HPCDNet: Hybrid position coding and dual-frquency domain transform network for low-light image enhancement.

Low-light image enhancement (LLIE) improves lighting to obtain natural normal-light images from images captured under poor illumination. However, existing LLIE methods do not effectively utilize positional and frequency domain image information. To address this limitation, we proposed an end-to-end low-light image enhancement network called HPCDNet. HPCDNet uniquely integrates a hybrid positional coding technique into the self-attention mechanism by appending hybrid positional codes to the query and key, which better retains spatial positional information in the image. The hybrid positional coding can adaptively emphasize important local structures to improve modeling of spatial dependencies within low-light images. Meanwhile, frequency domain image information lost under low-light is recovered via discrete wavelet and cosine transforms. The resulting two frequency domain feature types are weighted and merged using a dual-attention module. More effective use of frequency domain information enhances the network's ability to recreate details, improving visual quality of enhanced low-light images. Experiments demonstrated that our approach can heighten visibility, contrast and color properties of low-light images while better preserving details and textures than previous techniques.

BILSTM-SimAM: An improved algorithm for short-term electric load forecasting based on multi-feature.

With the growing number of user-side resources connected to the distribution system, an occasional imbalance between the distribution side and the user side arises, making short-term power load forecasting technology crucial for addressing this issue. To strengthen the capability of load multi-feature extraction and improve the accuracy of electric load forecasting, we have constructed a novel BILSTM-SimAM network model. First, the entirely non-recursive Variational Mode Decomposition (VMD) signal processing technique is applied to decompose the raw data into Intrinsic Mode Functions (IMF) with significant regularity. This effectively reduces noise in the load sequence and preserves high-frequency data features, making the data more suitable for subsequent feature extraction. Second, a convolutional neural network (CNN) mode incorporates Dropout function to prevent model overfitting, this improves recognition accuracy and accelerates convergence. Finally, the model combines a Bidirectional Long Short-Term Memory (BILSTM) network with a simple parameter-free attention mechanism (SimAM). This combination allows for the extraction of multi-feature from the load data while emphasizing the feature information of key historical time points, further enhancing the model's prediction accuracy. The results indicate that the R2 of the BILSTM-SimAM algorithm model reaches 97.8%, surpassing mainstream models such as Transformer, MLP, and Prophet by 2.0%, 2.7%, and 3.6%, respectively. Additionally, the remaining error metrics also show a reduction, confirming the validity and feasibility of the method proposed.

Multi-Scale Feature Interactive Fusion Network for RGBT Tracking.

The fusion tracking of RGB and thermal infrared image (RGBT) is paid wide attention to due to their complementary advantages. Currently, most algorithms obtain modality weights through attention mechanisms to integrate multi-modalities information. They do not fully exploit the multi-scale information and ignore the rich contextual information among features, which limits the tracking performance to some extent. To solve this problem, this work proposes a new multi-scale feature interactive fusion network (MSIFNet) for RGBT tracking. Specifically, we use different convolution branches for multi-scale feature extraction and aggregate them through the feature selection module adaptively. At the same time, a Transformer interactive fusion module is proposed to build long-distance dependencies and enhance semantic representation further. Finally, a global feature fusion module is designed to adjust the global information adaptively. Numerous experiments on publicly available GTOT, RGBT234, and LasHeR datasets show that our algorithm outperforms the current mainstream tracking algorithms.

Blind and low-complexity modulation format identification based on signal envelope flatness for autonomous digital coherent receivers.

Modulation format identification (MFI) is a critical technology for autonomous digital coherent receivers in next-generation elastic optical networks. A novel and simple MFI scheme, to the best of our knowledge, based on signal envelope flatness is proposed without requiring any training or other prior information. After amplitude normalization and partition, the incoming polarization division multiplexed (PDM) signals can be classified into quadrature phase shift keying (QPSK), 8 quadrature amplitude modulation (QAM), 16QAM, and 64QAM signals according to envelope flatnesses R1, R2, and R3 of signals in different amplitude ranges. The feasibility of the proposed MFI scheme is first verified via numerical simulations with 28 GBaud PDM-QPSK/-8QAM/-16QAM/-64QAM signals. Only by using 4000 symbols can the proposed MFI scheme achieve a 100% correct identification rate for the four modulation formats over a wide optical signal-to-noise ratio (OSNR) range. Proof-of-concept experiments among 28 GBaud PDM-QPSK/-8QAM/-16QAM systems under back-to-back and long-haul fiber transmission links are implemented to further demonstrate the effectiveness of the proposed MFI scheme. The experimental results show that the proposed MFI scheme can obtain a 100% correct identification rate when the OSNR value of each modulation format is higher than the threshold corresponding to 7% FEC and is resilient towards fiber nonlinearities. More importantly, the proposed MFI scheme can significantly reduce computational complexity.

