Fusion Performance Research Articles

Pedestrian Re-Identification Based on Fine-Grained Feature Learning and Fusion

Video-based pedestrian re-identification (Re-ID) is used to re-identify the same person across different camera views. One of the key problems is to learn an effective representation for the pedestrian from video. However, it is difficult to learn an effective representation from one single modality of a feature due to complicated issues with video, such as background, occlusion, and blurred scenes. Therefore, there are some studies on fusing multimodal features for video-based pedestrian Re-ID. However, most of these works fuse features at the global level, which is not effective in reflecting fine-grained and complementary information. Therefore, the improvement in performance is limited. To obtain a more effective representation, we propose to learn fine-grained features from different modalities of the video, and then they are aligned and fused at the fine-grained level to capture rich semantic information. As a result, a multimodal token-learning and alignment model (MTLA) is proposed to re-identify pedestrians across camera videos. An MTLA consists of three modules, i.e., a multimodal feature encoder, token-based cross-modal alignment, and correlation-aware fusion. Firstly, the multimodal feature encoder is used to extract the multimodal features from the visual appearance and gait information views, and then fine-grained tokens are learned and denoised from these features. Then, the token-based cross-modal alignment module is used to align the multimodal features at the token level to capture fine-grained semantic information. Finally, the correlation-aware fusion module is used to fuse the multimodal token features by learning the inter- and intra-modal correlation, in which the features refine each other and a unified representation is obtained for pedestrian Re-ID. To evaluate the performance of fine-grained features alignment and fusion, we conduct extensive experiments on three benchmark datasets. Compared with the state-of-art approaches, all the evaluation metrices of mAP and Rank-K are improved by more than 0.4 percentage points.

A temporally insensitive spatio-temporal fusion method for remote sensing imagery via semantic prior regularization

Spatio-temporal fusion has become a popular technology for generating remote sensing images with high spatial and high temporal resolutions, thus providing valuable data support for remote sensing monitoring applications, such as environmental monitoring and city planning. Currently, deep learning-based methods have garnered a significant amount of attention, and they mostly employ the fine image at the neighboring date as an auxiliary image. However, capturing usable neighboring fine images may be challenging due to the adverse effects of weather conditions on optical images. Moreover, the fusion performance drops sharply when the temporal interval is long (i.e., there are significant differences in images). In this paper, we proposed a bidirectional pyramid fusion network with semantic prior regularization (BPFN-SPR), which exhibits remarkable flexibility and robustness to temporal intervals.Specifically, the proposed BPFN-SPR contains dual-path operations (i.e., Semantic Extraction path and Image Reconstruction path). The semantic extraction path has two modes: parameter learning mode and parameter freezing mode. The parameter learning mode aims to learn the information representation of the auxiliary fine image, while the parameter freezing mode aims to perceive the accurate semantic information of the target fine image. The image reconstruction path progressively reconstructs spatial details of fine images from coarse images, which jointly optimizes the target fine image and the auxiliary fine image, reducing the temporal sensitivity of the reconstruction branch, and thereby improving its generalization ability. Experimental results show that the proposed method has competitive performance, especially for areas with land cover changes. In addition, extensive experiments using images at multi-temporal intervals as auxiliary images have also demonstrated the significant advantages of the proposed method. The mean PSNR value attains 31.0713, while the average spectral index SAM measures 0.1640 on the LGC test set. Meanwhile, for the CIA test set, the average PSNR is recorded at 29.5332, accompanied by an average spectral index SAM of 0.1865. Therefore, the proposed BPFN-SPR has considerable potential in monitoring Earth's surface dynamics.

A Study on Bilingual Deep Learning PIS Neural Network Model Based on Graph-Text Modal Fusion

This research investigates a multilingual cross-modal pedestrian information search (PIS) technique based on graph-text modal fusion. Initially, we used a combination of replacement neural networks to improve the English Language-Based Pedestrian Information Search model with Graph-Text Modal Fusion (GTMFLPIS) performance. In addition, existing research lacks GTMFLPIS models for other languages. Therefore, we propose to train GTMFLPIS models for Chinese. The Chinese GTMFLPIS model was trained using our previously constructed Chinese CUHK-PEDES dataset. The Rank1 of the Chinese RN50_PMML12V2 model reached 0.5989. In addition, we found that a single model could not adapt to the limitations of multiple languages. Therefore, we propose a novel architecture to implement a single-model multilingual cross-modal GTMFLPIS model in this research. We propose RN50_DBMCV2 and ENB7_DBM-CV2, both of which have improved performance over the existing ones. We constructed a bilingual dataset using our Chinese CUHK-PEDES dataset and existing English CUHK-PEDES dataset to test our novel multilingual cross-modal GTMFLPIS model. In addition, we found that the loss function significantly impacts the model during our experiments. Therefore, we optimized the performance of the existing loss functions for cross-modal GTMFLPIS models. Our proposed CCMPM loss function improves the performance of the model by 2%. The experimental results of this research show that our proposed model has advantages in improving the accuracy of PIS.

