ALBERT

Description: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Description: Presents information on forthcoming Engineering Management society conferences.

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

Description: Provides a listing of board members, committee members, editors, and society officers.

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: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Description: Objective: This work evaluates current 3-D image registration tools on clinically acquired abdominal computed tomography (CT) scans. Methods: Thirteen abdominal organs were manually labeled on a set of 100 CT images, and the 100 labeled images (i.e., atlases) were pairwise registered based on intensity information with six registration tools (FSL, ANTS-CC, ANTS-QUICK-MI, IRTK, NIFTYREG, and DEEDS). The Dice similarity coefficient (DSC), mean surface distance, and Hausdorff distance were calculated on the registered organs individually. Permutation tests and indifference-zone ranking were performed to examine the statistical and practical significance, respectively. Results: The results suggest that DEEDS yielded the best registration performance. However, due to the overall low DSC values, and substantial portion of low-performing outliers, great care must be taken when image registration is used for local interpretation of abdominal CT. Conclusion: There is substantial room for improvement in image registration for abdominal CT. Significance: All data and source code are available so that innovations in registration can be directly compared with the current generation of tools without excessive duplication of effort.

Description: Goal : In K-edge tomographic imaging with photon counting detectors, the energy window width of photon counting detectors significantly affects the signal-to-noise ratio (SNR) of measured intensity data and the contrast-to-noise ratio (CNR) of reconstructed images. In this paper, we present an optimization method to determine an optimal window width around a K-edge for optimal SNR and CNR. Methods : An objective function is designed to describe SNR of the projection data based on the Poisson distribution of detected X-ray photons. Then, a univariate optimization method is applied to obtain an X-ray energy window width. Results : Numerical simulations are performed to evaluate the proposed method, and the results show that the optimal energy window width obtained from the proposed method produces not only optimal SNR data in the projection domain but also optimal CNR values in the image domain. Conclusion : The proposed method in the projection domain can determine an optimal energy window width for X-ray photon counting imaging, and achieve optimality in both projection and image domains. Significance : Our study provides a practical way to determine the optimal energy window width of photon counting detectors, which helps improve contrast resolution for X-ray K-edge tomographic imaging.

Description: Objective: The propagation of electrophysiological activity measured by multichannel devices could have significant clinical implications. Gastric slow waves normally propagate along longitudinal paths that are evident in recordings of serosal potentials and transcutaneous magnetic fields. We employed a realistic model of gastric slow wave activity to simulate the transabdominal magnetogastrogram (MGG) recorded in a multichannel biomagnetometer and to determine characteristics of electrophysiological propagation from MGG measurements. Methods: Using MGG simulations of slow wave sources in a realistic abdomen (both superficial and deep sources) and in a horizontally-layered volume conductor, we compared two analytic methods (second-order blind identification, SOBI and surface current density, SCD) that allow quantitative characterization of slow wave propagation. We also evaluated the performance of the methods with simulated experimental noise. The methods were also validated in an experimental animal model. Results: Mean square errors in position estimates were within 2 cm of the correct position, and average propagation velocities within 2 mm/s of the actual velocities. SOBI propagation analysis outperformed the SCD method for dipoles in the superficial and horizontal layer models with and without additive noise. The SCD method gave better estimates for deep sources, but did not handle additive noise as well as SOBI. Conclusion: SOBI-MGG and SCD-MGG were used to quantify slow wave propagation in a realistic abdomen model of gastric electrical activity. Significance: These methods could be generalized to any propagating electrophysiological activity detected by multichannel sensor arrays.

Description:   Goal : This study aims at a systematic assessment of five computational models of a birdcage coil for magnetic resonance imaging (MRI) with respect to accuracy and computational cost. Methods : The models were implemented using the same geometrical model and numerical algorithm, but different driving methods (i.e., coil “defeaturing”). The defeatured models were labeled as: specific ( S2 ), generic ( G32 , G16 ), and hybrid ( H16, $hbox{H16}_{{rm fr}text{-}{rm forced}}$ ). The accuracy of the models was evaluated using the “symmetric mean absolute percentage error” (“SMAPE”), by comparison with measurements in terms of frequency response, as well as electric ( $|{vec E}|$ ) and magnetic ( $| {vec B} |$ ) field magnitude. Results : All the models computed the $| {vec B} |$ within 35% of the measurements, only the S2 , G32, and H16 were able to accurately model the $|{vec E}|$ inside the phantom with a maximum SMAPE of 16%. Outside the phantom, only the S2 showed a SMAPE lower than 11%. Conclusions : Results showed that assessing the accuracy of $| {vec B} |$ based only on comparison along the central longitudinal line of the coil can be misleading. Generic or hybrid coils — when properly modeling the currents along the rings/rungs — were sufficient to accur- tely reproduce the fields inside a phantom while a specific model was needed to accurately model $|{vec E}|$ in the space between coil and phantom. Significance : Computational modeling of birdcage body coils is extensively used in the evaluation of radiofrequency-induced heating during MRI. Experimental validation of numerical models is needed to determine if a model is an accurate representation of a physical coil.

Description: Objective: The objective of this research was to develop a bioimpedance platform for monitoring fluid volume in residual limbs of people with trans-tibial limb loss using prostheses. Methods: A customized multifrequency current stimulus profile was sent to thin flat electrodes positioned on the thigh and distal residual limb. The applied current signal and sensed voltage signals from four pairs of electrodes located on the anterior and posterior surfaces were demodulated into resistive and reactive components. An established electrical model (Cole) and segmental limb geometry model were used to convert results to extracellular and intracellular fluid volumes. Bench tests and testing on amputee participants were conducted to optimize the stimulus profile and electrode design and layout. Results: The proximal current injection electrode needed to be at least 25 cm from the proximal voltage sensing electrode. A thin layer of hydrogel needed to be present during testing to ensure good electrical coupling. Using a burst duration of 2.0 ms, intermission interval of 100 μs, and sampling delay of 10 μs at each of 24 frequencies except 5 kHz, which required a 200-μs sampling delay, the system achieved a sampling rate of 19.7 Hz. Conclusion: The designed bioimpedance platform allowed system settings and electrode layouts and positions to be optimized for amputee limb fluid volume measurement. Significance: The system will be useful toward identifying and ranking prosthetic design features and participant characteristics that impact residual limb fluid volume.

