Biomedical signal and image processing

Liquid is a fundamental element in our day-today life. Proper management, control, and monitoring of liquid, especially water, are still areas to be improved. Suppose we can control and monitor the liquid properly. In that case, we can save the waste of liquids like water, which is used massively in household work daily, or different kinds of liquids are used in medicine to treat patients, or many functions in the industry are related to the other liquids. So, every day, a lot of liquid is used. Proper control and monitoring systems can minimize wastage and ensure the precise usage of particular liquids. Our proposed design uses the Arduino Microcontroller to monitor the water flow by the water flow sensor. We used LCD to show the water usage values and precisely what amount a specific consumer takes. Also, the water supply will be cut off if the consumed value reaches a particular consumer's predefined value. The critical part of this work is that we built it for even water distribution in a house based on how much water is preserved. Though our design is mainly based on water, it can be applied to any liquid based application in any industry.

In this paper a new method for segmenting medical images is presented, the multiresolution diffused expectation-maximization (MDEM) algorithm. The algorithm operates within a multiscale framework, thus taking advantage of the fact that objects/regions to be segmented usually reside at different scales. At each scale segmentation is carried out via the expectation-maximization algorithm, coupled with anisotropic diffusion on classes, in order to account for the spatial dependencies among pixels. This new approach is validated via experiments on a variety of medical images and its performance is compared with more standard methods.

The need for efficient content-based image retrieval system has increased hugely. Efficient and effective retrieval techniques of images are desired because of the explosive growth of digital images. content based image retrieval (CBIR) is a promising approach because of its automatic indexing retrieval based on their semantic features and visual appearance. The similarity of images depends on the feature representation.However users have difficulties in representing their information needs in queries to content based image retrieval systems. In this paper we investigate two methods for describing the contents of images. The first one characterizes images by global descriptor attributes, while the second is based on color histogram approach.To compute feature vectors for Global descriptor, required time is much less as compared to color histogram. Hence cross correlation value & image descriptor attributes are calculated prior histogram implementation to make CBIR system more efficient.The performance of this approach is measured and results are shown. The aim of this paper is to compare various global descriptor attributes and to make CBIR system more efficient. It is found that further modifications are needed to produce better performance in searching images.

This essay presents a unified theory proposing that reality consists of interconnected "resonant fields" exhibiting scale-invariant properties across multiple domains, from quantum spacetime to artificial intelligence and to consciousness. The central thesis suggests that these diverse phenomena share common mathematical structures based on dimensional hierarchies (1D strings → 2D branes → 3D tori → 4D Clifford tori) that maintain scale invariance through conformal transformations and duality relationships. The authors propose that one-dimensional string theory, two-dimensional brane theory, three-dimensional torus geometry, and four-dimensional Clifford torus geometry constitute components of a unified theoretical framework. Each dimensional level allegedly exhibits scale-invariant properties. Building on the "Cemi Field Theory", and the holographic "Event Horizon Brain concept", this essay argues that consciousness emerges from coherent electromagnetic fields generated by synchronous neural activity. This proposes a fivecomponent architecture shared across systems. Artificial neural networks are reinterpreted as "computational fields" where information propagates through weighted connection patterns, analogous to wave propagation in physical media. The further framework suggests AGI emergence may occur as a phase transition when computational complexity reaches critical thresholds.

The paper presents an assessment and estimation of human exposure to extremely-low-frequency (ELF) magnetic field generated in 33/11kV electrical injection substations equipment in Kano metropolis using the magnetic field model. The magnetic fields level were measured with the aid of hand-held "Trifield TM 100XE Meter; frequency weighted" in different parts of the substations and the occupational exposure in a shift personnel was estimated by calculating the time weighted average exposure level (TWA) over the entire week and noting basically that the general public average exposure level to power frequency of 50 Hz is less than 2 mG (≡ 0.2 µT). The main objectives of this study are to assess the level of magnetic fields radiation in an injection substation and estimate induced current density level at the head region of the human body. Current density estimation at the head region was computed by using magnetic field model by assuming that the human body has a homogenous and isotropic conductivity and current path is circular. The result obtained was validated through comparison with the international guidelines and standards for safety occupational exposure set by the International Commission on Non-Ionising Radiation Protection (ICNIRP).

