Search

A carbon nanotube (CNT) has been used as electronic materials for functional electronic devices such as transistors, transparent electrodes, sensors, and Joule heating devices owing to their unique properties and high aspect ratios. However, the CNT network is randomly aligned on sheets fabricated by the spreading of a CNT suspension. In order to fabricate directional CNT network, we demonstrate the large scale alignment and assembly of CNTs by a dielectrophoretic external force, which is applied by an alternating current (AC). The alignment of the CNT network was monitored to have 2.6–17.5 times lower resistance values in the direction of the applied electrical field than in the vertical directions of the electrical field. We demonstrated that the resistance values were increased in the vertical direction by the multiple AC alignment process with layer-by-layer. Furthermore, the directionally aligned CNT network was transferred onto a nitrocellulose sheet via solvent evaporation and the electrical properties were measured. Even though we demonstrated only the limited MAAP process with CNT, the directional MAAP including transfer process could be used for diverse applications such as Joule heating transparent electrodes and flexible sensor devices.

Although the underwater optical communication system (UWOC) plays an important role in many marine applications, the acquisition, pointing and tracking (APT) problems are still great challenges in a long or turbid water channel. The Orthogonal Frequency Division Multiplexing (OFDM) and the space-time block coding (STBC) technologies have been introduced in UWOC systems to improve spectral efficiency and data transmission reliability. On these foundations, a multi-input multi-output (MIMO) mode is proposed and verified in this article to reduce the alignment requirement. In experiments, two sets of 450 nm blue laser diodes (LDs) and PIN photodiodes are selected as light sources and receivers, and then parallel light beams are generated and modulated using an Alamouti coding for its anti-interference ability. The receiving plane is rotated to simulate the various alignment situations, and the system performance of MIMO-OFDM and single-input single-output OFDM (SISO-OFDM) are compared in tap water and turbid water. The results show that the spatial diversity in MIMO can extend the communication range and its single-sided maximum detection angle range is 92% and 112% higher than the SISO system, respectively.

Hand gesture recognition (HGR) based on surface electromyogram (sEMG) and Accelerometer (ACC) signals is increasingly attractive where fusion strategies are crucial for performance and remain challenging. Currently, neural network-based fusion methods have gained superior performance. Nevertheless, these methods typically fuse sEMG and ACC either in the early or late stages, overlooking the integration of entire cross-modal hierarchical information within each individual hidden layer, thus inducing inefficient inter-modal fusion. To this end, we propose a novel Alignment-Enhanced Interactive Fusion (AiFusion) model, which achieves effective fusion via a progressive hierarchical fusion strategy. Notably, AiFusion can flexibly perform both complete and incomplete multimodal HGR. Specifically, AiFusion contains two unimodal branches and a cascaded transformer-based multimodal fusion branch. The fusion branch is first designed to adequately characterize modality-interactive knowledge by adaptively capturing inter-modal similarity and fusing hierarchical features from all branches layer by layer. Then, the modality-interactive knowledge is aligned with that of unimodality using cross-modal supervised contrastive learning and online distillation from embedding and probability spaces respectively. These alignments further promote fusion quality and refine modality-specific representations. Finally, the recognition outcomes are set to be determined by available modalities, thus contributing to handling the incomplete multimodal HGR problem, which is frequently encountered in real-world scenarios. Experimental results on five public datasets demonstrate that AiFusion outperforms most state-of-the-art benchmarks in complete multimodal HGR. Impressively, it also surpasses the unimodal baselines in the challenging incomplete multimodal HGR. The proposed AiFusion provides a promising solution to realize effective and robust multimodal HGR-based interfaces.

The growing complexity and interdisciplinary nature of Materials Science research demand efficient data management and exchange through structured knowledge representation. Domain-Level Ontologies (DLOs) for Materials Science have emerged as a valuable tool for describing materials properties, processes, and structures, enabling effective data integration, interoperability, and knowledge discovery. However, the harmonization of DLOs, and, more generally, the establishment of fully interoperable multi-level ecosystems, remains a challenge due to various factors, including the diverse landscape of existing ontologies. This work provides, for the first time in literature, a comprehensive overview of the state-of-the-art of DLOs for Materials Science, reviewing more than 40 DLOs and highlighting their main features and purposes. Furthermore, an alignment methodology including both manual and automated steps, making use of Top-Level Ontologies’ (TLO) capability of promoting interoperability, and revolving around the engineering of FAIR standalone entities acting as minimal data pipelines (“bridge concepts”), is presented. A proof of concept is also provided. The primary aspiration of this undertaking is to make a meaningful contribution towards the establishment of a unified ontology framework for Materials Science, facilitating more effective data integration and fostering interoperability across Materials Science subdomains.

In this article, we investigate the feasibility to align synthetic aperture radar (SAR) imagery based on a compensation for sea ice drift occurring between temporally shifted image acquisitions. The image alignment is a requirement for improving sea ice classification by combining multifrequency SAR images acquired at different times. Images obtained at different radar frequencies provide complementary information, thus reducing ambiguities in the separation of ice types and the retrieval of sea ice parameters. For the alignment we use ice displacement vectors obtained from a sea ice drift retrieval algorithm based on pattern matching. The displacement vectors are organized on a triangular mesh and used for a piecewise affine transformation of the slave image onto the master image. In our case study, we developed an alignment framework for pairs of Advanced Land Observing Satellite-2 PALSAR-2 (L-band) and Sentinel-1 (C-band) images. We demonstrate several successful examples of alignment for time gaps ranging from a few hours to several days, depending on the ice conditions. The data were acquired over three test sites in the Arctic: 1) Belgica Bank, 2) Fram Strait, and 3) Lincoln Sea. We assess the quality of the alignment using the structural similarity index. From the displacement vectors, locations and extensions of patches of strong ice deformation are determined, which allows to estimate the possible areal size of successful alignment over undeformed ice and a judgment of the expected quality for each image pair. The comprehensive assessment of hundreds of aligned L–C SAR pairs shows the potential of our method to work under various environmental conditions provided that the ice drift can be estimated reliably.

