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Recent developments in the field of non-local attention (NLA) have led to a renewed interest in self-similarity-based single image super-resolution (SISR). Researchers usually use the NLA to explore non-local self-similarity (NSS) in SISR and achieve satisfactory reconstruction results. However, a surprising phenomenon that the reconstruction performance of the standard NLA is similar to that of the NLA with randomly selected regions prompted us to revisit NLA. In this paper, we first analyzed the attention map of the standard NLA from different perspectives and discovered that the resulting probability distribution always has full support for every local feature, which implies a statistical waste of assigning values to irrelevant non-local features, especially for SISR which needs to model long-range dependence with a large number of redundant non-local features. Based on these findings, we introduced a concise yet effective soft thresholding operation to obtain high-similarity-pass attention (HSPA), which is beneficial for generating a more compact and interpretable distribution. Furthermore, we derived some key properties of the soft thresholding operation that enable training our HSPA in an end-to-end manner. The HSPA can be integrated into existing deep SISR models as an efficient general building block. In addition, to demonstrate the effectiveness of the HSPA, we constructed a deep high-similarity-pass attention network (HSPAN) by integrating a few HSPAs in a simple backbone. Extensive experimental results demonstrate that HSPAN outperforms state-of-the-art approaches on both quantitative and qualitative evaluations. Our code and a pre-trained model were uploaded to GitHub (https://github.com/laoyangui/HSPAN) for validation. [ABSTRACT FROM AUTHOR]

We address the problem of person re-identification (reID), that is, retrieving person images from a large dataset, given a query image of the person of interest. A key challenge is to learn person representations robust to intra-class variations, as different persons could have the same attribute, and persons’ appearances look different, e.g., with viewpoint changes. Recent reID methods focus on learning person features discriminative only for a particular factor of variations (e.g., human pose), which also requires corresponding supervisory signals (e.g., pose annotations). To tackle this problem, we propose to factorize person images into identity-related and -unrelated features. Identity-related features contain information useful for specifying a particular person (e.g., clothing), while identity-unrelated ones hold other factors (e.g., human pose). To this end, we propose a new generative adversarial network, dubbed identity shuffle GAN (IS-GAN). It disentangles identity-related and -unrelated features from person images through an identity-shuffling technique that exploits identification labels alone without any auxiliary supervisory signals. We restrict the distribution of identity-unrelated features, or encourage the identity-related and -unrelated features to be uncorrelated, facilitating the disentanglement process. Experimental results validate the effectiveness of IS-GAN, showing state-of-the-art performance on standard reID benchmarks, including Market-1501, CUHK03 and DukeMTMC-reID. We further demonstrate the advantages of disentangling person representations on a long-term reID task, setting a new state of the art on a Celeb-reID dataset. Our code and models are available online: https://cvlab-yonsei.github.io/projects/ISGAN/. [ABSTRACT FROM AUTHOR]

Deep generative models have emerged as a powerful class of priors for signals in various inverse problems such as compressed sensing, phase retrieval and super-resolution. In this work, we consider the compressed sensing problem and assume the unknown signal to lie in the range of some pre-trained generative model. A popular approach for signal recovery is via gradient descent in the low-dimensional latent space. While gradient descent has achieved good empirical performance, its theoretical behavior is not well understood. We introduce the use of stochastic gradient Langevin dynamics (SGLD) for compressed sensing with a generative prior. Under mild assumptions on the generative model, we prove the convergence of SGLD to the true signal. We also demonstrate competitive empirical performance to standard gradient descent. [ABSTRACT FROM AUTHOR]

Contrastive self-supervised representation learning methods maximize the similarity between the positive pairs, and at the same time tend to minimize the similarity between the negative pairs. However, in general the interplay between the negative pairs is ignored as they do not put in place special mechanisms to treat negative pairs differently according to their specific differences and similarities. In this paper, we present Extended Momentum Contrast (XMoCo), a self-supervised representation learning method founded upon the legacy of the momentum-encoder unit proposed in the MoCo family configurations. To this end, we introduce a cross consistency regularization loss, with which we extend the transformation consistency to dissimilar images (negative pairs). Under the cross consistency regularization rule, we argue that semantic representations associated with any pair of images (positive or negative) should preserve their cross-similarity under pretext transformations. Moreover, we further regularize the training loss by enforcing a uniform distribution of similarity over the negative pairs across a batch. The proposed regularization can easily be added to existing self-supervised learning algorithms in a plug-and-play fashion. Empirically, we report a competitive performance on the standard Imagenet-1K linear head classification benchmark. In addition, by transferring the learned representations to common downstream tasks, we show that using XMoCo with the prevalently utilized augmentations can lead to improvements in the performance of such tasks. We hope the findings of this paper serve as a motivation for researchers to take into consideration the important interplay among the negative examples in self-supervised learning. [ABSTRACT FROM AUTHOR]

Optimization modeling of real-world application problems usually involves noise from various sources. Noisy optimization imposes challenges to optimization methods since the objective values can be different for multiple evaluations. In this article, we propose a novel online population size learning (OPL) technique of evolution strategies for handling noisy optimization problems. By re-evaluating a fraction of the candidates, we measure the strength of noise level of the re-evaluated candidate solutions and adapt the population size according to the noise level. The proposed OPL combines the advantages of both explicit averaging by re-evaluations and the implicit averaging by large population size and overcomes their limitations. We incorporate it with the covariance matrix adaptation evolution strategy (CMA-ES) and obtain OPL-CMA-ES. Compared with the existing noise handling technique, the proposed OPL is much simpler in both concepts and computation. We conduct comprehensive experiments to evaluate the algorithm’s performance on standard problems with Gaussian noise. We further evaluate the performance of OPL-CMA-ES on the black-box optimization benchmarks (BBOBs) noisy testbed, which is a standard platform for comparing black-box optimization algorithms, compared with the state-of-the-art noise-handling algorithms. The experimental results show that OPL-CMA-ES achieves remarkable performance and outperforms the compared variants. [ABSTRACT FROM AUTHOR]

