Multispectral Palmprint Encoding and Recognition

Palmprints are emerging as a new entity in multi-modal biometrics for human identification and verification. Multispectral palmprint images captured in the visible and infrared spectrum not only contain the wrinkles and ridge structure of a palm, but also the underlying pattern of veins; making them a highly discriminating biometric identifier. In this paper, we propose a feature encoding scheme for robust and highly accurate representation and matching of multispectral palmprints. To facilitate compact storage of the feature, we design a binary hash table structure that allows for efficient matching in large databases. Comprehensive experiments for both identification and verification scenarios are performed on two public datasets -- one captured with a contact-based sensor (PolyU dataset), and the other with a contact-free sensor (CASIA dataset). Recognition results in various experimental setups show that the proposed method consistently outperforms existing state-of-the-art methods. Error rates achieved by our method (0.003% on PolyU and 0.2% on CASIA) are the lowest reported in literature on both dataset and clearly indicate the viability of palmprint as a reliable and promising biometric. All source codes are publicly available.Comment: Preliminary version of this manuscript was published in ICCV 2011. Z. Khan A. Mian and Y. Hu, "Contour Code: Robust and Efficient Multispectral Palmprint Encoding for Human Recognition", International Conference on Computer Vision, 2011. MATLAB Code available: https://sites.google.com/site/zohaibnet/Home/code

Studies on Buddleja asiatica antibacterial, antifungal, antispasmodic and Ca++ antagonist activities

Crude extract of Buddleja asiatica Lour and its fractions, chloroform (F1), ethyl acetate (F2) and nbutanol (F3) were evaluated for antibacterial, antifungal, antispasmodic and Ca++ antagonist activities. The antibacterial activity was performed against 11 types of bacteria. The crude extract and fractions F2 and F3 exhibited significant activity, while F1 showed low activity in killing the Shigella flexenari, Sternostoma boydi and Escherichia coli. In the rest bacteria, the crude extract and all the fractions (F1 to F3) revealed minimum to nil inhibitory effect. The fungicidal activity of the crude extract and all the fractions (F1 to F3) was also performed against six different fungi. The crude extract and fractions F1 and F3 displayed significant activity, while fraction F2 showed moderate activity against Fusarium solani. In the case of Microsporum canis, the crude extract and fraction F3 showed high activity but in the other four fungi, the inhibition area exhibited optimum to nil activity in crude extract and all the fractions (F1 to F3). In isolated rabbit jejunum preparations, B. asiatica crude extract caused concentration-dependent (0.03 to 1.0 mg/ml) relaxation of spontaneous and high K+ (80 mM)-induced contractions. The results indicate the antibacterial, antifungal, antispasmodic and Ca++ antagonist potential of B. asiatica Lour.Key words: Buddleja asiatica, antibacterial, antifungal, antispasmodic, Ca++ channel blocker

The Use of Bone Morphogenetic Protein in the Intervertebral Disk Space in Minimally Invasive Transforaminal Lumbar Interbody Fusion

Study Design: Retrospective Cohort. Objective: The objective of this study was to characterize one surgeon’s experience over a 10-year period using rhBMP-2 in the disk space for minimally invasive transforaminal lumbar interbody fusion (MIS TLIF). Summary of Background Data: MIS TLIF has been utilized as a technique for decreasing patients’ immediate postoperative pain, decreasing blood loss, and shortened hospital stays. Effectiveness and complications of rhBMP-2’s use in the disk space is limited because of its off-label status. Methods: Retrospective analysis of consecutive MIS TLIFs performed by senior author between 2004 and 2014. rhBMP-2 was used in the disk space in all cases. Patients were stratified based on the dose of rhBMP-2 utilized. Patients had 9 to 12 month computerized tomography scan to evaluate for bony fusion and continued follow-up for 18 months. Results: A total of 688 patients underwent a MIS TLIF. A medium kit of rhBMP-2 was utilized in 97 patients, and small kit was used in 591 patients. Fusion rate was 97.9% and this was not different between the 2 groups with 96/97 patients fusing in the medium kit group and 577/591 patients fusing in the small kit group. Five patients taken back to the operating room for symptomatic pseudoarthrosis, 4 reoperated for bony hyperostosis, and 10 radiographic pseudoarthroses that did not require reoperation. A statistically significant difference in the rate of foraminal hyperostosis was found when using a medium sized kit of rhBMP-2 was 4.12% (4/97 patients), compared with a small kit (0/591 patients, P=0.0004). Conclusions: Utilization of rhBMP-2 in an MIS TLIF leads to high fusion rate (97.9%), with an acceptable complication profile. The development of foraminal hyperostosis is a rare complication that only affected 0.6% of patients, and seems to be a dose related complication, as this complication was eliminated when a lower dose of rhBMP-2 was utilized