Banking sector deals with financial services became an essential service of today's society. Customers are the key components of banks. The banking products offered by every bank are standardized in nature. So, there is a need for every bank to differentiate itself from its competitors. Thus, customer satisfaction has become utmost important criteria for a bank's performance in long run. The significant predictor of customer satisfaction and organizational performance is customer empowerment. Customer empowerment is the ability of customers to take their own decisions consciously. It is highly required to survive in extensive competitive market and has been identified as a growing force of marketing. It eventually leads to customer satisfaction.At this juncture, a modest attempt is made in this paper to identify the innovative practices of banks to bring out customer satisfaction through customer empowerment.

Within the Unified Resonant Framework (URF), this paper proposes that thought-both conscious and subconscious-functions analogously to dark energy and dark matter, forming the invisible yet dominant substrates of both cognition and the cosmos. Photons are identified as the resonant quanta of informational coherence, mediating between conscious expansion (dark energy) and subconscious structure (dark matter). By merging cosmological field equations with neuroresonant dynamics, the URF formalism reveals a self-similar architecture between mind and universe, suggesting that consciousness operates as a localized cosmological field, and that cognition represents an emergent form of universal resonance symmetry.

This paper expands upon the Unified Resonant Framework (URF) by reinterpreting entropy not as an objective measure of disorder, but as a subjective resonance gradient between observer and field. Conventional thermodynamics defines entropy as the quantitative degree of disorder within a closed system. Under URF, however, disorder is not an intrinsic feature of the universe but a perceptual byproduct of misaligned resonance between local consciousness and the greater harmonic field. Entropy, therefore, is a measure of coherence deviation rather than chaos. This reinterpretation unites physics and phenomenology by proposing that order and disorder are observer-dependent expressions of field alignment.

This paper proposes that subjective experience-long excluded from the domain of empirical science-constitutes direct, measurable evidence for the Unified Resonant Framework (URF). Within URF, consciousness is not a passive observer but an active resonant participant in the formation of reality. Subjectivity, under this model, is the localized expression of resonance coherence between observer and field. Drawing upon interdisciplinary data from neuroscience, quantum theory, and psychophysiology, this work argues that internal experience can satisfy empirical criteria when redefined in resonance terms: coherence, repeatability, and intersubjective convergence. By reframing the observer as an empirical node within the field rather than an external spectator, URF resolves the mind-matter duality and positions consciousness as the first instrument of universal measurement.

The aim of the study was a selection of best ECG leads to get the significant T-wave alternans signal (TWA). The group of 16 patients with implantable cardioverterdefibrillator (ICD) was examined. The 64 lead ECG system was used. Three lead sets were analyzed: 64 BSPM lead set, orthogonal lead set (XYZ) and standard 12 lead set. Patients were divided into two groups: TWA positive (TWA(+)) 5 patients and TWA negative (TWA(-)) 11 patients. In TWA(+) group mean alternans ratio was equal 5.4 (SD 8.09) and in TWA(-) group 0.73 (SD 0.26). Both standard ECG leads and XYZ leads are less sensitive to TWA than specially selected leads from 64 lead set

We present photon assisted tunneling in multiferroics of type 𝐴𝐵𝑂 3. It is seen that the interactions of photons with this analytic molecule accelerate the electron transmissions. It is clarified that the existence of ferromagnetic, ferroelectric, and piezoelectric in multiferroic materials are consequence of magnetic anisotropy, dipole segregation and strain. The effect of photon is therefore, to disassemble existing electronic states or assembles to form new electronic states by enhancing its transmission rates. On the other hand, photons with definite frequency are expected to overcome, the Vander Waals interactions. It is expected that photon assisted tunneling/hopping will cause an electric dipole separation. Hence photon assisted tunneling in multferroic domain walls will be dealt.