The integration of phasor measurement units (PMUs) and phasor data concentrators (PDCs) in smart grids may be exploited by attackers to initiate new and sophisticated false data injection (FDI) attacks. Existing FDI attack mitigation approaches are generally less effective against sophisticated FDI attacks, such as collusive false data injection (CFDI) attacks launched by compromised PDCs (and PMUs) as we demonstrate in this paper. Thus, we propose a secure and resilience-enhanced scheme (SeCDM) to detect and mitigate such cyber threats in smart grids. Specifically, we design a decentralized homomorphic computation paradigm along with a hierarchical knowledge sharing algorithm to facilitate secure ciphertext calculation of state estimation residuals. Following this, a centralized FDI detector is implemented to detect FDI attacks. Findings from the security analysis demonstrate our approach achieves enhanced conventional FDI and CFDI attack resilience, and findings from our performance evaluations on the standard IEEE 14-, 24-, and 39-bus power systems also show that the communication overheads and computational complexity are reasonably “low”. [ABSTRACT FROM AUTHOR]

Existing person re-identification (Re-ID) methods usually rely heavily on large-scale thoroughly annotated training data. However, label noise is unavoidable due to inaccurate person detection results or annotation errors in real scenes. It is extremely challenging to learn a robust Re-ID model with label noise since each identity has very limited annotated training samples. To avoid fitting to the noisy labels, we propose to learn a prefatory model using a large learning rate at the early stage with a self-label refining strategy, in which the labels and network are jointly optimized. To further enhance the robustness, we introduce an online co-refining (CORE) framework with dynamic mutual learning, where networks and label predictions are online optimized collaboratively by distilling the knowledge from other peer networks. Moreover, it also reduces the negative impact of noisy labels using a favorable selective consistency strategy. CORE has two primary advantages: it is robust to different noise types and unknown noise ratios; it can be easily trained without much additional effort on the architecture design. Extensive experiments on Re-ID and image classification demonstrate that CORE outperforms its counterparts by a large margin under both practical and simulated noise settings. Notably, it also improves the state-of-the-art unsupervised Re-ID performance under standard settings. Code is available at https://github.com/mangye16/ReID-Label-Noise. [ABSTRACT FROM AUTHOR]

Power consumption for integrated circuits (ICs) fabricated at advanced technology nodes is a primary concern for application-specific IC (ASIC) designers. To reduce power consumption, designers use transistors with different threshold voltage ($V_{\mathrm {T}}$) options and may reduce the supply voltage, oftentimes to as low as transistor-threshold-voltage levels. This work investigates the effects of these two popular techniques, different $V_{\mathrm {T}}$ options, and near-threshold-voltage (NTV) operation on the single-event (SE) performance of conventional D flip-flop (D-FF) cells in low-power applications. Results indicate that at near NTV levels, the SE cross section increases by two orders of magnitude, and the low $V_{\mathrm {T}}$ (LVT) option yields the best SE performance in comparison to the standard $V_{\mathrm {T}}$ (SVT) and ultralow $V_{\mathrm {T}}$ (uLVT) options. [ABSTRACT FROM AUTHOR]

In this paper, we develop a novel visual-inertial navigation system with motion constraint (VINS-Motion), which extends the visual-inertial navigation system (VINS) to incorporate vehicle motion information for improving the autonomous vehicles localization accuracy. We introduce the vehicle stop detection module in the pre-processing measurement procedure. During the back-end processing, if the vehicle is detected to be in a stopping state, we perform optimization based on the stop constraint to help eliminate the abnormal jitter of the estimated pose, thus ensuring the reasonability of the trajectory. Otherwise, besides the prior information, IMU measurement residual, and visual measurement residual utilized in VINS, vehicle orientation/velocity constraint built by Ackerman steering model is first exploited to constitute residuals. We minimize the sum of priors and Mahalanobis norms of three kinds of residuals to obtain a maximum posterior estimation, thus increasing system consistency and accuracy. The proposed approach is validated on public datasets and compared with state-of-the-art algorithms, which demonstrates that the motion constraint proposed in this paper improves the standard VINS performance and achieves significantly higher positioning accuracy. [ABSTRACT FROM AUTHOR]

Computational surface modeling that underlies material recognition has transitioned from reflectance modeling using in-lab controlled radiometric measurements to image-based representations based on internet-mined single-view images captured in the scene. We take a middle-ground approach for material recognition that takes advantage of both rich radiometric cues and flexible image capture. A key concept is differential angular imaging, where small angular variations in image capture enables angular-gradient features for an enhanced appearance representation that improves recognition. We build a large-scale material database, Ground Terrain in Outdoor Scenes (GTOS) database, to support ground terrain recognition for applications such as autonomous driving and robot navigation. The database consists of over 30,000 images covering 40 classes of outdoor ground terrain under varying weather and lighting conditions. We develop a novel approach for material recognition called texture-encoded angular network (TEAN) that combines deep encoding pooling of RGB information and differential angular images for angular-gradient features to fully leverage this large dataset. With this novel network architecture, we extract characteristics of materials encoded in the angular and spatial gradients of their appearance. Our results show that TEAN achieves recognition performance that surpasses single view performance and standard (non-differential/large-angle sampling) multiview performance. [ABSTRACT FROM AUTHOR]

Three-phase pulsewidth-modulated converters require closed-loop controllers to meet the performance requirements and to achieve reliable responses over the entire operating range. Conventionally, by using the synchronous reference frame (SRF) representation, different linear and nonlinear control strategies have been proposed to improve the converter's dynamic performance. However, the achieved improvements cannot be objectively assessed due to the absence of a performance limit standard reference of the converter's dynamics. In this article, the theoretical minimum time solutions for three-phase converters in the SRF are derived and analyzed, providing an objective reference point for the system's dynamic performance evaluation. The normalized dynamic model of the converter in the SRF is derived and combined with the time-optimal control principle to find the theoretical minimum transient time solutions. The state-plane representation of these minimum time responses enables a straightforward and comprehensive interpretation of the converter's dynamic behavior, and it simplifies the derivation of closed-form expressions that characterize the minimum time transient parameters. The introduced analysis, minimum time dynamic responses, derived transient's expressions, and dynamic performance evaluation are validated by simulation and experimental results. [ABSTRACT FROM AUTHOR]

High-rate product codes (PCs) and staircase codes (SCs) are ubiquitous codes in high-speed optical communication achieving near-capacity performance on the binary symmetric channel. Their success is mostly due to very efficient iterative decoding algorithms that require very little complexity. In this paper, we extend the density evolution (DE) analysis for PCs and SCs to a channel with ternary output and ternary message passing, where the third symbol marks an erasure. We investigate the performance of a standard error-and-erasure decoder and of its simplification using DE. The proposed analysis can be used to find component code configurations and quantizer levels for the channel output. We also show how the use of even-weight BCH subcodes as component codes can improve the decoding performance at high rates. The DE results are verified by Monte-Carlo simulations, which show that additional coding gains of up to 0.6dB are possible by ternary decoding, at only a small additional increase in complexity compared to traditional binary message passing. [ABSTRACT FROM AUTHOR]

