Dual image watermarking based on NSST-LWT-DCT for color image

Advanced internet technology allows unauthorized individuals to modify and distribute digital images. Image watermarking is a popular solution for copyright protection and ensuring digital security. This research presents an embedding scheme with a set of conditions using non-subsampled Shearlet transform (NSST), lifting wavelet transform (LWT), and discrete cosine transform (DCT). Red and green channels are employed for the embedding process. The red channel is converted by NSST-LWT. The low-frequency area (LL) frequency is then split into small blocks of 8×8, each partition block is then transformed by DCT. The DCT coefficient of (3,4), (5,2), (5,3), (3,5), called matrix M1, and (2,5), (4,3), (6,2), (4,4), called matrix M2 are selected for singular value decomposition (SVD) process. With a set of conditions, the watermark bits are incorporated into those singular values. The green channel is cropped to get the center image before splitting into 4×4 pixels. The block components are then selected based on the least entropy value for the embedding regions. The selected blocks are then computed using LWT-SVD. A set of conditions for U(1,1) and U(2,1) are used to incorporate the watermark logo. The experimental findings reveal that the suggested scheme achieves high imperceptibility and resilience under various evaluating attacks with an average peak signal-to-noise ratio (PSNR) and correlation value (NC) values are up to 43.89 dB and 0.96, respectively

Smart object detection using deep learning algorithm and jetson nano for blind people

Visual impairment is often defined as a best corrected visual acuity of worse than either 20/40 or 20/60. The term blindness is used for complete or nearly complete vision loss. Visual impairment may cause people difficulties with normal daily activities such as driving, reading, socializing and walking. Therefore, this project develops a smart object detection using deep learning algorithm and jetson nano to improve object detection for blind people. Furthermore, an ultrasonic sensor and sound notification are added to the project development to notify blind people if any object located nearest around them

Smart kitchen mobile based application with Arduino module features

Kitchen is one of the important areas in the house as the kitchen serves many functions especially the cooking activity. Moreover, kitchen management such as managing the groceries items, electricity usage as well as gas management are also crucial activities in kitchen. Hence, a smart kitchen mobile based application with Arduino module features is developed which have four main functions: (i) to produce a recipe recommendation from available ingredients using fuzzy logic, (ii) to have a grocery tracker function, (iii) expiry notification function, and (iv) arduino features that enable user to control kitchen lamp, motion sensor and gas detection alarm

Home security system using face recognition

This is a Home Security System that focuses on using face recognition. The system is divided into two main parts which are an Android application and Raspbery Pi module. Using the Home Security System application, the user will be able to sign up, sign in, reset password, upload face images (for face recognition), retrieve uploaded images, retrieving door status and motion detection history and view CCTV footage. Raspberry Pi module is equipped with a camera module (face recognition purpose), passive infrared (PIR) motion sensor, door sensor and solenoid door lock

Iris segmentation

The quality of eye image data become degraded particularly when the image is taken in the non-cooperative acquisition environment such as under visible wavelength illumination. Consequently, this environmental condition may lead to noisy eye images, incorrect localization of limbic and pupillary boundaries and eventually degrade the performance of iris recognition system. Hence, this study has compared several segmentation methods to address the abovementioned issues. The results show that Circular Hough transform method is the best segmentation method with the best overall accuracy, error rate and decidability index that more tolerant to ‘noise’ such as reflection

Mobile smart door security system

Door have interconnection between the different interiors or departments and it is function to preserve privacy and protection for the areas when it is locked. Although there are several solutions for door lock such as using personal identification number, radio frequency identification or biometric system, these systems still have several limitations such as forget password, misplace card or error due to biometric sensor sensitivity. Hence, a smart door security system using a secret knock as well as mobile application are developed to resolve the problem. The developed system is not only can lock or unlock the door using secret knock, but also can monitor any user access the door through mobile

Distributed Denial of Service Attack Detection in IoT Networks using Deep Learning and Feature Fusion: A Review