Description: Surface electromyography (sEMG) has been the predominant method for sensing electrical activity for a number of applications involving muscle–computer interfaces, including myoelectric control of prostheses and rehabilitation robots. Ultrasound imaging for sensing mechanical deformation of functional muscle compartments can overcome several limitations of sEMG, including the inability to differentiate between deep contiguous muscle compartments, low signal-to-noise ratio, and lack of a robust graded signal. The objective of this study was to evaluate the feasibility of real-time graded control using a computationally efficient method to differentiate between complex hand motions based on ultrasound imaging of forearm muscles. Dynamic ultrasound images of the forearm muscles were obtained from six able-bodied volunteers and analyzed to map muscle activity based on the deformation of the contracting muscles during different hand motions. Each participant performed 15 different hand motions, including digit flexion, different grips (i.e., power grasp and pinch grip), and grips in combination with wrist pronation. During the training phase, we generated a database of activity patterns corresponding to different hand motions for each participant. During the testing phase, novel activity patterns were classified using a nearest neighbor classification algorithm based on that database. The average classification accuracy was 91%. Real-time image-based control of a virtual hand showed an average classification accuracy of 92%. Our results demonstrate the feasibility of using ultrasound imaging as a robust muscle–computer interface. Potential clinical applications include control of multiarticulated prosthetic hands, stroke rehabilitation, and fundamental investigations of motor control and biomechanics.

Description: Presents the introductory editorial for this issue of the publication.

Description: We have been developing an automated cardiovascular drug infusion system for simultaneous control of arterial pressure (AP), cardiac output (CO), and left atrial pressure (P LA ) in decompensated heart failure (HF). In our prototype system, CO and P LA were measured invasively through thoracotomy. Furthermore, the control logic inevitably required use of inotropes to improve hemodynamics, which was not in line with clinical HF guidelines. The goal of this study was to solve these problems and develop a clinically feasible system. We integrated to the system minimally invasive monitors of CO and pulmonary capillary wedge pressure (PCWP, surrogates for P LA ) that we developed recently. We also redesigned the control logic to reduce the use of inotrope. We applied the newly developed system to nine dogs with decompensated HF. Once activated, our system started to control the infusion of vasodilator and diuretics in all the animals. Inotrope was not infused in three animals, and infused at minimal doses in six animals that were intolerant of vasodilator infusion alone. Within 50 min, our system controlled AP, CO, and PCWP to their respective targets accurately. Pulmonary artery catheterization confirmed optimization of hemodynamics (AP, from 98 ± 4 to 74 ± 11 mmHg; CO, from 2.2 ± 0.5 to 2.9 ± 0.3 L·min −1 ·m −2 ; PCWP, from 27.0 ± 6.6 to 13.8 ± 3.0 mmHg). In a minimally invasive setting while reducing the use of inotrope, our system succeeded in automatically optimizing the overall hemodynamics in canine models of HF. The present results pave the way for clinical application of our automated drug infusion system.

Description: Image registration is a key problem in a variety of applications, such as computer vision, medical image processing, pattern recognition, etc., while the application of registration is limited by time consumption and the accuracy in the case of large pose differences. Aimed at these two kinds of problems, we propose a fast rotation-free feature-based rigid registration method based on our proposed accelerated-NSIFT and GMM registration-based parallel optimization (PO-GMMREG). Our method is accelerated by using the GPU/CUDA programming and preserving only the location information without constructing the descriptor of each interest point, while its robustness to missing correspondences and outliers is improved by converting the interest point matching to Gaussian mixture model alignment. The accuracy in the case of large pose differences is settled by our proposed PO-GMMREG algorithm by constructing a set of initial transformations. Experimental results demonstrate that our proposed algorithm can fast rigidly register 3-D medical images and is reliable for aligning 3-D scans even when they exhibit a poor initialization.

Description: Spare parts support services have received increasing management attention due to the growing number of critical systems in many business sectors. In this paper, we examine an integrated system design approach to customize spare parts support services based on response time with inventory pooling strategies. To provide customized services that meet user requirements for spare part response time, we depart from the traditional spare parts management and develop a systematic approach to design service parts support services based on axiomatic design theory. In particular, we focus on pricing discrimination decisions in service parts contracts for two-tier users under a mechanism design framework. Distinguishing between users of next-day and same-day contracts for service parts operations, we further evaluate the effect of various inventory pool structures with reserve strategies through a simulation model for the objective of cost minimization. These analytical results of this new approach provide guidance for managers in customizing spare parts support services with the holistic consideration of pricing scheme, response time, and inventory policy.

Description: This paper considers an auto parts supplier who receives order release updates from its customers and revises its production plan for future periods on a weekly basis. The inaccuracy of the order releases causes significant costs in the form of premium expedited transportation, production overtime, and excess inventory. This setting provides a rich context for studying order release variance, because the supply chain has adopted a just-in-time (JIT) approach where ideal inventory levels are kept at zero. This leads to a high reliance on order release accuracy in order to manage production quantities. This paper presents an optimization model that extends previous approaches focused on optimizing production plans to the JIT setting. Furthermore, based on real order release information provided to the supplier, two simple adjustment heuristic methods are developed. The performances of these approaches are compared with relying solely on order releases received from the customers. The simple median-based adjustment heuristic performs the best of all the approaches. Implications of the analysis are also discussed.

Description: This paper considers a manufacturer who outsources the production of a product to multiple competing suppliers, who differ in their cost structures and in their capabilities for producing high-quality products. The manufacturer must design the sourcing process to ensure that the selected supplier has sufficient quality capability, while encouraging competition among the suppliers. We develop and analyze a mathematical model of performance-based contracting, a sourcing method that is appropriate when the manufacturer has imperfect information regarding the suppliers’ costs and capabilities. We compare the performance of performance-based contracting with that of a two-stage sourcing process, an alternative sourcing method that is more commonly used in practice. The theoretical results and managerial insights derived from this research can enable manufacturing firms to improve the management of their sourcing processes. In particular, we demonstrate that performance-based contracting with a symmetric linear penalty/reward function will always outperform the two-stage sourcing process from the perspective of the buyer and that the optimal penalty/reward rate is less than or equal to the unit warranty cost. In addition, performance-based contracting generally leads to a higher quality level provided by the winning supplier. However, the winning supplier is generally better off under the two-stage sourcing process.

Description: We investigate when and how an original equipment manufacturer (OEM) should offer a trade-in rebate to recover used products in order to achieve better price discrimination and weaken competition from third-party remanufacturers (3PRs). This paper is motivated by a major IT equipment OEM, which negotiates with customers to offer them personalized trade-in rebates to induce them to return their old products and purchase new units. The company also faces increasing competition from 3PRs. We model such a trade-in program with negotiated rebates through a generalized Nash bargaining framework. Our main research question is whether the OEM should compete with a 3PR using only a trade-in program or by offering remanufactured products, or through both options. In the absence of 3PRs, the OEM always prefers to offer the trade-in program compared with not offering a trade-in program. As a trade-in program also helps to restrict the supply of used products to 3PRs, one would expect that offering a trade-in program would be more attractive in the presence of a 3PR. We show, however, that the OEM may find it detrimental to offer a trade-in program when faced with competition from a 3PR. We also show that despite the fact that cores are readily available via the trade-ins, the trade-in program makes it less attractive for the OEM to remanufacture. Finally, we show that offering a trade-in program may also lead to lower total environmental impact, but only in the presence of remanufactured products.