In order for a closed-loop radar system to adapt properly to changing operational conditions, the performance standards and requirements of the radar system must be known. Often, these performance standards are contained in statements made in language, which describe the different goals of the radar system. In this article, we explore the use of language-based cost functions (LBCFs) to encode performance standards made in imprecise language into an objective function, which a fully adaptive radar (FAR) can use to optimize its performance in real time. To enable the LBCFs, we also propose a statement decomposer, which takes in statements as inputs and yields the information required by LBCFs as outputs. A simulation study of a FAR tracking a running human is conducted with four different cost function methodologies: quadratic, weighted global criterion, fuzzy optimization, and LBCFs. Results show that when statements are properly formulated and presented, the statement decomposer is able to successfully extract the relevant information. Results also show that when paired with a previously developed optimization routine, the LBCF system is capable of autonomously creating cost functions that outperform the other cost functions in the simulated FAR target tracking scenario. [ABSTRACT FROM AUTHOR]

5G is envisioned to simultaneously provide diverse service types with heterogeneous needs under very different application scenarios and business models. Therefore, network slicing is included as a key feature of the 5G architecture to allow sharing a common infrastructure among different tenants, such as mobile communication providers, vertical market players, etc. In order to provide the Radio Access Network (RAN) with network slicing capabilities, mechanisms that efficiently distribute the available capacity among the different tenants while satisfying their needs are required. For this purpose, this paper proposes a multi-agent reinforcement learning approach for RAN capacity sharing. It makes use of the Deep Q-Network algorithm in a way that each agent is associated to a different tenant and learns the capacity to be provided to this tenant in each cell while ensuring that the service level agreements are satisfied and that the available radio resources are efficiently used. The consideration of multiple agents contributes to a better scalability and higher learning speed in comparison to single-agent approaches. In this respect, results show that the policy learnt by the agent of one tenant can be generalised and directly applied by other agents, thus reducing the complexity of the training and making the proposed solution easily scalable, e.g., to add new tenants in the system. The proposed approach is well aligned with the on-going 3GPP standardization work and guidelines for the parametrization of the solution are provided, thus enforcing its practical applicability. [ABSTRACT FROM AUTHOR]

We consider the problem of conducting frame rate dependent video quality assessment (VQA) on videos of diverse frame rates, including high frame rate (HFR) videos. More generally, we study how perceptual quality is affected by frame rate, and how frame rate and compression combine to affect perceived quality. We devise an objective VQA model called Space-Time GeneRalized Entropic Difference (GREED) which analyzes the statistics of spatial and temporal band-pass video coefficients. A generalized Gaussian distribution (GGD) is used to model band-pass responses, while entropy variations between reference and distorted videos under the GGD model are used to capture video quality variations arising from frame rate changes. The entropic differences are calculated across multiple temporal and spatial subbands, and merged using a learned regressor. We show through extensive experiments that GREED achieves state-of-the-art performance on the LIVE-YT-HFR Database when compared with existing VQA models. The features used in GREED are highly generalizable and obtain competitive performance even on standard, non-HFR VQA databases. The implementation of GREED has been made available online: https://github.com/pavancm/GREED. [ABSTRACT FROM AUTHOR]

This article presents a comparison of measurement methods for current and voltage distortions in low-voltage networks in the frequency range from 2 to 150 kHz (supraharmonics). The comparison encompasses the methods informatively described in IEC and CISPR international standards, as well as other innovative techniques presented in the literature. This work is carried out within a novel framework that includes advanced and complex synthetic test signals, as well as real grid recordings that allow an accurate comparison of the performance of the tested methods. Specifically designed indices are employed to characterize the accuracy of the tested methods in the frequency and amplitude assessments. In light of that, the strengths and weaknesses of the methods are identified. The results of this article contribute to the ongoing standardization work carried out by the IEC SC77A/WG9 with the purpose of defining a normative measurement method suitable for assessing grid disturbance levels in the range from 2 to 150 kHz. [ABSTRACT FROM AUTHOR]

This article proposes an online scheme for state estimation of a generic class of nonlinear dynamical systems in the presence of abnormal measurement data from sensors. We thoroughly illustrate why the performance of standard recursive Bayesian inference degrades in the presence of any measurement distortion. After demonstrating how different abnormalities can be accommodated explicitly inside a generic state–space model, we propose a robust mechanism to perform recursive Bayesian inference on the presented model to not only detect but also mitigate the effect of corrupted measurements in the final state estimates. Using simulations and experimentation, we demonstrate the success of the proposed framework in reducing the impact of different types of distortions in measurements. The ability to tackle different kinds of measurement abnormalities during online inference sets the proposed method apart from the existing techniques. [ABSTRACT FROM AUTHOR]

Atomic frequency standards are used to generate accurate and precise time and frequency, enabling many communications, synchronization, and navigation systems in modern life. GPS and other satellite navigation systems, voice and data telecommunications, and timestamping of financial transactions all rely on precise time and frequency enabled by atomic frequency standards. This review provides a snapshot and an outlook of commercial and emerging atomic frequency standards. We provide a concise summary of the performance and physics of operation of current atomic frequency standards. In addition, we discuss examples of emerging frequency standard technologies and prototype demonstrations with a focus on technologies expected to provide commercial or military utility within the next decade. We include a comparison of performance versus size and power for current atomic frequency standards. We develop and discuss an empirical relationship between frequency standard performance and product size. [ABSTRACT FROM AUTHOR]

