of joint acoustic emission) system for at-home monitoring. Methods: We used three types of microphones—electret, MEMS, and piezoelectric film microphones—to obtain joint sounds in healthy collegiate athletes during unloaded flexion/extension, and we evaluated the robustness of each microphone's measurements via: 1) signal quality and 2) within-day consistency. Results: First, air microphones acquired higher quality signals than contact microphones (signal-to-noise-and-interference ratio of 11.7 and 12.4 dB for electret and MEMS, respectively, versus 8.4 dB for piezoelectric). Furthermore, air microphones measured similar acoustic signatures on the skin and 5 cm off the skin (∼4.5× smaller amplitude). Second, the main acoustic event during repetitive motions occurred at consistent joint angles (intra-class correlation coefficient ICC(1, 1) = 0.94 and ICC(1, k) = 0.99). Additionally, we found that this angular location was similar between right and left legs, with asymmetry observed in only a few individuals. Conclusion: We recommend using air microphones for wearable joint sound sensing; for practical implementation of contact microphones in a wearable device, interface noise must be reduced. Importantly, we show that airborne signals can be measured consistently and that healthy left and right knees often produce a similar pattern in acoustic emissions. Significance: These proposed methods have the potential for enabling knee joint acoustics measurement outside the clinic/lab and permitting long-term monitoring of knee health for patients rehabilitating an acute knee joint injury.

Description: A new method was proposed for chirp signal detection and estimation built on the frame-based fast Fourier transform (FFT). The proposed method uses the peak frequency difference between FFT frames to detect a chirp signal and estimate chirp rate. This approach differs from conventional methods and is easy to implement. It generates more accurate chirp rate estimation especially under a low signal-to-noise ratio. Simulation and experimental data are used to verify the proposed methods.

Description: As telehealth applications emerge, the need for accurate and reliable biosignal quality indices has increased. One typical modality used in remote patient monitoring is the electrocardiogram (ECG), which is inherently susceptible to several different noise sources, including environmental (e.g., powerline interference), experimental (e.g., movement artifacts), and physiological (e.g., muscle and breathing artifacts). Accurate measurement of ECG quality can allow for automated decision support systems to make intelligent decisions about patient conditions. This is particularly true for in-home monitoring applications, where the patient is mobile and the ECG signal can be severely corrupted by movement artifacts. In this paper, we propose an innovative ECG quality index based on the so-called modulation spectral signal representation. The representation quantifies the rate of change of ECG spectral components, which are shown to be different from the rate of change of typical ECG noise sources. The proposed modulation spectral-based quality index, MS-QI, was tested on 1) synthetic ECG signals corrupted by varying levels of noise, 2) single-lead recorded data using the Hexoskin garment during three activity levels (sitting, walking, running), 3) 12-lead recorded data using conventional ECG machines (Computing in Cardiology 2011 dataset), and 4) two-lead ambulatory ECG recorded from arrhythmia patients (MIT-BIH Arrhythmia Database). Experimental results showed the proposed index outperforming two conventional benchmark quality measures, particularly in the scenarios involving recorded data in real-world environments.

Description: Platelet-rich plasma (PRP) is a volume of autologous plasma that has a higher platelet concentration above baseline. It has already been approved as a new therapeutic modality and investigated in clinics, such as bone repair and regeneration, and oral surgery, with low cost-effectiveness ratio. At present, PRP is mostly prepared using a centrifuge. However, this method has several shortcomings, such as long preparation time (30 min), complexity in operation, and contamination of red blood cells (RBCs). In this paper, a new PRP preparation approach was proposed and tested. Ultrasound waves (4.5 MHz) generated from piezoelectric ceramics can establish standing waves inside a syringe filled with the whole blood. Subsequently, RBCs would accumulate at the locations of pressure nodes in response to acoustic radiation force, and the formed clusters would have a high speed of sedimentation. It is found that the PRP prepared by the proposed device can achieve higher platelet concentration and less RBCs contamination than a commercial centrifugal device, but similar growth factor (i.e., PDGF-ββ). In addition, the sedimentation process under centrifugation and sonication was simulated using the Mason–Weaver equation and compared with each other to illustrate the differences between these two technologies and to optimize the design in the future. Altogether, ultrasound method is an effective method of PRP preparation with comparable outcomes as the commercially available centrifugal products.

