Road Deterioration detection A Machine Learning-Based System for Automated Pavement Crack Identification and Analysis

Road surfaces may deteriorate over time because of a number of external factors such as heavy traffic, unfavourable weather, and poor design. These flaws, which may include potholes, fissures, and uneven surfaces, can pose significant safety threats to both vehicles and pedestrians. This research aims to develop and evaluate an automated system for detecting and analyzing cracks in pavements based on machine learning. The research explores the utilisation of object detection techniques to identify and categorize different types of pavement cracks. Additionally, the proposed work investigates several approaches to integrate the outcome system with existing pavement management systems to enhance road maintenance and sustainability. The research focuses on identifying reliable data sources, creating accurate and effective object detection algorithms for pavement crack detection, classifying various types of cracks, and assessing their severity and extent. The research objectives include gathering reliable datasets, developing a precise and effective object detection algorithm, classifying different types of pavement cracks, and determining the severity and extent of the cracks. The study collected pavement crack images from various sources, including publicly available databases and images captured using mobile devices. Multiple object detection models, such as YOLOv5, YOLOv8, and CenterNet were trained and tested using the collected dataset. The proposed approaches were evaluated using different performance metrics, The achieved results indicated that the YOLOv5 model outperformed CenterNet by a significant margin

FQHE interferometers in strong tunneling regime. The role of compactness of edge fields

We consider multiple-point tunneling in the interferometers formed between edges of electron liquids with in general different filling factors in the regime of the Fractional Quantum Hall effect (FQHE). We derive an effective matrix Caldeira-Leggett models for the multiple tunneling contacts connected by the chiral single-mode FQHE edges. It is shown that the compactness of the Wen- Fr\"ohlich chiral boson fields describing the FQHE edge modes plays a crucial role in eliminating the spurious non-locality of the electron transport properties of the FQHE interferometers arising in the regime of strong tunneling.Comment: 5 page

CT texture analysis: a potential tool for prediction of survival in patients with metastatic clear cell carcinoma treated with sunitinib

BACKGROUND: To assess CT texture based quantitative imaging biomarkers in the prediction of progression free survival (PFS) and overall survival (OS) in patients with clear cell renal cell carcinoma undergoing treatment with Sunitinib. METHODS: In this retrospective study, measurable lesions of 40 patients were selected based on RECIST criteria on standard contrast enhanced CT before and 2 months after treatment with Sunitinib. CT Texture analysis was performed using TexRAD research software (TexRAD Ltd, Cambridge, UK). Using a Cox regression model, correlation of texture parameters with measured time to progression and overall survival were assessed. Evaluation of combined International Metastatic Renal-Cell Carcinoma Database Consortium Model (IMDC) score with texture parameters was also performed. RESULTS: Size normalized standard deviation (nSD) alone at baseline and follow-up after treatment was a predictor of OS (Hazard ratio (HR) = 0.01 and 0.02; 95% confidence intervals (CI): 0.00 – 0.29 and 0.00 – 0.39; p = 0.01 and 0.01). Entropy following treatment and entropy change before and after treatment were both significant predictors of OS (HR = 2.68 and 87.77; 95% CI = 1.14 – 6.29 and 1.26 – 6115.69; p = 0.02 and p = 0.04). nSD was also a predictor of PFS at baseline and follow-up (HR = 0.01 and 0.01: 95% CI: 0.00 – 0.31 and 0.001 – 0.22; p = 0.01 and p = 0.003). When nSD at baseline or at follow-up was combined with IMDC, it improved the association with OS and PFS compared to IMDC alone. CONCLUSION: Size normalized standard deviation from CT at baseline and follow-up scans is correlated with OS and PFS in clear cell renal cell carcinoma treated with Sunitinib

Ionospheric Delay Estimation during Ionospheric Depletion Events for Single Frequency Users of IRNSS

