A street lighting system is a very essential part of the highways and streets of a smart city. Managing power consumption and maintenance of a street light system will be a challenging task in huge countries. The proposed work is mainly focused on the minimization of power consumption in the implementation of a smart street lighting system. Also, use a mobile application for setting up the brightness levels of the lamps in an encrypted form so that an unauthorized person will not be able to modify the settings. In the existing streetlight system, wireless sensors are installed to control and monitor the streetlamps. In the proposed system, using an nRF24L01 radio transceiver module, a secured communication link is established to operate the streetlights depending on the ambient weather conditions, movement of humans, vehicles and any other objects. A failsafe mechanism is implemented in the modules for conventional lamp operation in the case of module failures. Light-dependent resistor (LDR) is used to determine the ambient brightness levels to automatically turn on/off the streetlights based on weather conditions and lighting on roads. Using smartphones, we access and control the brightness information from the master node at which the nRF24L01 radio transceiver module is installed, and the same information is relayed to all the slave nodes. The results show that we could effectively monitor and control the brightness of streetlights in a secure way and there is a significant amount of power savings. The proposed system saves the average powers of 53.45%, 44.76%, 39.39%, and 32.25% respectively for 10%, 20%, 30%, and 50% idle mode brightness compared to the state-of-the-art techniques present in the literature.

Background: Yogic breathing also called as “Pranayama” is practiced with inhalation (Pooraka), holding the breath for some time (Kumbhaka) and then exhalation (Rechaka). The effective methods of yogic breathing keep oneself healthy and also improves immunity power. The yogic breathing can be practiced irrespective of one's age and gender and even in the office which helps to reduce the stress. To get the best results through yoga, a person has to follow certain timings and sit in a correct posture. Although many devices are existing in the market to monitor heart rate, posture and breathing during physical activity, there is a need of a device which is simple, cheap, and easy to use without an additional requirement of a smartphone. Moreover, the proposed device is able to evaluate the breathing data by transmitting it to a webpage through a Wi-Fi hotspot of the Microcontroller. Methods: The developed device has two subsystems: (i) A wrist subsystem to measure the heart rate, visual aid of breathing and vibration feedback for kapalabhati. (ii) A waist subsystem to monitor the posture with help of flex sensor and the results are displayed on the display of the wrist device. It also provides vibration feedback. The inertial measurement unit is used for breath detection. The subsystems are communicated through SPI communication. The breathing data are transmitted to a webpage through a Wi-Fi hotspot of the microcontroller. Results: The various yogic breathing and normal breathing exercises are tested on different normal subjects using the developed device and analyzed. The heart rate and beats per minute are evaluated. The heart rate sensor is validated using a standard medical device and it is observed that there was a 97.4% accuracy. Conclusion: The results show that the device is able to accurately monitor different kinds of breathing and additionally provide heart rate and posture information while performing the breathing exercises.

6G systems require highly adaptive beamforming techniques to cope with rapid channel variations and strict latency constraints. The advent of Intelligent Reflecting Surfaces (IRS) has emerged as a transformative paradigm in the evolution of 6G wireless communication systems, enabling programmable radio environments through passive beam control. Driven by the potentials of IRS-assisted communication strategies, this work proposes a novel hybrid beamforming architecture for IRS-assisted Multiple-Input Multiple-Output (MIMO) systems. The architecture seamlessly integrates constrained optimization techniques—specifically Minimum Variance Distortionless Response (MVDR) and Linearly Constrained Minimum Variance (LCMV)—with advanced deep recurrent learning models, including Gated Recurrent Units (GRU) and Recurrent Neural Networks (RNN). The proposed approach enables precise signal alignment with improved spatial selectivity, effective interference suppression, and enhanced spectral efficiency. Monte Carlo simulations validate the efficient performance of the GRU-based MVDR beamforming scheme, achieving a spectral efficiency of ~5 bps/Hz observed at lower signal-to-noise ratios (SNRs), outperforming other hybrid approaches. Furthermore, Average Bit Error Probability (ABEP) analysis employing MVDR framework investigates the impact of transmit correlation and phase rotation in correlated Rayleigh fading scenarios. MVDR beamforming consistently boosts ABEP performance across all the transmit correlations by minimizing symbol misalignment during signal transmission. Additionally, the GRU-integrated MVDR approach achieves target BER levels at notably lower transmit power of ~8 dBm and enhancements of ~6 dBm under imperfect Channel State Information (CSI) scenarios in comparison with other schemes.