Description: Decision support systems (DSSs) for supply chain risk management benefit from a holistic approach for mitigating risks, which include identification and assessment of risks and evaluation and selection of measures to appease risks. However, previous studies in this area overlooked probability estimation, measure selection, and assessment of interdependence of risks and measures. We aim to fill these gaps in the literature by proposing a two-stage DSSs that will assist managers in not only select mitigation strategies for supply chain risks, but also mitigation tactics when risks occur. Our DSS employs a novel matrix formulation for decision-tree analysis, which integrates expert judgments. We applied our models to the supply chain of a fast-expanding offshore-wind industry, which faces high levels of exposure to risks because of the associated complexities in this domain. The results demonstrate how to select mitigation strategies and mitigation tactics for managing supply chain risks within the offshore-wind industry.

Description: Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder that has recently seen serious increase in the number of affected subjects. In the last decade, neuroimaging has been shown to be a useful tool to understand AD and its prodromal stage, amnestic mild cognitive impairment (MCI). The majority of AD/MCI studies have focused on disease diagnosis, by formulating the problem as classification with a binary outcome of AD/MCI or healthy controls. There have recently emerged studies that associate image scans with continuous clinical scores that are expected to contain richer information than a binary outcome. However, very few studies aim at modeling multiple clinical scores simultaneously, even though it is commonly conceived that multivariate outcomes provide correlated and complementary information about the disease pathology. In this article, we propose a sparse multi-response tensor regression method to model multiple outcomes jointly as well as to model multiple voxels of an image jointly. The proposed method is particularly useful to both infer clinical scores and thus disease diagnosis, and to identify brain subregions that are highly relevant to the disease outcomes. We conducted experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and showed that the proposed method enhances the performance and clearly outperforms the competing solutions.

Description: A plethora of techniques for cardiac deformation imaging with 3D ultrasound, typically referred to as 3D speckle tracking techniques, are available from academia and industry. Although the benefits of single methods over alternative ones have been reported in separate publications, the intrinsic differences in the data and definitions used makes it hard to compare the relative performance of different solutions. To address this issue, we have recently proposed a framework to simulate realistic 3D echocardiographic recordings and used it to generate a common set of ground-truth data for 3D speckle tracking algorithms, which was made available online. The aim of this study was therefore to use the newly developed database to contrast non-commercial speckle tracking solutions from research groups with leading expertise in the field. The five techniques involved cover the most representative families of existing approaches, namely block-matching, radio-frequency tracking, optical flow and elastic image registration. The techniques were contrasted in terms of tracking and strain accuracy. The feasibility of the obtained strain measurements to diagnose pathology was also tested for ischemia and dyssynchrony.

Description: Ultra-high field MRI is an area of great interest for clinical research and basic science due to the increased signal-to-noise, spatial resolution and magnetic-susceptibility-based contrast. However, the fact that the electromagnetic wavelength in tissue is comparable to the relevant body dimensions means that the uniformity of the excitation field is much poorer than at lower field strengths. In addition to techniques such as transmit arrays, one simple but effective method to counteract this effect is to use high permittivity “pads”. Very high permittivities enable thinner, flexible pads to be used, but the limiting factor is wavelength effects within the pads themselves, which can lead to image artifacts. So far, all studies have used simple continuous rectangular/circular pad geometries. In this work we investigate how the wavelength effects can be partially mitigated utilizing shaped pad with holes. Several arrangements have been simulated, including low order pre-fractal geometries, which maintain the overall coverage of the pad, but can provide better image homogeneity in the region of interest or higher sensitivity depending on the setup. Experimental data in the form of in vivo human images at 7T were acquired to validate the simulation results.

Description: In this paper, we consider combined TV denoising and diffusion tensor fitting in DTI using the affine-invariant Riemannian metric on the space of diffusion tensors. Instead of first fitting the diffusion tensors, and then denoising them, we define a suitable TV type energy functional which incorporates the measured DWIs (using an inverse problem setup) and which measures the nearness of neighboring tensors in the manifold. To approach this functional, we propose generalized forward- backward splitting algorithms which combine an explicit and several implicit steps performed on a decomposition of the functional. We validate the performance of the derived algorithms on synthetic and real DTI data. In particular, we work on real 3D data. To our knowledge, the present paper describes the first approach to TV regularization in a combined manifold and inverse problem setup.

Description: We present a generic method for automatic detection of abnormal regions in medical images as deviations from a normative data base. The algorithm decomposes an image, or more broadly a function defined on the image grid, into the superposition of a normal part and a residual term. A statistical model is constructed with regional sparse learning to represent normative anatomical variations among a reference population (e.g., healthy controls), in conjunction with a Markov random field regularization that ensures mutual consistency of the regional learning among partially overlapping image blocks. The decomposition is performed in a principled way so that the normal part fits well with the learned normative model, while the residual term absorbs pathological patterns, which may then be detected through a statistical significance test. The decomposition is applied to multiple image features from an individual scan, detecting abnormalities using both intensity and shape information. We form an iterative scheme that interleaves abnormality detection with deformable registration, gradually improving robustness of the spatial normalization and precision of the detection. The algorithm is evaluated with simulated images and clinical data of brain lesions, and is shown to achieve robust deformable registration and localize pathological regions simultaneously. The algorithm is also applied on images from Alzheimer’s disease patients to demonstrate the generality of the method.

Description: Staining and scanning of tissue samples for microscopic examination is fraught with undesirable color variations arising from differences in raw materials and manufacturing techniques of stain vendors, staining protocols of labs, and color responses of digital scanners. When comparing tissue samples, color normalization and stain separation of the tissue images can be helpful for both pathologists and software. Techniques that are used for natural images fail to utilize structural properties of stained tissue samples and produce undesirable color distortions. The stain concentration cannot be negative. Tissue samples are stained with only a few stains and most tissue regions are characterized by at most one effective stain. We model these physical phenomena that define the tissue structure by first decomposing images in an unsupervised manner into stain density maps that are sparse and non-negative. For a given image, we combine its stain density maps with stain color basis of a pathologist-preferred target image, thus altering only its color while preserving its structure described by the maps. Stain density correlation with ground truth and preference by pathologists were higher for images normalized using our method when compared to other alternatives. We also propose a computationally faster extension of this technique for large whole-slide images that selects an appropriate patch sample instead of using the entire image to compute the stain color basis.

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

Description: We present a methodology to recover the geometrical calibration of conventional X-ray settings with the help of an ordinary video camera and visible fiducials that are present in the scene. After calibration, equivalent points of interest can be easily identifiable with the help of the epipolar geometry. The same procedure also allows the measurement of real anatomic lengths and angles and obtains accurate 3D locations from image points. Our approach completely eliminates the need for X-ray-opaque reference marks (and necessary supporting frames) which can sometimes be invasive for the patient, occlude the radiographic picture, and end up projected outside the imaging sensor area in oblique protocols. Two possible frameworks are envisioned: a spatially shifting X-ray anode around the patient/object and a moving patient that moves/rotates while the imaging system remains fixed. As a proof of concept, experiences with a device under test (DUT), an anthropomorphic phantom and a real brachytherapy session have been carried out. The results show that it is possible to identify common points with a proper level of accuracy and retrieve three-dimensional locations, lengths and shapes with a millimetric level of precision. The presented approach is simple and compatible with both current and legacy widespread diagnostic X-ray imaging deployments and it can represent a good and inexpensive alternative to other radiological modalities like CT.

