Search

Visual feedback gain is a crucial factor influencing the performance of precision grasping tasks, involving multiple brain regions of the visual motor system during task execution. However, the dynamic changes in brain network during this process remain unclear. The aim of this study is to investigate the impact of changes in visual feedback gain during precision grasping on brain network dynamics. Sixteen participants performed precision grip tasks at 15% of MVC under low (0.1°), medium (1°), and high (3°) visual feedback gain conditions, with simultaneous recording of EEG and right-hand precision grip data during the tasks. Utilizing electroencephalogram (EEG) microstate analysis, multiple parameters (Duration, Occurrence, Coverage, Transition probability(TP)) were extracted to assess changes in brain network dynamics. Precision grip accuracy and stability were evaluated using root mean square error(RMSE) and coefficient of variation(CV) of grip force. Compared to low visual feedback gain, under medium/high gain, the Duration, Occurrence, and Coverage of microstates B and D increase, while those of microstates A and C decrease. The Transition probability from microstates A, C, and D to B all increase. Additionally, RMSE and CV of grip force decrease. Occurrence and Coverage of microstates B and C are negatively correlated with RMSE and CV. These findings suggest that visual feedback gain affects the brain network dynamics during precision grasping; moderate increase in visual feedback gain can enhance the accuracy and stability of grip force, whereby the increased Occurrence and Coverage of microstates B and C contribute to improved performance in precision grasping. Our results play a crucial role in better understanding the impact of visual feedback gain on the motor control of precision grasping.

Motor imagery electroencephalogram (MI-EEG) classification is essential in brain-computer interface (BCI), and many classification methods have been proposed recently. However, the MI-EEG classification accuracy of the public dataset still has room for improvement, and designing a suitable model to extract and fuse the multi-modality features efficiently is crucial. In this study, we proposed a Multi-scale Space-time Frequency fusion Network (MSFNet) to improve the MI-EEG classification accuracy. The MSFNet comprises data acquisition and preprocessing, multi-scale time-conv fusion unit, multi-scale frequency-conv fusion unit, feature fusion, and classification. Multi-scale time-conv fusion unit can extract multi-scale spatiotemporal features, and multi-scale frequency-conv fusion unit can extract five frequency sub-band features. These two features were concatenated to complete the multi-modality features fusion, and MI-EEG was classified. Average accuracy, kappa value, and F1 score were adopted as the evaluation metric, and BCI Competition 2008 IV 2a and High Gamma datasets were employed to demonstrate the effectiveness of the MSFNet. The superiority of this proposed model was demonstrated by comparison against the state-of-the-art methods, and the classification result is the highest. Overall, we achieved an average accuracy of 80.47%, kappa value of 0.783, and F1 score of 0.743 in the BCI Competition 2008 IV 2a dataset, and we achieved an average accuracy of 93.56%, kappa value of 0.933, and F1 score of 0.915 in High Gamma dataset. This model realized the extraction and efficient fusion of spatiotemporal and frequency domain features and the high-precision MI-EEG classification, which has an important application value.

Precise sustained force control of the fingers is important for achieving flexible hand movements. However, how neuromuscular compartments within a forearm multi-tendon muscle cooperate to achieve constant finger force remains unclear. This study aimed to investigate the coordination strategies across multiple compartments of the extensor digitorum communis (EDC) during index finger sustained constant extension. Nine subjects performed index finger extensions of 15%, 30%, and 45% maximal voluntary contractions, respectively. High-density surface electromyography signals were recorded from the EDC and then analyzed using non-negative matrix decomposition to extract activation patterns and coefficient curves of EDC compartments. The results showed two activation patterns with stable structures during all tasks: one pattern corresponding to the index finger compartment was named primary pattern; whereas the other corresponding to other compartments was named auxiliary pattern. Further, the intensity and stability of their coefficient curves were assessed using the root mean square value (RMS) and coefficient of variation (CV). The RMS and CV values of the primary pattern increased and decreased with time, respectively, while the corresponding values of the auxiliary pattern were both negatively correlated with the formers. These findings suggested a special coordination strategy across EDC compartments during index finger constant extension, manifesting as two compensations of the auxiliary pattern for the intensity and stability of the primary pattern. The proposed method provides new insight into the synergy strategy across multiple compartments within a forearm multi-tendon during sustained isometric contraction of a single finger and a new approach for constant force control of prosthetic hands.

