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

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

Adaptive Extended Kalman Filter for Orbit Estimation of GEO Satellites

The aim of this paper is to develop Adaptive Extended Kalman Filter (AEKF) algorithm for the precise orbit estimation of GEO satellites (viz., GSAT-10 – Geostationary satellite and IRNSS-1A – Geosynchronous satellite) using two-way CDMA range measurements data from different ranging stations located in India. It brings forward the effectiveness of AEKF algorithm over Extended Kalman Filter (EKF) algorithm. EKF algorithm is adapted by updating process noise covariance (Q), measure of uncertainty in state dynamics during the time interval between measurement updates and measurement noise covariance (R), function of measurement update based on measurement residual. This paper addresses the modeling of all errors in measurement domain and the computation of measurement residual using observed and modeled measurement ranges for all stations. The filter incorporates non-linear model for measurement update, non-linear dynamic model for time update and estimation is carried out at every second. This paper also elaborates the development of indigenous full force propagation model considering all the perturbations during orbit prediction period for GEO Satellites. Adaptation of EKF algorithm in precise orbit estimation is done primarily for making the algorithm more robust by countering the uncertainties in process and measurement noises, resolving the problem of manual tuning of the filter and also by keeping the error covariance (P) consistent with real performance. Adaptation of Q is implemented based on the error in system states with respect to estimated states while Adaptation of R is implemented based on the error in observed measurements with respect to measurements obtained from estimated state vectors (aposteriori measurement expectation). Analysis of the estimated results using the above proposed method is carried out by comparison of Station-wise range residues for both the methods (AEKF and EKF). Consistency of obtained orbit for GEO Satellites are validated using overlapping technique for both AEKF and EKF methods, orbit estimated from these methods are also validated by comparing with batch least squares method and filter behavior is continuously monitored during data gaps by observing error covariance(P) for both the methods. Keywords: Kalman Filtering, Process Noise Covariance, Measurement Noise Covariance, Orbit Estimation, CDM

The clinical utility of prostate cancer heterogeneity using texture analysis of multiparametric MRI

Purpose To determine if multiparametric MRI (mpMRI) derived filtration-histogram based texture analysis (TA) can differentiate between different Gleason scores (GS) and the D’Amico risk in prostate cancer. Methods We retrospectively studied patients whose pre-operative 1.5T mpMRI had shown a visible tumour and who subsequently underwent radical prostatectomy (RP). Guided by tumour location from the histopathology report, we drew a region of interest around the dominant visible lesion on a single axial slice on the T2, Apparent Diffusion Coefficient (ADC) map and early arterial phase post-contrast T1 image. We then performed TA with a filtration-histogram software (TexRAD -Feedback Medical Ltd, Cambridge, UK). We correlated GS and D’Amico risk with texture using the Spearman’s rank correlation test. Results We had 26 RP patients with an MR-visible tumour. Mean of positive pixels (MPP) on ADC showed a significant negative correlation with GS at coarse texture scales. MPP showed a significant negative correlation with GS without filtration and with medium filtration. MRI contrast texture without filtration showed a significant, negative correlation with D’Amico score. MR T2 texture showed a significant, negative correlation with the D’Amico risk, particularly at textures without filtration, medium texture scales and coarse texture scales. Conclusion ADC map mpMRI TA correlated negatively with GS, and T2 and post-contrast images with the D’Amico risk score. These associations may allow for better assessment of disease prognosis and a non-invasive method of follow-up for patients on surveillance. Further, identifying clinically significant prostate cancer is essential to reduce harm from over-diagnosis and over-treatment

Modernized IRNSS Broadcast Ephemeris Parameters

India has successfully stepped into satellite Navigation system with the launch of its first three IRNSS satellites IRNSS 1A, 1B and 1C.  IRNSS provides two types of services, Standard Posting Service (SPS), which is open for civilian use and the Restricted Service (RS), for authorized users.  The system is set to change the facet of navigation, surveying, transportation, precision agriculture, disaster management and telecommunication in India. In any navigation system, broadcast navigation parameters are of paramount importance in arriving user position solution at user receiver end. IRNSS Navigation data is classified as primary and secondary Navigation parameters. Primary navigation data of a satellite principally represents its own orbit and onboard clock offset in the form of quasi-keplerian elements and clock coefficients (Bias, Drift and Drifts rate) respectively. Whereas secondary navigation parameters includes satellite almanac, ionosphere delay correction messages, differential corrections, Earth orientation parameters and  IRNSS Time offset with respect to other GNSS. In existing IRNSS system satellite ephemeris of primary navigation parameters are broadcast in the form of 15 quasi-keplerian elements valid for a period of 2 hours or more. Spacecraft ephemeris which represents orbit in the form of 9 parameters, i.e., position, velocity and acceleration component of spacecraft in Cartesian coordinate system are chosen from Russian Global Navigation satellite system (Glonass) to improve Time to First Fix (TTFF) of IRNSS system with similar existing orbit accuracy. In addition, two models of user receiver orbit propagation algorithms with proposed ephemeris are briefed and their results are compared with standalone Glonass model. Generation of IRNSS ephemeris in Cartesian coordinate system and description of user receiver orbit propagation algorithms using new type of ephemeris to get user position solution is the scope of this paper.. Keywords: IRNSS, TTFF (Time to First Fix), Broadcast ephemeri

Effectiveness of high dose spinal cord stimulation for non-surgical intractable lumbar radiculopathy

