Arunai Engineering College

ABSTRACT Object detection in road‐vehicle images presents significant challenges due to variations in view angle, weather conditions, shooting height, noise, low contrast, geometric distortions, and atmospheric effects. The research proposes a novel methodology integrating Deep Learning and optimization techniques to enhance object detection performance. To address image quality issues, a Gaussian Blur with a Bilateral filter is employed for noise reduction and contrast enhancement. For feature extraction, a Lightweight Tree Multiplier–based Discrete Daubechies Wavelet Transform Convolutional Network is introduced, utilizing an advanced wavelet transform approach to capture both spatial and spectral features effectively. Feature selection is optimized using Adaptive Quantum‐Informed Recursive Multiscale Feature Selection, incorporating Quantum‐Informed Recursive Optimization to retain the most relevant features while reducing redundancy. For object detection, the extracted features are processed through the Improved Lightweight Harris Hawks YOLOv11 Network, for efficient and high‐speed object detection. Improved Harris Hawks Optimization is also applied to optimize the loss function, enhancing detection accuracy. The outcomes of the implementation reached a precision rate of 99.8% and an accuracy rate of 99.7%. The model that proposed intends to be a powerful tool for the detection of road vehicles in difficult images, giving short processing time together with high‐detection efficiency and reliability.

Tiruvannamalai district, located in the northern part of Tamil Nadu, is a landscape with an immense historical background both archaeologically and culturally. Specifically, Periyakolappadi village in Chengam Taluk falls under the ancient territory ruled by the Sangam-era chieftain Nannan Sei Nannan and serves as a literal storehouse of Pallava-era hero stones (Nadu-kal). This research paper provides a detailed archaeological analysis of: The etymology of the name "Periyakolappadi." Geographical landmarks mentioned in the Sangam literary work Malaipadukadam. Hero stones dating back to the reigns of Pallava kings Mahendravarman, Eswaravarman, and Nandivarman. Unique local rock names characteristic of the region. Furthermore, the study uses statistical data to explain the continuity of agriculture from the Sangam period to the present, as well as modern economic shifts like the impact of flower cultivation. Highlighting how natural resources and historical monuments are facing destruction due to commercial encroachment, this research emphasizes the urgent need for their preservation. தமிழகத்தின் வடபகுதியில் அமைந்துள்ள திருவண்ணாமலை மாவட்டம், தொல்லியல் ரீதியாகவும் பண்பாட்டு ரீதியாகவும் அளப்பரிய வரலாற்றுப் பின்னணியைக் கொண்ட ஒரு நிலப்பரப்பாகும். குறிப்பாக, செங்கம் வட்டத்தில் அமைந்துள்ள பெரியகோளாப்பாடி கிராமம், சங்க காலத் தலைவன் நன்னன் சேய் நன்னனின் ஆட்சிப் புலத்திற்கு உட்பட்ட பகுதியாகவும், பல்லவர் காலத்து நடுகற்களின் களஞ்சியமாகவும் திகழ்கிறது. இவ்வாய்வறிக்கை, பெரியகோளாப்பாடியின் ஊர்ப்பெயர் தோற்றம், சங்க இலக்கியமான மலைபடுகடாம் குறிப்பிடும் புவியியல் அடையாளங்கள், பல்லவ மன்னர்களான மகேந்திரவர்மன், ஈஸ்வரவர்மன் மற்றும் நந்திவர்மன் காலத்து நடுகற்கள், மற்றும் இப்பகுதியின் தனித்துவமான பாறைப் பெயர்கள் ஆகியவற்றைத் தொல்லியல் நோக்கில் விரிவாக ஆராய்கிறது. மேலும், சங்க காலம் முதல் இன்று வரை இப்பகுதியில் தொடர்ந்து வரும் உழவுத் தொழில் மற்றும் தற்காலப் பொருளாதார மாற்றமான மலர்ச் சாகுபடியின் தாக்கம் குறித்தும் புள்ளிவிவரங்களுடன் விளக்குகிறது. இயற்கை வளங்கள் மற்றும் வரலாற்றுச் சின்னங்கள் வணிக ரீதியான ஆக்கிரமிப்புகளால் அழியும் நிலையில் உள்ளதைச் சுட்டிக்காட்டி, அவற்றைப் பாதுகாக்க வேண்டியதன் அவசியத்தையும் இந்த ஆய்வு வலியுறுத்துகிறது.

Landslides are one of the most frequent and destructive natural disasters, posing severe threats to human lives, infrastructure, and the environment. Accurate prediction and classification of landslide occurrences using remote sensing and aerial imagery have become vital for disaster management and mitigation. However, existing deep learning models often fail to capture fine-grained spatial variations and multi-class distinctions leads to inaccurate segmentation and misclassification of landslide-affected regions. To overcome these challenges, a novel penta classification of landslide via deep learning based SegNet and RegNet (PENTA-SERE) model has been proposed. Initially, the 180 aerial images from INRIA Aerial Image Labelling Dataset were collected for landslides classification. The input aerial images are denoised using adaptive bilateral filter (ABF) to reduce the noise artifacts. The denoised images are fed into the SegNet to identify and separate the regions corresponding to different landslide areas. Then, the segmented regions are extracted using RegNet for efficient feature representation and enhanced discrimination among different types of landslides. Finally, the Deep Belief Network (DBN) is used for penta classification such as Rock falls, Debris flaws, Creep, Solifluction and earth slides. The performance analysis of the proposed PENTA-SERE model is evaluated in terms of accuracy, specificity, precision, recall and F1score based on the INRIA Aerial Image Labelling Dataset. The proposed PENTA-SERE model achieves an overall accuracy of 99.29%, and F1 Score of 96.62% based on the gathered dataset. The Proposed PENTA-SERE model improves the overall accuracy by 17.41, 27.45, 6.83, and 2.44% better than Bidirectional Encoder Representations from Transformers (BERT), Support Vector Machine (SVM), Deep convolutional neural network (DCNN), InceptionV3 respectively.

This paper proposes a wideband millimeter wave (mmW) antenna based on a frequency selective surface (FSS) for gain improvement for 5 G cellular communication. The presented antenna is a planar inverted cone antenna (PICA) which resonates at 28 GHz in the FR2 band and has a footprint of $11 \times 12 \times 1.6 \mathrm{~mm}^{3}$. The antenna gain is improved by integrating an FSS superstrate implemented with $6 \times 6$ unit elements antenna that used as a reflecting surface. Proposed antenna has a impedance bandwidth -10 dB at 12.6 GHz $(25.2-37.8 \mathrm{GHz})$ and a peak gain of 7.73 dB at 28 GHz. Due to FSS, the proposed antenna gain is improved by about 4.02 dB. The far-field patterns in H and E planes are at 90° to the antenna boresight axis. A prototype is fabricated to verify the simulation results. Due to compactness, wideband, and high gain, the proposed antenna may be a great choice for 5 G communication.