Current methods for skeleton-based human action recognition usually work with complete skeletons. However, in real scenarios, it is inevitable to capture incomplete or noisy skeletons, which could significantly deteriorate the performance of current methods when some informative joints are occluded or disturbed. To improve the robustness of action recognition models, a multi-stream graph convolutional network (GCN) is proposed to explore sufficient discriminative features spreading over all skeleton joints, so that the distributed redundant representation reduces the sensitivity of the action models to non-standard skeletons. Concretely, the backbone GCN is extended by a series of ordered streams which is responsible for learning discriminative features from the joints less activated by preceding streams. Here, the activation degrees of skeleton joints of each GCN stream are measured by the class activation maps (CAM), and only the information from the unactivated joints will be passed to the next stream, by which rich features over all active joints are obtained. Thus, the proposed method is termed richly activated GCN (RA-GCN). Compared to the state-of-the-art (SOTA) methods, the RA-GCN achieves comparable performance on the standard NTU RGB+D 60 and 120 datasets. More crucially, on the synthetic occlusion and jittering datasets, the performance deterioration due to the occluded and disturbed joints can be significantly alleviated by utilizing the proposed RA-GCN. [ABSTRACT FROM AUTHOR]

We propose the first fast and certifiable algorithm for the registration of two sets of three-dimensional (3-D) points in the presence of large amounts of outlier correspondences. A certifiable algorithm is one that attempts to solve an intractable optimization problem (e.g., robust estimation with outliers) and provides readily checkable conditions to verify if the returned solution is optimal (e.g., if the algorithm produced the most accurate estimate in the face of outliers) or bound its suboptimality or accuracy. Toward this goal, we first reformulate the registration problem using a truncated least squares (TLS) cost that makes the estimation insensitive to a large fraction of spurious correspondences. Then, we provide a general graph-theoretic framework to decouple scale, rotation, and translation estimation, which allows solving in cascade for the three transformations. Despite the fact that each subproblem (scale, rotation, and translation estimation) is still nonconvex and combinatorial in nature, we show that 1) TLS scale and (component-wise) translation estimation can be solved in polynomial time via an adaptive voting scheme, 2) TLS rotation estimation can be relaxed to a semidefinite program (SDP) and the relaxation is tight, even in the presence of extreme outlier rates, and 3) the graph-theoretic framework allows drastic pruning of outliers by finding the maximum clique. We name the resulting algorithm TEASER (Truncated least squares Estimation And SEmidefinite Relaxation). While solving large SDP relaxations is typically slow, we develop a second fast and certifiable algorithm, named TEASER++, that uses graduated nonconvexity to solve the rotation subproblem and leverages Douglas-Rachford Splitting to efficiently certify global optimality. For both algorithms, we provide theoretical bounds on the estimation errors, which are the first of their kind for robust registration problems. Moreover, we test their performance on standard benchmarks, object detection datasets, and the 3DMatch scan matching dataset, and show that 1) both algorithms dominate the state-of-the-art (e.g., RANSAC, branch-&-bound, heuristics) and are robust to more than $\text{99}\%$ outliers when the scale is known, 2) TEASER++ can run in milliseconds and it is currently the fastest robust registration algorithm, and 3) TEASER++ is so robust it can also solve problems without correspondences (e.g., hypothesizing all-to-all correspondences), where it largely outperforms ICP and it is more accurate than Go-ICP while being orders of magnitude faster. We release a fast open-source C++ implementation of TEASER++. [ABSTRACT FROM AUTHOR]

Multirobot fleets play an important role in industrial logistics, surveillance, and exploration applications. A wide literature exists on the topic, both resorting to reactive (i.e. collision avoidance) and to deliberative (i.e. motion planning) techniques. In this work, null space-based inverse kinematics (NSB-IK) methods are applied to the problem of fleet management. Several NSB-IK approaches existing in the literature are reviewed, and compared with a reverse priority approach, which originated in manipulator control, and is here applied for the first time to the considered problem. All NSB-IK approaches are here described in a unified formalism, which allows (i) to encode the property of each controller into a set of seven main key features, (ii) to study possible new control laws with an opportune choice of these parameters. Furthermore, motivated by the envisioned application scenario, we tackle the problem of task-switching activation. Leveraging on the iCAT TPC technique Simetti and Casalino, 2016, in this article, we propose a method to obtain continuity in the control in face of activation or deactivation of tasks, and subtasks by defining suitable damped projection operators. The proposed approaches are evaluated formally, and via simulations. Performances with respect to standard methods are compared considering a specific case study for multivehicles management. [ABSTRACT FROM AUTHOR]

Advanced receiver autonomous integrity monitoring (ARAIM) is under development to support both lateral and vertical guidance. A key component of ARAIM is the integrity support message (ISM), which contains validated input parameters including the probabilities of satellite and constellation failure. A service commitment for the former is provided in the global positioning system (GPS) performance standards, whereas further commitments from GLONASS, Galileo, and Beidou are expected to follow. However, air navigation service providers may demand some flexibility in setting the ISM. This article presents a procedure for setting ISM parameters employing a means to determine the a priori probabilities using the Bayesian inference of a Poisson process model given in the literature. This method and a novel way to accumulate the service history are used to analyze the effects of satellite block and age. Results show that while the type and age of satellite are clearly drivers in the likelihood of a fault; such effects may be accounted for without significant impact on the broadcast values. This article also addresses the probability of unscheduled satellite outages using the new method. Finally, this article gives a high-level scheme of how the validity of the ISM parameters may be assured. [ABSTRACT FROM AUTHOR]

Our impression about one person often updates after we see more aspects of him/her and this process keeps iterating given more meetings. We formulate such an intuition into the problem of person re-identification (re-ID), where the representation of a query (probe) image is iteratively updated with new information from the candidates in the gallery. Specifically, we propose a simple attentional aggregation formulation to instantiate this idea and showcase that such a pipeline achieves competitive performance on standard benchmarks including CUHK03, Market-1501 and DukeMTMC. Not only does such a simple method improve the performance of the baseline models, it also achieves comparable performance with latest advanced re-ranking methods. Another advantage of this proposal is its flexibility to incorporate different representations and similarity metrics. By utilizing stronger representations and metrics, we further demonstrate state-of-the-art person re-ID performance, which also validates the general applicability of the proposed method. [ABSTRACT FROM AUTHOR]

We present FoveaBox, an accurate, flexible, and completely anchor-free framework for object detection. While almost all state-of-the-art object detectors utilize predefined anchors to enumerate possible locations, scales and aspect ratios for the search of the objects, their performance and generalization ability are also limited to the design of anchors. Instead, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object. The scales of target boxes are naturally associated with feature pyramid representations. In FoveaBox, an instance is assigned to adjacent feature levels to make the model more accurate.We demonstrate its effectiveness on standard benchmarks and report extensive experimental analysis. Without bells and whistles, FoveaBox achieves state-of-the-art single model performance on the standard COCO and Pascal VOC object detection benchmark. More importantly, FoveaBox avoids all computation and hyper-parameters related to anchor boxes, which are often sensitive to the final detection performance. We believe the simple and effective approach will serve as a solid baseline and help ease future research for object detection. The code has been made publicly available at https://github.com/taokong/FoveaBox. [ABSTRACT FROM AUTHOR]