Description: Cloud storage offers a popular environment for users to store and share content. Yet, content sharing leads to multiple downloads of the same content when users synchronize devices. These downloads contribute to bandwidth waste and increase server workloads. To what extent does this occur? Would network caches alleviate the problem? The authors address these questions by investigating the traffic generated by Dropbox, a popular cloud storage service. Using data collected from four networks, they show that a large fraction (57-70 percent) of downloads generated by Dropbox users is associated with content shared among multiple devices. Moreover, despite optimizations such as the LAN Sync protocol, up to 25 percent of Dropbox download traffic comes from content replicas. The authors then evaluate an alternative synchronization architecture that uses caches to offload storage servers from such downloads. Their experiments show that the approach cost-effectively avoids most repetitive downloads, benefiting service providers, the network, and end users.

Description: Bot detection -- identifying a software program that's using a computer system -- is an increasingly necessary security task. Existing solutions balance proof of human identity with unobtrusiveness in users' workflows. Cognitive modeling and natural interaction might provide stronger security and less intrusiveness.

Description: The IPv6 Internet is an important component of the Internet's continued growth and evolution. By several metrics, IPv6 has grown exponentially and now carries nontrivial amounts of production traffic. Less well-understood, however, is IPv6's topology and the way in which providers are using their IPv6 address allocations. Rather than relying on passive measurements or heuristics, which can bias inferences, here the authors use uniform active probing. They execute Internet Control Message Protocol (ICMP)-Paris traceroute probes to an address in each /48 in all /32's advertised in the global IPv6 routing table (approximately 400 million traces from 26 globally distributed vantage points). At this granularity, they characterize the distribution of IPv6 interface addresses in the wild, and find significant differences among providers and regions. By providing insight into the structure of the current IPv6 Internet router interface addressing method, their hope is to better inform efforts to design future intelligent, active IPv6 topology-mapping algorithms and systems.

Description: Since Jeff Howe introduced the term Crowdsourcing in 2006, this human-powered problem-solving paradigm has gained a lot of attention and has been a hot research topic in the field of computer science. Even though a lot of work has been conducted on this topic, so far we do not have a comprehensive survey on most relevant work done in the crowdsourcing field. In this paper, we aim to offer an overall picture of the current state of the art techniques in general-purpose crowdsourcing. According to their focus, we divide this work into three parts, which are: incentive design, task assignment, and quality control. For each part, we start with different problems faced in that area followed by a brief description of existing work and a discussion of pros and cons. In addition, we also present a real scenario on how the different techniques are used in implementing a location-based crowdsourcing platform, gMission. Finally, we highlight the limitations of the current general-purpose crowdsourcing techniques and present some open problems in this area.

Description: Any important data management and analytics tasks cannot be completely addressed by automated processes. These tasks, such as entity resolution, sentiment analysis, and image recognition can be enhanced through the use of human cognitive ability. Crowdsouring platforms are an effective way to harness the capabilities of people (i.e., the crowd) to apply human computation for such tasks. Thus, crowdsourced data management has become an area of increasing interest in research and industry. We identify three important problems in crowdsourced data management. (1) Quality Control: Workers may return noisy or incorrect results so effective techniques are required to achieve high quality; (2) Cost Control: The crowd is not free, and cost control aims to reduce the monetary cost; (3) Latency Control: The human workers can be slow, particularly compared to automated computing time scales, so latency-control techniques are required. There has been significant work addressing these three factors for designing crowdsourced tasks, developing crowdsourced data manipulation operators, and optimizing plans consisting of multiple operators. In this paper, we survey and synthesize a wide spectrum of existing studies on crowdsourced data management. Based on this analysis we then outline key factors that need to be considered to improve crowdsourced data management.

Description: Principal component analysis and the residual error is an effective anomaly detection technique. In an environment where anomalies are present in the training set, the derived principal components can be skewed by the anomalies. A further aspect of anomaly detection is that data might be distributed across different nodes in a network and their communication to a centralized processing unit is prohibited due to communication cost. Current solutions to distributed anomaly detection rely on a hierarchical network infrastructure to aggregate data or models; however, in this environment, links close to the root of the tree become critical and congested. In this paper, an algorithm is proposed that is more robust in its derivation of the principal components of a training set containing anomalies. A distributed form of the algorithm is then derived where each node in a network can iterate towards the centralized solution by exchanging small matrices with neighboring nodes. Experimental evaluations on both synthetic and real-world data sets demonstrate the superior performance of the proposed approach in comparison to principal component analysis and alternative anomaly detection techniques. In addition, it is shown that in a variety of network infrastructures, the distributed form of the anomaly detection model is able to derive a close approximation of the centralized model.

Description: Many applications deal with moving object datasets, e.g., mobile phone social networking, scientific simulations, and ride-sharing services. These applications need to handle a tremendous number of spatial objects that continuously move and execute spatial queries to explore their surroundings. To manage such update-heavy workloads, several throwaway index structures have recently been proposed, where a static index is rebuilt periodically from scratch rather than updated incrementally. It has been shown that throwaway indices outperform specialized moving-object indices that maintain location updates incrementally. However, throwaway indices suffer from scalability due to their single-server design and the only distributed throwaway index (D-MOVIES), extension of a centralized approach, does not scale out as the number of servers increases, especially during query processing phase.

Description: Collaborative filtering (CF) is out of question the most widely adopted and successful recommendation approach. A typical CF-based recommender system associates a user with a group of like-minded users based on their individual preferences over all the items, either explicit or implicit, and then recommends to the user some unobserved items enjoyed by the group. However, we find that two users with similar tastes on one item subset may have totally different tastes on another set. In other words, there exist many user-item subgroups each consisting of a subset of items and a group of like-minded users on these items. It is more reasonable to predict preferences through one user's correlated subgroups, but not the entire user-item matrix. In this paper, to find meaningful subgroups, we formulate a new Multiclass Co-Clustering (MCoC) model, which captures relations of user-to-item, user-to-user, and item-to-item simultaneously. Then, we combine traditional CF algorithms with subgroups for improving their top- $N$ recommendation performance. Our approach can be seen as a new extension of traditional clustering CF models. Systematic experiments on several real data sets have demonstrated the effectiveness of our proposed approach.