We present a suitable ways for tuning superconductivity in 𝐴𝐵𝑂 3 type multiferroics nanocrystals. It is seen that as a smart materials, multiferroics nanocrystals present distinguishing properties including ferroelectricity, piezoelectricity, ferrotoroids, magneto electrics and ferromagnetism. The reasons for the existence of multiferroicity are interactions of photons with these analytic molecules, electron doping and crispr, which adds or alters the electronic, optical and magnetic properties dramatically. It is clarified that the existence of ferromagnetic, ferroelectric and piezoelectric in Multiferroic materials are consequence of magnetic anisotropy, dipole segregation and strain. The formations of copper pairs in Multiferroic nanocrystals are due to electron doping, photon activated tunneling, Zero field cooling and crispr methods are responsible for the resulting superconductivity. In addition, quantum phenomenon as photon assisted tunneling in multiferroics nanocrystals will reinforce the coupling of electron pair. Hence, the nature of superconductivity in giving Multiferroic nanocrystals is dealt in accordance to oxygen content and vacancies.

This paper introduces a novel Real-time Automated Non-invasive Cardiac Remote Health Monitoring Methodology for the detection of human condition by analyzing the ECG signal under the real-time environment. We proposed a novel System-on-Chip (SoC) architecture which has four folded modeling. After the data sensing, firstly, the classification module uses Hurst exponent as a metric in classifYing the condition of the ECG signal. Secondly, if there is an abnormal detection by classifier, the Feature Extraction (FE) extracts QRS complex, P wave and T wave from ECG frames. As there is demand of ECG frames, a robust Boundary Detection (BD) mechanism is introduced to identifY such frames. The fragmented-QRS (f-QRS) feature is also introduced as our third contribution to detect the fragmentation in the QRS complex and identifY its morphology. The fourth step is compressing the ECG data using Hybrid compression technique for low area implementation and communicating this data to the nearest health care center by incorporating the concept of an Adaptive Rule Engine (ARE) based classifier. The proposed SoC architecture is prototyped on Xilinx Virtex 7 FPGA and it is tested on JOO patient's data from PTBDB (MIT-BIH), CSE and in house 11TH DB, the percentage of accuracy obtained is 91% and it is under process for Tape-out.

In this paper, we propose a novel digital watermarking scheme in DCT domain based fuzzy inference system and the human visual system to adapt the embedding strength of different blocks. Firstly, the original image is divided into some 8×8 blocks, and then fuzzy inference system according to different textural features and luminance of each block decide adaptively different embedding strengths. The watermark detection adopts correlation technology. Experimental results show that the proposed scheme has good imperceptibility and high robustness to common image processing operators.

Evoked potentials are electrical signals produced by the nervous system in response to a stimulus. In general these signals are noisy with a low signal to noise ratio. The aim was to investigate ways of extracting the evoked response within an evoked potential recording, achieving a similar signal to noise ratio as conventional averaging but with less repetitions per average. In this thesis, evolutionary algorithms were used in three ways to extract the evoked potentials from a noisy background.