Multimodal machine learning (MML) is a tempting multidisciplinary research area where heterogeneous data from multiple modalities and machine learning (ML) are combined to solve critical problems. Usually, research works use data from a single modality, such as images, audio, text, and signals. However, real-world issues have become critical now, and handling them using multiple modalities of data instead of a single modality can significantly impact finding solutions. ML algorithms play an essential role in tuning parameters in developing MML models. This paper reviews recent advancements in the challenges of MML, namely: representation, translation, alignment, fusion and co-learning, and presents the gaps and challenges. A systematic literature review (SLR) was applied to define the progress and trends on those challenges in the MML domain. In total, 1032 articles were examined in this review to extract features like source, domain, application, modality, etc. This research article will help researchers understand the constant state of MML and navigate the selection of future research directions.

Alignment between the reference layout (or target pattern) and the corresponding scanning electron microscope (SEM) image is a crucial task for the die-to-database (D2DB) inspection in the semiconductor industry. However, it is challenging to align them accurately because the style and quality of reference layouts represented as a computer-aided design (CAD) are quite different from those of grayscale SEM images with noise. Direct application of conventional cross-correlation-based matching methods often leads to misalignment. Here, we propose a new method enabling the precise pattern alignment. The main idea is to transform SEM images into target-like CAD images using a generative adversarial network (GAN). As the generated and real layout images have similar style and quality, they can be precisely aligned using conventional matching methods. Then, the SEM image can be located correctly on the corresponding reference layout for inspection. A polygon-based clustering algorithm for target patterns is developed to avoid manual selection and minimize the number of training data. [ABSTRACT FROM AUTHOR]

Tandem repeats are repetitive structures present in some DNA sequences, consisting of many repeated copies of a single motif. They can serve as important markers for phylogenetic and population genetic studies, due to the high polymorphism in the number of motif copies as well as variations in the motif. The first step in using tandem repeats for phylogenetic studies is to estimate the evolutionary distance between a pair $D_1$ D 1 and $D_2$ D 2 of tandem repeat sequences with homologous motifs. This problem can be broken into two sub-problems: 1) Construct the most recent common ancestor of the sequences. 2) Calculate the evolutionary distance between each sequence and the hypothesised common ancestor. We present an algorithm that estimates the solution to the second problem. This takes the form of an asymmetric alignment algorithm to estimate the evolutionary distance between two tandem repeat sequences $A$ A and $D$ D , where $D$ D is assumed to have descended from $A$ A , under a model that allows block duplication, deletion, and variant substitution. The algorithm is asymmetric in the sense that the two input sequences $A$ A and $D$ D play different roles in the calculations, reflecting the assumption that $D$ D descends from $A$ A . Our model assumes static motif boundaries, meaning that motif duplication and deletion events must respect the motif boundaries. The algorithm may also be applied without modification to more complex repetitive structures with two or more motifs, such as nested tandem repeats. [ABSTRACT FROM AUTHOR]

The design of railway alignment and station locations involves two intertwined problems, which makes it a complex and time-consuming task. Especially in mountainous regions, the large 3-dimensional (3D) search spaces, complex terrain conditions, coupling constraints and infinite numbers of potential alternatives of this problem pose many challenges. However, most current optimization methods emphasize either alignment optimization or station locations optimization independently. Only a few methods consider coordinated optimization of alignment and stations, but optimize them sequentially. This paper proposes a concurrent optimization method based on a 3-dimensional distance transform algorithm (3D-DT) to solve this problem. It includes the following components: (1) To optimize the location of stations within specified spacing intervals, a novel perceptual search strategy is proposed and incorporated into the basic 3D-DT optimization process. (2) A combined-alignment-station 3D search neighboring mask is developed and employed to search for both the alignment and stations. In order to implement the perceptual process, two additional kinds of 3D reverse perceptual neighboring masks are also developed and employed in the algorithm. (3) Multiple coupling constraints between alignment and stations are also formulated and addressed during the search process. In this study, the effectiveness of the method is verified through a real-world case study in a complex mountainous region. The optimization results show that the proposed method can find high-quality alternatives satisfying multiple coupling constraints.

We describe a new toolset for the manipulation and analysis of ray clouds (3D maps defined by a set of rays from a moving lidar to the scanned surfaces). Unlike point clouds, ray clouds contain information on free-space (air) as well as surface geometry. This allows the toolset to perform volumetric functions and analysis that cannot be done on point clouds alone. The presented toolset consists of seventeen command-line functions, with a C++ library available for those who require more control or tight integration. Our aim is that RayCloudTools is as useful and simple as possible, and we use this paper to demonstrate its utility, and to assess its ease of use, with comparison to established cloud processing libraries.

Power analysis attacks pose a significant threat to the security of cryptographic devices as they can reveal a secret key. Performing cryptographic operations based on a randomly varying clock (RVC) is a practical countermeasure against such attacks. The countermeasure makes it difficult to align power traces, which is a prerequisite for power analysis attacks to succeed. This paper introduces a synchronous real-time sampling (SRTS) technique as an advanced hardware-implemented approach to collect traces for a power analysis attack that negates countermeasures involving practical RVCs. By recovering the RVC, the leakage signal corresponding to the recovered clock edge is synchronously sampled in real time. We propose an analog-based hardware system implemented with two circuit blocks for SRTS operations, namely, a clock recovery block and an analog signal-processing block. The target of the power analysis attack is an Advanced Encryption Standard (AES)-128 software-implemented smart card operated at 20 MHz, which is varied in the range of 30% by the RVC countermeasure. The traces captured by the SRTS show that the suboperations of the AES encryption are distinct in contrast to the indistinguishable waveforms captured at a fixed sample rate. The results of the power analysis attack demonstrate that the correct key is successfully extracted with a high correlation coefficient at the S-box output of the AES. The proposed SRTS method improves the relative distinguishing margin by 191.4% and reduces the required number of traces to 2.75% compared with the conventional correlation power analysis attack with a fixed sample clock.