The naval gun strikes against the sea need to measure the deviation of the fall point to determine the accuracy and hit probability. In order to improve the accuracy of measuring the fall point position of the naval gun, this paper proposes a maritime fall point measurement method based on cone angle intersection, which does not need to use the axis angle data of the equipment and eliminates the dependence on the equipment’s axis angle data when measuring accuracy. Firstly, the photographic measurement method is used to achieve automatic optical intersection of single equipment at different moments, using two observation stations and one cooperative target ship. Then, the image data within 10 km from the measured target are collected using two observation stations. The line of sight directions and angles of the two observation stations are combined to measure the target object and the cooperative target ship at each observation station. The constraint of the approximate equal radius of the Earth ellipsoid is added under close range conditions. The cone angle intersection model mapping relationship is established. Finally, the coordinate position of the target object in three-dimensional space is calculated using field tests at sea and compared with the actual position for analysis. The experimental results show that the measured fall point is within 5 m in the range of 700 m with a ratio of more than 75%. This has increased the range of measurement accuracy processing and proved the feasibility of the measurement method. The method can be used for calibrating large and medium caliber naval gun firing to the sea to improve the firing accuracy of the weapon. It has guiding significance for measuring the fall point position of naval guns at sea.

In order to adapt to new situation of cascade hydropower enterprises (CHEs) participating in the joint market of day-ahead energy and frequency regulation (FR), a short-term optimal scheduling strategy for cascade hydropower plants (CPPs) is proposed. Firstly, the development situation of AGC in China is introduced and the CPPs’ AGC mode of participation in FR from the perspective of economy and solvability is analyzed. Secondly, for maximizing the revenue, the economic dispatching model of CPPs based on the two-part FR compensation mechanism is proposed, which combines the benefits of both the energy and FR. Finally, a two-stage optimization method that couples the load distribution among multi plants with the economic operation of a single plant is proposed for solving the model above. The experimental results show that the two-stage optimization method has good convergence and robustness in solving the proposed model, and compared with the single energy model, the total benefits of the proposed model increases by $ \$ $ 72935.6 (5.54%) through optimizing the distribution of electricity quantity and AGC capacity, which provides a new idea for cascade hydropower enterprises (CHEs) to explore market dividend and optimize market decision-making.

Signal coordination plays an important role in urban traffic control systems. Studying the influence of some factors such as the link length on the potential benefits of signal coordination can result in more efficient traffic control. Researchers have observed a highly fluctuating or damped sine-wave type relationship between the link length and the delay in two-way coordinated control, but a clear explanation of this relationship is still lacking. This paper presents a thorough analysis of the mechanism by which link length affects the delay in two-way coordination. The impact of the link length involves two aspects. We first derived formulas for the delay using shockwave queuing profiles that included the impact of the offsets in the two directions. We then conducted numerical experiments employing Robertson’s platoon dispersion model to incorporate the impact of platoon dispersion. By comparing the results from the derived formulas and the numerical experiments, we concluded that the periodicity is due to the mutual relation between the offsets in the two directions, whereas the attenuation is due to platoon dispersion. The findings should provide valuable insights for developing a more reasonable correlation degree model for the coordination of adjacent intersections. In addition, we found that platoon dispersion has a negligible influence on the critical link lengths at which the delay cannot be reduced by signal coordination. This means that whether or not platoon dispersion is considered, it will not affect the choice between the simultaneous and the alternative progressions to minimize the delay under a given link length, which is very meaningful in practical work.