Description: The quasimaximum likelihood (QML) estimator of polynomial-phase signals (PPSs) is based on the maximization of the short-time Fourier transform and suffers from aliasing when signals are sampled below the Nyquist sampling rate. In this paper, a phase unwrapping procedure has been proposed as an additional step in the QML to estimate parameters of such signals. Statistical study has shown excellent performance of the proposed approach.

Description: A new wireless sensor was designed, fabricated, and applied for in situ monitoring of tensile force at a wound site. The sensor was comprised of a thin strip of magnetoelastic material with its two ends connected to suture threads for securing the sensor across a wound repair site. Since the sensor was remotely interrogated by applying an ac magnetic field and capturing the resulting magnetic field, it did not require direct wire connections to an external device or internal battery for long-term use. Due to its magnetoelastic property, the application of a tensile force changed the magnetic permeability of the sensor, altering the amplitude of the measured magnetic field. This study presents two sensor designs: one for high and one for low-force ranges. A sensor was fabricated by directly adhering the magnetoelastic strip to the suture. This sensor showed good sensitivity at low force, but its response saturated at about 1.5 N. To monitor high tensile force, the magnetoelastic strip was attached to a metal strip for load sharing. The suture thread was attached to the both ends of the metal strip so only a fraction of the applied force was directed to the sensor, allowing it to exhibit good sensitivity even at 44.5 N. The sensor was applied to two ex vivo models: a sutured section of porcine skin and a whitetail deer Achilles tendon. The results demonstrate the potential for in vivo force monitoring at a wound repair site.

Description: This study presents a precise way to detect the third ( $S_{3}$ ) heart sound, which is recognized as an important indication of heart failure, based on nonlinear single decomposition and time–frequency localization. The detection of the $S_{3}$ is obscured due to its significantly low energy and frequency. Even more, the detected $S_{3}$ may be misunderstood as an abnormal second heart sound with a fixed split, which was not addressed in the literature. To detect such $S_{3}$ , the Hilbert vibration decomposition method is applied to decompose the heart sound into a certain number of subcomponents while intactly preserving the phase information. Thus, the time information of all of the decomposed components are unchanged, which further expedites the identification and localization of any module/section of a signal properly. Next, the proposed localization step is applied to the decomposed subcomponents by using smoothed pseudo Wigner–Ville distribution followed by the reassignment method. Finally, based on the positional information, the $S_{3}$ is distinguished and confirmed by measuring time delays between the $S_{2}$ and $S_{3}$ . In total, 82 sets of cardiac cycles collected from different databases including Texas Heart Institute database are examined for evaluation of the proposed method. The result analysis shows that the proposed method can detect the $S_{3}$ correctly, even when the - ormalized temporal energy of $S_{3}$ is larger than 0.16, and the frequency of those is larger than 34 Hz. In a performance analysis, the proposed method demonstrates that the accuracy rate of $S_{3}$ detection is as high as 93.9%, which is significantly higher compared with the other methods. Such findings prove the robustness of the proposed idea for detecting substantially low-energized $S_{3}$ .

Description: Electromagnetic (EM) tracking systems are highly susceptible to field distortion. The interference can cause measurement errors up to a few centimeters in clinical environments, which limits the reliability of these systems. Unless corrected for, this measurement error imperils the success of clinical procedures. It is therefore fundamental to dynamically calibrate EM tracking systems and compensate for measurement error caused by field distorting objects commonly present in clinical environments. We propose to combine a motion model with observations of redundant EM sensors and compensate for field distortions in real time. We employ a simultaneous localization and mapping technique to accurately estimate the pose of the tracked instrument while creating the field distortion map. We conducted experiments with six degrees-of-freedom motions in the presence of field distorting objects in research and clinical environments. We applied our approach to improve the EM tracking accuracy and compared our results to a conventional sensor fusion technique. Using our approach, the maximum tracking error was reduced by 67% for position measurements and by 64% for orientation measurements. Currently, clinical applications of EM trackers are hampered by the adverse distortion effects. Our approach introduces a novel method for dynamic field distortion compensation, independent from preoperative calibrations or external tracking devices, and enables reliable EM navigation for potential applications.