Öz: İnternet kullanımının hızla yaygınlaşması, diğer alanlarda olduğu gibi, ses verilerinin de önemli ölçüde artış göstermesine yol açmıştır. Bu artış, ilgili verilerin organizasyonunda iş yükünün de paralel şekilde artmasına neden olmuştur. Bu bağlamda, veri organizasyonunun daha etkin gerçekleştirilmesi adına yeni ve daha verimli sistemlere duyulan ihtiyaç kaçınılmaz hale gelmiştir. Özellikle makine öğrenmesi ve derin öğrenme mimarilerine dayalı sınıflandırma yöntemleri, bu verilerin otomatik sınıflandırılmasında etkili bir şekilde kullanılmakta ve bu alanda önemli bir rol üstlenmektedir. Ses verilerinin artmasında, film endüstrisi de oldukça önemli bir yer tutmaktadır. Audio post prodüksiyon sürecinin unsurlarından olan foley pratiklerindeki kayıt süreci de bu artışın başat unsurlarındandır. Çalışma, foley kayıtlarından oluşturulan ses kütüphanelerinin organizasyonuna yönelik verimliliğin artırılması amacıyla makine öğrenimi ve derin öğrenme mimarileri üzerinden sınıflandırma yöntemlerini ele almaktadır. Çalışmada, halka açık foley kütüphaneleri üzerinden veri setleri oluşturulacak ve bu veri setlerindeki seslerin spektral ve psikoakustik öznitelikleri çıkarılacaktır. Çıkarılan öznitelikler, lojistik regresyon, k-en yakın komşu, karar ağacı, yapay sinir ağları gibi makine öğrenmesi ve derin öğrenme mimarileri aracılığıyla sınıflandırılacaktır. Sınıflandırma çalışmaları sonucunda veri setlerinin büyüklüğü ve çeşitliliği bağlamında ilgili mimarilerin başarıları karşılaştırılacaktır. Çalışma sonucunda elde edilecek bulguların, gerek film endüstrisinde gerekse akademik çalışmalarda kullanılacak seslerin organizasyonundaki verimliliği artırmaya yönelik uygulamalar konusunda katkı sağlayacağı öngörülmüştür.

The use of machine learning has become widespread in recent years, especially due to the impact of media content on the general population. In particular, the validation of truth in the context of news has become a critical necessity, due to its ready availability from verified and unverified sources, and its ability to influence people majorly. The present work outlines a LSTM (long short-term memory) based approach to news validation. The results obtained by the model are presented in terms of the training data set and contrasted with the results obtained from the test data set. Appreciable accuracy is achieved through the model, seen through the corresponding loss curves and confusion matrix.

This paper presents the subtraction method for interference cancellation in ECG. In contrast to the well-known hardware and software filters, the method does not affect the signal frequency components around the rated powerline frequency. The method searches for linear segments in the ECG signal. Then a digital filter is used to remove the interference from these segments. The samples of the interference signal are computed in these segments and later on these samples are used to remove by subtraction the interference from the signal where non-linear segments are encountered. A theoretical generalised approach of the method is developed in the paper.

Two-photon excitation fluorescence (2PEF) allows imaging of tissue up to about one millimeter in thickness. Typically, reducing fluorescence excitation exposure reduces the quality of the image. However, using deep learning super resolution techniques, these low-resolution images can be converted to high-resolution images. This work explores improving human tissue imaging by applying deep learning to maximize image quality while reducing fluorescence excitation exposure. We analyze two methods: a method based on U-Net, and a patch-based regression method. Both methods are evaluated on a skin dataset and an eye dataset. The eye dataset includes 1200 paired high power and low power images of retinal organoids. The skin dataset contains multiple frames of each sample of human skin. High-resolution images were formed by averaging 70 frames for each sample and low-resolution images were formed by averaging the first 7 and 15 frames for each sample. The skin dataset includes 550 images fo...

There is a growing need for constant improvement in biometric identity recognition systems to keep up with new and evolving security threats. This paper highlights the importance of thorough and ongoing research in the field of biometrics. Likewise, in the world of automated security systems, biometrics has made a major impact and is widely recognised in the academic world. Biometric systems work by capturing and analysing physical or behavioural traits (like iris, fingerprints, palmprint, or facial patterns) to verify individual identities. These security systems are not only useful for security purposes, but they are also being used in areas like the banking sector, law enforcement bodies, the healthcare sector, electronics design, and immigration services to improve human identity checks and control access to restricted areas. A sound literature review was conducted in this paper. The main goal of this review paper is to present a lengthy view of several research approaches, system setups, challenges in the field, and technical components required, such as how biometric features are extracted, different types of biometrics used, available biometric datasets, and how biometric data from other sources can be combined. This paper further explores the connections between various biometric types and provides insights from both Recent and Older Research (ROR). This paper also presents a view of where the field stands and where it's headed. Additionally, the paper revealed the current challenges in using multibiometric recognition systems and offers suggestions for future research directions.

