Browsing by Subject "fusion"

Now showing 1 - 4 of 4
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    A Fuzzy Logic Based Piezoresistive/Piezoelectric Fusion Algorithm for Carbon Nanocomposite Wide Band Strain Sensor
    (IEEE, 2021) Alotaibi, Ahmed; Anwar, Sohel; Mechanical and Energy Engineering, School of Engineering and Technology
    Polymer nanocomposites (PNC) have a great potential for in-situ strain sensing applications in both static and dynamic loading scenarios. These PNCs, having a polymer matrix of polyvinylidene fluoride (PVDF) with a conductive filler of multi-walled carbon nanotubes (MWCNT), have both piezoelectric and piezoresistive characteristics. Generally, this composite would accurately measure either low frequency dynamic strain using piezoresistive characteristic or high frequency dynamic strains using piezoelectric characteristics of the MWCNT/PVDF film sensor. This limits the frequency bands of the strain sensor to either piezoresistive or piezoelectric ranges. In this study, a novel weighted fusion technique, called piezoresistive/piezoelectric fusion (PPF), is proposed to combine both piezoresistive and piezoelectric characteristics to capture wide frequency bands of strain measurements in real time. This fuzzy logic (FL) based method combines the salient features (i.e. piezoresistive and piezoelectric) of the nanocomposite sensor via reasonably accurate models to extend the frequency range over a wider band. The FL determines the weight of each signal based on the error between the estimate and actual measurements. These weights indicate the contribution of each signal to the final fused measurement. The fuzzy inference system (FIS) was developed using both optimization and data clustering techniques. In addition, type-2 FIS was utilized to overcome the model's uncertainty limitations. The developed PPF methods were verified with experimental data at different dynamic frequencies that were obtained from existing literature. The fused measurements of the MWCNT/PVDF were found to correlate very well with the actual strain and a high degree of accuracy was achieved by the subtractive clustering PPF's FISs algorithm.
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    High Failure Rates of Concomitant Periprosthetic Joint Infection and Extensor Mechanism Disruption
    (Elsevier, 2018) Anderson, Lucas A.; Culp, Brian M.; Della Valle, Craig J.; Gililland, Jeremy M.; Meneghini, R. Michael; Browne, James A.; Springer, Bryan D.; Orthopaedic Surgery, School of Medicine
    Background Patients presenting with both chronic periprosthetic joint infection (PJI) and extensor mechanism disruption (EMD) pose a significant challenge. As there is little in the literature regarding outcomes of patients with concomitant PJI and EMD, we performed a multicenter study to evaluate the outcomes. Methods Sixty patients with concomitant diagnoses of PJI and EMD were evaluated from 5 institutions. Patient demographics, presentation type, surgical management, and outcomes including recurrent infections, final surgery, and ambulatory status were documented. Results Fifty-three of 60 patients had an attempted extensor mechanism reconstruction/repair (EMR) of which 12 (23%) were successful, averaging 3.5 (range, 2-7) intervening surgeries. Forty-one patients (77%) were considered failures with recurrence of infection as most common failure (80%); 26 ended in fusion, 10 in above knee amputation, 3 with chronic resection arthroplasty, and 2 with chronic spacers/EMD. Seven patients had no attempt at EMR but proceeded directly to fusion (n = 6) or amputation (n = 1). There was no statistical difference between groups that had success or failure of EMR in age, American Society of Anesthesiologists Physical Status Classification System, or body mass index. Conclusion Our study demonstrates that concomitant EMD and PJI is a dreaded combination with poor outcomes regardless of treatment. Eradication of infection and reconstruction of the extensor mechanism often require numerous surgeries and despite great effort often end in failure. Consideration of early fusion or amputation may be preferable in some patients to avoid the morbidity and mortality of repeated surgeries.
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    Multiresolution variance-based image fusion
    (2013-05) Ragozzino, Matthew; Salama, Paul; Christopher, Lauren; Rizkalla, Maher E.; King, Brian
    Multiresolution image fusion is an emerging area of research for use in military and commercial applications. While many methods for image fusion have been developed, improvements can still be made. In many cases, image fusion methods are tailored to specific applications and are limited as a result. In order to make improvements to general image fusion, novel methods have been developed based on the wavelet transform and empirical variance. One particular novelty is the use of directional filtering in conjunction with wavelet transforms. Instead of treating the vertical, horizontal, and diagonal sub-bands of a wavelet transform the same, each sub-band is handled independently by applying custom filter windows. Results of the new methods exhibit better performance across a wide range of images highlighting different situations.
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    Toward Resolution-Invariant Person Reidentification via Projective Dictionary Learning
    (IEEE, 2019-06) Li, Kai; Ding, Zhengming; Li, Sheng; Fu, Yun; Computer Information and Graphics Technology, School of Engineering and Technology
    Person reidentification (ReID) has recently been widely investigated for its vital role in surveillance and forensics applications. This paper addresses the low-resolution (LR) person ReID problem, which is of great practical meaning because pedestrians are often captured in LRs by surveillance cameras. Existing methods cope with this problem via some complicated and time-consuming strategies, making them less favorable, in practice, and meanwhile, their performances are far from satisfactory. Instead, we solve this problem by developing a discriminative semicoupled projective dictionary learning (DSPDL) model, which adopts the efficient projective dictionary learning strategy, and jointly learns a pair of dictionaries and a mapping function to model the correspondence of the cross-view data. A parameterless cross-view graph regularizer incorporating both positive and negative pair information is designed to enhance the discriminability of the dictionaries. Another weakness of existing approaches to this problem is that they are only applicable for the scenario where the cross-camera image sets have a globally uniform resolution gap. This fact undermines their practicality because the resolution gaps between cross-camera images often vary person by person in practice. To overcome this hurdle, we extend the proposed DSPDL model to the variational resolution gap scenario, basically by learning multiple pairs of dictionaries and multiple mapping functions. A novel technique is proposed to rerank and fuse the results obtained from all dictionary pairs. Experiments on five public data sets show the proposed method achieves superior performances to the state-of-the-art ones.