Description: A feasibility study on a new technique capable of monitoring localized sweat rate is explored in this paper. Wearable devices commonly used in clinical practice for sweat sampling (i.e., Macroducts) were positioned on the body of an athlete whose sweat rate was then monitored during cycling sessions. The position at which the sweat fills the Macroduct was indicated by a contrasting marker and captured via a series of time-stamped photos or a video recording of the device during an exercise period. Given that the time of each captured image/frame is known (either through time stamp on photos or the constant frame rate of the video capture), it was, therefore, possible to estimate the sweat flow rate through a simple calibration model. The importance of gathering such valuable information is described, together with the results from a number of exercise trials to investigate the viability of this approach.

Description: Objective: A novel high-precision approach [lifetime-decomposition measurement (LTDM)] for the assessment of the glomerular filtration rate (GFR) based on clearance measurements of exogenous filtration marker. Methods: The time-correlated single photon counting (TCSPC) acquisition in combination with a new decomposition method allows the separation of signal and background from transcutaneous measurements of GFR. Results: The performance of LTDM is compared versus the commercially available NIC-kidney patch-based system for transcutaneous GFR measurement. Measurements are performed in awake Sprague Dawley (SD) rats. Using the standard concentration required for the NIC-kidney system [7-mg/100-g body weight (b.w.) FITC-Sinistrin] as reference, the mean difference (bias) of the elimination curves GFR between LTDM and NIC-kidney was 4.8%. On the same animal and same day, the capability of LTDM to measure GFR with a FITC-Sinistrin dose reduced by a factor of 200 (35-μg/100-g b.w.) was tested as well. The mean differences (half lives with low dose using LTDM compared with those using first, the NIC-Kidney system and its standard concentration, and second, LTDM with the same concentration as for the NIC-Kidney system) were 3.4% and 4.5%, respectively. Conclusion: We demonstrate that with the LTDM strategy substantial reductions in marker concentrations are possible at the same level of accuracy. Significance: LTDM aims to resolve the issue of the currently necessary large doses of fluorescence tracer required for transcutaneous GFR measurement. Due to substantially less influences from autofluorescence and artifacts, the proposed method outperforms other existing techniques for accurate percutaneous organ function measurement.

Description: Classic brain–machine interface (BMI) approaches decode neural signals from the brain responsible for achieving specific motor movements, which subsequently command prosthetic devices. Brain activities adaptively change during the control of the neuroprosthesis in BMIs, where the alteration of the preferred direction and the modulation of the gain depth are observed. The static neural tuning models have been limited by fixed codes, resulting in a decay of decoding performance over the course of the movement and subsequent instability in motor performance. To achieve stable performance, we propose a dual sequential Monte Carlo adaptive point process method, which models and decodes the gradually changing modulation depth of individual neuron over the course of a movement. We use multichannel neural spike trains from the primary motor cortex of a monkey trained to perform a target pursuit task using a joystick. Our results show that our computational approach successfully tracks the neural modulation depth over time with better goodness-of-fit than classic static neural tuning models, resulting in smaller errors between the true kinematics and the estimations in both simulated and real data. Our novel decoding approach suggests that the brain may employ such strategies to achieve stable motor output, i.e., plastic neural tuning is a feature of neural systems. BMI users may benefit from this adaptive algorithm to achieve more complex and controlled movement outcomes.

Description: With shrinking process technology, decreasing supply voltage, and increasing clock frequency, noise reduction becomes more and more crucial to the success of modern mixed-signal system-on-chip design. To eliminate the switching noise due to crosstalk coupling between analog and digital signals, it is essential to fully separate the routing paths of analog and digital nets when generating mixed-signal layouts. Different from previous works which cannot fully separate analog and digital routing paths, this paper presents a novel hierarchical deterministic mixed-signal layout synthesis approach with the separation of analog and digital signal paths for switching noise elimination. Experimental results based on a third-order $ {Sigma Delta }$ modulator show that the proposed approach can result in various layouts with separated analog and digital signal paths while achieving better signal-to-noise and distortion ratio, and overall performance specifications.

Description: Efficient performance modeling of today’s analog and mixed-signal circuits is an important yet challenging task, due to the high-dimensional variation space and expensive circuit simulation. In this paper, we propose a novel performance modeling algorithm that is referred to as Bayesian model fusion (BMF) to address this challenge. The key idea of BMF is to borrow the information collected from an early stage (e.g., schematic level) to facilitate efficient performance modeling at a late stage (e.g., post layout). Such a goal is achieved by statistically modeling the performance correlation between early and late stages through Bayesian inference. Furthermore, to make the proposed BMF method of practical utility, four implementation issues, including: 1) prior mapping; 2) missing prior knowledge; 3) fast solver; and 4) prior and hyper-parameter selection, are carefully considered in this paper. Two circuit examples designed in a commercial 32 nm CMOS silicon on insulator process demonstrate that the proposed BMF method achieves up to $9times $ runtime speed-up over the traditional modeling technique without surrendering any accuracy.

