Speech Enhancement By Exploiting The Baseband Phase Structure Of Voiced Speech For Effective Non-Stationary Noise Estimation

Speech enhancement is one of the most important and challenging issues in the speech communication and signal processing field. It aims to minimize the effect of additive noise on the quality and intelligibility of the speech signal. Speech quality is the measure of noise remaining after the processing on the speech signal and of how pleasant the resulting speech sounds, while intelligibility refers to the accuracy of understanding speech. Speech enhancement algorithms are designed to remove the additive noise with minimum speech distortion.The task of speech enhancement is challenging due to lack of knowledge about the corrupting noise. Hence, the most challenging task is to estimate the noise which degrades the speech. Several approaches has been adopted for noise estimation which mainly fall under two categories: single channel algorithms and multiple channel algorithms. Due to this, the speech enhancement algorithms are also broadly classified as single and multiple channel enhancement algorithms.In this thesis, speech enhancement is studied in acoustic and modulation domains along with both amplitude and phase enhancement. We propose a noise estimation technique based on the spectral sparsity, detected by using the harmonic property of voiced segment of the speech. We estimate the frame to frame phase difference for the clean speech from available corrupted speech. This estimated frame-to-frame phase difference is used as a means of detecting the noise-only frequency bins even in voiced frames. This gives better noise estimation for the highly non-stationary noises like babble, restaurant and subway noise. This noise estimation along with the phase difference as an additional prior is used to extend the standard spectral subtraction algorithm. We also verify the effectiveness of this noise estimation technique when used with the Minimum Mean Squared Error Short Time Spectral Amplitude Estimator (MMSE STSA) speech enhancement algorithm. The combination of MMSE STSA and spectral subtraction results in further improvement of speech quality

Predictive Models for ABS and TPMS based on Gaussian Naïve Bays

The car industry is currently preoccupied with the issue of safety. The increasing number of accidents occurring around the world as a result of automobile problems is a major contributing factor to these incidents. The amount of complicated electronics that is used in vehicles is becoming more prevalent every day. A great effort has been made in evaluating vehicle features in relation to vehicle components. Through such systems, a smart architecture and complex function designs are involved. During all of this vehicle transformation and evolution, the automotive industry recognises a high demand for vehicle safety. While designing and manufacturing this system, automotive experts understand a need for a strict monitoring and feedback system for complex vehicle architecture, which can notify the end user if there is any indication of a failure ahead of time. In order to effectively participate in vehicle design activities, it is critical to grasp the significance of safety features. Tire system failures and braking system failures have played a large role in several recent traffic accidents. The failures of the tyre system and the braking system in the vehicle are addressed in this study. While investigating this system, it is discovered that it is supported by complex electrical systems, which include an ECU (electronic controller unit), sensors, and a wire system. Through the use of these technologies, censored data can be processed in a timely manner and made available for diagnostic purposes. Nevertheless, car diagnostics is needed after any vehicle failure but that does not serve the aim of maintaining vehicle safety. As a result, predictive analysis or predictive diagnostics may be a viable option for informing the driver about the health of a particular vehicle component in advance. In this study, the author discusses the concepts of vehicle prognostics for the tyre pressure monitor system and the antilock braking system, which are accomplished using a statistical method of machine learning. In today's world, machine learning is expanding in breadth, and the world is becoming more aware of its enormous potential in the field of data analytics. It is the purpose of this study to introduce methodologies by which machine learning can assist vehicle predictive analytics to attain the intended goal of vehicle safety.The author of this article discusses how Bayesian statistics may be used to produce predictions in the form of probability estimation. The prediction's outcome is thoroughly analysed

Footstep Power Generation using Piezo Ceramic

People move all the time. Wouldn’t it be great to harness that movement and help to power our cities with the movement of people living in them? Piezoelectric harvesting is one of the most reliable and energy efficient method. The crystalline structure of piezoelectric material provides the ability to transform mechanical strain energy into electrical energy. The power generated by piezo is D.C signal with A.C ripples, which is not used directly for battery charging so hence we use rectifier and filter to get pure D.C signal. Further boost converter circuit is used to step up the D.C signal and through battery charger circuit, battery is charged. This charge can be used to drive the a.c loads by converting D.C signal to A.C with help of inverter circuit

Multiclass Classification of Brain MRI through DWT and GLCM Feature Extraction with Various Machine Learning Algorithms

This study delves into the domain of medical diagnostics, focusing on the crucial task of accurately classifying brain tumors to facilitate informed clinical decisions and optimize patient outcomes. Employing a diverse ensemble of machine learning algorithms, the paper addresses the challenge of multiclass brain tumor classification. The investigation centers around the utilization of two distinct datasets: the Brats dataset, encompassing cases of High-Grade Glioma (HGG) and Low-Grade Glioma (LGG), and the Sartaj dataset, comprising instances of Glioma, Meningioma, and No Tumor. Through the strategic deployment of Discrete Wavelet Transform (DWT) and Gray-Level Co-occurrence Matrix (GLCM) features, coupled with the implementation of Support Vector Machines (SVM), k-nearest Neighbors (KNN), Decision Trees (DT), Random Forest, and Gradient Boosting algorithms, the research endeavors to comprehensively explore avenues for achieving precise tumor classification. Preceding the classification process, the datasets undergo pre-processing and the extraction of salient features through DWT-derived frequency-domain characteristics and texture insights harnessed from GLCM. Subsequently, a detailed exposition of the selected algorithms is provided and elucidates the pertinent hyperparameters. The study's outcomes unveil noteworthy performance disparities across diverse algorithms and datasets. SVM and Random Forest algorithms exhibit commendable accuracy rates on the Brats dataset, while the Gradient Boosting algorithm demonstrates superior performance on the Sartaj dataset. The evaluation process encompasses precision, recall, and F1-score metrics, thereby providing a comprehensive assessment of the classification prowess of the employed algorithms