Efficient 2D Graph SLAM for Sparse Sensing

Simultaneous localization and mapping (SLAM) plays a vital role in mapping unknown spaces and aiding autonomous navigation. Virtually all state-of-the-art solutions today for 2D SLAM are designed for dense and accurate sensors such as laser range-finders (LiDARs). However, these sensors are not suitable for resource-limited nano robots, which become increasingly capable and ubiquitous nowadays, and these robots tend to mount economical and low-power sensors that can only provide sparse and noisy measurements. This introduces a challenging problem called SLAM with sparse sensing. This work addresses the problem by adopting the form of the state-of-the-art graph-based SLAM pipeline with a novel frontend and an improvement for loop closing in the backend, both of which are designed to work with sparse and uncertain range data. Experiments show that the maps constructed by our algorithm have superior quality compared to prior works on sparse sensing. Furthermore, our method is capable of running in real-time on a modern PC with an average processing time of 1/100th the input interval time.Comment: Accepted for 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

Earth-Abundant Tin Sulfide-Based Photocathodes for Solar Hydrogen Production.

Tin-based chalcogenide semiconductors, though attractive materials for photovoltaics, have to date exhibited poor performance and stability for photoelectrochemical applications. Here, a novel strategy is reported to improve performance and stability of tin monosulfide (SnS) nanoplatelet thin films for H2 production in acidic media without any use of sacrificial reagent. P-type SnS nanoplatelet films are coated with the n-CdS buffer layer and the TiO2 passivation layer to form type II heterojunction photocathodes. These photocathodes with subsequent deposition of Pt nanoparticles generate a photovoltage of 300 mV and a photocurrent density of 2.4 mA cm-2 at 0 V versus reversible hydrogen electrode (RHE) for water splitting under simulated visible-light illumination (λ > 500 nm, Pin = 80 mW cm-2). The incident photon-to-current efficiency at 0 V versus RHE for H2 production reach a maximum of 12.7% at 575 nm with internal quantum efficiency of 13.8%. The faradaic efficiency for hydrogen evolution remains close to unity after 6000 s of illumination, confirming the robustness of the heterojunction for solar H2 production

A Comparative Study on Machine Learning Algorithms for Network Defense

Network security specialists use machine learning algorithms to detect computer network attacks and prevent unauthorized access to their networks. Traditionally, signature and anomaly detection techniques have been used for network defense. However, detection techniques must adapt to keep pace with continuously changing security attacks. Therefore, machine learning algorithms always learn from experience and are appropriate tools for this adaptation. In this paper, ten machine learning algorithms were trained with the KDD99 dataset with labels, then they were tested with different dataset without labels. The researchers investigate the speed and the efficiency of these machine learning algorithms in terms of several selected benchmarks such as time to build models, kappa statistic, root mean squared error, accuracy by attack class, and percentage of correctly classified instances of the classifier algorithms

Effectiveness of social media sentiment analysis tools with the support of emoticon/emoji

Organizations are increasingly interested in using microblogging platforms, such as Twitter, to get rapid feedback in several domains using sentiment analysis algorithms to rate, for example, whether a target audience is happy or unhappy. However, posts on microblogging platforms can differ from the source material used to train the sentiment analysis tools. For example, emojis and emoticons are increasingly employed in social media to clarify, enhance, or sometimes reverse the sentiment of a post but can be stripped out of a piece of text before it is processed. Responding to this interest, many sentiment analysis algorithms are being made available as web services, but as details of the algorithms used are not always published on the website, comparisons between web services and how well they deal with the peculiarities of microblogging posts can be difficult. To address this, a prototype web application was developed to compare the performance of nine tweet-related sentiment analysis web services and, through targeted hypotheses, to study the effect of emojis and emoticons on polarity classification. Twelve specific research test sets were created with the application, labelled by volunteers, and tested against the analysis web services with evaluation provided by two- and three-class accuracy measures. Distinct differences were found in how the web services used emoticons and emojis in assigning a positive or negative sentiment value to a tweet, with some services seeming to ignore their presence. It was found in general that web services classified polarity sensitive tweets significantly less accurately than tweets where the sentiment of the emoji/emoticon supported the sentiment of the text