B5G networks are envisioned to meet higher spectral efficiencies through advanced transmission technologies. Consequently, it is important to comprehend the typical propagation characteristics of wireless communication medium that experience multipath effects, fading, shadowing, and nonlinear distortions. Most of these challenging issues arise under non-line of sight conditions concerning the propagation medium, which deteriorates the quality of the signal received due to uncontrollable channel variations. The recent advances in Intelligent Reflecting Surface (IRS), enable a dynamically alterable wireless environment to achieve flexible reconfigurable channels. The deployment of IRS can evidently bypass the obstacles through smart reflections and shall mitigate channel fading impairments. Motivated by the potentials of the IRS, this paper investigates the performance of IRS-assisted multi-input-multi-output (MIMO) systems to analyze the effect of nonlinear distortions and system adaptation over IID and non-IID Gaussian channel variations. The simulations show the distortion levels with the help of power spectral density for correlated or uncorrelated channel conditions. Furthermore, Monte Carlo simulations are performed to understand the behavior of the wireless system through the analysis of bit error rate (BER) vs signal-to-noise ratio (SNR), Ergodic Channel capacity vs SNR in the presence of IID-Gaussian/ non-IID Gaussian and in-band nonlinear distortions. Additionally, in realistic channel conditions, it is observed that the effect of increasing the number of IRS elements increases the BER performance of ~8dB in both IID and non-IID Gaussian variations. Finally, improvement in the performance of ergodic capacity of 5 bps/Hz has been observed at a fixed SNR of 5dB.

The proposed research study investigates the relationship between Bone Mineral Density (BMD) values extracted from computed tomography (CT) scans, with a primary focus on their significance in vertebral segmentation. The study includes 25 subjects spanning five age groups; the study reveals a continuous reduction in mean Hounsfield Unit (HU) values with aging, validated by statistical analysis that emphasizes the importance of these findings across different vertebral levels. The data exposes a steady decline in mean BMD values with age, ranging from 175.05 HU in the 40s to 51.8 HU in the over-80s age group, exhibiting statistically significant differences (p<0.05). Subgroup analysis provides granular insights into age-related variations at specific lumbar vertebrae levels by contributing valuable information to the complexities of bone health assessment. The research connects mathematics and medical imaging to improve our understanding of vertebral characteristics by introducing the Radon Transform as a fundamental tool in CT image reconstruction. These findings include both p and mean values which have the potential to revolutionize the field of bone health assessment by affecting public health policy and opening the way for more personalized and successful treatment options. This includes the earlier detection of conditions like osteoporosis and improvements in patient care.

This study proposes a comprehensive framework for the segmentation and identification of vertebrae in CT scans using a combination of deep learning and traditional machine learning techniques. The Res U-Net architecture is employed to achieve a high model accuracy of 93.62% on the VerSe’20 dataset demonstrating effective performance in segmenting lumbar and thoracic vertebrae. Feature extraction is enhanced through the application of Otsu’s method which effectively distinguishes the vertebrae from the surrounding tissue. The proposed method achieves a Dice Similarity Coefficient (DSC) of 87.10% ± 3.72%, showcasing its competitive performance against other segmentation techniques. By accurately extracting vertebral features this framework assists medical professionals in precise preoperative planning, allowing for the identification and marking of critical anatomical features required during spinal fusion procedures. This integrated approach not only addresses the challenges of vertebrae segmentation but also offers a scalable and efficient solution for analyzing large-scale medical imaging datasets with the potential to significantly improve clinical workflows and patient outcomes.