Daily Archives: September 24, 2026

Authors: Hosea, J. O., Ogbonna, B. O.

Abstract: One of the problems associated with wind energy is fluctuations in wind speed, which cause unstable and inefficient operation of variable-speed wind turbines, especially above the rated operating range where blade pitch control is necessary to adjust aerodynamic power and maintain generator speed within the rated range. This study investigates the effect of a Fuzzy Logic Controller (FLC) on pitch-angle regulation compared with the dynamic performance of a proportional-integral (PI) controller. The same wind turbine model, generator reference speed, wind-speed profile, and operating conditions were used for both controllers. The proposed Mamdani-type FLC is based on generator-speed error and its rate of change, using scaling, saturation, fuzzification, rule-based inference, and defuzzification to derive an appropriate pitch-angle command. The FLC adjusts the pitch command according to changing turbine operating conditions, enabling control of the aerodynamic power coefficient, aerodynamic torque, generator speed, and output power under wind-speed disturbances. Controller performance was evaluated using rise time, settling time, percentage overshoot, steady-state error, RMSE, IAE, ISE, ITAE, and ITSE. The FLC increased rise time from 1.7677 s to 3.2920 s, while reducing settling time from 17.2793 s to 5.5018 s and overshoot from 7.662% to 0.880%. Furthermore, the FLC reduced steady-state error, IAE, ITAE, and ITSE by 19.20%, 20.76%, 71.56%, and 61.97%, respectively. The PI controller produced lower RMSE and ISE. Overall, the results showed that FLC-based pitch-angle control provides better damping, disturbance rejection, steady-state accuracy, and long-term tracking performance under variable wind operation, although its initial response is slower than conventional PI control.

Published by: vikaspatanker

Authors: Mr. Murari Kumar, Dr. Deepti shinghal

Abstract: In modern electronic systems, analog-to-digital converters (ADCs) play a critical role in bridging the analog and digital domains. At the heart of ADC architecture lies the comparator, a fundamental circuit responsible for precise and fast voltage comparison. With the rapid growth of portable and battery-powered devices, there is an increasing need for high-speed and low-power CMOS comparators to meet performance and energy efficiency demands. This review paper presents a comprehensive analysis of various design techniques and topologies used to achieve high speed and low power consumption in CMOS comparators. Key performance parameters such as propagation delay, power consumption, input offset voltage, and resolution are discussed in detail. Additionally, the paper highlights the trade-offs involved in comparator design and explores recent advancements, including dynamic comparators, adaptive biasing, and low-voltage operation techniques. The role of high-performance comparators in different ADC architectures—such as flash, SAR, and pipeline ADCs—is also reviewed, providing insights into future research directions in this critical field of analog circuit design.

DOI: http://doi.org/10.5281/zenodo.22935146

Published by: vikaspatanker

Authors: Professor Vikram Singh, Research Scholar Naresh Kumar

Abstract: The rapid generation of deepfake videos poses significant challenges to real-time detection systems, which often encounter a wide range of quality variations and novel manipulation techniques. In order to deal with the challenges faced we propose a hybrid InceptionV3-LSTM network that encapsulates spatial feature extraction and action sequence over time. This architecture employs InceptionV3 pretrained on ImageNet for modeling frame-level artifacts, then stacks a bidirectional LSTM to jointly learn the temporal problem from multiple sequences. The model displays robustness under high compression and when tested on low compression, it shows a decline of just 2.5% in accuracy which implies that the model is sensitive. According to temporal analysis, LSTM proves capable of detecting manipulations as illustrated due to reconstruction loss variance being higher by 3.2× of low-fidelity deepfakes. The deepfake inference data under tool-based evaluation shows this detection. The InceptionV3-LSTM’s full-fledged version achieves 150 ms latency and 96% accuracy while real-time performance granted 20 ms/frame latency using MobileNet-LSTM with no accuracy loss (96%). The solution presented in this study can be deployed, balancing detection accuracy and computational efficiency. However, limitations still exist for novel manipulation techniques and multi-modal integration could be an avenue for future work. The objective of this experimental study is to achieve the enhanced detection accuracy using separate training and testing datasets of deepfake videos which can help get a better security performance and robustness. By integrating these advances, the application allows users to check the authenticity of people and deep fake videos. A framework for real-time detection of visual deepfake videos analyzes pixel-level artifacts including blending boundaries, double edges, eyes, ears, nose, head pose inconsistencies and other possible parameters sub-pixel noise patterns unique to manipulation videos.

DOI: http://doi.org/10.5281/zenodo.22934344

Published by: vikaspatanker

Authors: Assistant Professor Hakam Chand

Abstract: The paper analyses infrastructure and market connectivity as joint determinants of climate-resilient horticulture in Kangra district, Himachal Pradesh. It uses a secondary-data case-study approach and the latest consistent state horticulture series available in the supplied publications, covering 2011-12 to 2024-25. Although horticulture output expanded during the period, climate variability, fragmented production, seasonal irrigation, post-harvest losses and costly transport threaten the stability of smallholder returns. Kangra’s traditional kuhl systems, road network, urban centres and crop diversity provide a strong base, but infrastructure must function as an integrated service chain. The study develops an Infrastructure Readiness Matrix covering water, planting material, extension, protection, aggregation, grading, cold chain, processing, roads, digital information and finance. It also proposes indicators for reliability, utilisation and inclusion. The analysis suggests that water harvesting and micro-irrigation should be combined with crop planning; pack houses should be located only where verified throughput exists; and producer aggregation should precede capital-intensive cold-chain investment. Climate resilience requires diversified crops and seasons, quality planting material, local weather and pest advisories, and multiple market channels. A phased investment and monitoring framework is recommended for Kangra.

DOI: http://doi.org/10.5281/zenodo.22933192

Published by: vikaspatanker

Authors: Parav Sharma, Rajesh Chauhan, Akshay Bhardwaj

Abstract: In today's complex cloud-native microservice architectures, the traditional rule-based monitoring approaches are insufficient for system reliability. The solution to this is the integration of Artificial Intelligence (AI) into DevOps, often known as AIOps. This paper introduces an AI-powered observability framework that combines the LSTM (Long Short-Term Memory) autoencoder with the Prometheus-Grafana observability stack for anomaly detection in Kubernetes-based microservice environments. The system collects real-time CPU utilization metrics from different microservices, trains an LSTM model on metric records, and detects anomalies using reconstruction error thresholding. Under controlled CPU stress injection, the characteristic scale of reconstruction error, measured as the mean plus two standard deviations of each condition's own reconstruction errors, is approximately 27 times higher than under baseline conditions, indicating that the model's reconstruction error responds strongly to abnormal workload patterns. Because a single training run may not be representative, the model is retrained ten times on a 6,407-timestep dataset; the reconstruction-error threshold is 0.000676 +/- 0.000038 and the anomaly rate 4.38% +/- 0.26% (mean +/- SD), a coefficient of variation of 5.6%, indicating that the result is reproducible rather than an artifact of one random initialization. The trained model is then deployed as a continuous detector that scores live metrics every 60 seconds; in a controlled test it flagged all 17 readings taken while an injected workload was active and returned to normal after its removal. The LSTM-based model detects anomalies without relying on fixed alert boundaries, offering a more adaptive alternative to static threshold approaches and enabling proactive detection of system abnormalities in cloud-native DevOps environments.

DOI: http://doi.org/10.5281/zenodo.22932622

Published by: vikaspatanker