The growing market of smart devices make them appealing for various applications. Motion tracking can be achieved using such devices, and is important for various applications such as navigation, search and rescue, health monitoring, and quality of life-style assessment. Step detection is a crucial task that affects the accuracy and quality of such applications. In this paper, a new step detection technique is proposed, which can be used for step counting and activity monitoring for health applications as well as part of a Pedestrian Dead Reckoning (PDR) system. Inertial and Magnetic sensors measurements are analyzed and fused for detecting steps under varying step modes and device pose combinations using a free-moving handheld device (smartphone). Unlike most of the state of the art research in the field, the proposed technique does not require a classifier, and adaptively tunes the filters and thresholds used without the need for presets while accomplishing the task in a real-time operation manner. Testing shows that the proposed technique successfully detects steps under varying motion speeds and device use cases with an average performance of 99.6% and outperforms some of the state of the art techniques that rely on classifiers and commercial wristband products. [ABSTRACT FROM AUTHOR]

This study explored the relationship between the information system (IS) capability with the alignment of IS strategy and social capital and the relationship between the alignment of IS strategy and social capital with business performance. Moreover, this study also aimed to investigate the mediating role of the alignment of IS strategy and social capital in the relationship between IS capability and business performance. A cross-sectional field study (i.e. questionnaire survey) was conducted on 179 national scale bank branches across Indonesia. Partial least square (PLS) is utilized to test all of the research hypotheses. The result of the study showed that there are significant relationships between IS capability with the alignment of IS strategy and social capital and significant relationships between the alignment of IS strategy and social capital with business performance. Besides, this study also proved that the alignment of IS strategy with social capital has a significant role in mediating the relationship between IS Capability and Business Performance. This study contributes to the theory development by analyzing the research gap from the previous researchers considering social capital as the strategic factor in the relationship between IS capability and business performance. The findings of this study can provide a reference for companies (i.e. board of directors) in determining investment and information system use policy.

Granular computing has the advantage of discovering complex data knowledge, and manifold alignment has proven of great value in a lot of areas of machine learning. We propose a novel algorithm of fuzzy granule manifold alignment (FGMA), where we define some new operations, measurements, and local topology of fuzzy granular vectors in fuzzy granular space. Furthermore, the algorithm is very different from Semi-supervised and Procrustes algorithm because predetermining correspondence is not necessary. A projection is learned that can map instances described by two types of features to a low-dimensional space. Meanwhile, the local topology of the fuzzy granular vector induced by the instance is also preserved and matched within each set in lower dimensional space. This approach makes it possible to directly compare between data instances in different spaces. We convert an alignment problem of data in feature space into fuzzy granular manifold alignment problem of granular space. Specifically, we first define fuzzy granule, fuzzy granular vector, operations, and measurements in fuzzy granular space and gave proofs of theorems and deductions. Next, the local topology around the fuzzy granular vector is introduced and the optimal local topology matching can be achieved by minimizing their Frobenius norm. Finally, two manifolds are connected and the optimal mapping can be calculated to obtain dimensionality reduction of the joint structure. Thus, the corresponding relationship between two data instances can be got. We verified this algorithm in Oxford image and Alzheimer's disease voice dataset. Theoretical analysis and experiments demonstrate the algorithm proposed is robust and effective.

This article presents a novel substrate-agnostic self-alignment technology with submicrometer alignment accuracy targeted for heterogeneous integrated systems. This self-alignment technology, referred to as PSAS-to-PSAS self-alignment, incorporates positive self-alignment structures (PSAS) or reflowed patterned photoresist structures that couple with other PSAS to achieve submicrometer alignment accuracy without the need for an advanced placement tool. The PSAS-to-PSAS self-alignment technology is non-substrate invasive and hence does not consume any substrate real estate. These PSAS structures can also be fabricated atop any material substrate; hence, this technology is suitable for heterogeneous integrated systems where a variety of different material substrates may be incorporated. This article discusses this PSAS-to-PSAS self-alignment technology and it provides a design analysis relating the inter-substrate gap between aligning substrates and the dimensions of the PSAS structures. The fabrication of the PSAS on two separate substrates is then performed, followed by a manual assembly process to couple the PSAS on one substrate to the PSAS on the other aligning substrate. Vernier scales are incorporated on these substrates to measure the misalignment between said substrates; submicrometer alignment is recorded. As these structures may also enable a non-permanent system configuration, alignment repeatability measurements were also performed. [ABSTRACT FROM AUTHOR]

Chemical and biological processes are reliant on the pH value of the elements involved in the reaction. Transparent pH sensors with a large detection area are desirable in transparent electronics and microfluidic analytical platforms. This work reports the fabrication of pH sensors using the dielectrophoresis (DEP) method. Carboxyl-functionalized multi-walled carbon nanotubes (MWCNTs) were assembled across indium tin oxide (ITO) electrodes separated with a gap width of $50 ~\mu \text{m}$. The DEP process was assisted with airflow and thermal annealing to increase the stability of the aligned MWCNT connections and reduce the variation in sensor resistance. The fabricated pH sensors were exposed to analytes with different pH values. The sensors showed high sensitivity between 170% (pH=4) to 2000% (pH=10) and response times between 4 to 6 s. The long-term performance and repeatability of the sensors were improved, where the sensors can recover their initial resistance after each testing cycle. [ABSTRACT FROM AUTHOR]

Initialization is a fundamental task for the state estimation of multi-sensor integration. In this paper, we propose a novel approach to achieve a fast and accurate initialization for the integrated system. With the outputs from GNSS, the rough roll, pitch, yaw, and direction of gravity are calculated. Based on these initial states, the GNSS/INS fusion and Visual-Inertial Odometry (VIO) are simultaneously launched whenever the observations are available. Subsequently, a coarse scale is retrieved by assigning the initial states of GNSS/INS fusion to the VIO procedure. The scale can be utilized to get poses and points very close to the real world. Once the VIO estimation converges, the transformation parameters between the poses of VIO and GNSS/INS fusion are estimated via non-linear optimization. Thus, we can align these two trajectories and evaluate the scale error. Through six vehicular tests, we analyze the performance of the proposed method with different illumination, obstructions, and motion patterns, and compare the results of the proposed approach with the state-of-the-art algorithms. The results indicated that the Visual-Inertial parameters converge within 9 seconds in our method. The scale errors of both our method are mostly within 10%. The alignment errors are mostly at centimeter-level in our method, which is comparable to the existing approaches. [ABSTRACT FROM AUTHOR]