The fusion quality of infrared and visible image is very important for subsequent human understanding of image information and target processing. The fusion quality of the existing infrared and visible image fusion methods still has room for improvement in terms of image contrast, sharpness and richness of detailed information. To obtain better fusion performance, an infrared and visible image fusion algorithm based on latent low-rank representation (LatLRR) nested with rolling guided image filtering (RGIF) is proposed that is a novel solution that integrates two-level decomposition and three-layer fusion. First, infrared and visible images are decomposed using LatLRR to obtain the low-rank sublayers, saliency sublayers, and sparse noise sublayers. Then, RGIF is used to perform further multiscale decomposition of the low-rank sublayers to extract multiple detail layers, which are fused using convolutional neural network (CNN)-based fusion rules to obtain the detail-enhanced layer. Next, an algorithm based on improved visual saliency mapping with weighted guided image filtering (IVSM-GIF) is used to fuse the low-rank sublayers, and an algorithm for adaptive weighting of regional energy features based on Laplacian pyramid decomposition is used to fuse the saliency sublayers. Finally, the fused low-rank sublayer, saliency sublayer, and detail-enhanced layer are used to reconstruct the final image. The experimental results show that the proposed method outperforms other state-of-the-art fusion methods in terms of visual quality and objective evaluation, achieving the highest average values in six objective evaluation metrics.

Although the traditional image fusion method can obtain rich image results, obvious artificial noise and artifacts are often present in the resulting image. Fusion algorithms based on neural networks can avoid the shortcomings of traditional methods, but they are more complex and less flexible. In this study, we proposed a fusion method using the deep residual neural network ResNet152, which can not only effectively suppress artificial noise but also preserve the edge details of the image and improve the efficiency of the neural network. The proposed method is characterized by a multiscale transformation of an infrared image and visible light image in the optimized nonsubsampled contourlet transformation domain, and the deep residual neural network ResNet152 is used to extract the deep features of the low-pass component to guide the fusion of the low-pass component. The bandpass component is fused by taking the modulus maximum. This method can fully retain the global features and structural information of the source image in the result image. Compared to existing fusion methods using public test image sets, the experimental results show that on a subjective level, the fusion method creates sharper depth edges and fewer noise artifacts than traditional fusion methods. From an objective perspective, the average value for the results of the evaluation function is greater than that of other fusion methods.

Trackers based on fully-convolutional Siamese networks regard tracking as a process of learning a similarity function. By utilizing shallow networks and off-line training, Siamese trackers can achieve high tracking speed and perform well in some simple scenes. However, due to the less semantic information and the invariant template, Siamese trackers still have a gap compared with the state-of-the-art methods in complex scenes and other challenging problems (Occlusion, Deformation, etc.). In this paper, we propose a Siamese tracking algorithm with deep features and robust feature fusion (SiamDF). The improved ResNet-18 network is utilized to replace the traditional shallow network and extract the deep features with more semantic information. For eliminating the negative effect of padding and making better use of the deep network, the proposed algorithm adopts the spatial aware sampling strategy to overcome the strict translation invariance. Meanwhile, a final response map with high quality can be obtained by using the multi-layer feature fusion. Thus, the tracker can significantly reduce the impact of the distractors in complex scenes. In addition, an adaptive feature information fusion is adopted to update the template, so that the algorithm can adapt to various changes of the target appearance. Objective evaluation on the OTB100 dataset shows that the precision and the overlap success can reach 0.852 and 0.658 respectively. Moreover, the EAO value evaluated on the VOT2016 database can reach 0.336. These results demonstrate that our algorithm can effectively improve the tracking performance and perform favorably in both robustness and accuracy.

Existing Siamese network based trackers are easily disturbed by large deformation, occlusion and distractor objects in the background. By comparing these trackers, we observe that the monotonous positive pairs usually have limited challenging factors (Occlusion, Deformation, etc.), which may make the learned features less robust. In addition, the foreground information of the substantial training data is utilized directly without deeper exploration. Thus, the trackers cannot effectively discriminate the foreground from the semantic backgrounds. In this paper, we focus on modifying the Siamese tracker by enriching the positive pairs and taking further advantage of the foreground information. During the offline training phase, a simple sampling strategy is adopted to enrich the challenging factors in positive pairs, which can effectively enhance the robustness of the tracker. At the same time, we highlight the foreground information by padding the background, and the information is utilized to generate a novel padding loss, which guides the tracker to pay less attention to the distractors in the background. Moreover, an improved feature information fusion is adopted to update the template, so that the tracker can adapt to the drastic appearance changes. Comprehensive experiments on the OTB and the VOT benchmarks demonstrate that our proposed tracker can achieve outstanding performance in both accuracy and robustness.