Description: With the rapid development of Web 2.0 and Online To Offline (O2O) marketing model, various online e vent- b ased s ocial n etwork s (EBSNs) are getting popular. An important task of EBSNs is to facilitate the most satisfactory event-participant arrangement for both sides, i.e., events enroll more participants and participants are arranged with personally interesting events. Existing approaches usually focus on the arrangement of each single event to a set of potential users, or ignore the conflicts between different events, which leads to infeasible or redundant arrangements. In this paper, to address the shortcomings of existing approaches, we first identify a more general and useful event-participant arrangement problem, called G lobal E vent-participant A rrangement with C onflict and C apacity ( $GEACC$ ) problem, focusing on the conflicts of different events and making event-participant arrangements in a global view. We find that the GEACC problem is NP-hard due to the conflicts among events. Thus, we design two approximation algorithms with provable approximation ratios and an exact algorithm with pruning technique to address this problem. In addition, we propose an online setting of GEACC, called OnlineGEACC, which is also practical in real-world scenarios. We further design an online algorithm with provable performance guarantee. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real and synthetic datasets.

Description: Given a point $p$ and a set of points $S$ , the kNN operation finds the $k$ closest points to in $S$ . It is a computational intensive task with a large range of applications such as knowledge discovery or data mining. However, as the volume and the dimension of data increase, only distributed approaches can perform such costly operation in a reasonable time. Recent works have focused on implementing efficient solutions using the MapReduce programming model because it is suitable for distributed large scale data processing. Although these works provide different solutions to the same problem, each one has particular constraints and properties. In this paper, we compare the different existing approaches for computing kNN on MapReduce, first theoretically, and then by performing an extensive experimental evaluation. To be able to compare solutions, we identify three generic steps for kNN computation on MapReduce: data pre-processing, data partitioning, and computation. We then analyze each step from load balancing, accuracy, and complexity aspects. Experiments in this paper use a variety of datasets, and analyze the impact of data volume, data dimension, and the value of k from many perspectives like time and space complexity, and accuracy. The experimental part brings new advantages and shortcomings that are discussed for each algorithm. To the best of our knowledge, this is the first pape- that compares kNN computing methods on MapReduce both theoretically and experimentally with the same setting. Overall, this paper can be used as a guide to tackle kNN-based practical problems in the context of big data.

Description: Mining communities or clusters in networks is valuable in analyzing, designing, and optimizing many natural and engineering complex systems, e.g., protein networks, power grid, and transportation systems. Most of the existing techniques view the community mining problem as an optimization problem based on a given quality function(e.g., modularity), however none of them are grounded with a systematic theory to identify the central nodes in the network. Moreover, how to reconcile the mining efficiency and the community quality still remains an open problem. In this paper, we attempt to address the above challenges by introducing a novel algorithm. First, a kernel function with a tunable influence factor is proposed to measure the leadership of each node, those nodes with highest local leadership can be viewed as the candidate central nodes. Then, we use a discrete-time dynamical system to describe the dynamical assignment of community membership; and formulate the serval conditions to guarantee the convergence of each node's dynamic trajectory, by which the hierarchical community structure of the network can be revealed. The proposed dynamical system is independent of the quality function used, so could also be applied in other community mining models. Our algorithm is highly efficient: the computational complexity analysis shows that the execution time is nearly linearly dependent on the number of nodes in sparse networks. We finally give demonstrative applications of the algorithm to a set of synthetic benchmark networks and also real-world networks to verify the algorithmic performance.

Description: When a microblogging user adopts some content propagated to her, we can attribute that to three behavioral factors, namely, topic virality , user virality , and user susceptibility . Topic virality measures the degree to which a topic attracts propagations by users. User virality and susceptibility refer to the ability of a user to propagate content to other users, and the propensity of a user adopting content propagated to her, respectively. In this paper, we study the problem of mining these behavioral factors specific to topics from microblogging content propagation data. We first construct a three dimensional tensor for representing the propagation instances. We then propose a tensor factorization framework to simultaneously derive the three sets of behavioral factors. Based on this framework, we develop a numerical factorization model and another probabilistic factorization variant. We also develop an efficient algorithm for the models’ parameters learning. Our experiments on a large Twitter dataset and synthetic datasets show that the proposed models can effectively mine the topic-specific behavioral factors of users and tweet topics. We further demonstrate that the proposed models consistently outperforms the other state-of-the-art content based models in retweet prediction over time.

Description: We propose an algorithm for detecting patterns exhibited by anomalous clusters in high dimensional discrete data. Unlike most anomaly detection (AD) methods, which detect individual anomalies, our proposed method detects groups ( clusters ) of anomalies; i.e., sets of points which collectively exhibit abnormal patterns. In many applications, this can lead to a better understanding of the nature of the atypical behavior and to identifying the sources of the anomalies. Moreover, we consider the case where the atypical patterns exhibit on only a small (salient) subset of the very high dimensional feature space. Individual AD techniques and techniques that detect anomalies using all the features typically fail to detect such anomalies, but our method can detect such instances collectively, discover the shared anomalous patterns exhibited by them, and identify the subsets of salient features. In this paper, we focus on detecting anomalous topics in a batch of text documents, developing our algorithm based on topic models. Results of our experiments show that our method can accurately detect anomalous topics and salient features (words) under each such topic in a synthetic data set and two real-world text corpora and achieves better performance compared to both standard group AD and individual AD techniques. All required code to reproduce our experiments is available from https://github.com/hsoleimani/ATD .

Description: Multilabel classification is prevalent in many real-world applications where data instances may be associated with multiple labels simultaneously. In multilabel classification, exploiting label correlations is an essential but nontrivial task. Most of the existing multilabel learning algorithms are either ineffective or computationally demanding and less scalable in exploiting label correlations. In this paper, we propose a co-evolutionary multilabel hypernetwork (Co-MLHN) as an attempt to exploit label correlations in an effective and efficient way. To this end, we firstly convert the traditional hypernetwork into a multilabel hypernetwork (MLHN) where label correlations are explicitly represented. We then propose a co-evolutionary learning algorithm to learn an integrated classification model for all labels. The proposed Co-MLHN exploits arbitrary order label correlations and has linear computational complexity with respect to the number of labels. Empirical studies on a broad range of multilabel data sets demonstrate that Co-MLHN achieves competitive results against state-of-the-art multilabel learning algorithms, in terms of both classification performance and scalability with respect to the number of labels.

Description: Query auto completion (QAC) methods recommend queries to search engine users when they start entering a query. Current QAC methods mostly rank query completions based on their past popularity, i.e., on the number of times they have previously been submitted as a query. However, query popularity changes over time and may vary drastically across users. Accordingly, the ranking of query completions should be adjusted. Previous time-sensitive and user-specific QAC methods have been developed separately, yielding significant improvements over methods that are neither time-sensitive nor personalized. We propose a hybrid QAC method that is both time-sensitive and personalized. We extend it to handle long-tail prefixes, which we achieve by assigning optimal weights to the contribution from time-sensitivity and personalization. Using real-world search log datasets, we return top $N$ query suggestions ranked by predicted popularity as estimated from popularity trends and cyclic popularity behavior; we rerank them by integrating similarities to a user's previous queries (both in the current session and in previous sessions). Our method outperforms state-of-the-art time-sensitive QAC baselines, achieving total improvements of between 3 and 7 percent in terms of mean reciprocal rank (MRR). After optimizing the weights, our extended model achieves MRR improvements of between 4 and 8 percent.

