Image Quality Meter Using Compression

ABSTRAC

Image Quality Assessment using Image Details in Frequency Domain

This research proposes a RR (Reduced Reference) DIQAM (Detailed Image Quality Assessment Meter) for DCT (Discrete Cosine Transform) based compressed images. DCT technique divides image in sub blocks to achieve image compression.Therefore, it degrades the IQ (Image Quality) by introducing the distortions called blockiness and blurriness in the compressed image.In the telecommunication systems scenario, the systems available bandwidth is limited. The proposed IQ assessment technique requires fewer image details parameters called RR parameters at the receiver, rather than the complete reference image. This paper suggests a method for receiving end to estimate the objective quality of the received image in frequency domain.The proposed IQ meter starts by taking the image through edge detection method, then converting it into frequency domain by Fourier transform and estimating the image details. The image details calculations include the vertical and horizontal ac harmonics as well as all other ac coefficients present in the image. It has been shown in the presented work that using dc coefficients with the other ac coefficients further improves the quality assessment.The calculated strength of coded image details at receiver is compared with the received RR parameter for the estimation of distortions, blockiness and blurriness. The accuracy of the designed RR DIQAM algorithm is proved by correlating the estimated objective values of the distortions with the LIVE image database2 subjective DMOS values of blockiness and blurriness. The results obtained by the proposed technique are well matched with the LIVE database values and provide 94-96% correlation

Image Quality Meter Using Compression

This paper proposed a new technique to compressed image blockiness/blurriness in frequency domain through edge detection method by applying Fourier transform. In image processing, boundaries are characterized by edges and thus, edges are the problems of fundamental importance. The edges have to be identified and computed thoroughly in order to retrieve the complete illustration of the image. Our novel edge detection scheme for blockiness and blurriness shows improvement of 60 and 100 blocks for high frequency components respectively than any other detection technique

Physicochemical and rheological study of orange pulp fortified cookies

Abstract This study was aimed to find out the effects of supplementation of wheat cookies with orange pulp fiber at 0%. 3%, 6%, 9% levels. The orange pulp fiber used was contained of protein 7.40%, fat 2.19%, moisture 8.10%, fiber 7.31%, ash 2.55%, water holding capacity 4.71%, oil holding capacity 1.91% and pH 3.81. The rheological analysis showed that with the addition of orange pulp fiber, water absorption and mixing tolerance index were increased while dough development time and dough stability were decreased among the treatments. Variance analysis of the formulations used for making cookies showed significant differences (P≤0.05) for appearance, flavor, texture, and overall acceptance. So it can be concluded that orange pulp fiber may be used as a food additives to gain nutritional and health benefits

Increasing the Efficiency of Smart Patient Room Using Internet of Things (IoT)

Researchers are developing more applications based on Internet of Things (IoT) in healthcare services. Proper health care services are the major requirements now a days due to constant increase in the population. These days a major problem faced by critical patients or victims of any kind of accident is of receiving treatment on time, which in some cases becomes a huge problem specially when hospitals refused to take patients in their ER due to unavailability of beds. Travel time consumed while moving patients from one hospital to another at times results in death. The modern technology is able to manage the needs by using IoT technologies that can connect smart objects together. This paper provides the solution to the users who want prompt and timely medical treatment for their loved ones, especially in case of emergency. Smart IoT based patient room is a system that enables paramedic staff /user to get the information about beds availability in the Emergency Room in real time. The smart patient room is different from other emergency rooms by allowing the user to see the status of bed inside emergency room from anywhere in the world through internet using Raspberry Pi. This application also helps hospital staff to monitor basic vitals of patient including the body temperature, heartbeat, etc. and helps user to save life by saving time searching for hospitals where bed is available for treatment in Emergency room. Smart IoT based emergency room consist of android mobile device, cloud network, wireless means of communication, hardware having Wi-Fi module, that sends the data to the cloud which indicates the users about the status of bed. This system deploys pressure sensors on beds that will automatically sense pressure and indicates users regarding the status of the bed inside the emergency room of the desired hospital

Active Machine Learning for Heterogeneity Activity Recognition Through Smartwatch Sensors

Smartwatches with cutting-edge sensors are becoming commonplace in our daily lives. Despite their widespread use, it can be challenging to interpret accelerometer and gyroscope data efficiently for Human Activity Recognition (HAR). This study explores active learning integrated with machine learning, intending to maximize the use of smartwatch technology across a range of applications. The previous research on the HAR lacks promising performance, which could make it difficult to make highly accurate recognition. This paper proposes a novel approach to predict human activity from the Heterogeneity Human Activity Recognition (HHAR) dataset that integrates active learning with machine learning models: Random Forest (RF), Extreme Gradient Boosting (XGBoost), K-nearest Neighbors (KNN), Decision Tree (DT), Gradient Boosting (GB) and Light Gradient Boosting Machine (LGBM) classifier to predict heterogeneous activities accurately. We evaluated our approach to these models on the HHAR dataset that was generated using an accelerometer and gyroscope of smartwatches. The experiments are evaluated on 3 iterations where the results demonstrated that the proposed approach predicts human activities with the highest F1-Score of 99.99%. The results indicate that this approach is the most accurate and effective compared to the conventional approaches and baseline

Hybrid precoding design for secure smart-grid enabled MIMO wireless communications in Industry 5.0

Secure and reliable wireless communication systems are essential for the successful integration of smart grid-enabled wireless communications and Industry 5.0 applications. This work proposes a hybrid precoding design for secure smart-grid multiple-input multiple-output (MIMO) wireless communications in Industry 5.0 to maximize the average secrecy capacity of the system while minimizing user outages crucial for maintaining the security and privacy of sensitive data in critical infrastructure. To achieve this, we develop a mathematical model that considers the impact of various system parameters, such as eavesdropping and interference caused by the active eavesdropper and noise, on the secrecy capacity. To find an efficient solution to the optimization problem, we propose an algorithm that decouples the original optimization problem into a series of subproblems and uses iterative techniques to find the optimal values, thereby improving computational efficiency. The hybrid precoding scheme is an effective technique for optimizing the design parameters of MIMO-based secure wireless communication systems. Our proposed approach provides a practical solution for achieving this optimization. Our numerical results demonstrate that our proposed scheme outperforms traditional benchmark schemes, maximizing the average secrecy capacity while minimizing user outages. Our work highlights the importance of secure wireless communication systems in Industry 5.0 and smart grid applications. The proposed approach provides an efficient method for designing secure wireless communication systems that can effectively address the unique challenges posed by these critical infrastructure systems