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Description: Layout-dependent effects (LDEs) have become a critical issue in modern analog and mixed-signal circuit designs. The three major sources of LDEs, well proximity, length of oxide diffusion, and oxide-to-oxide spacing, significantly affect the threshold voltage and mobility of devices in advanced technology nodes. In this paper, we propose the first work to consider the three major sources of LDEs during analog placement. We first transform the three LDE models into nonlinear analytical placement models. Then an LDE-aware analytical analog placement algorithm is presented to mitigate the influence of the LDEs while improving circuit performance. Experimental results show that our placement algorithm can effectively and efficiently reduce the LDE-induced variations and improve circuit performance.

Description: Reconfigurable digital filter is being widely used in applications such as communication and signal processing. Its performance, power consumption, and logic resource utilization are the major factors to be taken into consideration when designing the filters. This paper proposes a concise canonic signed digit coefficient grouping method aiming at reducing the number of common subexpressions (CSs). Further, we statistically analyze every CS occurance for numerous sorts of the finite-impulse response (FIR) filters and obtain characterization of the distribution behavior for all the possible CS patterns in a 16-bit coefficient. Thus, a novel processing element structure is proposed to form a medium-grain array for computationally efficient realization of reconfigurable FIR filter. The experiment results suggest such design implementations typically achieve 21% reduction in silicon area, 20% decrease in power consumption, and 14% improvement in operation speed in comparison to other conventional FIR architectures.

Description: Digital microfluidic biochips (DMFBs) are gaining increasing attention with promising applications for automating and miniaturizing laboratory procedures in biochemistry. In DMFBs, cross-contamination of droplets with different biomolecules is a major issue, which causes significant errors in bioassays. Washing operations are introduced to clean the cross-contamination spots. However, existing works have oversimplified assumptions on the washing behavior, which either assume infinite washing capacity, or ignore the routing conflicts between functional and washing droplets. This paper proposes the first integrated functional and washing droplet routing flow, which considers practical issues including the finite washing capacity constraint, and the routing conflicts between functional and washing droplets. Washing droplets of different sizes are also proposed to wash the congested cross-contamination spots. Effectiveness of the proposed method is validated by real-life biochemical applications.

Description: It is known that bus-oriented escape routing and area routing are necessary in a high-speed printed circuit board (PCB) design. In this paper, given a set of global routed buses in a high-speed PCB design, it is assumed that the routed nets in a single bus are represented as a bus-oriented net between two escaped boundary pins. Based on the construction of a virtual wall between two circuit components, the connection transformation of the given bus-oriented nets inside a closed region and the construction of a covering graph for the represented intervals, an iterative modified left-edge algorithm is proposed to minimize the number of the assigned layers and assign all the bus-oriented nets onto the available layers. Compared with Tsai’s algorithm, the experimental results show that our proposed algorithm reduces 15.0% of the layer number and 21.9% of CPU time for six tested examples on the average, respectively. Compared with Chin’s algorithm, the experimental results show that our proposed algorithm use less CPU time to reduce 15.0% of the layer number for six tested examples on the average.

Description: Provides a listing of the editors, board members, and current staff for this issue of the publication.

Description: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Description: Applications executed on multicore embedded systems interact with system software [such as the operating system (OS)] and hardware, leading to widely varying thermal profiles which accelerate some aging mechanisms, reducing the lifetime reliability. Effectively managing the temperature therefore requires: 1) autonomous detection of changes in application workload and 2) appropriate selection of control levers to manage thermal profiles of these workloads. In this paper, we propose a technique for workload change detection using density ratio-based statistical divergence between overlapping sliding windows of CPU performance statistics. This is integrated in a runtime approach for thermal management, which uses reinforcement learning to select workload-specific thermal control levers by sampling on-board thermal sensors. Identified control levers override the OSs native thread allocation decision and scale hardware voltage–frequency to improve average temperature, peak temperature, and thermal cycling. The proposed approach is validated through its implementation as a hierarchical runtime manager for Linux, with heuristic-based thread affinity selected from the upper hierarchy to reduce thermal cycling and learning-based voltage–frequency selected from the lower hierarchy to reduce average and peak temperatures. Experiments conducted with mobile, embedded, and high performance applications on ARM-based embedded systems demonstrate that the proposed approach increases workload change detection accuracy by an average ${3.4times }$ , reducing the average temperature by 4 °C–25 °C, peak temperature by 6 °C–24 °C, and thermal cycling by 7%–35% over state-of-the-art approaches.