In this paper, we consider an IoT dedicated network corresponding to a non licensed LoRa Low Power Wide Area Network. The LoRa network operates in the unlicensed 868 MHz band within a total bandwidth of 1 MHz divided into 8 orthogonal channels of 125 kHz each. Despite the high level of interference, this network offers long range communications in the order of 2 to 5 km in urban areas and 10 to 30 km in rural areas. To efficiently mitigate this high level of interference, LoRa network essentially relies on a Chirp Spread Spectrum (CSS) modulation and on repetition diversity mechanisms. The LoRa CSS modulation spreads the signal within a band of 125 kHz using 6 possible spreading factors (from 7 to 12) to target data rates (starting from 5 kbps for the closest node to 300 bps for the furthest ones). The repetition diversity mechanisms enable the data recovery when the transmission is subject to bad channel conditions or/and high interference levels. Although the CSS modulation protects edge-cell's devices from the high level of interference induced by nodes in the proximity of the gateway, it fails to protect nodes at the edge of a given SF region and several trials are required to recover the packet. In this paper, we propose an adaptive multi-channels allocation policy that attributes multiple adjacent channels of 125 kHz for nodes situated at the edge of SF zones. We study the impact of this adaptive sub-band allocation on the gateways' intensities, the rate distribution and the power consumption. Our results are based on a statistical characterization of the interference in the network as well as the outage probability in a typical cell.

Travel time is inherently uncertain in urban networks due to volatile traffic flows, signal controls, bus stops and disturbances from pedestrians. An effective way to characterize such uncertainty is by estimating Travel Time Distribution (TTD). However, conventional TTD models are lack of considering the interactions between turning movements within the link. Whereas the phenomenon of the Channelization Section Spillover (CSS) is very common, leading to strong interactions between turning movements. In this study, by incorporating the correlation of turning movements in the TTD model, that is, considering CSS, copula-based link-level and path-level TTD models are built. First, based on the empirical data, the correlations of turning movements are analyzed, and then the applicability of the various copula models are examined. The marginal distribution of each turning movement is described using parametric and non-parametric regression analysis, respectively. Then, the best-fitting copula is determined based on correlation parameters and the goodness-of-fit tests. As a case study, the chosen model is applied in estimating link-level and path-level TTD in an arterial road in Hangzhou, China, and compared with the model that did not consider the CSS before. Both results indicate that the copula-based approach can precisely capture the positively correlated relationship between turning movements during peak hours. Furthermore, higher TTD estimation accuracy demonstrates the significance to consider CSS, particularly in peak hours.

A 7.07-kW Yb:YAG composite crystal zigzag slab amplifier at room temperature is developed and demonstrated, the optical-to-optical efficiency is about 29.5% and the slope efficiency is 39.5%, respectively, with the pumping power of 19.98 kW and the incident seed laser power of 1.18 kW. The influence of the pump coupling efficiency, the intensity of the seed laser, and the temperature to the extracted power from the Yb:YAG slab are analyzed by simulation. The fluorescence distribution at the end face and the transmission wavefront of the Yb:YAG slab are measured, the nonuniformity of the fluorescence distribution is less than 6%, and the P-V value of the transmission wave front distortion is less than 1.6 μm.

We investigate the intensity and phase response of optical switches based on a photonic crystal waveguide coupled to a nanocavity. In particular, we compare the performances of switches with traditional Lorentzian transmission spectrum to switches displaying an asymmetric Fano shape, as obtained by incorporating a partially transmitting element in the waveguide. Compared to traditional Lorentzian structures, the Fano structure shows improved switching contrast and speed without adding any extra phase modulation, corresponding to a much lower chirp parameter. Using a simple and ultracompact InP photonic-crystal Fano structure with broken mirror symmetry, we experimentally demonstrate 20-Gb/s all-optical switching with low-energy consumption.