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This paper proposes a gesture-based control system for industrial robots. To achieve that goal, the performance of an image classifier trained on three different American Sign Language (ASL) fingerspelling image datasets is considered. Then, the three are combined into a single larger dataset, and the classifier is trained on that. The result of this process is then compared with the original three. [ABSTRACT FROM AUTHOR]

This paper introduces a methodology for precise object orientation determination using principal component analysis, with robust performance under significant noise conditions. It validates the potential to mitigate the challenges associated with axis-aligned bounding boxes in smart manufacturing environments. The proposed approach paves the way for improved alignment in robotic grasping tasks, positioning it as a computationally efficient alternative to ML methods employing oriented bounding boxes. the methodology demonstrated a maximum angle deviation of 3.5 degrees under severe noise conditions through testing with bolts in orientations of 0 to 180 degrees. [ABSTRACT FROM AUTHOR]

Climate change has had a significant impact on glacier recession, particularly in the Arctic, where glacier meltwater is an important contributor to global sea-level rise. Therefore, it is important to accurately quantify glacier recession within this sensitive region, using multiple observations of glacier extent. In this study, we mapped 480 glaciers in Novaya Zemlya, Russian Arctic, using object-based image analysis applied to multispectral Landsat satellite imagery in Google Earth Engine and quantify the area changes between 1986–89 and 2019–21. The results show that in 1986–89, the total glacierized area was 22 990 ± 301 km2, in 2000–01 the area was 22 525 ± 308 km2 and by 2019–21 the glacier area reduced to 21 670 ± 292 km2, representing a total of 5.8% reduction in glacier area between 1986–89 and 2019–21. Higher glacier area loss was observed on the Barents Sea coast (7.3%) compared to the Kara (4.2%), reflecting previously observed differences in warming trends. The accuracy of the automatically generated outlines of each layer (1986–89, 2000–01 and 2019–21) was evaluated by comparing with manually corrected outlines (reference data) using random sampling, resulting in an overall accuracy estimate of between 96 and 97% compared to the reference data. This automated approach in Google Earth Engine is a promising tool for rapidly mapping glacier change that reduces the amount of time required to generate accurate glacier outlines. [ABSTRACT FROM AUTHOR]

Glaciers have been retreating over the last century as a result of climate change, particularly in the Arctic, causing sea levels to rise, affecting coastal communities and potentially changing global weather and climate patterns. In this study, we mapped 2203 glaciers in Novaya Zemlya (Russian Arctic), Penny Ice Cap (Baffin Island), Disko Island (Qeqertarsuaq, Greenland) and part of Kenai (Alaska), using Object-Based Image Analysis (OBIA) applied to multispectral Landsat satellite imagery in Google Earth Engine (GEE) to quantify the glacier area changes over three decades. Between 1985–89 and 2019–21, the results show that the overall glacier area loss in Novaya Zemlya is 1319 ± 419 km² (5.7% of area), 452 ± 227 km2 (6.6%) for Penny Ice Cap, 457 ± 168 km² (23.6%) in Disko Island and 196 ± 84 km2 (25.7%) in Kenai. A total of seventy-three glaciers have disappeared completely, including sixty-nine on Disko Island, three in Novaya Zemlya and one in Kenai. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence technology, many researchers have begun to focus on visual language navigation, which is one of the most important tasks in multi-modal machine learning. The focus of this multi-modal field is how to fuse multiple inputs, which is crucial for the integrated feedback of intrinsic information. However, the existing models are only implemented through simple data augmentation or expansion, and are obviously far from being able to tap the intrinsic relationship between modalities. In this paper, to overcome these challenges, a novel multi-modal matching feedback self-tuning model is proposed, which is a novel neural network called Vital Information Matching Feedback Self-tuning Network (VIM-Net). Our VIM-Net network is mainly composed of two matching feedback modules, a visual matching feedback module (V-mat) and a trajectory matching feedback module (T-mat). Specifically, V-mat matches the target information of visual recognition with the entity information extracted by the command; T-mat matches the serialized trajectory feature with the direction of movement of the command. Ablation experiments and comparative experiments are conducted on the proposed model using the Matterport3D simulator and the Room-to-Room (R2R) benchmark datasets, and the final navigation effect is shown in detail. The results prove that the model proposed in this paper is indeed effective on the task. [ABSTRACT FROM AUTHOR]

