A Practical Approach to 3D Scanning in the Presence of Interreflections, Subsurface Scattering and Defocus

Global or indirect illumination effects such as interreflections and subsurface scattering severely degrade the performance of structured light-based 3D scanning. In this paper, we analyze the errors in structured light, caused by both long-range (interreflections) and short-range (subsurface scattering) indirect illumination. The errors depend on the frequency of the projected patterns, and the nature of indirect illumination. In particular, we show that long-range effects cause decoding errors for low-frequency patterns, whereas short-range effects affect high-frequency patterns. Based on this analysis, we present a practical 3D scanning system which works in the presence of a broad range of indirect illumination. First, we design binary structured light patterns that are resilient to individual indirect illumination effects using simple logical operations and tools from combinatorial mathematics. Scenes exhibiting multiple phenomena are handled by combining results from a small ensemble of such patterns. This combination also allows detecting any residual errors that are corrected by acquiring a few additional images. Our methods can be readily incorporated into existing scanning systems without significant overhead in terms of capture time or hardware. We show results for several scenes with complex shape and material properties

Pattern Clustering using Soft-Computing Approaches

Clustering is the process of partitioning or grouping a given set of patterns into disjoint clusters. This is done such that patterns in the same cluster are alike and patterns belonging to two dierent clusters are dierent. . Clustering Process can be divided into two parts Cluster formation Cluster validation The most trivial K-means algorithm is rst implemented on the data set obtained from UCI machine repository. The comparison is extended to Fuzzy C-means algorithm where each data is a member of every cluster but with a certain degree known as membership value. Finally, to obtain the optimal value of K Genetic K-means algorithm in implemented in which GA nds the value of K as generation evolves.The ecieny of the three algorithms can be judged on the two measuring index such as : the silhouette index and Davies-Bouldin Index

Implementation of reed solomon error correcting codes

In the present world, communication has got many applications such as telephonic conversations etc. in which the messages are encoded into the communication channel and then decoding it at the receiver end. During the transfer of message, the data might get corrupted due to lots of disturbances in the communication channel. So it is necessary for the decoder tool to also have a function of correcting the error that might occur. Reed Solomon codes are type of burst error detecting codes which has got many applications due to its burst error detection and correction nature. My aim of the project is to implement this reed Solomon codes in a VHDL test bench waveform and also to analyse the error probability that is occurring during transmission. To perform this check one can start with simulating reed Solomon codes in MATLAB and then going for simulation in XILINX writing the VHDL code. The encoder and decoder design of reed Solomon codes have got different algorithms. Based on your requirements you can use those algorithms. The difference between the algorithms is that of the computational calculations between them. The complexity of the code depends on the algorithm used. I will be using Linear Feedback Shift Register circuit for designing the encoder

GANTouch: An Attack-Resilient Framework for Touch-based Continuous Authentication System

Previous studies have shown that commonly studied (vanilla) implementations of touch-based continuous authentication systems (V-TCAS) are susceptible to active adversarial attempts. This study presents a novel Generative Adversarial Network assisted TCAS (G-TCAS) framework and compares it to the V-TCAS under three active adversarial environments viz. Zero-effort, Population, and Random-vector. The Zero-effort environment was implemented in two variations viz. Zero-effort (same-dataset) and Zero-effort (cross-dataset). The first involved a Zero-effort attack from the same dataset, while the second used three different datasets. G-TCAS showed more resilience than V-TCAS under the Population and Random-vector, the more damaging adversarial scenarios than the Zero-effort. On average, the increase in the false accept rates (FARs) for V-TCAS was much higher (27.5% and 21.5%) than for G-TCAS (14% and 12.5%) for Population and Random-vector attacks, respectively. Moreover, we performed a fairness analysis of TCAS for different genders and found TCAS to be fair across genders. The findings suggest that we should evaluate TCAS under active adversarial environments and affirm the usefulness of GANs in the TCAS pipeline.Comment: 11 pages, 7 figures, 2 tables, 3 algorithms, in IEEE TBIOM 202

On the Inference of Soft Biometrics from Typing Patterns Collected in a Multi-device Environment

In this paper, we study the inference of gender, major/minor (computer science, non-computer science), typing style, age, and height from the typing patterns collected from 117 individuals in a multi-device environment. The inference of the first three identifiers was considered as classification tasks, while the rest as regression tasks. For classification tasks, we benchmark the performance of six classical machine learning (ML) and four deep learning (DL) classifiers. On the other hand, for regression tasks, we evaluated three ML and four DL-based regressors. The overall experiment consisted of two text-entry (free and fixed) and four device (Desktop, Tablet, Phone, and Combined) configurations. The best arrangements achieved accuracies of 96.15%, 93.02%, and 87.80% for typing style, gender, and major/minor, respectively, and mean absolute errors of 1.77 years and 2.65 inches for age and height, respectively. The results are promising considering the variety of application scenarios that we have listed in this work.Comment: The first two authors contributed equally. The code is available upon request. Please contact the last autho

Biogenic Preparation, Characterization, and Biomedical Applications of Chitosan Functionalized Iron Oxide Nanocomposite

Chitosan (CS) functionalization over nanomaterials has gained more attention in the biomedical field due to their biocompatibility, biodegradability, and enhanced properties. In the present study, CS functionalized iron (II) oxide nanocomposite (CS/FeO NC) was prepared using Sida acuta leaf extract by a facile and eco-friendly green chemistry route. Phyto-compounds of S. acuta leaf were used as a reductant to prepare CS/FeO NC. The existence of CS and FeO crystalline peaks in CS/FeO NC was confirmed by XRD. FE-SEM analysis revealed that the prepared CS/FeO NC were spherical with a 10–100 nm average size. FTIR analyzed the existence of CS and metal-oxygen bands in the prepared NC. The CS/FeO NC showed the potential bactericidal activity against E. coli, B. subtilis, and S. aureus pathogens. Further, CS/FeO NC also exhibited the dose-dependent anti-proliferative property against human lung cancer cells (A549). Thus, the obtained outcomes revealed that the prepared CS/FeO NC could be a promising candidate in the biomedical sector to inhibit the growth of bacterial pathogens and lung cancer cells