Description: This paper proposes a three-tier algorithmic framework as the basis for the flexible design of data-driven structural health monitoring (SHM) systems. The three major functions of the SHM system, including data normalization, feature extraction, and hypothesis testing (HT), are mapped to the three layers of the framework. The first tier of the framework is devoted to data normalization. Machine learning (ML) methods are adopted to normalize available data sets by binning data sets to similar environmental and operational conditions (EOCs) of the system. Specifically, affinity propagation clustering is used to delineate data into groups of similar EOC. Once data are normalized by EOC, the second tier of the framework extracts features from the data to serve as condition parameters (CPs) for damage assessment. To ascertain the health state of the structure, the third tier of the framework is devoted to statistical analysis of the CP through HT. An intrinsic goal of the study is to explore the modularity of the three tier framework as a means of offering SHM system designers opportunity to explore and test different computational block sets at each layer to maximize the detection capability of the SHM system. Various realizations of the three-tier modular framework are presented and applied to acceleration and EOC data collected from an operational 3-kW wind turbine. In total, 354 data sets are collected from the turbine, including tower lateral accelerations in two orthogonal directions at six heights, wind speed and wind direction; 317 of the data sets correspond to the wind turbine in a healthy state and 37 with the wind turbine in a damage state. Using quantitative metrics derived from receiver operating characteristic (ROC) curves, the damage classification capabilities of the framework are validated and shown to accurately identify intentionally introduced damage in the turbine.

Description: Reinforced concrete infrastructure assets built during the last century are required to stay in-service beyond their intended design lives, which requires techniques that allow the effects of deterioration and overloading to be monitored to ensure these assets are still fit for purpose. One such indicator of distress in a reinforced concrete structure is crack movement; models have been proposed that allow the internal stresses within a structure to be estimated if crack width and slip can be measured. This paper introduces a technique that uses digital image correlation (DIC) to measure not only crack width but also crack slip. The effect of curvature on this measurement technique is discussed and a method for minimizing the errors due to curvature is presented. The technique is used to measure crack width and slip in reinforced concrete beams with both small crack slip and significant crack slip. The results suggest that the technique can be used to measure crack movement in beams once the effects of curvature are accounted for. Future research in this area will focus on development of this technique for use in the field.

Description: Ultrasonic guided waves are an attractive tool for structural health monitoring due to their capability to rapidly assess large regions of a structure. Yet, most guided wave based methods for detecting, locating, and classifying structural damage rely on our ability to accurately predict guided wave behavior. Characterizing and predicting guided wave behavior is difficult, particularly in mechanically complex materials such as fiber-reinforced composites. In this paper, we address this challenge through a sparse wavenumber analysis framework. Sparse wavenumber analysis integrates physics-based models, signal processing algorithms for compressive sensing, and a small number of local measurements to predict global wave behavior. We implement sparse wavenumber analysis for three wave systems: standing waves on a string, Lamb waves in an isotropic plate, and guided waves in a unidirectional, anisotropic plate. Through the use of simulation and experimental data, we show that sparse wavenumber analysis can accurately recover the sparse representations (i.e., the eigenmodes) of each system and then use these representations to predict global wave behavior. For the anisotropic plate, we accurately predict 149765 experimental time-domain measurements from only 36 local measurements.

Description: Guided wave sensors are widely used in a number of industries and have found particular application in the oil and gas industry for the inspection of pipework. Traditionally this type of sensor was used for one-off inspections, but in recent years there has been a move towards permanent installation of the sensor. This has enabled highly repeatable readings of the same section of pipe, potentially allowing improvements in defect detection and classification. This paper proposes a novel approach using independent component analysis to decompose repeat guided wave signals into constituent independent components. This separates the defect from coherent noise caused by changing environmental conditions, improving detectability. This paper demonstrates independent component analysis applied to guided wave signals from a range of industrial inspection scenarios. The analysis is performed on test data from pipe loops that have been subject to multiple temperature cycles both in undamaged and damaged states. In addition to processing data from experimental damaged conditions, simulated damage signals have been added to “undamaged” experimental data, so enabling multiple different damage scenarios to be investigated. The algorithm has also been used to process guided wave signals from finite element simulations of a pipe with distributed shallow general corrosion, within which there is a patch of severe corrosion. In all these scenarios, the independent component analysis algorithm was able to extract the defect signal, rejecting coherent noise.