The explosive growth of Internet of Things (IoT) devices has led to escalating threats from distributed denial of service (DDoS) attacks. Moreover, the scale and heterogeneity of IoT environments pose unique security challenges, and intelligent solutions tailored for the IoT are needed to defend critical infrastructure. The deep learning technique shows great promise because automatic feature learning capabilities are well suited for the complex and high-dimensional data of IoT systems. Additionally, feature fusion approaches have gained traction in enhancing the performance of deep learning models by combining complementary feature sets extracted from multiple data sources. This paper aims to provide a comprehensive literature review focused specifically on deep learning techniques and feature fusion for DDoS attack detection in IoT networks. Studies employing diverse deep learning models and feature fusion techniques are analysed, highlighting key trends and developments in this crucial domain. This review provides several significant contributions, including an overview of various types of DDoS attacks, a comparison of existing surveys, and a thorough examination of recent applications of deep learning and feature fusion for detecting DDoS attacks in IoT networks. Importantly, it highlights the current challenges and limitations of these deep learning techniques based on the literature surveyed. This review concludes by suggesting promising areas for further research to enhance deep learning security solutions, which are specifically tailored to safeguarding the fast-growing IoT infrastructure against DDoS attacks

Fusion Iris and Periocular Recognitions in Non-Cooperative Environment

The performance of iris recognition in non-cooperative environment can be negatively impacted when the resolution of the iris images is low which results in failure to determine the eye center, limbic and pupillary boundary of the iris segmentation. Hence, a combination with periocular features is suggested to increase the authenticity of the recognition system. However, the texture feature of periocular can be easily affected by a background complication while the colour feature of periocular is still limited to spatial information and quantization effects. This happens due to different distances between the sensor and the subject during the iris acquisition stage as well as image size and orientation. The proposed method of periocular feature extraction consists of a combination of rotation invariant uniform local binary pattern to select the texture features and a method of color moment to select the color features. Besides, a hue-saturation-value channel is selected to avoid loss of discriminative information in the eye image. The proposed method which consists of combination between texture and colour features provides the highest accuracy for the periocular recognition with more than 71.5% for the UBIRIS.v2 dataset and 85.7% for the UBIPr dataset. For the fusion recognitions, the proposed method achieved the highest accuracy with more than 85.9% for the UBIRIS.v2 dataset and 89.7% for the UBIPr dataset

The Earth & Universe Augmented Reality Educational Book

Students have been perceiving even simple concepts in science are particularly difficult to grasp, since many ideas involve three-dimensional thinking. So, an Augmented Reality learning tool was developed to provide easy-to-use teaching/learning tools for learners and educators. The unique capability that allows virtual objects to appear in real world can serve as an effective tool to facilitate students to acquire better understandings on science and assist teachers to teach concepts that cannot be easily seen in a natural environment. Thus, AR-SMB has a bright potential for expansion and among potential markets include schools, parents and collaboration with publisher like Sasbadi/Pelangi

Internet of Things (IoT) Based Fire Alert Monitoring System for Car Parking

Safety is one of the important factors that should be considered either in the parking area, workplace, home area and so forth. In the university parking area, the students are unable to receive any information regarding a fire smoke or an accident near their vehicle. In addition, the parking safety also not assured due to the shortage of car superintendence and there is no any strict parking management by the security officer. Therefore, a fire smoke alert monitoring system in the university parking area is necessary in order to prevent any accidents that may cause property breakdown and loss of life as happens inside the university area. This system should be introduced since the existing parking is unsystematic and less efficient as it unable to response the complications that are regularly happen to the students because they do not receive any information regarding a fire smoke or an accident near their vehicle in the parking area. With this new system, a few advancements are implemented in order to help the students in various aspects by using multiple and distinct Arduino devices. Moreover, an android application is developed to facilitate the security officer in order to identify the car information that are involved in the accident that might be occur in the university parking area