The automatic dependent surveillance—broadcast (ADS-B) system adopts an open communication mode, and the lack of designed-in security measures in the ADS-B system makes it vulnerable to various types of attacks (jamming, spoofing, etc.). In view of the low-bandwidth and less-data-bit features of the ADS-B, this paper studies the integrity and authenticity of information by signing messages and proposes an ADS-B message authentication method based on certificateless short signature. This method uses short signature and does not require certificate management and has efficient performance. Compared with the existing approach, the computation costs of the proposed method in the signature phase are reduced by 1/2, and the signature length is reduced by 3/4. Additionally, we used the extended NS2 simulation platform to simulate 1090ES data link in different scenarios of the network; the simulation results show that our solution is suitable for minimum operational performance standard of ADS-B. [ABSTRACT FROM AUTHOR]

This article introduces signal-flow graphs for linear time-periodic systems to streamline and visually describe the frequency-domain modeling of complex phase-locked loop (PLL) systems used in grid-connected converters. Small-signal modeling using the proposed graphs is demonstrated for two commonly used single-phase PLL structures: SOGI-PLL and Park-PLL. Loop-gain models are developed for these PLLs to evaluate how an orthogonal signal generator (OSG), which is required in single-phase PLLs using the synchronous reference frame (SRF) architecture, modifies the PLL loop gain compared to that of a three-phase SRF-PLL, which does not require an OSG. It is shown that the OSG in the SOGI-PLL and Park-PLL introduces a significant phase lag in the PLL loop gain, limiting the maximum bandwidth for which either PLL can be designed. Slow-frequency adaptation (SFA) of OSG is proposed to mitigate the influence of the OSG dynamics on the PLL loop gain. Experimental results are presented to validate the developed loop-gain models and show that the proposed SFA-SOGI-PLL and SFA-Park-PLL have better transient performance, they do not suffer from the bandwidth limit, and they preserve the steady-state performance of the standard SOGI-PLL and Park-PLL. [ABSTRACT FROM AUTHOR]

Computer generated holograms represent a novel digital media that with adequate processing tools and the advance of holographic display technologies will foster the development of true immersive video technologies applications. A digital hologram typically conveys a considerable amount of data making compression and image quality evaluation tools necessary for the development of practical applications. This paper proposes an objective evaluation of the performances of state-of-the-art compression techniques applied to digital holographic data. The experimental analysis takes into account several holographic images reconstructing the holograms from different perspectives and distances. The results of the exploratory study are reported, discussed and complemented with a statistical analysis of the hologram data under test, providing insights for computer-generated holography compression techniques design. [ABSTRACT FROM AUTHOR]

A Ku -band low cross-polarization conical horn based on thin metasurface (MTS) walls is presented in this article, along with the relevant design method. The necessary boundary conditions are realized by printing a tensor MTS on the internal walls of the horn, which yields a radiation performance similar to a standard corrugated Gaussian horn yet with the benefits of thin walls. The design method is based on the adiabatic approximate solution for the hybrid mode of a conical waveguide with generic balanced impedance walls, which allows the determination of the impedance profile supporting a single balanced mode. The impedance design is based on the usual locally flat approximation of the wall and on the assumption of plane wave incidence. This approach results in an analytic design method that requires no optimization. The design has been first validated by solving the problem with homogenized impedance boundary conditions through a body of revolution (BoR) method of moments. The actual structure, including impedance wall implementation, is then simulated in full detail with a full-wave fast code. Finally, the horn has been realized and experimentally characterized; measurements show excellent agreement both with theory and with simulations, with a cross-polarization level below −20 dB over a 2 GHz bandwidth. [ABSTRACT FROM AUTHOR]

Gluing an antenna-on-chip (AoC) directly on a ground plane or heat sink in the lead frame is a standard process for commercial applications. To reduce the effect of losses in the silicon substrate and improve the antenna’s radiation efficiency, the wafer thickness is often drastically reduced. However, the close proximity of the antenna and ground plane can cause an impedance mismatch. Metal tiles, which are embedded in silicon dioxide layers, are designed as an artificial magnetic conductor (AMC) to increase the radiation efficiency and tune the antenna impedance. A double-layer AMC is applied instead of a single-layer AMC to reduce the silicon dielectric loss. Additionally, the double-layer AMC has more capacitance to tune the impedance when integrating with a monopole on-chip antenna. The resulting −10 dB impedance bandwidth is increased to 5.8 GHz around the 60 GHz band, the simulated realized gain increases by 3.3 dB, and the simulated antenna radiation efficiency is increased from 11% to 21%. The measured input impedance of the AoC shows good agreement with the simulation results after probe calibration. [ABSTRACT FROM AUTHOR]

Recently, fluorescence imaging using shortwave infrared light (SWIR, 1,000-2,000 nm) has been proposed as having advantage over conventional near-infrared fluorescence (NIRF) imaging due to the reduced tissue scattering, negligible autofluorescence, comparable tissue absorption, and the discovery that indocyanine green (ICG), used clinically as a NIRF contrast agent, also has fluorescence emission in SWIR regime. Images of ICG in small animals acquired by commercial Si-based and InGaAs-based imaging cameras have been qualitatively compared, however the lack of working standards to quantify performance of these imaging systems limits quantitative comparison. Without quantification using a traceable in vitro test, clinical adoption of rapidly evolving advances in both NIRF and SWIR imaging devices will become limited. In this work, we developed an ICG based fluorescent solid working standard calibrated with SI units (mW⋅cm-2⋅sr−1) for quantification of measurement sensitivity of Si, GaAs-intensified Si, and InGaAs based camera systems, their signal-to-noise ratio (SNR), and contrast in non-clinical tests. In addition, we present small animal and large animal imaging with ICG for qualitative comparison of the same SWIR fluorescence and NIRF imaging systems. Results suggest that SWIR fluorescence imaging of ICG may have superior resolution in small animal imaging compared to NIRF imaging, but lack of measurement sensitivity, SNR, contrast, as well as water absorption limits deep penetration in large animals. [ABSTRACT FROM AUTHOR]