Description: Integrated structural health monitoring and damage prognosis (SHM-DP) methodologies, coupled with sensor-based nondestructive evaluation (NDE) techniques, are becoming increasingly important for the near-real-time condition assessment (i.e., SHM) and future performance predictions (i.e., DP) of aging mechanical systems, civil structures, and infrastructure networks, as well as automotive, naval, and aerospace vehicles. A successful SHM-DP strategy, capable of identifying all critical damage mechanisms while accounting for all relevant sources of uncertainty, can be used as an advanced tool to effectively and optimally manage the life-cycle of the monitored system, recursively forecast its remaining useful life (RUL), and ultimately reduce the overall ownership cost through dynamic reliability-based inspection and maintenance (RBIM) plans, system downtime minimization, catastrophic failure prevention, and potential RUL extension. In this perspective, fatigue damage propagation is one of the most critical and unpredictable deterioration processes for a large variety of structural and mechanical systems that are subjected repeatedly to cyclic and/or random operational loading during their service life. Within this limited scope, the authors developed a comprehensive NDE-based SHM-DP framework for recursively predicting the time-varying system reliability and the remaining fatigue life (RFL) of monitored systems subjected to deterioration by multi-site fatigue damage propagation. This paper provides a brief overview of the proposed framework and then uses a set of experimental fatigue test data to perform a thorough statistical performance assessment of the developed methodology at the local reliability component level (i.e., single damage mechanism and single damage location) including NDE detectability and measurement uncertainty as well as both load and model parameter uncertainty.

Description: Accurate and reliable damage characterization (i.e., damage detection, localization, and evaluation of extent) in civil structures and infrastructure is an important objective of structural health monitoring (SHM). Highly accurate and reliable characterization of damage at early stages requires continuous or quasi-continuous direct sensing of the critical parameters. Direct sensing requires deploying dense arrays of sensors, to enhance the probability that damage will result in signals that can be directly acquired by the sensors. However, coverage by dense arrays of sensors over the large areas that are of relevance represents an enormous challenge for current technologies. Large area electronics (LAE) is an emerging technology that can enable the formation of dense sensor arrays spanning large areas (several square meters) on flexible substrates. This paper explores the requirements and technology for a sensing sheet for SHM based on LAE and crystalline silicon CMOS integrated circuits (ICs). The sensing sheet contains a dense array of thin-film full-bridge resistive strain sensors, along with the electronics for strain readout, full-system self-powering, and communication. Research on several stages is presented for translating the sensing sheet to practical SHM applications. This includes experimental characterization of an individual sensor’s response when exposed to cracks in concrete and steel; theoretical and experimental performance evaluation of various geometrical parameters of the sensing sheet; and development of the electronics necessary for sensor readout, power management, and sensor-data communication. The concept of direct sensing has been experimentally validated, and the potential of a sensing sheet to provide direct sensing and successful damage characterization has been evaluated in the laboratory setting. A prototype of the sensing sheet has also been successfully developed and independently characterized in the laboratory, meeting the- required specifications. Thus, a sensing sheet for SHM applications shows promise both in terms of practicality and effectiveness.

Description: The inverse problem of synthetic aperture imaging radiometers (SAIRs) has been demonstrated to be not well posed. The regularization methods are crucial for providing unique and stable solutions in the reconstruction of radiometric brightness temperature (BT) maps. Different to deterministic ones, a new approach is presented by referring to the rule of Bayesian inference, providing a probability model of regularized constraints to combat the ill-posedness of finite-dimensional discrete inverse problems. In addition, the SAIR inverse problem can be converted into the probability estimation of the reconstructed BT. Furthermore, in application to both uniformly and nonuniformly spaced arrays, our method can obtain the optimal solution adaptively and avoid the dilemma of choosing the optimal regularization parameter. Finally, simulation results illustrating the effectiveness and performance of the proposed method are provided.