Instrument alignment is a common task in various surgical interventions using navigation. The goal of the task is to position and orient an instrument as it has been planned preoperatively. To this end, surgeons rely on patient-specific data visualized on screens alongside preplanned trajectories. The purpose of this manuscript is to investigate the effect of instrument visualization/non visualization on alignment tasks, and to compare it with virtual extensions approach which augments the realistic representation of the instrument with simple 3D objects. 18 volunteers performed six alignment tasks under each of the following conditions: no visualization on the instrument; realistic visualization of the instrument; realistic visualization extended with virtual elements (Virtual extensions). The first condition represents an egocentric-based alignment while the two other conditions additionally make use of exocentric depth estimation to perform the alignment. The device used was a see-through device (Microsoft HoloLens 2). The positions of the head and the instrument were acquired during the experiment. Additionally, the users were asked to fill NASA-TLX and SUS forms for each condition. The results show that instrument visualization is essential for a good alignment using see-through devices. Moreover, virtual extensions helped achieve the best performance compared to the other conditions with medians of 2 mm and 2° positional and angular error respectively. Furthermore, the virtual extensions decreased the average head velocity while similarly reducing the frustration levels. Therefore, making use of virtual extensions could facilitate alignment tasks in augmented and virtual reality (AR/VR) environments, specifically in AR navigated surgical procedures when using optical see-through devices. [ABSTRACT FROM AUTHOR]

We present a new approach for redirected walking in static and dynamic scenes that uses techniques from robot motion planning to compute the redirection gains that steer the user on collision-free paths in the physical space. Our first contribution is a mathematical framework for redirected walking using concepts from motion planning and configuration spaces. This framework highlights various geometric and perceptual constraints that tend to make collision-free redirected walking difficult. We use our framework to propose an efficient solution to the redirection problem that uses the notion of visibility polygons to compute the free spaces in the physical environment and the virtual environment. The visibility polygon provides a concise representation of the entire space that is visible, and therefore walkable, to the user from their position within an environment. Using this representation of walkable space, we apply redirected walking to steer the user to regions of the visibility polygon in the physical environment that closely match the region that the user occupies in the visibility polygon in the virtual environment. We show that our algorithm is able to steer the user along paths that result in significantly fewer resets than existing state-of-the-art algorithms in both static and dynamic scenes. Our project website is available at https://ganuna.umd.edu/vis.poly/. [ABSTRACT FROM AUTHOR]

Calibration is important for digital monopulse radars since the target tracking performance would be degraded by the amplitude and phase imbalance among channels. However, the alignment between the radar to be calibrated and the test probe is a challenge, especially for antennas with high gain and narrow beamwidth. In this letter, we propose a fast alignment algorithm for the autocalibration of digital monopulse radars using the gradient ascent method based on the tentative radiation pattern gradient model. We verify the feasibility and the high efficiency of the proposed algorithm by simulations and an experiment of a W-band digital monopulse radar. The elapsed time of the proposed algorithm is 0.01 % of the existing method and requires lower equipment complexity compared with the scanning-based method. [ABSTRACT FROM AUTHOR]

Due to the complex underwater environment, the assisted velocity provided by Doppler velocity log (DVL) may be contaminated by non-Gaussian noise. This will degrade the performance of the traditional optimisation-based alignment (OBA) method. To solve this problem, this paper proposes a robust OBA (ROBA) based on projection statistics (PS) algorithm. The ROBA contains two main steps which are also the contributions of the work presented here. Firstly, ROBA makes use of PS algorithm to identify the stored abnormal auxiliary velocity accurately under the framework of Kalman filter (KF), and then a robust strategy of the observation vector is designed to achieve the robustness of OBA. The semi physical experiment of SINS underwater in-motion coarse alignment is carried out based on the ship-mounted measured data. The experimental results demonstrate the superiority of the proposed method over the traditional ones, and the yaw alignment accuracy of ROBA is at least 30% improvement over the traditional ones under the non-Gaussian conditions. [ABSTRACT FROM AUTHOR]

Process models are used to describe or verify the correctness of business processes before their implementation by enterprise information systems. With the development of data mining technologies, a lot of event logs generated from such systems can be used to check conformance of business processes and process models. For models containing a choice with concurrency structure, although the model repaired by the existing method can replay the event log, it is quite different from the business process. Therefore, we propose a model repair method based on logic Petri net. First, we give an algorithm to find the position of the deviation. Then, we add directed arcs from a place to a transition or from a transition to a place via logic Petri nets. Finally, we repair the model by adding logic transitions with logic functions. The repaired model can efficiently enhance conformance dimensions, such as simplicity and precision. The correctness and effectiveness of the method are verified by experimental results.

Various conventional and novel fixing strategies for dielectric resonator antennas (DRAs) in K band and beyond are presented and investigated. Their effects on DRAs are studied in depth and explained theoretically through the perturbation theory and basic principles of DRAs. Multifunctional fixtures which achieve different antenna performance are then proposed and discussed. Their mechanisms for offering flexible bandwidth, miniaturization, frequency diversity, low cross polarization, and circular polarization are explained. In practical applications, glue is used besides fixtures to increase reliability, and its influence on antenna performance is difficult to predict. By considering various glue distributions in simulation beforehand, potential performance degradation is successfully mitigated. For validation, a four-element DRA array for 24-GHz automotive radars is fabricated. The results demonstrate that the proposed fixing strategy provides superior characteristics such as shock-resistance, fabrication and assembly simplicity, and robust antenna performance.

Business processes are usually described and verified by the process models before they are implemented in the information systems. However, there exist deviations between the activities of a process model and those in event logs generated from the information systems. To replay the activities in the logs, the existing model needs to be repaired. In the current repair methods, the models with concurrent blocks cannot be simply repaired. Therefore, this paper proposes a new model repair approach based on logic Petri nets. Concurrent transition pairs and choice transition pairs are constructed based on process trees. By traversing optimal alignments, the transitions between the head and tail of concurrent blocks are determined, deviations are used to judge whether the activities in a process model and event logs have concurrent transition pairs or choice transition pairs, and the model is then repaired via logic Petri nets. Finally, the correctness and effectiveness of the proposed model repair method are illustrated by the experiments.