A survey of Advanced Spectrum Sensing Techniques in Cognitive Radio Networks

Radio spectrum resource demand has increased extraordinarily due to emerging broadband wireless applications which have resulted to critical spectrum shortage problem.  Cognitive radio technology is promising technology that can effectively use unutilized licensed spectrum and can solve spectrum shortage problem. Spectrum sensing is the key element of cognitive radio network to find unused spectrum. Hence effective and accurate spectrum sensing is compulsory for cognitive radio network. This paper is a survey of various advanced spectrum sensing techniques. This paper covers basics of spectrum sensing along with its classification and challenges of spectrum sensing

DIAGNOSIS AND MANAGEMENT OF KATISHOOLA (LOW BACK PAIN) IN AYURVEDA: A CRITICAL REVIEW

Low back pain (LBP) is an important clinical, social, economic, and public health problem affecting the population indiscriminately. It is a disorder with many possible etiologies, occurring in many groups of the population, and with many definitions. Consequently, the vast literature available on LBP is not only heterogeneous but also contradictory. A clear description regarding the Samprapti, Lakshana of Kati Graha is explained by the Shodhala in the Kayachikitsa Khanda, Vataroga Adhikara. He has described various formulations for Kati Shoola and has specifically indicated Trayodashanga Guggulu for Kati Graha. The prevalence of LBP in Indian population has been found to vary between 6.2% (in general population) to 92% (in construction workers). Low back pain can be medically and economically devastating and is the number one cause for disability in patients younger than forty-five years of age and number three cause for disability in patients older than forty-five years of age. This problem, supposedly has a favourable natural history, although it can be remarkably disabling, has challenged the health care providers. Understanding the role of different medical systems in the management of backache is important for the cost-effective management of the disease. Physician treating backache patients should understand this, so that they can co-ordinate and integrate functionally based programs, because no single medication, modality, exercise regimen or other treatment technique may result in low backache recovery. Here in this article, the diagnostic and treatment aspects are discussed critically using Ayurvedic and modern literatures. 

PRINCIPLE AND PRACTICE OF YAPANA BASTI - A CRITICAL REVIEW

Basti Chikitsa regarded as the prime treatment modality among the Panchakarma. It is having not only curative action but also preventive and promotive actions. Basti therapy is considered as Chikitsardha among all therapy and some physician recognize it as complete therapy because Basti has a vast field of therapeutic action. Basti is not merely the enema; rather it is a highly complex, sophisticated, and systemic therapy having wider range of therapeutic actions and indications. It is considered as prime treatment modality for Vata Dosha. Yapana Basti is a subtype of Asthapana Basti, which is having the property to support life and promote longevity and widely used in various disorders such as Pakshaaghata, Siragata Vata, Snayugata Vata, Mamsagata Vata, Asthigata Vata, Majjagta Vata, Shukragata Vata, Sarvanga Vata and Ekangavata. Yapana Basti can be administered at OPD level without any specific restrictions, and hence it can be considered as an ideal therapeutic modification of Basti therapy for the present life style. Yapana Basti and Madhutailika Basti are regarded as one and same, so in this article both are discussed separately. Here, an effort has made to compile and critically analyse the principles, concepts and practices by referring the Brihatrayi, Laghutrayis, commentaires and current researches

Customized CNN Model for Multiple Illness Identification in Rice and Maize

Crop diseases imperil global food security and economies, demanding early detection and effective management. Convolutional Neural Networks (CNNs), particularly in rice and maize leaf disease classification, have gained traction due to their automatic feature extraction capabilities. CNN models eliminate manual feature extraction, enabling precise disease diagnosis based on learned features. Researchers have rapidly advanced these models, achieving promising results. Leaf disease characteristics like color changes, texture variations, and lesion appearance have been identified as useful for automated diagnosis using machine learning. Developing CNN models involves crucial stages: dataset preparation, architecture selection, hyperparameter tuning, and model training and evaluation. Diverse and accurately annotated datasets are pivotal, and appropriate CNN architecture selection, such as ResNet101 and XceptionNet, ensures optimal performance. These architectures' pre-training on vast image datasets enhances feature extraction. Hyperparameter tuning fine-tunes the model, and training and evaluation gauge its precision. CNN models hold potential to enhance rice and maize productivity and global food security by effectively detecting and managing diseases

Construction Project of Residential Building in 3-Tier City

In this project, we are going to study how the construction works are carried out, executed in the 3-tier city and what kind of problems are faced in the construction process.As a saturation has taken place in the constructions in important cities of India, a number of II-tier and III-tiercities have been found to give a successful growth in the construction field. As the real-estate sector of our country is moving higher, a several state capitals and smaller cities that have the relatively better infrastructure and are able to support higher growth in the economy are already into the limelight. These II-Tier and III-Tier cities are characterized by low costs in real estates, land availability for development, untouched manpower and increasing quality of life