Description: A range-cell-focusing algorithm is proposed in order to improve the quality of the target image. In a synthetic aperture radar (SAR) imaging system, the range resolution depends on the frequency bandwidth and determines the ability to distinguish between targets that are very close to each other. In cases where the resolution and the SNR from the environment are not adequate, targets cannot be accurately visualized. In order to successively classify targets that are close, we are combining an enhanced-multiple-signal-classification spectrum as a weighting function to reproduce the raw data. The proposed algorithm improves classification and separation for close targets while suppressing artifacts in the final images. The targets of interest are stationary point scatterers. The results are obtained from both simulated and experimental data to demonstrate that the proposed algorithm provides better performance than a conventional SAR imaging algorithm, the range migration algorithm.

Description: This letter presents a multiscale edge detection method for multilook polarimetric synthetic aperture radar (PolSAR) images based on the nonsubsampled contourlet transform (NSCT). The NSCT can provide flexible multiscale and directional decomposition. In the multiscale decomposition, the coefficients of the nonsubsampled pyramid in the NSCT are calculated via maximizing the polarimetric contrast between the adjacent subband levels, instead of using the difference of the adjacent subbands as used in the additive noise model. By this way, we make the NSCT applicable to PolSAR data and multiband data. Then, the edges are detected in the NSCT domain based on a fusion of the directional subband coefficients at different scales. Experimental results with both simulated and real PolSAR data show that the present approach is robust to noise and the extracted edges are complete and continuous.

Description: A method for defining the spatial resolution of a Global Navigation Satellite System reflectometry delay–Doppler map (DDM) and of any derived geophysical product is proposed. An effective spatial resolution is derived as a function of measurement geometry and delay–Doppler (DD) interval, and as a more appropriate representation of resolution than the geometric resolution previously used in the literature. The definition more accurately accounts for variations in the scattered power across different pixels of the DDM and more accurately includes the power spreading effect caused by the Woodward ambiguity function. The dependence of the effective resolution on incidence angle, receiver altitude, and DD interval is analyzed and compared with the dependence of the geometric resolution with similar parameters.

Description: Laser scanner-captured 3-D point cloud data analysis is becoming more commonly used for remote sensing and plant science applications. Because of nonrigidity and complexity, reconstructing a 3-D model of a plant is extremely challenging. Existing algorithms often fail to find correct correspondences for plantlike thin structures. We address the problem of finding 3-D junction points in plant point cloud data as a first step of this correspondence matching process. Temporarily, we transform the 3-D problem into 2-D by performing appropriate coordinate transformations to the neighborhood of each 3-D point. Our proposed method has two steps. First, a statistical dip test of multimodality is performed to detect the nonlinearity of the local 2D structure. Then, each branch is approximated by sequential random-sample-consensus line fitting and a Euclidean clustering technique. The straight line parameters of each branch are extracted using total-least-squares estimation. Finally, the straight line equations are solved to determine if they intersect in the local neighborhood. Such junction points are good candidates for subsequent correspondence algorithms. Using these detected junction points, we formulate a correspondence algorithm as a subgraph matching problem and show that, without using traditional descriptor similarity-based matching, good correspondences can be obtained by simply considering geodesic distances among graph nodes. Experiments on synthetic and real ( Arabidopsis plant) data show that the proposed method outperforms the state of the art.

Description: Repeat-track analysis is commonly utilized to generate elevation change time series from satellite radar altimetry over ice sheets. It requires surface gradient (SG) correction due primarily to orbital drifts and radar-related empirical corrections caused by radar scatters from ice surface and potential subsurface. In this letter, two approaches, namely, the use of a digital elevation model (DEM) and the modified repeat-track analysis, which uses the accumulated Envisat altimetry profiles, are applied to correct the SG over both Greenland ice sheet (GrIS) and Antarctic ice sheet (AIS). By comparing the root mean square (rms) of elevation change time series after SG correction, the percentage of data (rms< 1 m) obtained by using modified repeat-track analysis is found to be 85% and 88% for the GrIS and AIS, respectively, as opposed to 45% and 44% if the DEM method is used. Furthermore, three cases are studied to assess empirical corrections for elevation retrieved from both ice-1 and ice-2 algorithms over the AIS. We conclude that the modified repeat-track analysis is more effective to remove topographic induced error. For the ice-2 algorithm, waveform shape parameters are needed in addition to applying corrections from changes in backscatter coefficients. The trend of elevation changes from the ice-1 algorithm with only backscatter analysis agrees with that from the ice-2 algorithm with corrections from backscatter coefficient changes and waveform shape parameters. This study could provide a potential data processing recipe for generating improved satellite radar altimetry elevation time series over ice sheets.