In this paper, a comparative study between proposed hyper kurtosis based modified duo-histogram equalization (HKMDHE) algorithm and contrast limited adaptive histogram enhancement (CLAHE) has been presented for the implementation of contrast enhancement and brightness preservation of low contrast human brain CT scan images. In HKMDHE algorithm, contrast enhancement is done on the hyper-kurtosis based application. The results are very promising of proposed HKMDHE technique with improved PSNR values and lesser AMMBE values than CLAHE technique.

CT scan images of human brain of a particular patient in different cross sections are taken, on which wavelet transform and multi-fractal analysis are applied. The vertical and horizontal unfolding of images are done before analyzing these images. A systematic investigation of de-noised CT scan images of human brain in different cross-sections are carried out through wavelet normalized energy and wavelet semi-log plots, which clearly points out the mismatch between results of vertical and horizontal unfolding. The mismatch of results confirms the heterogeneity in spatial domain. Using the multi-fractal de-trended fluctuation analysis (MFDFA), the mismatch between the values of Hurst exponent and width of singularity spectrum by vertical and horizontal unfolding confirms the same.

The COVID-19 outbreak prompts the need for new ways to detect and prevent epidemics. Since cough is one of the COVID-19 symptoms, our work proposes a sound recognition system based on our previous works which are able to detect different type of cough through a continuously home sound analysis. It is a part of EpiSemioWatch AMI COVID UTC funded project which aims to develop a detection system of epidemic risk in retirement home using sound and movement sensors. This paper presents the sound part of the global system and more explicitly the differentiation of cough from other environmental sound. The proposed system detects automatically the sound activity through a wavelet based system, realize an acoustic segmentation speech/sound and classify the cough sounds. A first evaluation of the system in our lab is presented.

Alnoor Model defines light as a stable soliton in a universal scalar field, interacting deterministically with matter. This approach resolves wave-particle duality and observerdependent collapse, explaining the photoelectric effect, interference, and gravitational lensing via phase-synchronized coherence. A nonlinear wave equation enables soliton propagation over cosmological scales, predicting resonance-gated transparency, critical coupling thresholds, and coherence-controlled radiation pressure. The model reproduces the electron's anomalous magnetic moment (aₑ ≈ 0.001159) as a structural resonance and explains the excess redshift of 3C 273 (z=0.158) through cosmic phase gradients. It also predicts suppressed photoemission without temporal coherence, providing a clear experimental test.

Alnoor Model defines light as a stable soliton in a universal scalar field, interacting deterministically with matter. This approach resolves wave-particle duality and observer dependent collapse, explaining the photoelectric effect, interference, and gravitational lensing via phase-synchronized coherence. A nonlinear wave equation enables soliton propagation over cosmological scales, predicting resonance-gated transparency, critical coupling thresholds, and coherence-controlled radiation pressure. The model reproduces the electron's anomalous magnetic moment (aₑ ≈ 0.001159) as a structural resonance and explains the excess redshift of 3C 273 (z=0.158) through cosmic phase gradients. It also predicts suppressed photoemission without temporal coherence, providing a clear experimental test.