Description: Wrappers are pieces of software used to extract data from websites and structure them for further application processing. Unfortunately, websites are continuously evolving and structural changes happen with no forewarning, which usually results in wrappers working incorrectly. Thus, wrappers maintenance is necessary for detecting whether wrapper is extracting erroneous data. The solution consists of using verification models to detect whether wrapper output is statistically similar to the output produced by the wrapper itself when it was successfully invoked in the past. Current proposals present some weaknesses, as the data used to build these models are supposed to be homogeneous, independent, or representative enough, or following a single predefined mathematical model. In this paper, we present MAVE, a novel multilevel wrapper verification system that is based on one-class classification techniques to overcome previous weaknesses. The experimental results show that our proposal outperforms accuracy of current solutions.

Description: Provides a listing of board members, committee members, editors, and society officers.

Description: Certain inner feelings and physiological states like pain are subjective states that cannot be directly measured, but can be estimated from spontaneous facial expressions. Since they are typically characterized by subtle movements of facial parts, analysis of the facial details is required. To this end, we formulate a new regression method for continuous estimation of the intensity of facial behavior interpretation, called Doubly Sparse Relevance Vector Machine (DSRVM). DSRVM enforces double sparsity by jointly selecting the most relevant training examples (a.k.a. relevance vectors) and the most important kernels associated with facial parts relevant for interpretation of observed facial expressions. This advances prior work on multi-kernel learning, where sparsity of relevant kernels is typically ignored. Empirical evaluation on challenging Shoulder Pain videos, and the benchmark DISFA and SEMAINE datasets demonstrate that DSRVM outperforms competing approaches with a multi-fold reduction of running times in training and testing.

Description: During at-speed test of high performance sequential ICs using scan-based Logic BIST, the IC activity factor (AF) induced by the applied test vectors is significantly higher than that experienced during its in field operation. Consequently, power droop (PD) may take place during both shift and capture phases, which will slow down the circuit under test (CUT) signal transitions. At capture, this phenomenon is likely to be erroneously recognized as due to delay faults. As a result, a false test fail may be generated, with consequent increase in yield loss. In this paper, we propose two approaches to reduce the PD generated at capture during at-speed test of sequential circuits with scan-based Logic BIST using the Launch-On-Shift scheme. Both approaches increase the correlation between adjacent bits of the scan chains with respect to conventional scan-based LBIST. This way, the AF of the scan chains at capture is reduced. Consequently, the AF of the CUT at capture, thus the PD at capture, is also reduced compared to conventional scan-based LBIST. The former approach, hereinafter referred to as Low-Cost Approach (LCA), enables a 50 percent reduction in the worst case magnitude of PD during conventional logic BIST. It requires a small cost in terms of area overhead (of approximately 1.5 percent on average), and it does not increase the number of test vectors over the conventional scan-based LBIST to achieve the same Fault Coverage (FC). Moreover, compared to three recent alternative solutions, LCA features a comparable AF in the scan chains at capture, while requiring lower test time and area overhead. The second approach, hereinafter referred to as High-Reduction Approach (HRA), enables scalable PD reductions at capture of up to 87 percent, with limited additional costs in terms of area overhead and number of required test vectors for a given target FC, over our LCA approach. Particularly, compared to two of the three recent alternative solutions mentioned above, HRA en- bles a significantly lower AF in the scan chains during the application of test vectors, while requiring either a comparable area overhead or a significantly lower test time. Compared to the remaining alternative solutions mentioned above, HRA enables a similar AF in the scan chains at capture (approximately 90 percent lower than conventional scan-based LBIST), while requiring a significantly lower test time (approximately 4.87 times on average lower number of test vectors) and comparable area overhead (of approximately 1.9 percent on average).

Description: Glaucoma is a neurological disorder leading to blindness initially through the loss of retinal ganglion cells, followed by loss of neurons higher in the visual system. Some work has been undertaken to develop prostheses for glaucoma patients targeting tissues along the visual pathway, including the lateral geniculate nucleus (LGN) of the thalamus, but especially the visual cortex. This review makes the case for a visual prosthesis that targets the LGN. The compact nature and orderly structure of this nucleus make it a potentially better target to restore vision than the visual cortex. Existing research for the development of a thalamic visual prosthesis will be discussed along with the gaps that need to be addressed before such a technology could be applied clinically, as well as the challenge posed by the loss of LGN neurons as glaucoma progresses.

Description: Alterations in the health of muscles can be evaluated through the use of electrical impedance myography (EIM). To date, however, nearly all work in this field has relied upon the measurement of muscle at rest. To provide an insight into the contractile mechanisms of healthy and disease muscle, we evaluated the alterations in the spectroscopic impedance behavior of muscle during the active process of muscle contraction. The gastrocnemii from a total of 13 mice were studied (five wild type, four muscular dystrophy animals, and four amyotrophic lateral sclerosis animals). Muscle contraction was induced via monophasic current pulse stimulation of the sciatic nerve. Simultaneously, multisine EIM (1 kHz to 1 MHz) and force measurements of the muscle were performed. Stimulation was applied at three different rates to produce mild, moderate, and strong contractions. We identified changes in both single and multifrequency data, as assessed by the Cole impedance model parameters. The processes of contraction and relaxation were clearly identified in the impedance spectra and quantified via derivative plots. Reductions in the center frequency ${f}_{mathrm{c}}$ were observed during the contraction consistent with the increasing muscle fiber diameter. Different EIM stimulation rate-dependencies were also detected across the three groups of animals.

Description: We describe a novel motion-tracking system, called MASK (magnetoarticulography for the assessment of speech kinematics) designed to track detailed orofacial movements during magnetoencephalographic (MEG) measures of human brain activity. A three-dimensional electromagnetic-tracking method was employed using lightweight coils energized with high-frequency sinusoidal currents, creating magnetic dipoles that can be continuously localized by the MEG sensors. In addition to being compatible with commercial MEG devices, this system has advantages over optical or video methods in that it can record nonline-of-sight movements (e.g., tongue movements) and advantages over surface electromyographic recordings, which are prone to movement-related artifacts and signal crosstalk. Static and dynamic tracking accuracy was evaluated using calibration devices with fixed intercoil distances. MEG data were collected in two healthy adult volunteers to test feasibility of tracking movements during tongue and facial movement, and during overt speech. The MASK system was shown to have sufficient static and dynamic accuracy to track orofacial movements within the MEG helmet. We successfully acquired spatially precise kinematic information time-locked to brain activity with high temporal resolution. We demonstrated successful tracking of oromotor and speech movements together with brain activity using the MASK system. This novel technology will provide an innovative tool in support of research and clinical applications for individuals with speech and other oromotor disorders.

Description: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.