The transient response of electrocardiogram (ECG) amplifiers is investigated in the light of the IEC 60601 performance standard for electrocardiographs. Simulations and bench tests carried out on models of several electrodes reveal that the undershoot limit of $100~\mu \text{V}$ and the recovery slope limit of 300 $\mu $ Vs−1 in response to a narrow 3-mV, 100-ms rectangular pulse are violated when the recommended input impedance of 10 $\text{M}\Omega $ is used in the amplifier. Analyses of dc and ac coupling of the electrodes to the amplifier are carried out, and the constraints are established for the amplifier input impedance based on the electrical properties of the electrodes. We recommend that the amplifier input resistance needs to be determined as $\text{R}_{\mathrm {in}} >60\text{R}_{P}$ or $>25/{\text{C}}_{P}$ , whichever is greater, where ${\text{R}}_{P}$ and $\text{C}_{P}$ are the resistance and capacitance of a simple electrical model of the electrode. In the case of ac coupling of the electrode to the amplifier, $\text{C}_{C}\text{R}_{\mathrm {in}} > 6$ s, where ${\text{C}}_{C}$ is the coupling capacitor. The results of simulations and tests suggest that an amplifier input impedance exceeding 3 $\text{G}\Omega $ and a cutoff frequency no higher than 0.05 Hz are necessary in order to meet the IEC 60601 performance criteria and avoid distortion of the recorded ECG signal. [ABSTRACT FROM AUTHOR]

This article presents the design and implementation of an active noise control (ANC) headphone system with a directional hear-through capability and compares the performance of this system to that of a standard hear-through headphone system. The directional hear-through ANC headphones are a novel integration of microphone array beamforming and ANC technologies into a pair of headphones, which provide the consumer with additional functionality and new, digitally augmented ways to interact with their acoustic environment. As the microphone array is necessarily compact, superdirective beamforming is utilised to increase its low and mid frequency directional performance. In this unique integration of two current consumer technologies, first, the ANC subsystem attempts to maximise the attenuation and then the beamformer output is added to the control signal and reproduced by the headphones’ loudspeakers, with the appropriate compensation to avoid self-cancellation. The experimental study demonstrates that the proposed spatially selective ANC headphones provide a hear-through capability in the look direction, whilst reducing ambient noise and enabling the wearer to experience reduced noise communication in a noisy environment. The proposed system thus offers the consumer the potential for an electronically enhanced acoustic experience, allowing a selective reduction in environmental noise whilst desired exterior noise remains audible. [ABSTRACT FROM AUTHOR]

This paper proposes and evaluates a systematic method of scheduling energy storage and conventional generation capacities in a day-ahead frequency regulation market, based on compliance to control performance standard 1 (CPS1), during each hour of the following day. The salient feature of this method is the development of two simple piecewise linear curves that represent the relationship between first, conventional generation and energy storage power capacities, and second, energy storage power and energy capacities, to satisfy CPS1 compliance in the presence of stochastic load variations. These curves are then modeled inside a mixed integer linear program to solve for resource allocations by minimizing total capacity cost. As such, the formulation avoids complex algorithms that are otherwise required to model frequency dynamics inside the optimization problem. To reduce computation time, a scenario reduction algorithm is used to obtain a small set of scenarios to represent the stochastic load. The proposed method is evaluated using comprehensive dynamic simulations of the two-area IEEE reliability test system implemented in PSS/E. The results prove that the proposed method is effective for scheduling capacities to meet CPS1 compliance with minimum cost. [ABSTRACT FROM AUTHOR]

Feature-aided tracking can often yield improved tracking performance over the standard multiple target tracking (MTT) algorithms. However, in many applications, the feature signal of the targets consists of sparse Fourier-domain signals. It changes quickly and nonlinearly in the time domain, and the feature measurements are corrupted by missed detections and misassociations. In this paper, we develop a feature-aided multiple hypothesis tracker for joint MTT and feature extraction in dense target environments. We use the atomic norm constraint to formulate the sparsity of feature signal and use the $\ell _1$ -norm to formulate the sparsity of the corruption induced by misassociations. Based on the sparse representation, the feature signal are estimated by solving a semidefinite program. With the estimated feature signal, refiltering is performed to estimate the kinematic states of the targets, where the association makes use of both kinematic and feature information. Simulation results are presented to illustrate the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The need for higher efficiency motors is becoming more relevant due to recent policies adopted in most advanced economies, which are adopting new minimum energy performance standards. A great effort to optimize several parts of rotating electric machines is being done to fulfill that requirement. Recent studies focus on stator winding optimization, because of its high loss share. In this paper, a general multilayer winding configuration is proposed for winding design optimization for multiphase symmetrical machines, by combining multilayer integer-slot, fractional-slot, and fractional-slot concentrated windings. The constrained minimization of the airgap magnetomotive force harmonic distortion and winding resistance is solved by the hybridization of differential evolution with a non-dominating sorting algorithm. A framework is provided for replication purposes. An experimental application example of standard and optimized windings is included for performance assessment. The optimized winding leads to a significant motor efficiency gain, while reducing the amount of copper needed for manufacturing. [ABSTRACT FROM AUTHOR]

One of the main distinctive features of multiphase machines is the appearance of new degrees of freedom (${\boldsymbol{x}}$ – ${\boldsymbol{y}}$ voltages/currents) that do not exist in their three-phase counterparts. As a direct consequence, control approaches that apply a single switching state during the sampling period cannot achieve zero average ${\boldsymbol{x}}$ – ${\boldsymbol{y}}$ voltage production. In direct torque control (DTC) this implies that ${\boldsymbol{x}}$ – ${\boldsymbol{y}}$ currents are not regulated, whereas in finite-control-set model predictive control (FCS-MPC) an enhanced ${\boldsymbol{x}}$ – ${\boldsymbol{y}}$ current regulation is feasible only at the expense of disturbing the flux/torque production. Aiming to avoid these shortcomings, this work makes use of the concept of synthetic/virtual voltage vectors (VVs) to nullify/limit the ${\boldsymbol{x}}$ – ${\boldsymbol{y}}$ voltage production in order to improve the current regulation in the secondary planes. Two strategies using two and four virtual voltage vectors (2-VV and 4-VV, respectively) are proposed and compared with the standard case that applies a single switching state. Since standard MPC has the capability to indirectly regulate ${\boldsymbol{x}}$ – ${\boldsymbol{y}}$ currents, the improvements with the inclusion of VVs are expected to be more significant in DTC strategies. Experimental results validate the proposed VVs and confirm the expectations through a detailed performance comparison of standard, 2-VV and 4-VV approaches for DTC and MPC strategies. [ABSTRACT FROM AUTHOR]