YOLO-APDM: Improved YOLOv8 for Road Target Detection in Infrared Images.

A new algorithm called YOLO-APDM is proposed to address low quality and multi-scale target detection issues in infrared road scenes. The method reconstructs the neck section of the algorithm using the multi-scale attentional feature fusion idea. Based on this reconstruction, the P2 detection layer is established, which optimizes network structure, enhances multi-scale feature fusion performance, and expands the detection network's capacity for multi-scale complicated targets. Replacing YOLOv8's C2f module with C2f-DCNv3 increases the network's ability to focus on the target region while lowering the amount of model parameters. The MSCA mechanism is added after the backbone's SPPF module to improve the model's detection performance by directing the network's detection resources to the major road target detection zone. Experimental results show that on the FLIR_ADAS_v2 dataset retaining eight main categories, using YOLO-APDM compared to YOLOv8n, mAP@0.5 and mAP@0.5:0.95 increased by 6.6% and 5.0%, respectively. On the M3FD dataset, mAP@0.5 and mAP@0.5 increased by 8.1% and 5.9%, respectively. The number of model parameters and model size were reduced by 8.6% and 4.8%, respectively. The design requirements of the high-precision detection of infrared road targets were achieved while considering the requirements of model complexity control.

EPVC: a novel initialization approach of visual-inertial integrated navigation

Abstract The fusion of visual and inertial measurements in robotics community is growing in popularity since both of them have complementary perceptual information. Pre-initializing gyroscope bias and accelerometer bias of the inertial measurement unit (IMU) is a critical issue to achieve a better fusion performance, and the metric scale is another crucial element to be estimated. Current mainstream loosely-coupled initialization methods are unstable as they do not incorporate IMU information into the visual structure from motion. In addition, the accuracy of the tightly-coupled methods is limited since they do not use visual observations to compensate gyroscope bias and usually ignore them in close-form solution. In this paper, a visual-inertial (VI) initialization method which we refer to as epipolar plane normal vectors coplanarity constraint (EPVC) method is proposed to solve gyroscope bias. A step further, a novel analytical solution is presented to optimize other parameters. Comparing the proposed method with VI navigation systems-mono and inertial-only optimization through the publicly available EuRoC dataset, the results demonstrate that the proposed method outperforms the existing methods in estimating the gyroscope bias and scale, and with the increase of initialization time, the accelerometer bias error and gravity direction error have a clear diminishing tendency.

Transport coefficient sensitivities in a semi-analytic model for magnetized liner inertial fusion

Performance of magnetized liner inertial fusion (MagLIF) experiments is highly dependent on transport processes including magnetized heat flows and magnetic flux losses. Magnetohydrodynamic simulations used to model these experiments require a choice of model for the transport coefficients, which are the constants of proportionality relating driving terms, such as temperature gradients and currents, to the associated heat and magnetic field transport. The coefficients have been the subject of repeated recalculation using various methods throughout the years. Using a semi-analytic MagLIF model [McBride and Slutz, Phys. Plasmas 22, 052708 (2015)], we compare models for the transport coefficients provided by Braginskii [Reviews of Plasma Physics, edited by M. A. Leontovich (Consultants Bureau, New York, 1965), Vol. 1, p. 205], Epperlein and Haines [Phys. Fluids 29, 1029 (1986)], Ji and Held [Phys. Plasmas 20, 042114 (2013)], Davies et al. [Phys. Plasmas 28, 012305 (2021)], and Sadler et al. [Phys. Rev. Lett. 126, 075001 (2021)]. The choice of model modifies magnetic-flux losses caused by the Nernst thermoelectric effect and thermal conduction losses. We present simulated results from parameter scans conducted in order to compare the effects of the different models on parameters of interest in MagLIF. In some regions of parameter space, discrepancies of up to 38% are found in integrated quantities like the fusion yield. These results may serve as a guide for experimental validation of the various models, particularly as laser preheat energies and initial axial field strengths are increased on MagLIF experiments.