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Description: Fiber based fluorescence lifetime imaging has shown great potential for intraoperative diagnosis and guidance of surgical procedures. Here we describe a novel method addressing a significant challenge for the practical implementation of this technique, i.e., the real-time display of the quantified biochemical or functional tissue properties superimposed on the interrogated area. Specifically, an aiming beam (450 nm) generated by a continuous-wave laser beam was merged with the pulsed fluorescence excitation light in a single delivery/collection fiber and then imaged and segmented using a color-based algorithm. We demonstrate that this approach enables continuous delineation of the interrogated location and dynamic augmentation of the acquired frames with the corresponding fluorescence decay parameters. The method was evaluated on a fluorescence phantom and fresh tissue samples. Current results demonstrate that 34 frames per second can be achieved for augmenting videos of 640 $times $ 512 pixels resolution. Also we show that the spatial resolution of the fluorescence lifetime map depends on the tissue optical properties, the scanning speed, and the frame rate. The dice similarity coefficient between the fluorescence phantom and the reconstructed maps was estimated to be as high as 93%. The reported method could become a valuable tool for augmenting the surgeon's field of view with diagnostic information derived from the analysis of fluorescence lifetime data in real-time using handheld, automated, or endoscopic scanning systems. Current method provides also a means for maintaining the tissue light exposure within safety limits. This study provides a framework for using an aiming beam with other point spectroscopy applications.

Description: Simultaneous Multi-Slice (SMS) magnetic resonance imaging (MRI) is a rapidly evolving technique for increasing imaging speed. Controlled aliasing techniques utilize periodic undersampling patterns to help mitigate the loss in signal-to-noise ratio (SNR) in SMS MRI. To evaluate the performance of different undersampling patterns, a quantitative description of the image SNR loss is needed. Additionally, eddy current effects in echo planar imaging (EPI) lead to slice-specific Nyquist ghosting artifacts. These artifacts cannot be accurately corrected for each individual slice before or after slice-unaliasing. In this work, we propose a hybrid-space sensitivity encoding (SENSE) reconstruction framework for SMS MRI by adopting a three-dimensional representation of the SMS acquisition. Analytical SNR loss maps are derived for SMS acquisitions with arbitrary phase encoding undersampling patterns. Moreover, we propose a matrix-decoding correction method that corrects the slice-specific Nyquist ghosting artifacts in SMS EPI acquisitions. Brain images demonstrate that the proposed hybrid-space SENSE reconstruction generates images with comparable quality to commonly used split-slice-generalized autocalibrating partially parallel acquisition reconstruction. The analytical SNR loss maps agree with those calculated by a Monte Carlo based method, but require less computation time for high quality maps. The analytical maps enable a fair comparison between the performances of coherent and incoherent SMS undersampling patterns. Phantom and brain SMS EPI images show that the matrix-decoding method performs better than the single-slice and slice-averaged Nyquist ghosting correction methods under the hybrid-space SENSE reconstruction framework.

Description: Photoacoustic (PA) images utilize pulsed lasers and ultrasound transducers to visualize targets with higher optical absorption than the surrounding medium. However, they are susceptible to acoustic clutter and background noise artifacts that obfuscate biomedical structures of interest. We investigated three spatial-angular compounding methods to improve PA image quality for biomedical applications, implemented by combining multiple images acquired as an ultrasound probe was rotated about the elevational axis with the laser beam and target fixed. Compounding with conventional averaging was based on the pose information of each PA image, while compounding with weighted and selective averaging utilized both the pose and image content information. Weighted-average compounding enhanced PA images with the least distortion of signal size, particularly when there were large (i.e., 2.5 mm and 7 $^{circ}$ ) perturbations from the initial probe position. Selective-average compounding offered the best improvement in image quality with up 181, 1665, and 1568 times higher contrast, CNR, and SNR, respectively, compared to the mean values of individual PA images. The three presented spatial compounding methods have promising potential to enhance image quality in multiple photoacoustic applications.

Description: Analysis of cranial nerve systems, such as the anterior visual pathway (AVP), from MRI sequences is challenging due to their thin long architecture, structural variations along the path, and low contrast with adjacent anatomic structures. Segmentation of a pathologic AVP (e.g., with low-grade gliomas) poses additional challenges. In this work, we propose a fully automated partitioned shape model segmentation mechanism for AVP steered by multiple MRI sequences and deep learning features. Employing deep learning feature representation, this framework presents a joint partitioned statistical shape model able to deal with healthy and pathological AVP. The deep learning assistance is particularly useful in the poor contrast regions, such as optic tracts and pathological areas. Our main contributions are: 1) a fast and robust shape localization method using conditional space deep learning, 2) a volumetric multiscale curvelet transform-based intensity normalization method for robust statistical model, and 3) optimally partitioned statistical shape and appearance models based on regional shape variations for greater local flexibility. Our method was evaluated on MRI sequences obtained from 165 pediatric subjects. A mean Dice similarity coefficient of 0.779 was obtained for the segmentation of the entire AVP (optic nerve only $=0.791$ ) using the leave-one-out validation. Results demonstrated that the proposed localized shape and sparse appearance-based learning approach significantly outperforms current state-of-the-art segmentation approaches and is as robust as the manual segmentation.

Description: Analytical (closed-form) inversion schemes have been the standard approach for image reconstruction in optoacoustic tomography due to their fast reconstruction abilities and low memory requirements. Yet, the need for quantitative imaging and artifact reduction has led to the development of more accurate inversion approaches, which rely on accurate forward modeling of the optoacoustic wave generation and propagation. In this way, multiple experimental factors can be incorporated, such as the exact detection geometry, spatio-temporal response of the transducers, and acoustic heterogeneities. The model-based inversion commonly results in very large sparse matrix formulations that require computationally extensive and memory demanding regularization schemes for image reconstruction, hindering their effective implementation in real-time imaging applications. Herein, we introduce a new discretization procedure for efficient model-based reconstructions in two-dimensional optoacoustic tomography that allows for parallel implementation on a graphics processing unit (GPU) with a relatively low numerical complexity. By on-the-fly calculation of the model matrix in each iteration of the inversion procedure, the new approach results in imaging frame rates exceeding 10 Hz, thus enabling real-time image rendering using the model-based approach.

Description: This paper introduces a statistical estimation framework for magnetic resonance (MR) fingerprinting, a recently proposed quantitative imaging paradigm. Within this framework, we present a maximum likelihood (ML) formalism to estimate multiple MR tissue parameter maps directly from highly undersampled, noisy ${bf k}$ -space data. A novel algorithm, based on variable splitting, the alternating direction method of multipliers, and the variable projection method, is developed to solve the resulting optimization problem. Representative results from both simulations and in vivo experiments demonstrate that the proposed approach yields significantly improved accuracy in parameter estimation, compared to the conventional MR fingerprinting reconstruction. Moreover, the proposed framework provides new theoretical insights into the conventional approach. We show analytically that the conventional approach is an approximation to the ML reconstruction; more precisely, it is exactly equivalent to the first iteration of the proposed algorithm for the ML reconstruction, provided that a gridding reconstruction is used as an initialization.