Dynamic Routings in Satellite Networks: An Overview.

The Satellite network is an important part of the global network. However, the complex architecture, changeable constellation topology, and frequent inter-satellite connection switching problems bring great challenges to the routing designs of satellite networks, making the study of the routing methods in satellite networks a research hotspot. Therefore, this paper investigates the latest existing routing works to tackle the dynamic routing problems in satellite networks. The architecture and development of satellite networks are presented and analyzed first. Afterward, dynamic routing problems in satellite networks are analyzed in detail based on the time-varying network topology. According to the latest works, the advanced satellite network routing schemes, including single-layer and multi-layer dynamic routing are introduced and analyzed. In addition, the merits, shortcomings, and applications of these schemes are analyzed and summarized. Finally, potential technologies and future directions are discussed.

A new underdetermined NMF based anti-collision algorithm for RFID systems.

Radio Frequency Identification (RFID) has been one of the critical technologies of the Internet of Things (IoT). With the rapid development of the IoT, the RFID systems are required to be more efficient and with high throughput capacity. In the widespread IoT application scenes, the collision problem of the RFID tags has become the increasingly remarkable problem in RFID systems. Traditionally, the anti-collision algorithms of RFID systems are always based on time division multiple access (TDMA). Although the TDMA based anti-collision algorithms are simple and easy to implement, it often misses tags and costs high time. Afterwards, the anti-collision algorithms based on blind source separation (BSS) have been introduced. These BSS based anti-collision algorithms are more efficient and stable, but they are mostly suitable for the determined or overdetermined case, i.e., the number of tags is less than that of the readers in RFID systems. Only a few anti-collision algorithms are taken into account of the underdetermined collision model. Because this underdetermined RFID collision model will give rise to more difficult solution but with very meaningfully practical IoT applications. Therefore, to investigate high quality underdetermined anti-collision algorithm for RFID system plays an important role in improving the efficiency of RFID system, and enable RFID implement more wide applications in future IoT systems. As a motivation, this paper proposes a new anti-collision algorithm for underdetermined RFID mixed system for performance improvement. In this work, the nonnegative matrix factorization (NMF) with minimum correlation and minimum volume constrains, i.e., the new MCV_NMF algorithm is proposed for anti-collision application in underdetermined RFID systems. This algorithm combines the independent principle of the tag signals with the NMF mechanism to achieve performance enhancement. The experimental results and analysis corroborate that this new algorithm can implement the underdetermined collision problem well and enhance the throughput capacity of RFID system.

Attention reduces the burstiness of V1 neurons involved in attended target enhancement.

Attention-dependent reduction in the tendency for neurons to fire bursts (burstiness) is widely observed in the visual cortex. However, the underlying mechanism and the functional role of this phenomenon remain unclear. We recorded well-isolated single-unit activities in primary visual cortex (V1) from two primates (Macaca mulatta) while they performed a detection task engaging spatial attention with two levels of difficulty (hard/easy). We found that attention modulated burstiness of V1 neurons in a cell-type specific manner. For neurons whose net response enhanced with the increase of task difficulty (difficulty-enhanced neuron), representing their involvement in boosting the signal of the attended stimulus, attention led to a reduction in burstiness during hard task but not during easy task. In contrast, regardless of the level of task difficulty, attention-dependent reduction in burstiness was not observed in neurons that showed a net suppression in firing rate with the increase of task difficulty (difficulty-suppressed neuron), indicating their commitment in filtering out the interference of distractor. This differentiation in the effects of attentional modulation on burstiness among the cells with distinct functional roles in attention suggests that the reduction in burstiness by attention is linked to target enhancement and is not associated with distractor suppression.

Presurgical localization and spatial shift of resting state networks in patients with brain metastases.

Brain metastases are the most prevalent cerebral tumors. Resting state networks (RSNs) are involved in multiple perceptual and cognitive functions. Therefore, precisely localizing multiple RSNs may be extremely valuable before surgical resection of metastases, to minimize neurocognitive impairments. Here we aimed to investigate the reliability of independent component analysis (ICA) for localizing multiple RSNs from resting-state functional MRI (rs-fMRI) data in individual patients, and further evaluate lesion-related spatial shifts of the RSNs. Twelve patients with brain metastases and 14 healthy controls were recruited. Using an improved automatic component identification method, we successfully identified seven common RSNs, including: the default mode network (DMN), executive control network (ECN), dorsal attention network (DAN), language network (LN), sensorimotor network (SMN), auditory network (AN) and visual network (VN), in both individual patients and controls. Moreover, the RSNs in the patients showed a visible spatial shift compared to those in the controls, and the spatial shift of some regions was related to the tumor location, which may reflect a complicated functional mechanism - functional disruptions and reorganizations - caused by metastases. Besides, higher cognitive networks (DMN, ECN, DAN and LN) showed significantly larger spatial shifts than perceptual networks (SMN, AN and VN), supporting a functional dichotomy between the two network groups even in pathologic alterations associated with metastases. Overall, our findings provide evidence that ICA is a promising approach for presurgical localization of multiple RSNs from rs-fMRI data in individual patients. More attention should be paid to the spatial shifts of the RSNs before surgical resection.