Information fusion for large-scale multi-source data based on the Dempster-Shafer evidence theory

There exists many large-scale multi-source data, ranging from genetic information to medical records, and military intelligence. The inherent intricacies and uncertainties embedded within these data sources pose significant challenges to the process of information fusion. Owing to its exceptional capacity to represent data uncertainty, Dempster-Shafer (D-S) evidence theory has emerged as a widely utilized approach in information fusion. However, the evidence theory encounters three significant issues when applied to multi-source data information fusion: (1) the conversion of sample information into evidence and the construction of the basic probability assignment (BPA) function; (2) the resolution of conflicting evidence; and (3) the mitigation of exponential explosion in computation. Addressing the aforementioned challenges, this paper delves into the information fusion strategies for large-scale multi-source data based on Dempster-Shafer evidence theory. Initially, the concept of support matrix is introduced and the data matrix is transformed into a support matrix to address the construction challenges associated with BPA. Next, a method for addressing evidence conflicts is introduced by incorporating an additional data source composed of average values. Furthermore, a solution for mitigating high computational complexity is presented through the utilization of a hierarchical fusion approach. Finally, experimental results show that compared with other five advanced information fusion methods, our information method has improved the classification accuracy by 4.66% on average and reduced the time by 66.35% on average. Hence, our method is both efficient and effective, demonstrating exceptional performance in information fusion.

Investigation of the effects of heat treatment and hot isostatic pressing in micro-drilling of Ti6Al4V alloy fabricated by laser powder bed fusion

In this study, micro-drilling performance of laser powder bed fusion (LPBF) Ti6Al4V alloy with heat treatment (HT) and hot isostatic pressing (HIP) post-processing conditions was investigated and compared to wrought material. The tests were performed at two spindle rotational speeds (5000 and 10,000 rev/min) and a feed per tooth (4 μm/tooth) under dry and minimum quantity lubrication (MQL) conditions using conventional drilling (CD) and peck drilling (PD) strategies. Micro-drilling performance was evaluated in terms of thrust force, surface roughness, subsurface microstructure and microhardness, burr formation, and tool wear. The test results showed that LPBF-HT and LPBF-HIP Ti6Al4V alloys exhibited different micro-drilling performances than the wrought material due to their different microstructural and mechanical properties. The LPBF-HIP Ti6Al4V characterized by higher strength and lamellar microstructure with higher β phase fraction (33.2 %) led to higher thrust force, lower surface quality and higher burr height. On the other hand, the wrought Ti6Al4V with higher ductility, lower hardness, and lower β phase fraction (16.3 %) showed lower thrust forces, surface roughness, and burr height. Notably, employing MQL conditions with the PD strategy provided good surface quality and reduced burr formation. A higher degree of tool wear was obtained in micro-drilling of LPBF-HIP Ti6Al4V. The major wear types were adhesion, BUE, and flank wear. Besides, chippings were observed in micro-drilling of LPBF-HT and LPBF-HIP Ti6Al4V.

Hierarchical Fusion of Infrared and Visible Images Based on Channel Attention Mechanism and Generative Adversarial Networks.

In order to solve the problem that existing visible and infrared image fusion methods rely only on the original local or global information representation, which has the problem of edge blurring and non-protrusion of salient targets, this paper proposes a layered fusion method based on channel attention mechanism and improved Generative Adversarial Network (HFCA_GAN). Firstly, the infrared image and visible image are decomposed into a base layer and fine layer, respectively, by a guiding filter. Secondly, the visible light base layer is fused with the infrared image base layer by histogram mapping enhancement to improve the contour effect. Thirdly, the improved GAN algorithm is used to fuse the infrared and visible image refinement layer, and the depth transferable module and guided fusion network are added to enrich the detailed information of the fused image. Finally, the multilayer convolutional fusion network with channel attention mechanism is used to correlate the local information of the layered fusion image, and the final fusion image containing contour gradient information and useful details is obtained. TNO and RoadSence datasets are selected for training and testing. The results show that the proposed algorithm retains the global structure features of multilayer images and has obvious advantages in fusion performance, model generalization and computational efficiency.