According to the World Health
Organization, pneumonia sickness claimed over two
million lives in 2019. Children between the ages of
one and five as well as elderly individuals
frequently die. The healthcare system faces
numerous obstacles, including a decline in
radiologists as a result of widespread migration
overseas, an increase in medical image volume,
challenges in interpreting criteria, rising workloads,
and an abundance of complex images that could
overwhelm radiologists. This calls for deliberate
actions to lessen the impact of pneumonia. Clinical
decision-making technology has advanced due to
increased research in medical image processing,
which has been made possible by the geometric
growth in diverse machine learning applications.
Clinical decision-making technology has evolved as
a result of increased research in medical image
processing, which has been enabled by the
exponential rise of various machine learning
applications. Combining machine learning and deep
learning in hybrid and ensemble forms provides a
speedy solution to challenges that have profoundly
altered society, either by saving lives or easing those
afflicted. This comprehensive overview of the
literature addresses various methods for classifying
pneumonia utilizing machine, deep, hybrid, and
ensemble learning techniques.In addition to
providing a summary of the reviews in a table and
deep insight into the results of the classifications,
the review highlights various research gaps in the
current models and potential solutions, enabling a
better understanding of the model's accuracy
performance and suitability for pneumonia
classification. It also offers recent trends,
illustrations, and an evaluation of these models'
efficacy in the classification of pneumonia disease.

This paper extends the Sentient Superfluid Framework (SSF) to propose a mechanism by which highly ordered crystalline structures, both natural and biological, can amplify coherent conscious intent. Building upon the understanding that consciousness is a stable, self-maintaining vortex within a superfluid cosmic medium, we formalize the process of attunement and intentional projection. We posit that a crystal's highly ordered atomic lattice, as well as the crystalline lens of the human eye, serves as a natural amplifier for conscious will, acting as a resonant cavity that increases the phase coherence of an individual's conscious actualization bias (SC). This amplified bias is then shown to exert a more significant non-local potential on the probability distribution of quantum outcomes. Furthermore, we reexamine the concept of entropy in this context, demonstrating how these structures can serve as a local "heat pump," absorbing and dissipating the surrounding conceptual entropy that would otherwise obstruct a desired outcome. This work provides a theoretical basis for the long-held intuitive belief in the power of crystals, elevating it from metaphysics to a testable physical phenomenon within a unified model of reality.

Plastic Waste is one of the most contemporary and major issues on global level. But since the use of plastic is quite widespread in our daily activities it is difficult to eradicate the use of plastic completely from our day to day lives. So, the only solution to this problem is to bring an alternative of plastic which is both eco-friendly and biodegradable. A material which possesses all the properties of plastic and can act as its replacement. One such replacement of plastic is biodegradable bio-plastic. Bio-plastics are plastic material produced from renewable biomass sources. The said research experiment attempts to find a solution for non-biodegradable plastic use. The "formulation of bio-plastic" by the use of natural raw resources available in our surroundings, has the potential to act as an alternative to harmful and hazardous non-biodegradable plastic. The research successfully provides a sustainable solution by shifting from plastic to Biodegradable Bio-plastic.

This paper shows how to implement carry-save arithmetic with reconfigurable adder. The architecture of the adder is such that it should add two operands fast. The adder will be design as an IP (Intellectual Property) core. The reconfigurable adder is then implementing on FPGA, Spartan 3E series board. A reconfigurable system is one that changes its form in order to alter its function. For it to be more than “configurable” it must be possible to change it either whilst it is in use, or by taking it out of use for a short time.

The main theme of this paper is to reduce noise from the noisy composite signal and reconstruct the input signals from the composite signal by designing FIR digital filter bank. In this work, three sinusoidal signals of different frequencies and amplitudes are combined to get composite signal and a low frequency noise signal is added with the composite signal to get noisy composite signal. Finally noisy composite signal is filtered by using FIR digital filter bank to reduce noise and reconstruct the input signals.