In this paper, a data assimilation network is proposed to tackle the challenges of domain generalization for person re-identification (ReID). Most of the existing research efforts only focus on single-dataset issues, and the trained models are difficult to generalize to unseen scenarios. This paper presents a distinctive idea to improve the generality of the model by assimilating three types of images: style-variant images, misaligned images and unlabeled images. The latter two are often ignored in the previous domain generalization ReID studies. In this paper, a non-local convolutional block attention module is designed for assimilating the misaligned images, and an attention adversary network is introduced to correct it. A progressive augmented memory is designed for assimilating the unlabeled images by progressive learning. Moreover, we propose an attention adversary difference loss for attention correction, and a labeling-guide discriminative embedding loss for progressive learning. Rather than designing a specific feature extractor that is robust to style shift as in most previous domain generalization work, we propose a data assimilation meta-learning procedure to train the proposed network, so that it learns to assimilate style-variant images. It is worth mentioning that we add an unlabeled augmented dataset to the source domain to tackle the domain generalization ReID tasks. Extensive experiments demonstrate that our approach significantly outperforms the state-of-the-art domain generalization methods. [ABSTRACT FROM AUTHOR]

Most existing attention mechanisms have no supervised signal during the training phase, which limits the model feature learning capability. To solve this problem, we propose a novel parameter-free attention mechanism based on class activation mapping. Attention mechanisms usually consist of spatial attention and channel attention, which indicates that “where” and “what” is more meaningful, respectively. Our attention also contains both types of attention. For Spatial Attention, we use class activation mapping as a supervision signal to guide the generation of it directly in space. Thus our spatial attention can pay more attention to the informative pedestrian parts of the scene and reduce background interference. For Channel Attention, the importance of each channel is obtained by the similarity between the aforementioned spatial attention and the feature map of each channel. In this manner, our channel attention is indirectly guided by class activation mapping. In addition, our attention is parameter-free, which reduces the risk of over-fitting. Finally, we conduct extensive evaluations on three popular benchmark datasets including Market1501, DukeMTMC-reID, and MSMT17, demonstrating the effectiveness of our approach on discriminative person representations. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder is an umbrella term for a group of neurodevelopmental disorders that is associated with impairments to social interaction, communication, and behaviour. Typically, autism spectrum disorder is first detected with a screening tool (e.g. modified checklist for autism in toddlers). However, the interpretation of autism spectrum disorder behavioural symptoms varies across cultures: the sensitivity of modified checklist for autism in toddlers is as low as 25 per cent in Sri Lanka. A culturally sensitive screening tool called pictorial autism assessment schedule has overcome this problem. Low- and middle-income countries have a shortage of mental health specialists, which is a key barrier for obtaining an early autism spectrum disorder diagnosis. Early identification of autism spectrum disorder enables intervention before atypical patterns of behaviour and brain function become established. This article proposes a culturally sensitive autism spectrum disorder screening mobile application. The proposed application embeds an intelligent machine learning model and uses a clinically validated symptom checklist to monitor and detect autism spectrum disorder in low- and middle-income countries for the first time. Machine learning models were trained on clinical pictorial autism assessment schedule data and their predictive performance was evaluated, which demonstrated that the random forest was the optimal classifier (area under the receiver operating characteristic (0.98)) for embedding into the mobile screening tool. In addition, feature selection demonstrated that many pictorial autism assessment schedule questions are redundant and can be removed to optimise the screening process. [ABSTRACT FROM AUTHOR]

Tinnitus is a problem that affects a diverse range of people. One common trait amongst people with tinnitus is the presence of hearing loss, which is apparent in over 90% of the cohort. It is postulated that the remainder of people with tinnitus have hidden hearing loss in the form of cochlear synaptopathy. The loss of hearing sensation is thought to cause a reduction in the bottom-up excitatory signals of the auditory pathway leading to a change in the frequency of thalamocortical oscillations known as thalamocortical dysrhythmia (TCD). The downward shift in oscillatory behavior, characteristic of TCD, has been recorded experimentally but the underlying mechanisms responsible for TCD in tinnitus subjects cannot be directly observed. This paper investigates these underlying mechanisms by creating a biologically faithful model of the auditory periphery and thalamocortical network, called the central auditory processing (CAP) model. The proposed model replicates tinnitus related activity in the presence of hearing loss and hidden hearing loss in the form of cochlear synaptopathy. The results of this paper show that, both the bottom-up and top-down changes are required in the auditory system for tinnitus related hyperactivity to coexist with TCD, contrary to the theoretical model for TCD. The CAP model provides a novel modeling approach to account for tinnitus related activity with and without hearing loss. Moreover, the results provide additional clarity to the understanding of TCD and tinnitus and provide direction for future approaches to treating tinnitus. [ABSTRACT FROM AUTHOR]

