Electronics and Communication Engineering

This paper proposes the technique of storing of image data in the FPGA memory and subsequent processing of the stored image data using MicroBlaze processor core of the Xilinx FPGA device. Though related research work exists in processing image data using DSP blocks available in FPGA devices but very little work exists in processing the same using FPGA based processor core. This type of design is extremely important for real time embedded system design for image processing applications. Our work deals with the ...

The Internet of Things (IoT) is one of the major technological trends which is utilized to monitor natural and human made resources to help in predicting and detecting exigence events like flood, fire, gas and water leak that can pose an intimidation to human life. This paper proposes a novel wireless prototype of a live weather monitoring station that uploads weather information received from the array of sensors to cloud database from a remote location which can be monitored from anywhere. The weather data is recorded, monitored and processed to forecast the different weather events and predict the upcoming disasters. The proposed battery powered cost-effective system can be installed anywhere within a locality or building to serve the maximum people. It is composed of three major modules-the wireless sensor module (IEEE802.15.4/Zigbee), the Arduino microcontroller with Ethernet/Wi-Fi network connectivity and an android app for users. Instant alert messages will be received to pho...

Biometrics has become important in security applications. In comparison with many other biometric features, iris recognition has very high recognition accuracy because it depends on iris which is located in a place that still stable throughout human life and the probability to find two identical iris's is close to zero. The identification system consists of several stages including segmentation stage which is the most serious and critical one. The current segmentation methods still have limitation in localizing the iris due to circular shape consideration of the pupil. In this research, Daugman method is done to investigate the segmentation techniques. Eyelid detection is another step that has been included in this study as a part of segmentation stage to localize the iris accurately and remove unwanted area that might be included. The obtained iris region is encoded using haar wavelets to construct the iris code, which contains the most discriminating feature in the iris pattern. Hamming distance is used for comparison of iris templates in the recognition stage. The dataset which is used for the study is UBIRIS database. A comparative study of different edge detector operator is performed. It is observed that canny operator is best suited to extract most of the edges to generate the iris code for comparison. Recognition rate of 89% and rejection rate of 95% is achieved.

In this paper we propose a design using both a microprocessor and light sensors for automatic room light detection and control. Our design, the HLCM (Home Light Control Module) which will be installed in every light fixture of a family, is made up of four blocks: the pyroelectric infrared (PIR) sensor circuit, the light sensor circuit, the microprocessor and the RF module. By using the PIR sensor circuit, the HLCM detects if a human body enters the detection area or not. If there is no human body present, all controlled lights are turned off. If there is, the HLCM detects the light intensity under the environment and maintains sufficient light by controlling the number of lights. We have also integrated an RF module to transmit and receive the data from each HLCM so we can control different lights in different regions. The result of using the HLCM shows that the total power consumption can be reduced 1 .

A new type of brain tumor identification system is proposed and analyzed in this paper by detecting Infected Region using a combination of Region Growing Algorithm, Cryptography and Digital Watermarking. The information related to patients contained in the Electronic Patient Record (EPR), Region of Infection (ROI), doctor's name and diagnosis from symptoms are encrypted and embedded in the tomographic image itself using the proposed methodology-a combination of the Rivest-Shamir-Adelman (RSA) encryption and bit plane slicing watermarking technique. The infected region is identified through region growing and contour detection algorithm which needs to be perfect for accurate ROI identification resulting in a better treatment. The image quality metrics show that the proposed GUI based brain tumor diagnostic system is good enough for successful recovery of all the embedded information and is found to be similar to the embedded information.

Willingness-To-Pay (WTP) is the most a person is willing to pay for a good or service. Conversely, Willingness-To-Accept (WTA) is the minimum amount a person is willing to accept for giving up a good or service. People often attribute a higher value for privacy in the WTA condition when compared to the WTP condition. In behavioral economics of privacy and security, this discrepancy between WTP and WTA has been explained by the endowment effect and the status-quo bias. In this study, we aim to emulate the effects of WTP and WTA through interface design. Specifically, we employed the principles of status-quo bias to build two versions of the IoT Marketplace. While one design of the marketplace emulated the WTA condition, the other emulated the WTP condition. In both versions of the marketplace, we communicated the aggregate privacy rating associated with the IoT device using the same indicator. We evaluated the effect the two interfaces had on product selection by conducting a study where participants were asked to purchase an IoT device using either a WTA interface or a WTP interface. Our results show that participants using the interface that emulated the WTA condition were more likely to pay a premium to purchase devices with a higher privacy rating when compared to the participants using the interface that emulated the WTP condition. We also append results showing that when asked to select items without making the purchases the same effect was more pronounced.