A railway track gradually deviates from the originally designed vertical alignment in the operation. To ensure the safety and comfort, railway maintenance department must periodically recreate vertical alignment and calibrate existing track to it. The recreated alignment should be as close as possible to the measured points along the existing railway track, while satisfying multiple constraints. A vertical alignment consists of tangents and curves. Identifying the geometric element attribution of the measured points accurately and recreating the vertical alignment efficiently and accurately with all the constraints satisfied are the key problems. Existing methods need manual assistance and few constraints are considered. This paper proposes an automatic global iterations method considering multiple constraints. Firstly, geometric elements are identified based on the change rate of gradient and are set as initial values for the iterations. Then, the geometric elements are fitted iteratively. In each iteration, all the tangents are fitted simultaneously followed by the fitting of all vertical curves and the constraints are handled while the geometric elements are fitted. The attributions of all points are adjusted after each iteration. If the attributions of all the points are consistent with the ranges of fitted geometric elements, the iteration terminates. A software is developed based on this method, and applications demonstrate this method can identify all geometric elements automatically and recreate an optimized vertical alignment while satisfying all the constraints.

Fluorescent quantum rods (QRs) have received much attention recently because of their properties of polarized light emission with narrow full width at half-maximum. Therefore, QRs can be used in liquid crystal display (LCD) backlight, as an active polarizer. In order to emit light with a high degree of polarization (DOP), QRs need to be controlled and aligned along one direction on large-scaled devices. However, most of the proposed effective alignment approaches cannot be applied to a large area. In this paper, we report for the first time the contact ink-jet printing (CIJP) method to align CdSe/CdS core/shell QRs. With the ink being printed, the QRs can align along the fluid ink flow direction resulting in aligned QRs along printing direction and forming a film with a high DOP of 42%. Furthermore, CIJP can realize arbitrarily large area fabrication without reducing DOP value. It is expected that this method can be utilized to print on large-scaled substrates directly to fabricate polarized optoelectronic devices, and further applied to LCD backlights and any other applications where polarized light is needed.

Person re-identification has become increasing popular because of its widely application in computer vision. In this paper, we propose a novel, simple and efficient person re-id network called MPLFN. The network combines two tasks: the classification task and the metric learning task. In the classification task, we uniformly partition N feature parts from an image, and compute the person classification loss in each part separately. Computing the part loss separately guides the network to focus on every body part and learn discriminative representations for each of them. And then in the metric learning task, we recalculate the distance of two images by the shortest path between two sets of feature parts. Then the distances are put into a triplet loss to perform a dynamic part alignment during the training. With the joint learning of these two tasks, the performance of the network is significantly enhanced. Compared with existing person re-id works, MPLFN achieves a better performance on three mainstream person re-identification datasets. Extensive experiments have been conducted to validate our proposed method.

A large class of Person Re-identification (ReID) approaches identify pedestrians with the TriHard loss. Though the TriHard loss is a robust ReID method, pose variance and viewpoint in pedestrians constrain the performance. To address this problem, we introduce a spatial transformer network (STN) to align pedestrians. Then, we illustrate the generality of the STN module in pose variance problem through the evaluations on feature representation network (FRN) like VGG, ResNet and DenseNet architectures respectively. Furthermore, based on the evaluation results, we propose a robust and high-performance ReID model which consists of the STN module, DenseNet backbone and TriHard loss. And finally, we prove that our ReID model is whole differentiable by formula derivation, therefore achieving an end-to-end high-performance ReID system. The experiments show that our ReID system outperforms the state-of-art methods on Market-1501, DukeMTMC-reID and CUHK03 datasets.

Process mining is an important technique for complex operational process management. It aims to extract valuable process-related information and build process models from event logs of enterprise information systems. Process models can describe the characteristics and verify the correctness of the system with the function of providing feedback to system designers. However, there may be some deviations between the activities in event logs and a process model because of the system upgrade and business process improvement. Thus the original model needs to be repaired because it does not describe the real business process well. For the models with non-free-choice structures, event logs can be replayed by the existing repair methods, but their repaired models are quite different from the business processes. Therefore, a new model repair method is proposed based on logic Petri nets in this paper. First, the concept of transition pairs and successor relationship is proposed. A successor relationship matrix is constructed. Then, the position to be repaired can be determined by traversing the transition pairs. Finally, the correctness and effectiveness of the proposed method are illustrated in our experiments.

We present a novel redirected walking controller based on alignment that allows the user to explore large and complex virtual environments, while minimizing the number of collisions with obstacles in the physical environment. Our alignment-based redirection controller, ARC, steers the user such that their proximity to obstacles in the physical environment matches the proximity to obstacles in the virtual environment as closely as possible. To quantify a controller's performance in complex environments, we introduce a new metric, Complexity Ratio (CR), to measure the relative environment complexity and characterize the difference in navigational complexity between the physical and virtual environments. Through extensive simulation-based experiments, we show that ARC significantly outperforms current state-of-the-art controllers in its ability to steer the user on a collision-free path. We also show through quantitative and qualitative measures of performance that our controller is robust in complex environments with many obstacles. Our method is applicable to arbitrary environments and operates without any user input or parameter tweaking, aside from the layout of the environments. We have implemented our algorithm on the Oculus Quest head-mounted display and evaluated its performance in environments with varying complexity. Our project website is available at https://ganuna.umd.edu/arc/. [ABSTRACT FROM AUTHOR]

Specifying precise relationships among graphic elements is often a time-consuming process with traditional alignment tools. Automatic beautification of roughly designed layouts can provide a more efficient solution but often lead to undesired results due to ambiguity problems. To facilitate ambiguity resolution in layout beautification, we present a novel user interface for visualizing and editing inferred relationships through an automatic global layout beautification process. First, our interface provides a preview of the beautified layout with inferred constraints without directly modifying an input layout. In this way, the user can easily keep refining beautification results by interactively repositioning and/or resizing elements in the input layout. Second, we present a gestural interface for editing automatically inferred constraints by directly interacting with the visualized constraints via simple gestures. Our technique is applicable to both 2D and 3D global layout beautification, supported by efficient system implementation that provides instant user feedback. Our user study validates that our tool is capable of creating, editing, and refining layouts of graphic elements, and is significantly faster than the standard snap-dragging or command-based alignment tools for both 2D and 3D layout tasks. [ABSTRACT FROM AUTHOR]