A novel and efficient metric learning strategy for person re‐identification is proposed. Person re‐identification is formulated as a multi‐domain learning problem. The assumption that the feature distributions from different camera views are the same is overthrown in this Letter. ID‐based transfer component analysis (IDB‐TCA) is proposed to learn a shared subspace, in which the differences in the feature distribution between source domain and target domain are significantly reduced. Experimental evaluation on the CUHK01 dataset demonstrates that metric learning with IDB‐TCA embedded outperforms state‐of‐art metric methods for person re‐identification. [ABSTRACT FROM AUTHOR]

Despite the emerging applications of diffusion tensor imaging (DTI) to mild traumatic brain injury (mTBI), very few investigations have been reported related to temporal changes in quantitative diffusion patterns, which may help to assess recovery from head injury and the long term impact associated with cognitive and behavioral impairments caused by mTBI. Most existing methods are focused on detection of mTBI affected regions rather than quantification of temporal changes following head injury. Furthermore, most methods rely on large data samples as required for statistical analysis and, thus, are less suitable for individual case studies. In this paper, we introduce an approach based on spatial group independent component analysis (GICA), in which the diffusion scalar maps from an individual mTBI subject and the average of a group of controls are arranged according to their data collection time points. In addition, we propose a constrained GICA (CGICA) model by introducing the prior information into the GICA decomposition process, thus taking available knowledge of mTBI into account. The proposed method is evaluated based on DTI data collected from American football players including eight controls and three mTBI subjects (at three time points post injury). The results show that common spatial patterns within the diffusion maps were extracted as spatially independent components (ICs) by GICA. The temporal change of diffusion patterns during recovery is revealed by the time course of the selected IC. The results also demonstrate that the temporal change can be further influenced by incorporating the prior knowledge of mTBI (if available) based on the proposed CGICA model. Although a small sample of mTBI subjects is studied, as a proof of concept, the preliminary results provide promising insight for applications of DTI to study recovery from mTBI and may have potential for individual case studies in practice. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the design and implementation of a distributed proportional–derivative (PD) controller of a 7-degrees of freedom (DOF) robot manipulator using the Takagi–Sugeno (T–S) fuzzy framework. Existing machine learning approaches to visual servoing involve system identification of image and kinematic Jacobians. In contrast, the proposed approach actuates a control signal primarily as a function of the error and derivative of the error in the desired visual feature space. This approach leads to a significant reduction in the computational burden as compared to model-based approaches, as well as existing learning approaches to model inverse kinematics. The simplicity of the controller structure will make it attractive in industrial implementations where PD/PID type schemes are in common use. While the initial values of PD gain are learned with the help of model-based controller, an online adaptation scheme has been proposed that is capable of compensating for local uncertainties associated with the system and its environment. Rigorous experiments have been performed to show that visual servoing tasks such as reaching a static target and tracking of a moving target can be achieved using the proposed distributed PD controller. It is shown that the proposed adaptive scheme can dynamically tune the controller parameters during visual servoing, so as to improve its initial performance based on parameters obtained while mimicking the model-based controller. The proposed control scheme is applied and assessed in real-time experiments using an uncalibrated eye-in-hand robotic system with a 7-DOF PowerCube robot manipulator. [ABSTRACT FROM PUBLISHER]

In diffusion-weighted imaging (DWI), reliable fiber tracking results rely on the accurate reconstruction of the fiber orientation distribution function (fODF) in each individual voxel. For high angular resolution diffusion imaging (HARDI), deconvolution-based approaches can reconstruct the complex fODF and have advantages in terms of computational efficiency and no need to estimate the number of distinct fiber populations. However, HARDI-based methods usually require relatively high b-values and a large number of gradient directions to produce good results. Such requirements are not always easy to meet in common clinical studies due to limitations in MRI facilities. Moreover, most of these approaches are sensitive to noise. In this study, we propose a new framework to enhance the performance of the spherical deconvolution (SD) approach in low angular resolution DWI by employing a single channel blind source separation (BSS) technique to decompose the fODF initially estimated by SD such that the desired fODF can be extracted from the noisy background. The results based on numerical simulations and two phantom datasets demonstrate that the proposed method achieves better performance than SD in terms of robustness to noise and variation in b-values. In addition, the results show that the proposed method has the potential to be applied to low angular resolution DWI which is commonly used in clinical studies. [ABSTRACT FROM PUBLISHER]