Description: Extracting ships from complex backgrounds is the bottleneck of ship detection in high-resolution optical satellite images. In this letter, we propose a nearly closed-form ship rotated bounding box space used for ship detection and design a method to generate a small number of highly potential candidates based on this space. We first analyze the possibility of accurately covering all ships by labeling rotated bounding boxes. Moreover, to reduce search space, we construct a nearly closed-form ship rotated bounding box space. Then, by scoring for each latent candidate in the space using a two-cascaded linear model followed by binary linear programming, we select a small number of highly potential candidates. Moreover, we also propose a fast version of our method. Experiments on our data set validate the effectiveness of our method and the efficiency of its fast version, which achieves a close detection rate in near real time.

Description: In this letter, a two-stage method for airport detection on remote sensing images is proposed. In the first stage, a new algorithm composed of several line-based processing steps is used for extraction of candidate airport regions. In the second stage, the scale-invariant feature transformation and Fisher vector coding are used for efficient representation of the airport and nonairport regions and support vector machines employed for classification. In order to evaluate the performance of the proposed method, extensive experiments are conducted on airports around the world with different layouts. The measures used in the evaluation are accuracy, sensitivity, and specificity. The proposed method achieved an accuracy of 94.6%, which was benchmarked with two previous methods to prove its superiority.

Description: High-resolution wide-swath synthetic aperture radar (SAR) systems are very attractive for the observation of dynamic processes on the Earth's surface, but they require the downlink of a huge volume of data. In order to comply with azimuth ambiguity requirements, in fact, a pulse repetition frequency much higher than the required processed Doppler bandwidth is often desirable. The volume of downlinked data, however, can be drastically reduced by performing Doppler filtering and decimation on board. A finite-impulse-response filter with a relatively small number of taps suffices to suppress the additional ambiguous components and to recover the original impulse response. This strategy is of special relevance for staggered SAR systems, which are typically characterized by a high oversampling factor. The proposed data reduction technique is also baseline for Tandem-L, where onboard Doppler filtering, resampling, and decimation will be jointly implemented.

Description: In this letter, we generate anisotropic bicontinuous media with different vertical and horizontal correlation functions. With the computer-generated bicontinuous medium, we then use numerical solutions of Maxwell equations in 3-dimensions (NMM3D) to calculate the anisotropic effective permittivities and the effective propagation constants of V and H polarizations. The copolarization phase difference (CPD) of VV and HH is then derived. The CPDs have recently been applied to the retrieval of snow water equivalent, snow depth, and anisotropy. The NMM3D simulation results are also compared with the results of the strong permittivity fluctuations in the low frequency limit and compared against the Maxwell–Garnett mixing formula.

Description: The IEEE GRS Society is grateful for the support given by the organizations listed and invites applications for Institutional Listings from other firms interested in the field of geoscience and remote sensing.

Description: The mucosal microanatomy of the large intestine is characterized by the presence of crypts of Lieberkühn, which is associated predominantly with goblet cells. Such cellular-level intestinal microstructures undergo morphological changes during the progression of bowel diseases, such as colon cancer or ulcerative colitis. As an indicator of gastric cancers, intestinal metaplasia in the large intestine is characterized by the appearance of goblet cells in gastric epithelium, and therefore, visualization of intestinal microstructure changes in cross-sectional view, particularly in vivo , in a high-speed fashion would assist early disease diagnosis and its treatment. In this paper, we investigated the capability of micro-optical coherence tomography $(mutext{OCT})$ for high-speed cellular-level crypt and goblet cell structures imaging ex vivo and in vivo . The adopted $mutext{OCT}$ system achieved a resolution of 2.0 $mutext{m}$ in both the lateral and axial directions in air. Ex vivo and video-rate in vivo images acquired in 3-D at respective imaging rates of 20 and 60 frames/s are presented and compared with the histology images. Imaging results show that the detailed microstructures, such as the crypt lumen and the goblet cells, could be clearly identified and are also comparable with those in histology images. Such comparisons also indicate that high-resolution $mutext{OCT}$ could be a powerful tool to perform “optical biopsy” in colorectal tissue. This is the first work, to the best of our knowledge, on cellular-level structure imaging in intestinal mucosa using spectral-- omain OCT.