Altered connectivity patterns among resting state networks in patients with ischemic white matter lesions.

White matter lesions (WMLs) have been associated with cognitive and motor decline. Resting state networks (RSNs) are spatially coherent patterns in the human brain and their interactions sustain our daily function. Therefore, investigating the altered intra- and inter-network connectivity among the RSNs may help to understand the association of WMLs with impaired cognitive and motor function. Here, we assessed alterations in functional connectivity patterns based on six well-defined RSNs-the default mode network (DMN), dorsal attention network (DAN), frontal-parietal control network (FPCN), auditory network (AN), sensory motor network (SMN) and visual network (VN)-in 15 patients with ischemic WMLs and 15 controls. In the patients, Spearman's correlation analysis was further performed between these alterations and cognitive test scores, including Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores. Our results showed wide alterations of inter-network connectivity mainly involving the SMN, DMN, FPCN and DAN, and some alterations correlated with cognitive test scores in the patients. The reduced functional connectivities in the SMN-AN, SMN-VN, FPCN-AN, DAN-VN pairs may account for the cognitive and motor decline in patients with ischemic WMLs, while the increased functional connectivities in the DMN-AN, DMN-FPCN and DAN-FPCN pairs may reflect a functional network reorganization after damage to white matter. It is unexpected that altered intra-network connectivities were found within the AN and VN, which may explain the impairments in verbal fluency and information retrieval associated with WMLs. This study highlights the importance of functional connectivity in understanding how WMLs influence cognitive and behavior dysfunction.

Abnormal cortical functional activity in patients with ischemic white matter lesions: A resting-state functional magnetic resonance imaging study.

There is increasing evidence that white matter lesions (WMLs) are associated with cognitive impairments. The purpose of this study was to explore the relationship of WMLs with cognitive impairments from the aspect of cortical functional activity. Briefly, Sixteen patients with ischemic WMLs and 13 controls participated in this study. A regional homogeneity (ReHo) approach was used to investigate altered neural coherence in patients with ischemic WMLs during the resting state. A correlation analysis was further performed between regions with altered ReHo and cognitive test scores, including Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), in the patient group. Finally, we found regions with altered ReHo values in patients with ischemic WMLs to be involved in default mode network (DMN), frontal-parietal control network (FPCN), dorsal attention network (DAN), motor network and right temporal cortex. Moreover, some altered regions belonging to DMN, FPCN and motor network were significantly correlated with cognitive test scores. Our results provide neuroimaging evidence for the impairments of memory, attention, executive and motor function in patients with ischemic WMLs. It is interesting to note that the decreased ReHo was mainly in the anterior brain regions, while increased ReHo in the posterior brain regions, which may indicate a failure down regulation of spontaneous activity in posterior regions. In summary, this study indicates an important role of specific cortical dysfunction in cognitive associated with WMLs.

Abnormal functional connectivity density in patients with ischemic white matter lesions: An observational study.

White matter lesions (WMLs) are frequently detected in elderly people. Previous structural and functional studies have demonstrated that WMLs are associated with cognitive and motor decline. However, the underlying mechanism of how WMLs lead to cognitive decline and motor disturbance remains unclear. We used functional connectivity density mapping (FCDM) to investigate changes in brain functional connectivity in 16 patients with ischemic WMLs and 13 controls. Both short- and long-range FCD maps were computed, and group comparisons were performed between the 2 groups. A correlation analysis was further performed between regions with altered FCD and cognitive test scores (Mini-Mental State Examination [MMSE] and Montreal Cognitive Assessment [MoCA]) in the patient group. We found that patients with ischemic WMLs showed reduced short-range FCD in the temporal cortex, primary motor cortex, and subcortical region, which may account for inadequate top-down attention, impaired motor, memory, and executive function associated with WMLs. The positive correlation between primary motor cortex and MoCA scores may provide evidence for the influences of cognitive function on behavioral performance. The inferior parietal cortex exhibited increased short-range FCD, reflecting a hyper bottom-up attention to compensate for the inadequate top-down attention for language comprehension and information retrieval in patients with WMLs. Moreover, the prefrontal and primary motor cortex showed increased long-range FCD and the former positively correlated with MoCA scores, which may suggest a strategy of cortical functional reorganization to compensate for motor and executive deficits. Our findings provide new insights into how WMLs cause cognitive and motor decline from cortical functional connectivity perspective.