Human poses play a critical role in human-centric product design. Despite considerable researches on pose synthesis and pose-driven product design, most of them adopt the simple stick figure model that captures only skeletons rather than real body geometries and do not link human poses to the environment (e.g., chairs for sitting). This paper focuses on user-tailored ergonomic design and rating of chairs using scanned human geometries. Fully utilizing the anthropometric information of the human models, our method considers more ergonomic guidelines of chair design (such as pressure distribution and support intensity) and links the geometry of 3D chair models and human-to-chair interactions into the pose deformation constraints of the human avatars. The core of our method is a pose generation algorithm which rigs the user's successive poses through coarse- and fine-level pose deformations. We define a non-linear energy function with contact, collision, and joint limit terms, and solve it using a hill-climbing algorithm. The fitting results allow us to quantitatively evaluate the chair model in terms of various ergonomic criteria. Our method is flexible and effective and can be applied to users with varying body shapes and a wide range of chairs. Moreover, the proposed technique can be easily extended to other furniture, such as desk, bed, and cabinet. Extensive evaluations and a user study demonstrate the efficiency and advantages of the proposed virtual fitting method. Given that our method avoids tedious on-site trying, facilitates the exploration/evaluation of various chair products, and provides valuable feedback for the designers and manufacturers to deliver customized products, it is ideal for online shopping of chairs. [ABSTRACT FROM AUTHOR]

Low-rank tensor recovery in the presence of sparse but arbitrary errors is an important problem with many practical applications. In this work, we propose a general framework that recovers low-rank tensors, in which the data can be deformed by some unknown transformations and corrupted by arbitrary sparse errors. We give a unified presentation of the surrogate-based formulations that incorporate the features of rectification and alignment simultaneously, and establish worst-case error bounds of the recovered tensor. In this context, the state-of-the-art methods ‘RASL’ and ‘TILT’ can be viewed as two special cases of our work, and yet each only performs part of the function of our method. Subsequently, we study the optimization aspects of the problem in detail by deriving two algorithms, one based on the alternating direction method of multipliers (ADMM) and the other based on proximal gradient. We provide convergence guarantees for the latter algorithm, and demonstrate the performance of the former through in-depth simulations. Finally, we present extensive experimental results on public datasets to demonstrate the effectiveness and efficiency of the proposed framework and algorithms. [ABSTRACT FROM AUTHOR]

Zero-shot learning aims at recognizing unseen classes (no training example) with knowledge transferred from seen classes. This is typically achieved by exploiting a semantic feature space shared by both seen and unseen classes, i.e., attribute or word vector, as the bridge. One common practice in zero-shot learning is to train a projection between the visual and semantic feature spaces with labeled seen classes examples. When inferring, this learned projection is applied to unseen classes and recognizes the class labels by some metrics. However, the visual and semantic feature spaces are mutually independent and have quite different manifold structures. Under such a paradigm, most existing methods easily suffer from the domain shift problem and weaken the performance of zero-shot recognition. To address this issue, we propose a novel model called AMS-SFE. It considers the alignment of manifold structures by semantic feature expansion. Specifically, we build upon an autoencoder-based model to expand the semantic features from the visual inputs. Additionally, the expansion is jointly guided by an embedded manifold extracted from the visual feature space of the data. Our model is the first attempt to align both feature spaces by expanding semantic features and derives two benefits: first, we expand some auxiliary features that enhance the semantic feature space; second and more importantly, we implicitly align the manifold structures between the visual and semantic feature spaces; thus, the projection can be better trained and mitigate the domain shift problem. Extensive experiments show significant performance improvement, which verifies the effectiveness of our model. [ABSTRACT FROM AUTHOR]

Computational photography has been a new research focus over the last two decades. Most of the computational imaging applications involve pixel-to-pixel correspondence adjustment between the spatial light modulator and the sensor. In this study, an accuracy pixel-to-pixel alignment method with six-axis adjustment is proposed. Specifically, the relations between the moiré fringe distribution and the six degree of freedom (DoF) displacements are characterized. A special pattern called five-grating pattern is designed for monitoring and adjusting the six DoF displacements in a four-step alignment procedure. Finally, the experimental results verify the performance of the proposed method by discussing the alignment accuracy.

Remote sensing of life detection or a non-contact monitor of vital signals is an important application for Ultra-wideband (UWB) radar, such as health monitoring of a vehicle driver. Using the UWB radar to detect physiological signals of a dynamic human, three kind movement features (body motion, breathing, and heartbeat) must be considered generally to be extracted from echo pulses. Usually, moving body echo signal is much larger than other twos, which will cause signal interference and interaction problems. Meanwhile, since moving body causes breathing and heartbeat to act in dynamic spatial position, conventional fixed feature with maximum spectrum or local peak spectrum detection methods are not likely to find those movable physiological active features. Thus, how to reduce physiological feature bias from body motion, and efficiently obtain valuable physiological information are problems. To solve these problems, a novel multi-feature alignment (MFA) two-layer EEMD method is proposed. The proposed method simultaneously detects breathing and heartbeat information from a slightly swinging human (driver). The simulation and experiment results show the proposed method can effectively and reliably evaluate breathing and heart rates in static or/and dynamic body situation, both in laboratory and car. [ABSTRACT FROM AUTHOR]

A template matching (TM) algorithm has been widely employed in a visual inspection process of wafer-level flip-chip packaging, but the structures of traditional TM algorithms are always direct feedforward, which leads to the difficulty in achieving fast speed and high accuracy at the same time. The motivation of this article is to combine the ability to enable the chip visual measurement running in a fast-speed and high-accuracy manner. First, a novel self-feedback intelligent template matching (SFI-TM) structure is proposed, which can enable the intermediate information in the matching process to be fully utilized. The intelligent speed and resolution regulation rules are combined to construct the SFI-TM algorithm. Then, the reliability and robustness of the SFI-TM algorithm are theoretically analyzed to make sure it works in a stable manner. Finally, a series of practical chip alignment visual inspection experiments, including parameter testing, comparing experiments with the other five proposed visual detection algorithms, and robustness testing, are carried out in detail, respectively. The experimental results indicate that the SFI-TM algorithm can achieve the highest average measurement accuracy (1.08  $\mu$ m) with almost the same measurement speed as fast as Tiny YOLOv2, and that it can resist the brightness variation from $-$ 20 to 30 gray value, the pepper–salt noise with a density of 0.5/pixel, and the Gaussian noise with a large variance of 2.5. In addition, it has good robustness against blurring and distortion of images under the dynamic speed motion processes. [ABSTRACT FROM AUTHOR]