This research surveyed and investigated the quality of sandcrete hollow blocks produced for construction works within the Enugu metropolis. The study aimed to ascertain quality control, in terms of the compressive strength of sandcrete blocks, the grading of sands used, and the dimensions of blocks produced. The methodology used in this research involved a survey of nine block industries that were randomly sampled within the Enugu metropolis and practical moulding of blocks used as control. Three-block industries use the machine for mixing and moulding while the other six use manual hand moulding. Six samples of hollow sandcrete blocks were randomly selected from the nine-block industries (3 samples each from 6-inches and9inches sizes) making it a total of 56 blocks. However, 6-block samples (3 samples each of 6 inches and 9 inches) were produced manually as control, using Nigeria Industrial Specificationwith cement/sand of 1:6, and water/cement of 0.8. The whole blocks were cured by sprinkling water on them for 14 days after which they were weighed and crushed to determine their compressive strengths. The results observed indicated that the quality of sandcrete hollow Blocksproduced within the Enugu metropolis was poor, with variations in dimensions, low compressive strength values ranging from 1.28 to 2.2N/mm ^2 for 6 inches blocks, and 0.95to 2.54N/mm ^2 for 9 inches blocks. The values were below the specification of 3.45N/mm ^2 load-bearing blocks are expected to have. The research concluded that the qualities of sandcrete hollow blocks produced within the Enugu metropolis were poor and that there was no quality control, inspection, and regulation of activities of block industries within the metropolis. The research recommended regulation and frequent inspection and monitoring of block industry activities within the state for certification of their products and ensuring conformation to specifications.

This paper describes a novel method for estimating dense disparity maps, based on wavelet representations. Within the proposed set theoretic framework, the stereo matching problem is formulated as a constrained optimization problem in which a quadratic objective function is minimized under multiple convex constraints. These constraints arise from the prior knowledge and the observations. In order to obtain a smooth disparity field, while preserving edges, we consider appropriate wavelet based regularization constraints. The resulting optimization problem is solved with a block iterative method which offers great flexibility in the incorporation of several constraints. Experimental results on both synthetic and real data sets show the excellent performance and robustness w.r.t. noise of our method.

Recent findings on biphoton entanglement in myelinated axons suggest quantum optical coherence in biological systems. This paper extends the Helix-Light-Vortex (HLV) Theory to interpret these results, integrating a quasicrystalline spacetime framework, a triadic spiral-time operator, and a QFT-compatible Lagrangian. Biphoton coherence is modeled as a transient perturbation in a discrete informational field, modulated by spiral time components (U1, U2, U3) and embedded in a 6D cut-and-project lattice. We derive an interaction Lagrangian with spiral-time corrections, propose informational gravitational backreaction, and introduce a symbolic grammar for state transitions. Experimental predictions include gravitational anomalies in neuronal systems, CMB low-ℓ modulations, and axion-like signatures. Technological implications, such as bio-quantum interfaces, are explored, unifying quantum neural phenomena with gravitational and consciousness dynamics.

We show how we implemented an end-to-end process to automatically develop a clinical practice knowledge base acquiring from SOAP notes. With our contribution we intend to overcome the "Knowledge Acquisition Bottleneck" problem by jump-starting the knowledge gathering from the most widely available source of clinical information that are natural language reports. We present the different phases of our process to populate automatically a proposed ontology with clinical assertions extracted from daily routine SOAP notes. The enriched ontology becomes a reasoning able knowledge base that depicts accurately and realistically the clinical practice represented by the source reports. With this knowledge structure in place and novel stateof-the-art reasoning capabilities, based in consequence driven reasoners, a clinical QA system based in controlled natural language is introduced that reveals breakthrough possibilities regarding the applicability of Artificial Intelligence techniques to the medical field.

Abstract—Recent years have seen significant research in finding closed form expressions for the delay of the RLC interconnect which improves upon the Elmore delay model. However, several of these formulae assume a step excitation. But in practice, the input waveform does have a non zero time of flight. There are few works reported so far which do consider the ramp inputs but lacks in the explicit nature which could work for a wide range of possible input slews. Elmore delay has been widely used as an analytical estimate of ...