With the increasing popularity of mobile phones and tablets, the explosive growth of query-by-capture applications calls for a compact representation of the query image feature. Compact descriptors for visual search (CDVS) is a recently released standard from the ISO/IEC moving pictures experts group, which achieves state-of-the-art performance in the context of image retrieval applications. However, they did not consider the matching characteristics in local space in a large-scale database, which might deteriorate the performance. In this paper, we propose a more compact representation with scale invariant feature transform (SIFT) descriptors for the visual query based on Grassmann manifold. Due to the drastic variations in image content, it is not sufficient to capture all the information using a single transform. To achieve more efficient representations, a SIFT manifold partition tree (SMPT) is initially constructed to divide the large dataset into small groups at multiple scales, which aims at capturing more discriminative information. Grassmann manifold is then applied to prune the SMPT and search for the most distinctive transforms. The experimental results demonstrate that the proposed framework achieves state-of-the-art performance on the standard benchmark CDVS dataset. [ABSTRACT FROM AUTHOR]

Image descriptors based on activations of Convolutional Neural Networks (CNNs) have become dominant in image retrieval due to their discriminative power, compactness of representation, and search efficiency. Training of CNNs, either from scratch or fine-tuning, requires a large amount of annotated data, where a high quality of annotation is often crucial. In this work, we propose to fine-tune CNNs for image retrieval on a large collection of unordered images in a fully automated manner. Reconstructed 3D models obtained by the state-of-the-art retrieval and structure-from-motion methods guide the selection of the training data. We show that both hard-positive and hard-negative examples, selected by exploiting the geometry and the camera positions available from the 3D models, enhance the performance of particular-object retrieval. CNN descriptor whitening discriminatively learned from the same training data outperforms commonly used PCA whitening. We propose a novel trainable Generalized-Mean (GeM) pooling layer that generalizes max and average pooling and show that it boosts retrieval performance. Applying the proposed method to the VGG network achieves state-of-the-art performance on the standard benchmarks: Oxford Buildings, Paris, and Holidays datasets. [ABSTRACT FROM AUTHOR]

The existing tunable circuits mainly concentrate on the control of frequency and amplitude. The control of phase characteristics becomes more and more important in the modern system for further performance enhancement. But none of existing coupler configurations can achieve this tunability. A novel hybrid coupler with simultaneous phase- and frequency-tunable characteristics is presented for the first time. Varying the difference between the capacitances for two rows of tunable capacitors loaded on the branch lines can provide different phase differences between two output ports with equal magnitude. Based on the same circuit, a tunable operating frequency can be realized while maintaining the equal power division and desired phase characteristics. This is achieved by simply changing the capacitances for two rows of tunable capacitors in unison. The high flexibility to achieve tunability in phase and frequency simultaneously is implemented. For demonstration purposes, a hybrid coupler operating at 1 GHz is designed and measured to validate the concept. It exhibits a variable phase difference from 45° to 135° while maintaining equal power division. Meanwhile, the same circuit gives a tunable operating frequency extending from 0.8 to 1.1 GHz while maintaining good performance for a 3 dB coupler with a tunable phase difference from 50° to 130°. [ABSTRACT FROM PUBLISHER]

Modern vehicles employ a large amount of distributed computation and require the underlying communication scheme to provide high bandwidth and low latency. Existing communication protocols like Controller Area Network (CAN) and FlexRay do not provide the required bandwidth, paving the way for adoption of Ethernet as the next generation network backbone for in-vehicle systems. Ethernet would co-exist with safety-critical communication on legacy networks, providing a scalable platform for evolving vehicular systems. This requires a high-performance network gateway that can simultaneously handle high bandwidth, low latency, and isolation; features that are not achievable with traditional processor based gateway implementations. We present VEGa, a configurable vehicular Ethernet gateway architecture utilising a hybrid FPGA to closely couple software control on a processor with dedicated switching circuit on the reconfigurable fabric. The fabric implements isolated interface ports and an accelerated routing mechanism, which can be controlled and monitored from software. Further, reconfigurability enables the switching behaviour to be altered at run-time under software control, while the configurable architecture allows easy adaptation to different vehicular architectures using high-level parameter settings. We demonstrate the architecture on the Xilinx Zynq platform and evaluate the bandwidth, latency, and isolation using extensive tests in hardware. [ABSTRACT FROM AUTHOR]

The lossless intra-prediction coding modality of the High Efficiency Video Coding standard provides high coding performance while allowing frame-by-frame basis access to the coded data. This is of interest in many professional applications, such as medical imaging, automotive vision, and digital preservation in libraries and archives. Various improvements to lossless intra-prediction coding have been proposed recently, most of them based on sample-wise prediction using differential pulse code modulation (DPCM). Other recent proposals aim at further reducing the energy of intra-predicted residual blocks. However, the energy reduction achieved is frequently minimal due to the difficulty of correctly predicting the sign and magnitude of residual values. In this paper, we pursue a novel approach to this energy-reduction problem using piecewise mapping (pwm) functions. In particular, we analyze the range of values in residual blocks and apply accordingly a pwm function to map specific residual values to unique lower values. We encode the appropriate parameters associated with the pwm functions at the encoder, so that the corresponding inverse pwm functions at the decoder can map values back to the same residual values. These residual values are then used to reconstruct the original signal. This mapping is, therefore, reversible and introduces no losses. We evaluate the pwm functions on $4\times 4$ residual blocks computed after DPCM-based prediction for lossless coding of a variety of camera-captured and screen content sequences. Evaluation results show that the pwm functions can attain the maximum bit-rate reductions of 5.54% and 28.33% for screen content material compared with DPCM-based and block-wise intra-prediction, respectively. Compared with intra-block copy, piecewise mapping can attain the maximum bit-rate reductions of 11.48% for a camera-captured material. [ABSTRACT FROM PUBLISHER]

