Model Predictive Control for Autonomous Driving Based on Time Scaled Collision Cone

In this paper, we present a Model Predictive Control (MPC) framework based on path velocity decomposition paradigm for autonomous driving. The optimization underlying the MPC has a two layer structure wherein first, an appropriate path is computed for the vehicle followed by the computation of optimal forward velocity along it. The very nature of the proposed path velocity decomposition allows for seamless compatibility between the two layers of the optimization. A key feature of the proposed work is that it offloads most of the responsibility of collision avoidance to velocity optimization layer for which computationally efficient formulations can be derived. In particular, we extend our previously developed concept of time scaled collision cone (TSCC) constraints and formulate the forward velocity optimization layer as a convex quadratic programming problem. We perform validation on autonomous driving scenarios wherein proposed MPC repeatedly solves both the optimization layers in receding horizon manner to compute lane change, overtaking and merging maneuvers among multiple dynamic obstacles.Comment: 6 page

DIGITAL AGRICULTURE: ANALYSIS OF VIBRATION TRANSMISSION FROM SEAT TO BACK OF TRACTOR DRIVERS UNDER MULTI-DIRECTIONAL VIBRATION CONDITIONS

The present research examines the impact of vibrations on seat-to-back transmissibility in tractor drivers. This study utilized a smart device for real-time data transmission to improve the experimentation by eliminating potential sources of error. Data was assessed using metrics such as weighted acceleration, daily exposure, power spectral density, and seat-to-back transmissibility. The seat pan and backrest were found to have high vibration levels on the vertical axis. Daily exposure response exceeded the exposure action limit of 0.5 m/s2, as specified in Directive 2002/44/EU. Power spectral densities at the seat pan and the backrest revealed dominant frequencies in the low-frequency range. Seat-to-back transmissibility demonstrated primary and secondary resonance within the 4.1-7.2 Hz and 8.2-11.8 Hz frequency ranges. Tractor manufacturers and designers could utilize the findings of this study to decrease the excessive vibration intensities and crucial resonating frequencies and thus enhance the operator's ride comfort

Stock Prediction using Machine Learning

The paper give detailed on the work that was done using regression techniques as stock market price prediction. The report describes Linear regression methods that were applied with accuracy obtained using this methods, it was found this model is effective from other although there are several opportunities to expand the research further with additional techniques and parameters

Prediction of strength enhancement of subgrade soil reinforced with geotextile using artificial neural network and M5P model tree

Geosynthetics layers are being implemented as reinforcement to enhance the strength of subgrade soil (which is calculated in terms of CBR). Present research work, aims at investigating the strength enhancement in terms of CBR through experimental study. Experiments were conducted on subgrade soil reinforcing it with single and double layer woven and non-woven geotextile layer were placed at depth M/3, M/2 and 2/3M from the top of CBR specimen, where Mis height of CBR specimen. Result indicate that woven geotextile offers more strength to subgrade soil than non-woven geotextile, further as depth of placement of reinforcement increases from top lesser is increase in strength for both the geotextile. Strength also increases when double layer was placed in comparison to single layer for both the geotextile. ANN and M5P was used to predict the CBR value, result suggest improved performance of ANN over M5P for present data

Association of Fructose Enriched Foods with Metabolic Syndrome and Cardiovascular Diseases

oai:ojs2.ojs.ijcp.in:article/1Cardiovascular diseases (CVDs) are the major causes of mortality and morbidity worldwide as well as in the Indian subcontinent, causing more than 25% of deaths. It has been predicted that these diseases will increase rapidly in India, making it a host to more than half the cases of heart disease in the world within the next 15 years. The World Health Organization (WHO) reports that in the year 2005 CVDs caused 17.5 million (30%) of the 58 million deaths that occurred worldwide. In the recent times, the association of metabolic syndrome (MS) is strongly linked with CVDs. MS is defined as a constellation of metabolic disorders in an individual. The main components of MS are dyslipidemia (higher triglyceride, low-density lipoproteins [LDL] and low high-density lipoproteins [HDL]), elevated blood pressure (BP), dysregulated glucose homeostasis, abdominal obesity and insulin resistance. Being one of the most widespread diseases in the world, almost half of the population of specific age groups in developed countries is affected by it. Studies have shown that the independent risk factors associated with MS increase the likelihood of CVDs. It has been postulated that excess intake of fructose promotes cell dysfunction, inflammation, intra-abdominal (visceral) adiposity, atherogenic dyslipidemia, weight gain, insulin resistance, hypertension thereby aggravating the chances for developing MS, type 2 diabetes and coronary heart disease