Description: We propose a unified Bayesian framework to detect both hyper- and hypo-active communities within whole-brain fMRI data. Specifically, our model identifies dense subgraphs that exhibit population-level differences in functional synchrony between a control and clinical group. We derive a variational EM algorithm to solve for the latent posterior distributions and parameter estimates, which subsequently inform us about the afflicted network topology. We demonstrate that our method provides valuable insights into the neural mechanisms underlying social dysfunction in autism, as verified by the Neurosynth meta-analytic database. In contrast, both univariate testing and community detection via recursive edge elimination fail to identify stable functional communities associated with the disorder.

Description: A recent entry into the rapidly evolving field of integrated PET/MR scanners is presented in this paper: a whole body hybrid PET/MR system (SIGNA PET/MR, GE Healthcare) capable of simultaneous acquisition of both time-of-flight (TOF) PET and high resolution MR data. The PET ring was integrated into an existing 3T MR system resulting in a (patient) bore opening of 60 cm diameter, with a 25 cm axial FOV. PET performance was evaluated both on the standalone PET ring and on the same detector integrated into the MR system, to assess the level of mutual interference between both subsystems. In both configurations we obtained detector performance data. PET detector performance was not significantly affected by integration into the MR system. The global energy resolution was within 2% (10.3% versus 10.5%), and the system coincidence time resolution showed a maximum change of 〈 3% (385 ps versus 394 ps) when measured outside MR and during simultaneous PET/MRI acquisitions, respectively. To evaluate PET image quality and resolution, the NEMA IQ phantom was acquired with MR idle and with MR active. Impact of PET on MR IQ was assessed by comparing SNR with PET acquisition on and off. B0 and B1 homogeneities were acquired before and after the integration of the PET ring inside the magnet. In vivo brain and whole body head-to-thighs data were acquired to demonstrate clinical image quality.

Description: The performance of photoacoustic microscopy (PAM) degrades due to the turbidity of the skull that introduces attenuation and distortion of both laser and stimulated ultrasound. In this manuscript, we demonstrated that a newly developed skull optical clearing solution (SOCS) could enhance not only the transmittance of light, but also that of ultrasound in the skull in vitro. Thus the photoacoustic signal was effectively elevated, and the relative strength of the artifacts induced by the skull could be suppressed. Furthermore in vivo studies demonstrated that SOCS could drastically enhance the performance of photoacoustic microscopy for cerebral microvasculature imaging.

Description: Cryo-balloon catheters have attracted an increasing amount of interest in the medical community as they can reduce patient risk during left atrial pulmonary vein ablation procedures. As cryo-balloon catheters are not equipped with electrodes, they cannot be localized automatically by electro-anatomical mapping systems. As a consequence, X-ray fluoroscopy has remained an important means for guidance during the procedure. Most recently, image guidance methods for fluoroscopy-based procedures have been proposed, but they provide only limited support for cryo-balloon catheters and require significant user interaction. To improve this situation, we propose a novel method for automatic cryo-balloon catheter detection in fluoroscopic images by detecting the cryo-balloon catheter's built-in X-ray marker. Our approach is based on a blob detection algorithm to find possible X-ray marker candidates. Several of these candidates are then excluded using prior knowledge. For the remaining candidates, several catheter specific features are introduced. They are processed using a machine learning approach to arrive at the final X-ray marker position. Our method was evaluated on 75 biplane fluoroscopy images from 40 patients, from two sites, acquired with a biplane angiography system. The method yielded a success rate of 99.0% in plane A and 90.6% in plane B, respectively. The detection achieved an accuracy of $1.00~{rm mm}pm 0.82~{rm mm}$ in plane A and $1.13~{rm mm}pm 0.24~{rm mm}$ in plane B. The localization in 3-D was associated with an average error of $0.36~{rm mm}pm 0.86~{rm mm}$ .

Description: Segmentation of the wrist bones in CT images has been frequently used in different clinical applications including arthritis evaluation, bone age assessment and image-guided interventions. The major challenges include non-uniformity and spongy textures of the bone tissue as well as narrow inter-bone spaces. In this work, we propose an automatic wrist bone segmentation technique for CT images based on a statistical model that captures the shape and pose variations of the wrist joint across 60 example wrists at nine different wrist positions. To establish the correspondences across the training shapes at neutral positions, the wrist bone surfaces are jointly aligned using a group-wise registration framework based on a Gaussian Mixture Model. Principal component analysis is then used to determine the major modes of shape variations. The variations in poses not only across the population but also across different wrist positions are incorporated in two pose models. An intra-subject pose model is developed by utilizing the similarity transforms at all wrist positions across the population. Further, an inter-subject pose model is used to model the pose variations across different wrist positions. For segmentation of the wrist bones in CT images, the developed model is registered to the edge point cloud extracted from the CT volume through an expectation maximization based probabilistic approach. Residual registration errors are corrected by application of a non-rigid registration technique. We validate the proposed segmentation method by registering the wrist model to a total of 66 unseen CT volumes of average voxel size of 0.38 mm. We report a mean surface distance error of 0.33 mm and a mean Jaccard index of 0.86.

Description: The Internet began as something much smaller; in certain ways less user-friendly but far more manageable. As the Internet evolves and the Internet of Things comes into focus, how can researchers and engineers customize diverse and open information to handle the Internet's rapid growth while ensuring a certain level of manageability?

Description: As the Internet grows in size, complexity, and the role it plays in modern society, measuring the Internet is increasingly critical to guide its continued evolution. Yet the scale, diversity, opacity, and ethical implications of conducting Internet experiments make it difficult to obtain an accurate and representative understanding of the network's behavior. The articles in this special issue provide but a glimpse into the wide range of activities that explore this rich area of investigation.

Description: Presents the 2016-2017 editorial calendar for this issue of the publication.

Description: The number of mobile devices and apps have grown tremendously in recent years, resulting in a dramatic increase in mobile traffic. This trend is expected to create a nearly 10-fold increase in global mobile data over the next 5 years, bringing mobile traffic analysis into focus. However, traditional traffic analysis approaches don't work well for mobile traffic. Mobile apps possess unique characteristics that make it exceedingly difficult to determine which app generated a flow. Here, the author discusses the challenges in mobile traffic analysis and presents a survey of techniques that address these issues.

Description: As Web APIs become the backbone of Web, cloud, mobile, and machine learning applications, the services computing community will need to expand and embrace opportunities and challenges from these domains.

Description: By leveraging the uneven distribution of traffic among network flows, the authors improve the query throughput of Cuckoo hashing. They achieve this by placing the most frequently used items in the table that's accessed first during the Cuckoo query operation. Their scheme is named Cuckoo cache, as it's conceptually similar to a cache but implemented inside the Cuckoo hash with little additional cost. Cuckoo cache is evaluated using a traffic monitoring application fed with real traces. The results are compared with existing Cuckoo hashing implementations and show significant improvements.

Description: As we move toward an era of the Internet of Things, loss of security and privacy becomes a greater risk, and responsible programming becomes a greater concern. It's only a matter of time until programmers are held accountable for the quality and safety of their work, so how can the industry plan accordingly?