MapReduce has become a major computing model for data intensive applications. Hadoop, an open source implementation of MapReduce, has been adopted by an increasingly growing user community. Cloud computing service providers such as Amazon EC2 Cloud offer the opportunities for Hadoop users to lease a certain amount of resources and pay for their use. However, a key challenge is that cloud service providers do not have a resource provisioning mechanism to satisfy user jobs with deadline requirements. Currently, it is solely the user's responsibility to estimate the required amount of resources for running a job in the cloud. This paper presents a Hadoop job performance model that accurately estimates job completion time and further provisions the required amount of resources for a job to be completed within a deadline. The proposed model builds on historical job execution records and employs Locally Weighted Linear Regression (LWLR) technique to estimate the execution time of a job. Furthermore, it employs Lagrange Multipliers technique for resource provisioning to satisfy jobs with deadline requirements. The proposed model is initially evaluated on an in-house Hadoop cluster and subsequently evaluated in the Amazon EC2 Cloud. Experimental results show that the accuracy of the proposed model in job execution estimation is in the range of 94.97 and 95.51 percent, and jobs are completed within the required deadlines following on the resource provisioning scheme of the proposed model. [ABSTRACT FROM AUTHOR]

Accurate distance perception and natural interactions are mandatory conditions when training precision aiming tasks in VR. However, many factors specific to virtual environments (VEs) lead to differences in the way users execute a motor task in VR versus the real world. To investigate these differences, the authors performed a study on basketball beginners' free-throw performance in VEs under different visual conditions. Although the success rate is not statistically different, some adaptations occurred in the way the users performed the task, depending on the visual conditions. In the third-person perspective visual condition, the release parameters indicate that the users more accurately estimated distance to target. Adding visual guidance information (gradual depth information showing the ideal ball trajectory) also led to more natural motor behavior. The final aim of this study was to develop a reliable basketball free-throw training system in VEs, so the authors compared beginners' performances in VR with experts' models of performance. Their results show that most of the performance variables tended to evolve closer to the experts' performance during the training in the VE. [ABSTRACT FROM PUBLISHER]

The impact of channel estimation errors is investigated in two-way relay networks (TWRNs) with two different channel state information (CSI) estimation schemes, namely separate and cascaded. In the former scheme, the relay node is chosen in accordance with the CSI of the individual channels between the source nodes and the relay node. By contrast, in the latter scheme, relay node selection is performed in accordance with the CSI of the cascaded channel between the two source nodes. For both estimation schemes, the effect of the CSI estimation error on the outage probability is examined by means of the Cramér–Rao lower bound. Moreover, in contrast to previous studies, the source nodes in the TWRN are assumed to have different estimation errors. The numerical results confirm the validity of the derived expressions and show that the presence of different estimation errors at the two source nodes has a significant effect on the performance of the TWRN. [ABSTRACT FROM PUBLISHER]

Two of the main drawbacks of the current broadcasting services are, on the one hand, the lack of flexibility to adapt to the new generation systems requirements, and on the other hand, the incapability of taking a piece of the current mobile services market. In this paper, layered division multiplexing (LDM), which grew out of the concept of Cloud Txn, is presented as a very promising technique for answering those challenges and enhancing the capacity of broadcasting systems. The major contribution of this paper is to present the first comprehensive study of the LDM performance behavior. In particular, in this paper, the theoretical considerations of the LDM implementation are completed with the first computer based simulations and laboratory tests, covering a wide range of stationary channels and the mobile TU-6 channel. The results will support LDM as a strong candidate for multiplexing different services in the next generation broadcasting systems, increasing both flexibility and performance. [ABSTRACT FROM PUBLISHER]

The article reports that Standards are fundamental in creating and maintaining an open and competitive market for equipment manufacturers and in assuring national and international interoperability of telecommunications technology and processes. Our interactive world is aided and achieved by standards which may be based on patented technologies that enable all types of devices to communicate efficiently with each other.

We call the Häring-iterative scheme with the clipping-nulling scheme as the preprocessing impulsive noise (IN) mitigation scheme: Mengi-Häring (MH)-iterative scheme. In this paper, we report two ideas that can significantly improve its performance. The first idea is to use the replacement-nulling scheme as the preprocessing IN mitigation scheme. The second idea is to use the output vector of the preprocessing IN mitigation scheme in all iterations. To show the performance comparison between the MH-iterative scheme and our proposed scheme, we conduct some simulations in the simplified model of the Middelton's additive white class A noise model and present performance in terms of the bit-error rate as a function of the signal-to-noise ratio. [ABSTRACT FROM PUBLISHER]

The use of alternate fuels and raw materials in the cement manufacturing process has evolved substantially over the last two to three decades, along with approaches to acquire regulatory approval. Most recently, the Environmental Protection Agency (EPA) has published new regulations as an overlay to what has been in place, having the potential to significantly impact this practice. Instead of dealing with the state agencies that usually have permitting authority, the new rules add another layer of regulatory hurdles by EPA. The new regulations also force a more significant overlap of the air permitting and solid waste rules. As a result, implementing an alternate material program and seeking approval has the potential to be considerably more challenging for the regulated community, in addition to adding complexity for the state agency permitting staff. This paper provides an update on the latest solid waste and air regulations applicable to alternative material use in cement plants, and potential implications for the U.S. industry. Early experiences will be explored, in addition to contemplating potential permitting and/or approval pathways expected to be encountered with the newest regulations in place. [ABSTRACT FROM AUTHOR]

The recently published NEMA NU 4-2008 Standards has been specially designed for evaluating the performance of small animal PET scanners used in preclinical applications. In this paper, we report on the NU 4 performance of a clinical positron emission mammography (PEM) system. Since there are no PEM specific performance test protocols available, and the NU 2 protocol (intended for whole-body PET scanners) cannot be applied without modification due to the compact design of the PEM scanner, we decided to evaluate the NU 4 Standards as an alternative. We obtained the following results: Trans-axial spatial resolution 1.8mmFWHMfor high resolution reconstruction mode and 2.4 mm FWHM for standard resolution reconstruction mode with no significant variation within the field of view. The total system sensitivity was 0.16 cps/Bq. In image quality testing, the uniformity was found to be 3.9% STD at the standard resolution mode and 5.6% at the high resolution mode when measured with a 34 mm paddle separation. The NEMA NU 4-2008 Standards were found to be a practicable tool to evaluate the performance of the PEM scanner after some modifications to address the specifics of its detector configuration. Furthermore, the PEM scanner's in-plane spatial resolution was comparable to other small animal PET scanners with good image quality. [ABSTRACT FROM AUTHOR]