OBJECTIVES: Spinal cord stimulation (SCS) is being increasingly used in non-surgical intractable low back pain. This study was designed to evaluate the efficacy of high-dose (HD) SCS utilizing sub-perception stimulation with higher frequency and pulse width in non-surgical predominant low-back pain population at 12 months. MATERIALS AND METHODS: A total of 20 patients were recruited (280 screened between March 2017 and July 2018) to undergo percutaneous fluoroscopic-guided SCS (Medtronic 8 contact standard leads and RestoreR IPG), with T8 and T9 midline anatomical parallel placement. Sixteen patients completed 12 months follow-up (500 Hz frequency, 500 μs pulse width, and 25% pulse density). Differences in patients’ clinical outcome (NRS back, NRS leg, ODI, PGIC, and PSQ) and medication usage (MQS) at 1, 3, and 12 months from the baseline were assessed using non-parametric Wilcoxon paired test. RESULTS: The mean NRS scores for back pain (baseline 7.53) improved significantly at 1, 3, and 12 months; 2.78 (p < 0.001), 4.45 (p = 0.002), and 3.85 (p = 0.002), respectively. The mean NRS score for leg pain (baseline 6.09) improved significantly at 1 and 3 months; 1.86 (p < 0.001) and 3.13 (p = 0.010), respectively. Mean NRS for leg pain at 12 months was 3.85 (p = 0.057). ODI and sleep demonstrated significant improvement as there was consistent improvement in medication particularly opioid usage (MQS) at 12 months. CONCLUSIONS: This study demonstrates that anatomical placement of leads with sub-perception HD stimulation could provide effective pain relief in patients who are not candidates for spinal surgery

MRI texture analysis parameters of contrast-enhanced T1-weighted images of Crohn's disease differ according to the presence or absence of histological markers of hypoxia and angiogenesis

PURPOSE: To investigate if texture analysis parameters of contrast-enhanced MRI differ according to the presence of histological markers of hypoxia and angiogenesis in Crohn's disease (CD). METHODS: Seven CD patients (mean age 38 (19-75), 3 male)) undergoing ileal resection underwent 3T MR enterography including axial ultrafast spoiled gradient-echo T1 post IV gadolinium chelate. Regions of interest were placed in bowel destined for resection and registered to trans-mural histological sections (n = 28 across 7 bowel sections) via MRI of the resected specimen. Microvessel density (MVD) and staining for markers of hypoxia (HIF 1α) and angiogenesis (VEGF) were performed. Texture analysis features were derived utilizing an image filtration-histogram technique at spatial scaling factor (SSF) 0-6 mm, including mean, standard deviation, mean of positive pixels, entropy, kurtosis and skewness and compared according to the presence or absence of histological markers of hypoxia/angiogenesis using Mann-Whitney U/Kruskal-Wallis tests and with the log of MVD using simple linear regression. RESULTS: Mean, standard deviation and mean of positive pixels were significantly lower in sections expressing VEGF. For example at SSF 6 mm, median (inter-quartile range) of mean, standard deviation and mean of positive pixels in those with VEGF expression were 150.1 (134.7), 132.4 (49.2) and 184.0 (91.4) vs. 362.5 (150.2), 216.3 (100.1) and 416.6 (80.0) in those without (p = 0.001, p = 0.004 and p = 0.001), respectively. There was a significant association between skewness and MVD (ratio 1.97 (1.15-3.41)) at SSF = 2 mm. CONCLUSIONS: Contrast-enhanced MRI texture analysis features significantly differ according to the presence or absence of histological markers of hypoxia and angiogenesis in CD

CT-based texture analysis potentially provides prognostic information complementary to interim fdg-pet for patients with hodgkin's and aggressive non-hodgkin's lymphomas

OBJECTIVES: The purpose of this study was to investigate the ability of computed tomography texture analysis (CTTA) to provide additional prognostic information in patients with Hodgkin's lymphoma (HL) and high-grade non-Hodgkin's lymphoma (NHL). METHODS: This retrospective, pilot-study approved by the IRB comprised 45 lymphoma patients undergoing routine 18F-FDG-PET-CT. Progression-free survival (PFS) was determined from clinical follow-up (mean-duration: 40 months; range: 10-62 months). Non-contrast-enhanced low-dose CT images were submitted to CTTA comprising image filtration to highlight features of different sizes followed by histogram-analysis using kurtosis. Prognostic value of CTTA was compared to PET FDG-uptake value, tumour-stage, tumour-bulk, lymphoma-type, treatment-regime, and interim FDG-PET (iPET) status using Kaplan-Meier analysis. Cox regression analysis determined the independence of significantly prognostic imaging and clinical features. RESULTS: A total of 27 patients had aggressive NHL and 18 had HL. Mean PFS was 48.5 months. There was no significant difference in pre-treatment CTTA between the lymphoma sub-types. Kaplan-Meier analysis found pre-treatment CTTA (medium feature scale, p=0.010) and iPET status (p<0.001) to be significant predictors of PFS. Cox analysis revealed that an interaction between pre-treatment CTTA and iPET status was the only independent predictor of PFS (HR: 25.5, 95% CI: 5.4-120, p<0.001). Specifically, pre-treatment CTTA risk stratified patients with negative iPET. CONCLUSION: CTTA can potentially provide prognostic information complementary to iPET for patients with HL and aggressive NHL