Conversational agents have transcended into multiple industries with increased ability for user engagement in intelligent conversation. Conversations with chatbots are different from interpersonal communication in terms of turntaking, intentions, and behavior. We study de-identified chat logs across 30 conversations with a well-recognized chatbot to understand (i) how people create turns in conversation to perform 'social action', extending human experiences and knowledge (ii) how people express typical human constructs like emotion in their interaction with chatbots, and, (iii) what are the discursive strategies used by people to create 'shared meaning' and identity for themselves. Our findings reveal conversational expectations and behavior of users being similar to those in human-to-human sharing (how people talk), but greater diversity in the nature of information shared (what they talk about). This can advance discussion both in how we can design conversational agents to be more intelligible, as well as sensitive to unnecessary information disclosure.

Individual concerns about account takeover and subversion are well-documented. Surveys indicate that concerns for the privacy and security of online accounts are widely shared. Adopting Two-Factor Authentication (2FA) is an action that individuals can take to secure their own accounts, including many popular consumerfacing services. Given that, why is two-factor hardware not more widely adopted? What usability and acceptability factors drive the adoption, or lack of adoption of 2FA in the form of trusted hardware? Passwords are inherently misaligned with human cognition, and hardware keys designed for ease of use are readily available in the marketplace. Yet passwords remain the dominant online authentication method. In order to be er understand relevant issues driving or impinging adoption of Two-Factor Authentication, we implemented a two-phase study of the Yubico FIDO U2F security key. e Yubico security key is a 2FA device designed to be user friendly. We examined the usability of the device by implementing a think-aloud protocol, and documented the halt and confusion

A large proportion of email messages in an average Internet user's inbox are unwanted commercial messages from mailing lists, bots, and so on. Although such messages often include instructions to unsubscribe, people still struggle with stopping unwanted email. We investigated the user experience of unsubscribing from unwanted email messages by recruiting 18 individuals for via a lab study followed by semi-structured interviews. Based on unsubscribing practices of the study participants, we synthesized eight common unsubscription mechanisms and identified the corresponding user experience challenges. We further uncovered alternative practices aimed at circumventing the need to unsubscribe. Our findings reveal frustration with the prevailing options for limiting access to the self by managing email boundaries. We apply our insight to offer design suggestions that could help commercial providers improve the user experience of unsubscribing and provide users more control over the email they receive.

The purpose of this study is to understand the privacy concerns and behavior of non-WEIRD populations in online messaging platforms. Analysis of surveys (n = 674) of WhatsApp users in Saudi Arabia and India revealed that Saudis had significantly higher concerns about being contacted by strangers. In contrast, Indians showed significantly higher concerns with respect to social contact from professional colleagues. Demographics impinge privacy preferences in both populations, but in different ways. Results from regression analysis show that there are statistically significant differences between the privacy behaviors of Saudis and Indians. In both cases, privacy concerns were strongly correlated with their reported privacy behaviors. Despite the differences, we identified technical solutions that could address the concerns of both populations of participants. We close by discussing the applicability of our recommendations, specifically those on transparency and consent, to other appli...

Technology is uniquely positioned to help us analyze large amounts of information to provide valuable insight during widespread public health concerns, like the ongoing COVID-19 pandemic. In fact, information technology companies like Apple and Google have recently launched tools for contact tracing-the ability to process location data to determine the people who have been in contact with a possible patient, in order to contain the spread of the virus. While China and Singapore have successfully led the effort, more and more countries are now implementing such surveillance systems, raising potential privacy concerns about this long term surveillance. For example, it is not clear what happens to the information post-pandemic because people are more likely to share their information during a global crisis without governments having to elaborate on their data policies. Digital Ethnography on Twitter, which has over 330 million users worldwide, with a majority in the United States where...

