Comprehensive Analysis of UFMC with OFDM and FBMC

The Braille script to voice conversion explores a wearable messaging device that uses embedded technology for visually impaired person. The main objective of this project is to establish a means of communication for specially abled people. It has a special keyboard that is integrated with the braille for the use of blind. It converts the message that the person wants to communicate to other person into a voice and text, so that he understands what the blind and dumb intend to say. It is the first of its kind that would be of great help to the specially abled, as blindness is considered to be the highest of all other disabilities. One can build communication between blind and dumb people and the community by using this Braille system. Moreover, the system is implemented on the open source Arduino platform which makes it more efficient and also cost effective.

Cognitive radio networks have emerged as a promising solution to the growing problem of spectrum scarcity and inefficient spectrum utilization. By enabling secondary users to opportunistically access underutilized frequency bands while avoiding harmful interference to primary users, cognitive radio networks offer the potential for more efficient and dynamic spectrum allocation. In this paper, we propose an intelligent approach to dynamic spectrum access using fuzzy logic in cognitive radio networks. The key challenge in cognitive radio networks lies in spectrum sensing, decision-making, and channel selection, where uncertainties and variations in the radio frequency spectrum are prevalent. To address these challenges, we employ fuzzy logic as a powerful tool to handle the inherent imprecision and ambiguity in the decision-making process. Fuzzy-based spectrum sensing algorithms allow for robust detection of spectrum opportunities by considering the “fuzziness” in the received signal strength and noise conditions.

The automatic plant monitoring system has recently attracted tremendous interest due to the potential application in emerging technology. More importantly, this technique is used to enhance the performance of existing techniques or to develop and design new techniques for the growth of plants. The plant monitoring system is helpful for watering the plants and to monitor few parameters for growth of plants. This system is very used in few areas like nursery farms and in agriculture. In this system a mechanism is established to find the moisture content in the soil with the help of soil moisture sensor and depending upon the condition of the sensor the water is controlled. Another important parameter is by capturing the images of the plant by using Arduino interfaced camera, and processing the image by using image processing to analyze and determine the disease effected by creating the feature vector database and retrieval of images from database similar to query image. This helps in providing the appropriate amount of water for plants so reduces some situations like mud cracks, water logging. This helps in irrigating the field even during night time, so does not require the farmer to switch ON the motor manually.

This paper aims to predict malnutrition in newborn babies using various machine learning techniques. Malnutrition is characterized by the insufficient acquisition of fat and muscle mass during intrauterine growth. It is primarily caused by poor maternal nutrition and placental insufficiency, resulting in increased neonatal morbidity and mortality worldwide. In this study, we calculate the Z-score of newborns, taking into account factors such as age in months, weight, height, and sex, to determine the presence of malnutrition. The dataset utilized for this project is obtained from UNICEF for network training. The dataset is divided into two parts: one for validation and another for testing. We calculate WAZ (underweight) and LAZ (stunting) and train the models to detect neonatal malnutrition. Various machine learning models, including SVM, KNN, logistic regression, Naïve Bayes, and a two-layer neural network, are employed to identify malnutrition in children. Among these models, logistic regression demonstrates superior accuracy compared to the other algorithms.

This research addresses the challenges of high Peak-to-Average Power Ratio (PAPR), sideband leakage, and spectrum efficiency in 5G wireless networks. We compare Universal Filtered Multi-Carrier (UFMC), Scrambled UFMC (S-UFMC), and Orthogonal Frequency Division Multiplexing (OFDM) techniques using Amplitude Phase Shift Keying (APSK) modulation. Our findings show that APSK modulation significantly reduces PAPR compared to traditional methods. Integrating Particle Swarm Optimization (PSO) with Partial Transmit Sequences (PTS)-OFDM and S-UFMC minimizes PAPR and computational complexity. Results demonstrate that S-UFMC with PSO optimization achieves superior performance, offering lower PAPR, reduced complexity, and enhanced spectral efficiency, positioning it as a promising 5G waveform. This research highlights the potential of advanced waveform designs to improve 5G communication systems. UFMC demonstrated improved spectral efficiency with sidelobe levels reaching as low as 0.2, indicating efficient spectrum utilization. The research achieved a PAPR of 7.393 dB for 16-APSK with 512 subcarriers and 7.414 dB for 1024 subcarriers after optimization. The PSO algorithm significantly reduced PAPR values, with the Scrambled UFMC system outperforming PTS-OFDM and standard UFMC in terms of PAPR.