Structural design and biomechanical analysis of a combined titanium and polyetheretherketone cage based on PE-PLIF fusion.

In lumbar spinal fusion, the titanium cage tends to cause stress shielding due to their high elastic modulus, which can lead to degenerative lesions in adjacent spinal segments. Furthermore, polyetheretherketone (PEEK) cages have certain material characteristics that do not promote bone ingrowth and fusion stability. In this study, a new cage was designed, and its biomechanical performance in percutaneous endoscopic posterior lumbar interbody fusion (PE-PLIF) was analyzed using the finite element (FE) method. A complete model of the L4-L5 lumbar spine was established, and static and harmonic vibration FE analysis models were developed based on it. The biomechanical properties of titanium, PEEK, and combined cage in PE-PLIF fusion were compared. The strain capacity of the combined fusion increased by 9.5% when compared to the titanium fusion. The surgical model under the combined fusion reduces the L5 endplate stress by 12% in the forward flexion condition and the fusion stress by 17% in the vibration condition compared to the model supported by the titanium fusion, and the differences in screw stress and mobility among the three models are not significant in multiple conditions. Consequently, the combined cage demonstrates a certain reduction in the stress-shielding effect when compared to the titanium cage; it has better fusion effect and provides theoretical support and guidance for the design of the clinical fusion cage.

IFNet: Data-driven multisensor estimate fusion with unknown correlation in sensor measurement noises

In recent years, multisensor fusion for state estimation has gained considerable attention. The effectiveness of the optimal fusion estimation method heavily relies on the correlation among sensor measurement noises. To enhance estimate fusion performance by mining unknown correlation in the data, this paper introduces a novel multisensor fusion approach using an information filtering neural network (IFNet) for discrete-time nonlinear state space models with cross-correlated measurement noises. The method presents three notable advantages: First, it offers a data-driven perspective to tackle uncertain correlation in multisensor estimate fusion while preserving the interpretability of the information filtering. Second, by harnessing the RNN’s capability to manage data streams, it can dynamically update the fusion weights between sensors to improve fusion accuracy. Third, this method has a lower complexity than the state-of-the-art KalmanNet measurement fusion method when dealing with the fusion problem involving a large number of sensors. Numerical simulations demonstrate that IFNet exhibits better fusion accuracy than traditional filtering methods and KalmanNet fusion filtering when correlation among measurement noises is unknown.

Multiexposed Image-Fusion Strategy Using Mutual Image Translation Learning with Multiscale Surround Switching Maps

The dynamic range of an image represents the difference between its darkest and brightest areas, a crucial concept in digital image processing and computer vision. Despite display technology advancements, replicating the broad dynamic range of the human visual system remains challenging, necessitating high dynamic range (HDR) synthesis, combining multiple low dynamic range images captured at contrasting exposure levels to generate a single HDR image that integrates the optimal exposure regions. Recent deep learning advancements have introduced innovative approaches to HDR generation, with the cycle-consistent generative adversarial network (CycleGAN) gaining attention due to its robustness against domain shifts and ability to preserve content style while enhancing image quality. However, traditional CycleGAN methods often rely on unpaired datasets, limiting their capacity for detail preservation. This study proposes an improved model by incorporating a switching map (SMap) as an additional channel in the CycleGAN generator using paired datasets. The SMap focuses on essential regions, guiding weighted learning to minimize the loss of detail during synthesis. Using translated images to estimate the middle exposure integrates these images into HDR synthesis, reducing unnatural transitions and halo artifacts that could occur at boundaries between various exposures. The multilayered application of the retinex algorithm captures exposure variations, achieving natural and detailed tone mapping. The proposed mutual image translation module extends CycleGAN, demonstrating superior performance in multiexposure fusion and image translation, significantly enhancing HDR image quality. The image quality evaluation indices used are CPBDM, JNBM, LPC-SI, S3, JPEG_2000, and SSEQ, and the proposed model exhibits superior performance compared to existing methods, recording average scores of 0.6196, 15.4142, 0.9642, 0.2838, 80.239, and 25.054, respectively. Therefore, based on qualitative and quantitative results, this study demonstrates the superiority of the proposed model.