The IETF Manufacturer Usage Description (MUD) standard was designed to protect IoT devices through network micro-segmentation. In practice, this is implemented using per-device access control that is defined by the manufacturer. This access control is embedded in a "MUD-File", which is transferred to the user's network during the onboarding process, and may contain from one to hundreds of rules. Validating these rules for each device can be a challenge, particularly when devices are interacting. In response, MUD-Visualizer was designed to simplify the validation of individual and interacting MUD-Files through straightforward visualizations. In this work, we report on an evaluation of the usability and efficacy of MUD-Visualizer. The results illustrate that not only it is more usable compared to manual analysis, but the participants that used MUD-Visualizer also had more accurate results in less time. CCS CONCEPTS • Security and privacy → Human and societal aspects of security and privacy; Network security; • Computer systems organization → Embedded and cyber-physical systems.

The behaviour of nonlinear turbo codes that use nonlinear finite state sequential machines (FSSMs), designed over the finite field GF(4), as constituent encoders is studied. These FSSMs show a nonzero output for the all-zero input case. The state-transitions structure and the randomness of the output for the all-zero input response of these FSSMs are studied for different initial state conditions. A relationship is found between these two characteristics and the BER performance or, equivalently, the form of the extrinsic information transfer chart of the corresponding turbo code. It is found that FSSMs with responses of the form of long closed cycles, and good randomness properties of the corresponding output are the best options as constituent encoders of nonlinear turbo codes designed over GF(4). A coefficient called the random behaviour coefficient is defined, to provide a quantitative measure of the behaviour of these FSSMs as constituent encoders of a turbo code.

Graphical elements: particularly tables and figures contain a visual summary of the most valuable information contained in a document. Therefore, localization of such graphical objects in the document images is the initial step to understand the content of such graphical objects or document images. In this paper, we present a novel end-to-end trainable deep learning based framework to localize graphical objects in the document images called as Graphical Object Detection (GOD). Our framework is data-driven and does not require any heuristics or meta-data to locate graphical objects in the document images. The GOD explores the concept of transfer learning and domain adaptation to handle scarcity of labeled training images for graphical object detection task in the document images. Performance analysis carried out on the various public benchmark data sets: ICDAR-2013, ICDAR-POD 2017 and UNLV shows that our model yields promising results as compared to state-of-the-art techniques.

In this conference proceeding, a novel intensity profile measuring systems is presented that is controlled by gesture recognition principles. This low-cost device works much faster and can be used to measure profiles of wide variety of light sources oriented in elevation and azimuth. Presently, it provides relative intensity measurement but can potentially be used for absolute intensity, if suitably modified. From, the profiles of the standard sources clearly the faulty light sources can be identified and therefore, can save a lot effort.

The extraction of blood vessels by morphological segmentation and the detection of the severity of diabetic retinopathy are proposed in this paper. The proposed algorithm extracts the finest vessels in the retina within a remarkably reduced computational time. The extracted blood vessel features being fed to KNN classifier determine the stage of Diabetic Retinopathy. The performance analysis is carried out which comes out to be of 94% along with the sensitivity (81.45%), specificity (96.25%), and accuracy (95.3%) defines the efficiency of the proposed system.

Fingerprint recognition, one of the important biometric verification tools plays an important role in maintaining good security in any sort of systems. The fingerprint recognition in this paper deals with the extraction of features followed by codebook formation by Linde Buzo Gray Vector Quantization. The system also adds a sophisticated multi class Support Vector Machine classifier with a comparator technique which enhances the efficacy of the system. The classifier obtains an overall accuracy of 94% fingerprint verification with no rejects.

Diabetic Retinopathy, one of the most common diseases occurring in diabetic patients results in partial or complete vision loss. So, its early detection is needed and needs to be taken care of. The paper deals with the filter based retinal vessel extraction, exudates detection using Fuzzy C means, detection and removal of optical disk using Convex Hull. Support Vector Machines (SVM) is used to classify the fundus images into Normal and Non-proliferative Diabetic Retinopathy (NPDR) or Proliferative Diabetic Retinopathy (PDR). The system attains an efficiency of 96.23% which defines the efficacy of the proposed system.