TOWARDS DATA STORAGE SCHEME IN BLOCKCHAIN BASED SERVERLESS ENVIRONMENT: AES ENCRYPTION AND DECRYPTION ALGORITHM APPROACH

In today's digital age, data storage has become an increasingly important issue. The exponential growth of data has led to the need for secure and private storage solutions. Unfortunately, without a standardized protocol, the security and privacy of sensitive data can be a challenge. However, blockchain technology has emerged as a promising solution for secure data storage. Blockchain's decentralized and immutable nature provides a comprehensive solution for the security and privacy of all types of data. In this research, we propose an innovative framework that leverages the benefits of blockchain technology to securely handle and store data in a serverless environment of distributed nodes. To ensure the highest level of security, we evaluated four encryption algorithms - Blowfish, RC4, DES, and AES - for storing data in a permissioned blockchain network. We found that AES encryption and decryption algorithms provide the best solution for creating a decentralized, immutable coordinate system. Our proposed framework is based on a permissioned blockchain network that enables multiple users to join the network through suitable identity verification processes, and each user is assigned certain special and designated permissions to perform actions. This framework provides a secure and efficient solution for the storage of all types of data, ensuring privacy and security. Our proposed framework offers an innovative solution for secure data storage and management in a decentralized environment. This research has practical implications for organizations that need to store sensitive data securely, and it also contributes to the ongoing development of blockchain technology

Real Time Object Detection with Noisy Sensors Using Deep Learning

In this paper, we introduce a first of its kind, radio-signal based object detection system for controlled environments, which substitutes complex signal processing and expensive hardware with deep learning networks to detect patterns from low-quality, inexpensive sensors. Our system operates in the less crowded low- frequency range of 433 MHz in contrast to existing RF-based sensing methods and uses mini-Doppler maps generated from raw I/Q data, thereby allowing us to use cheap, off-the-shelf software defined radios. We demonstrate that our system is versatile enough to handle occlusions and is also sensitive to multiple objects; additionally, it does not use visual data and hence is not hampered by bad lighting. The core of our system is a VGG-16 based CNN architecture trained on the mini-Doppler maps. We achieve an accuracy of 0.96 on a binary classification task of detecting the presence or absence of an object in an enclosed space. Furthermore, we observe that our system shows promise for more complicated detection algorithms as it is able to successfully differentiate between the presence of a single object and two identical objects placed together. Our results indicate that convolutional networks can learn features important enough from spectrograms that enable it to distinguish the presence of objects, thereby eliminating the need of sophisticated signal processing methods to do the same

Transforming farming with intelligence : smart vibration monitoring and alert system

ABSTRACT: During the evolution towards digital agriculture, the pivotal role of tractor riding necessitates a focus on improving operator performance and well-being. While most research has centered around vibration analysis, tangible solutions to control elevated vibration levels remain rare. The study aims to introduce an intelligent ThingSpeak-Enabled IoT (Internet of Things) solution that provides real-time monitoring and generates prompt warning alerts for tractor operators when vibrations exceed safe thresholds. The initial phase involved the real-time measurement of WBV (whole-body vibration) and SEAT (seat effective amplitude transmissibility). Following this, the secondary phase encompassed the analysis and validation of the system in cases where WBV and SEAT exceeded the recommended limits. The experimental design comprised 135 trials by systematically varying tractor ride parameters, including average speed (m/s), average depth (m), and pulling force (kN) levels. Daily vibration exposure response ranged from 0.43 m/s² to 0.87 m/s² with a mean exposure of 0.64 m/s², surpassing the EAV (exposure action value) threshold of 0.5 m/s². The SEAT values ranged between 91.37 and 133.08 with a mean of 108.35, that indicates insufficient seat isolation capacity, i.e., < 100. Statistically, the study ascertained a significant influence of average speed and average depth WBV and SEAT responses at a 5% significance level. It underscores the potential efficacy of altering speed and depth parameters to attenuate vibration exposure levels. Further, the effectiveness of the system was tested through the automatic transmission of warning alerts via emails, text messages, and flashing red LED light on the IoT system. This critical feature provides considerable utility for tractor operators to adjust ride settings, ensuring that the ride remains within safe vibration limits. Furthermore, adopting such an advanced warning system in tractor manufacturing signifies a pioneering step towards sustainably enhancing operator well-being