An efficient cross-view image fusion method based on selected state space and hashing for promoting urban perception

In the field of cross-view image geolocation, traditional convolutional neural network (CNN)-based learning models generate unsatisfactory fusion performance due to their inability to model global correlations. The Transformer-based fusion methods can well compensate for the above problems, however, the Transformer has quadratic computational complexity and huge GPU memory consumption. The recent Mamba model based on the selection state space has a strong ability to model long sequences, lower GPU memory occupancy, and fewer GFLOPs. It is thus attractive and worth studying to apply Mamba to the cross-view image geolocation task. In addition, in the image-matching process (i.e., fusion of satellite/aerial and street view data.), we found that the storage occupancy of similarity measures based on floating-point features is high. Efficiently converting floating-point features into hash codes is a possible solution. In this study, we propose a cross-view image geolocation method (S6HG) based purely on Vision Mamba and hashing. S6HG fully utilizes the advantages of Vision Mamba in global information modeling and explicit location information encoding and the low storage occupancy of hash codes. Our method consists of two stages. In the first stage, we use a Siamese network based purely on vision Mamba to embed features for street view images and satellite images respectively. Our first-stage model is called S6G. In the second stage, we construct a cross-view autoencoder to further refine and compress the embedded features, and then simply map the refined features to hash codes. Comprehensive experiments show that S6G has achieved superior results on the CVACT dataset and comparable results to the most advanced methods on the CVUSA dataset. It is worth noting that other floating-point feature-based methods (4096-dimension) are 170.59 times faster than S6HG (768-bit) in storing 90,618 retrieval gallery data. Furthermore, the inference efficiency of S6G is higher than ViT-based computational methods.

Denoiser Learning for Infrared and Visible Image Fusion.

Infrared image (IR) and visible image (VI) fusion creates fusion images that contain richer information and gain improved visual effects. Existing methods generally use the operators of manual design, such as intensity and gradient operators, to mine the image information. However, it is hard for them to achieve a complete and accurate description of information, which limits the image fusion performance. To this end, a novel information measurement method is proposed to achieve IR and VI fusion. Its core idea is to guide a generator in achieving image fusion by learning the denoisers. Specifically, by using denoisers to restore fusion images with different noise interference to source images, a mutual competition relationship is formed between denoisers, which helps the generator thoroughly explore the data specificity of the source images and guide it to achieve more accurate feature representation. In addition, a semantic adaptive measurement loss function is proposed to constrain the generator, which fuses semantic information adaptively by considering the semantic information density of different source images. The results of quantitative and qualitative experiments have shown that the proposed method can achieve a higher quality information fusion and has a faster fusion speed on three public datasets when compared with advanced methods.

Prone transpsoas lateral interbody fusion with retractor-mounted camera and heads-up display: illustrative case.

The prone transpsoas (PTP) technique involves the performance of a lateral lumbar interbody fusion with the patient prone. One challenge encountered in the early experience with the PTP technique is that with the patient in the prone position rather than the lateral decubitus position, the surgeon is often forced to raise the bed significantly and perform the interbody work in a hunched and seated position with their neck extended and shoulders abducted. The authors report on the use of a sterile retractor-mounted camera during PTP that allows for the surgery to be performed with surgical anatomy visualized on a heads-up display. The PTP procedure is performed without significant modification from standard practice. After the self-retaining retractor is inserted, a sterile retractor-mounted camera can be introduced down one of the blades of the self-retaining retractor, preferably the dorsally situated blade to avoid camera line-of-sight issues with dependent irrigation or bleeding. The surgical field can now be visualized on a high-definition monitor, allowing the surgeon to stand at the patient's side instead of sitting. A sterile retractor-mounted camera with a heads-up display is a novel way to address ergonomic challenges as well as improve educational opportunities in the operating room. https://thejns.org/doi/10.3171/CASE24318.

ASIFusion: An Adaptive Saliency Injection-Based Infrared and Visible Image Fusion Network

The purpose of infrared and visible image fusion (IVIF) is to acquire a more informative fused image by leveraging complementary information, facilitating human perception and machine vision. Among the existing fusion methods, the saliency-based methods conform to human perception characteristics and achieve relatively advantageous fusion performance. However, such methods fail to adaptively maintain the edge and intensity of salient objects, resulting in fixed fusion performance. To address these issue, we present ASIFusion, an adaptive saliency injection-based IVIF network. First, source images are inputted to the feature extraction encoder for fully extracting features. Meanwhile, the proposed adaptive saliency injection module detects salient objects in the infrared image and then learns the fusion weights of each channel, which serve as supplementary information for further fusion. These learned weights are utilized to merge the source images’ extracted features. Finally, the feature reconstruction decoder produces a fused image with injected saliency. The fused image maintains the intensity and edge of the salient objects and fully preserves the complementary information. Extensive experiments demonstrate that our proposed network outperforms state-of-the-art (SOTA) approaches with regard to fusion performance and computational efficiency.