Analysis of RNA-sequence (RNA-seq) data is widely used in transcriptomic studies and it has many applications. We review RNA-seq data analysis from RNA-seq reads to the results of differential expression analysis. In addition, we perform a descriptive comparison of tools used in each step of RNA-seq data analysis along with a discussion of important characteristics of these tools. A taxonomy of tools is also provided. A discussion of issues in quality control and visualization of RNA-seq data is also included along with useful tools. Finally, we provide some guidelines for the RNA-seq data analyst, along with research issues and challenges which should be addressed. [ABSTRACT FROM AUTHOR]

Track geometry is an important parameter, used by railways, for routine track maintenance. Laser based Track Recording Coaches (LTRCs) are widely availed by railways, for recording track geometry defects, such as: vertical profile, alignment, gauge etc. In this paper, a novel method for monitoring vertical profile irregularities by in-service rail vehicle dynamics measurements has been proposed. The procedure takes as input, data from bogie installed inertial measurement unit (IMU) measuring vertical acceleration and pitch-rate, and encoder measuring longitudinal velocity. Extended Kalman Filter (EKF) with Rauch-Tung-Striebel (RTS) smoothing and Extended Kalman Particle Filter (EPF) are reviewed, for detecting vertical profile irregularities. Process model of nonlinear state estimation filters, EKF and EPF, has been developed by applying a novel analytical technique that approximates osculating circle of a point on the vertical profile curve. Further, measurement model has been devised by a novel procedure estimating curvatures from circles approximating trajectory sensed by vertical acceleration and pitch-rate curvature. Proposed method has been field-tested by comparing its output with LTRC’s recorded data. [ABSTRACT FROM AUTHOR]

In obstetric ultrasound (US) scanning, the learner's ability to mentally build a three-dimensional (3D) map of the fetus from a two-dimensional (2D) US image represents a significant challenge in skill acquisition. We aim to build a US plane localization system for 3D visualization, training, and guidance without integrating additional sensors. This work builds on top of our previous work, which predicts the six-dimensional (6D) pose of arbitrarily oriented US planes slicing the fetal brain with respect to a normalized reference frame using a convolutional neural network (CNN) regression network. Here, we analyze in detail the assumptions of the normalized fetal brain reference frame and quantify its accuracy with respect to the acquisition of transventricular (TV) standard plane (SP) for fetal biometry. We investigate the impact of registration quality in the training and testing data and its subsequent effect on trained models. Finally, we introduce data augmentations and larger training sets that improve the results of our previous work, achieving median errors of 2.97 mm and 6.63° for translation and rotation, respectively.

Multitemporal domain adaptation (DA) is very useful for solving the spectral drift problem between different images and is a basis step of multitemporal classification. However, for high-resolution images, they always have a few spectral bands. A few spectral bands are difficult to establish accurate alignment model. In order to achieving accurate multitemporal alignment on a few spectral bands’ high-resolution images, source label learning step is proposed in this letter and used to optimize traditional manifold alignment (MA). The core of this method is to improve the erroneous manifold structure by combining majority voting and weighting coefficients. Besides, this method is a universal step and can be used for optimizing all MA methods. Two groups of data sets captured by Chinese GF1 and GF2 satellites are used for performance evaluation. The experimental results demonstrate the effectiveness of our method and indicate our method significantly outperforms the traditional DA methods. [ABSTRACT FROM AUTHOR]

Alignment is very important for Kibble balances and the joule balance at the measurement uncertainty region of several parts in 108. In the joule balance, if the suspended coil is in a misalignment state, parasitic displacements including the horizontal displacements along the ${x}$ - and ${y}$ -axes and the angular displacements around the ${x}$ -, ${y}$ -, and z-axes, will be produced and introduce alignment errors during the measurement of the joule balance. It is key for the joule balance to measure these parasitic displacements of the suspended coil during the alignment stages. So this paper describes the parasitic displacements measurement method based on our laser interferometer system. By studying and discussing the decoupling methods, we can calculate the five degrees of freedom parasitic displacements without complicating the structures of the suspended coil. In addition, several uncertainty sources have been theoretically analyzed to simplify the adjustment procedure during the parasitic displacements measurement. The experimental results show that the decoupling methods are effective for the parasitic displacements of the suspended coil, and this paper can contribute to a precise alignment in the joule balance. [ABSTRACT FROM AUTHOR]

Quality assessment during postconstruction of buildings is an indispensable procedure in construction industry. This paper describes the design and development of a quality inspection and assessment robot (QuicaBot) that can autonomously scan the entire room using cameras and laser scanners to pick up building defects, such as hollowness, crack, evenness, alignments, and inclination. A robotic system consisting of four types of sensors and a mobile platform as well as the corresponding five types of assessment algorithms is proposed. To the best of our knowledge, this paper is the first attempt to have a complete robotic system for postconstruction quality assessment of buildings. The aim of the developed system is twofold: first, to systematize the manual inspection work through automation resulting in more reliable and objective inspection reports, and then, to speed up the inspection process resulting in a more efficient end product. Based on our experimental on-site tests, the developed novel robot takes only half of the manual inspection time when inspecting the same room. We have also observed that the autonomous assessment results have better inspection accuracy when compared to manual assessments. Last but not least, the results provided by QuicaBot have more consistent measurement accuracy when compared to a manual assessor. Motivated by the initial successful on-site tests and as being a practical mechatronic system illustrating how sensing, sensor fusion, and actuation can be integrated to achieve an intelligent system for building defects assessment, we believe that the QuicaBot-like robots are going to become an integral part of construction industry in the near future. Note to Practitioners—This robotic system has the capability of simultaneously assessing five types of defects occupying 80% of the total common defects for the construction quality assessment. It can be used in assessing a building quality during and after the construction process, which can assist the constructors to construct a building with high quality. This proposed system significantly decreases the assessment time and increases the assessment accuracy and consistency. In fact, this system is developed with the main purpose of applying in practical construction automation-related engineering applications. [ABSTRACT FROM AUTHOR]