The IRNSS (Indian Regional Navigation System) navigation users estimate their position by using a receiver which receives the navigation signal from the IRNSS satellites which will be operating on L5 (1176.45MHz) and S (2492.028MHz) frequencies. There are 3 types of IRNSS users: 1) Dual frequency (L5 and S), 2) Single frequency (L5) and 3) Single frequency (S). The signal from the satellites before reaching the user receiver passes through the ionospheric layer of the atmosphere and suffers a delay. The delay in the signal introduces error in the position computed by the user. The dual frequency users of IRNSS correct the ionospheric error by taking advantage of the dispersive nature of ionosphere. On the other hand, single frequency user requisite an algorithm for computing the ionospheric delay along his line of sight. In IRNSS, the ionospheric error corrections for single frequency (L5 or S) users will be provided by two ways: 1) Grid based and 2) Coefficient based. These corrections may not be valid when an abnormal behavior of ionosphere occurs due to geomagnetic storm, solar coronal mass ejections or any other disturbances in the earth’s magnetic field. The abnormal behavior may result in increase or decrease of the TEC (Total Electron Content) in the ionosphere. Ionospheric depletion event is one such, where there is a sudden drop in TEC forming plasma bubbles travelling through the ionosphere. A user, whose line-of-sight when crosses such a TEC depleted area of ionosphere suffers from an extra error due to depletion. The amount of error is proportional to the depth of depletion. This error in the range ultimately results in the user position accuracy degradation. In this paper a novel algorithm has been designed and developed which will estimate the ionospheric delay, thereby providing ionospheric corrections even at times of depletions. The developed technique in turn provides achievable position accuracy during times of ionospheric depletions. The developed technique has been tested with GAGAN (GPS Aided GEO Augmented Navigation) INRES (Indian Reference Stations) data and IRNSS IRIMS (IRNSS Range and Integrity Monitoring Stations) data having deep ionospheric depletions. The fully tested and validated ionospheric delay estimation algorithm is proposed to be implemented in IRNSS single frequency (L5/S) receivers. Keywords: IRNSS Single Frequency User, Ionospheric Error, Ionospheric Depletion, Ionospheric Delay Estimation, Kalman Filte

Modeling of IRNSS System Time-Offset with Respect to other GNSS

The IRNSS System Time started at 00:00 UT on Sunday August 22nd 1999 (midnight between August 21st and 22nd). At the start epoch, IRNSS system time was ahead of UTC by 13 leap seconds. (i.e. IRNSS time, August 22nd 1999, 00:00:00 corresponds to UTC time August 21st 1999,23:59:47). IRNSS time is a continuous time without leap second corrections determined by the IRNSS System Precise Timing Facility (IRNPT) with an ensemble of Caesium and Hydrogen maser standard atomic clocks.Combining of multi GNSS satellites provides very significant advantages a) paves the way for computing the user position with increased number of satellites. b) Reduced horizontal and vertical Dilution of Precision (DOP) factors. And c) Decreased occupation time which means faster positioning results.This paper presents the 1.IRNSS time offset generation with respect to other GNSS timescales such as GPS, GLONASS system and traceability to UTC,UTC(NPLI)/UTC(K) 2.Validation of predicted time offsets with actual offsets.3.The IRNSS time offsets are derived from GNSS navigation message using UTC offsets to validate the predicted IRNSS time offsets. IRNSS times offset from GNSS are broadcasted in the form of coefficients in one of the IRNSS navigation messages. This broadcast message also allows the user to recover UTC and UTC (NPLI)/UTC(K) time for precise timings. Keywords: IRNSS, IRNSS Time offsets, IRNWT, UTC, UTC (NPLI) and GNS

MR texture analysis in differentiating renal cell carcinoma from Lipid-poor angiomyolipoma and oncocytoma

OBJECTIVES: To assess the utility of Magnetic resonance texture analysis (MRTA) in differentiating renal cell carcinoma (RCC) from lipid-poor angiomyolipoma (lpAML) and oncocytoma. METHODS: After ethical approval, 42 patients with 54 masses (34 RCC, 14 lpAML and six oncocytomas) who underwent MRI on a 1.5 T scanner (Avanto, Siemens, Erlangen, Germany) between January 2011 and December 2012 were retrospectively included in the study. MRTA was performed on the TexRAD research software (Feedback Plc., Cambridge, UK) using free-hand polygonal region of interest (ROI) drawn on the maximum cross-sectional area of the tumor to generate six first-order statistical parameters. The Mann-Whitney U test was used to look for any statically significant difference. The receiver operating characteristic (ROC) curve analysis was done to select the parameter with the highest class separation capacity [area under the curve (AUC)] for each MRI sequence. RESULTS: Several texture parameters on MRI showed high class separation capacity (AUC > 0.8) in differentiating RCC from lpAML and oncocytoma. The best performing parameter in differentiating RCC from lpAML was mean of positive pixels (MPP) at SSF 2 (AUC: 0.891) on DWI b500. In differentiating RCC from oncocytoma, the best parameter was mean at SSF 0 (AUC: 0.935) on DWI b1000. CONCLUSIONS: MRTA could potentially serve as a useful non-invasive tool for differentiating RCC from lpAML and oncocytoma. ADVANCES IN KNOWLEDGE: There is limited literature addressing the role of MRTA in differentiating RCC from lpAML and oncocytoma. Our study demonstrated several texture parameters which were useful in this regard