Tailoring thickness debit for high-temperature fatigue resistance of Inconel 718 superalloy fabricated by laser powder bed fusion

The thickness debit often leads to uncertainty regarding the fatigue performance of laser powder bed fusion (LPBF)-fabricated Inconel 718 thin-walled components and restricts the structural design of these components. Aiming to address this issue, fatigue properties of LPBF-fabricated Inconel 718 homogenized at various temperatures were investigated at 650 °C using specimens with different thicknesses. The results reveal a pronounced influence of both the thickness debit and the intricate interplay between the microstructural and geometrical scales of the thin-walled specimens on their fatigue life at 650 °C. The fatigue life of the thin-walled specimens with the same microstructural scale reduces with decreasing the ratio (t/d) of the specimen thickness (t) to the grain length (d). The coupling effect is described by a mechanism model correlated with the geometrical and microstructural scales of the specimens, in which continuous damage mechanics (CDM) and calculation of the yield strength have been considered. Based on the model, a criterion of t/d > 6.2 for the LPBF-fabricated Inconel 718 specimens homogenized at 1100 °C, and t/d > 8.8 for those homogenized at 1065 °C are proven to be satisfied to ensure a longer and more stable fatigue life of the thin-walled specimens serving at 650 °C. Elevating the homogenization temperature from 1065 °C to 1100 °C results in an extension of the fatigue life for specimens of the same thickness. This enhancement is attributed to the improved ability of grains to coordinate local deformation, as well as the reduced prevalence of elongated Laves and other phases, which typically serve as preferential sites for crack initiation and propagation. The finding suggests that the thickness debit in high-temperature fatigue resistance of LPBF-fabricated components can be minimized by tailoring the heat treatment strategy.

A perturbative multi-mode model with finite parallel electric field for fast-ion-driven Alfvén eigenmodes

Abstract Alfvén eigenmodes are of great interest in any fusion device as they can be excited by fast ions in the plasma. If the modes grow to large amplitudes, they can cause transport and redistribution of the fast ions, thus limiting fusion performance. To save computational resources, the resonant kinetic interaction between the fast-particle species and the modes is often modelled by MHD-kinetic hybrid codes. Here, we present such a hybrid model which is applicable to three-dimensional magnetic fields, accounts for a finite parallel electric field and multiple MHD modes present at the same time. The model extends the one previously implemented in the CKA-EUTERPE code allowing for a better estimate of the damping due to the parallel electric field and nonlinear mode-mode interaction. The capabilities of our model are illustrated by applying the code to model nonlinear frequency chirping and fast-ion profile flattening.

AFIRE: Adaptive FusionNet for illumination-robust feature extraction in heterogeneous imaging environments

The fusion of infrared and visible images aims to synthesize a fused image that incorporates richer information by leveraging the distinct characteristics of each modality. However, the disparate quality of input images in terms of infrared and visible light significantly impacts fusion performance. To address this issue, we propose a novel deep adaptive fusion method called Adaptive FusionNet for Illumination-Robust Feature Extraction (AFIRE). This method involves the interactive processing of two input features and dynamically adjusts the fusion weights based on varying illumination conditions. Specifically, we introduce a novel interactive extraction structure during the feature extraction stage for both infrared and visible light, enabling the capture of more complementary information. Additionally, we design a Deep Adaptive Fusion module to assess the quality of input features and perform weighted fusion through a channel attention mechanism. Finally, a new loss function is formulated by incorporating the entropy and median of input images to guide the training of the fusion network. Extensive experiments demonstrate that AFIRE outperforms state-of-the-art methods in preserving pixel intensity distribution and texture details. Source code is available at: https://www.github.com/ISCLab-Bistu/AFIRE.

M-ary Distributed Decision Fusion for Multihop Relay Wireless Sensor Networks: Decision Fusion Rule, Implementation Framework, and Performance Analysis

M-ary Distributed Decision Fusion for Multihop Relay Wireless Sensor Networks: Decision Fusion Rule, Implementation Framework, and Performance Analysis