A new domain adaptation algorithm based on the class centroid and covariance alignment (CCCA) is proposed for classification of remote sensing images. This approach exploits both the first- and second-order statistics to describe the data distribution and aligns the data distribution between domains on a per-class basis. Since the predicted labels of target data are used to estimate the two statistics, we applied overall centroid alignment (OCA) as a coarse domain adaptation strategy to improve the estimation accuracy. In addition, the OCA coarse adaptation in conjunction with CCCA refined adaptation can also benefit by incorporation of spatial information, resulting in a Spa_OCA_CCCA approach. The proposed approach is easy to implement, and only one parameter is required in the spatial filtering step. It does not require labeled information in the target domain and can achieve labor-free classification. The experimental results using Hyperion, National Center for Airborne Laser Mapping, and Worldview-2 remote sensing images demonstrated the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

We propose a new simple and low-cost alignment method utilizing electric contact for die stacking on an interposer or a printed circuit board. Without any expensive aligning/bonding equipment assisted by microscopy, we successfully stacked and bonded two silicon dies on a printed circuit board achieving alignment accuracy of $20~\mu \text{m}$ by utilizing only cheap equipment: manual micropositioners, a stage, a stand, a heat gun, a hot plate, and a power supply. In the proposed method, conductive alignment marks are fabricated on the bonding surfaces, and the electric contact between them are used to characterize misalignment. Therefore, the proposed method does not require any expensive complex microscopy equipment for alignment. In addition, alignment can be quickly and simply examined in situ by detecting electrical conduction during bonding. In experiment, alignment accuracy better than $20~\mu \text{m}$ and 100% bonding yield was measured in testing of 1800 bonds connecting two silicon interposer dies which were stacked on a printed circuit board by the proposed method. [ABSTRACT FROM AUTHOR]

Covering alignment problems arise from recent developments in genomics; so called pan-genome graphs are replacing reference genomes, and advances in haplotyping enable full content of diploid genomes to be used as basis of sequence analysis. In this paper, we show that the computational complexity will change for natural extensions of alignments to pan-genome representations and to diploid genomes. More broadly, our approach can also be seen as a minimal extension of sequence alignment to labelled directed acyclic graphs (labeled DAGs). Namely, we show that finding a covering alignment of two labeled DAGs is NP-hard even on binary alphabets. A covering alignment asks for two paths $R_1$ (red) and $G_1$ (green) in DAG $D_1$ and two paths $R_2$ (red) and $G_2$ (green) in DAG $D_2$ that cover the nodes of the graphs and maximize the sum of the global alignment scores: $\mathsf {as}(\mathsf {sp}(R_1),\mathsf {sp}(R_2))+\mathsf {as}(\mathsf {sp}(G_1),\mathsf {sp}(G_2))$ , where $\mathsf {sp}(P)$ is the concatenation of labels on the path $P$ . Pair-wise alignment of haplotype sequences forming a diploid chromosome can be converted to a two-path coverable labelled DAG, and then the covering alignment models the similarity of two diploids over arbitrary recombinations. We also give a reduction to the other direction, to show that such a recombination-oblivious diploid alignment is NP-hard on alphabets of size 3. [ABSTRACT FROM AUTHOR]

Due to the rapid progress of biological networks for modeling biological systems, a lot of biomolecular networks have been producing more and more protein-protein interaction (PPI) data. Analyzing protein-protein interaction networks aims to find regions of topological and functional (dis)similarities between molecular networks of different species. The study of PPI networks has the potential to teach us as much about life process and diseases at the molecular level. Although few methods have been developed for multiple PPI network alignment and thus, new network alignment methods are of a compelling need. In this paper, we propose a novel algorithm for a global alignment of multiple protein-protein interaction networks called MAPPIN. The latter relies on information available for the proteins in the networks, such as sequence, function, and network topology. Our algorithm is perfectly designed to exploit current multi-core CPU architectures, and has been extensively tested on a real data (eight species). Our experimental results show that MAPPIN significantly outperforms NetCoffee in terms of coverage. Nevertheless, MAPPIN is handicapped by the time required to load the gene annotation file. An extensive comparison versus the pioneering PPI methods also show that MAPPIN is often efficient in terms of coverage, mean entropy, or mean normalized. [ABSTRACT FROM AUTHOR]

We consider the problem of multi-message private information retrieval (MPIR) from $N$ non-communicating replicated databases. In MPIR, the user is interested in retrieving $P$ messages out of $M$ stored messages without leaking the identity of the retrieved messages. The information-theoretic sum capacity of MPIR $C_{s}^{P}$ is the maximum number of desired message symbols that can be retrieved privately per downloaded symbol, where the symbols are defined over the same field. For the case $P \geq M/2$ , we determine the exact sum capacity of MPIR as $C_{s}^{P}=1/(1+(M-P)/(PN))$. The achievable scheme in this case is based on downloading MDS-coded mixtures of all messages. For $P \leq {M}/{2}$ , we develop lower and upper bounds for all $M,P,N$. These bounds match if the total number of messages $M$ is an integer multiple of the number of desired messages $P$ , i.e., $M/P \in \mathbb {N}$. In this case, $C_{s}^{P}=(1+1/N+\cdots +1/N^{M/P-1})^{-1}$ , i.e., $C_{s}^{P}=(1-1/N)/(1-1/N^{M/P})$ for $N>1$ , and $C_{s}^{P}=P/M$ for $N=1$. The achievable scheme in this case generalizes the single-message capacity achieving scheme to have unbalanced number of stages per round of download. For all the remaining cases, the difference between the lower and upper bound is at most 0.0082, which occurs for $M=5$ , $P=2$ , $N=2$. Our results indicate that joint retrieval of desired messages is more efficient than successive use of single-message retrieval schemes even after considering the free savings that result from downloading undesired symbols in each single-message retrieval round. [ABSTRACT FROM AUTHOR]