Wavelets are numerical capacities that cut up information into various recurrence parts, and afterward concentrate every segment with a determination coordinated to its scale. They have favorable circumstances over conventional Fourier strategies in dissecting physical circumstances where the sign contains discontinuities and sharp spikes. Wavelets were produced freely in the fields of arithmetic, information correspondence, quantum material science, electrical building, and seismic topography. Trades between these fields amid the most recent ten years have prompted numerous new wavelet applications, for example, picture pressure, turbulence, human vision, radar, and tremor forecast. This paper acquaints wavelets with the intrigued specialized individual outside of the wavelets are utilized as a part of picture pressure. I portray the historical backdrop of wavelets starting with Fourier, contrast wavelet changes and Fourier changes, state properties and other exceptional parts of wavelets, and completion with some fascinating applications, for example, picture pressure.

Accurate classification of soil surface texture plays a pivotal role in agriculture, environmental management, and land-use planning. However, the classification of soil textures from RGB images obtained under uncontrolled field conditions remains a chall a novel approach for Soil Surface Texture Classification using Convolutional Neural Networks to address these challenges. Keywords: texture enhancement Soil surface texture is crucial for agriculture and land labor-intensive, but there's a growing need for real in digital imaging and machine learning, RGB images of soil can be analyzed using Convolutional Neural efficient classification. This research aims to develop a robust system for soil texture classification using CNNs under uncontrolled field condition soil texture classification, enabling better decision The integration of deep learning techniques in soil science This literature review examines the current state of research on deep learning recommendation, highlighting key methodologies, challenges, and future technique involves preprocessing soil images and extracting features using the Gabor wavelet transform.

We can now manufacture AI-based instruments. Data analytics allow for more precise and varied measurements. This has also made the cost of the system decrease a lot. This paper discusses the latest trends in using artificial intelligence in instrumentation. It is now easier to use multiphase flow metering technology. In this system, we can easily measure the flow rates of gas, liquid, and solids in a pipeline or any system. Oil, gas and chemical industry have now started this usage. Chemical processing and mining can also use this technology. This allows for simple calculation of multiphase flows, which can be used in process optimization monitoring and control. There is a growing trend in combustion monitoring because of the use of AI. Consequently, more accurate observation and analysis of the combustion process are possible. Environmental regulations can also be easily checked. This is useful in carbon dioxide flow measurement using carbon capture and storage aspects of it.

Image inpainting refers to the task of filling in the missing or damaged regions of an image in an undetectable manner. There are a large variety of image inpainting algorithms existing in the literature. They can broadly be grouped into two categories such as Partial ...

Image Inpainting is the process of filling in missing regions in an image. The objective of inpainting is to reconstruct the missing regions in a visually plausible way. Several algorithms are available in the literature for the same. Many researchers have ...

Kidney stones are a widespread health problem that can cause severe pain and require urgent medical attention. Accurately identifying and distinguishing kidney stones from other structures in medical images can be challenging due to their varying size, shape, and location. To assist healthcare professionals in making a diagnosis, computer-aided diagnosis systems can be useful, but precise segmentation of the region of interest is crucial. Although CT imaging is a commonly used diagnostic tool for kidney stones, it can be difficult to differentiate stones from other structures, leading to false positives. This study proposes a method to minimize false positives and extract essential structural information for the accurate diagnosis of kidney stones using image processing techniques for kidney organ segmentation with a mean Intersection over Union (IoU) of 76.79%. Additionally, the study aims to leverage machine learning algorithm, Linear Regression, to classify CT scans with multiple stones and improved the accuracy and reliability of disease diagnosis by 93.93%.

This study proposes the integration of principal component analysis (PCA), convolution, dense residual network (DRN) for the image classification of healthy kidneys, renal cysts, stones, and tumors. PCA significantly reduces lowvariance features in the feature-map derived by the stack of convolution-blocks, without losing information content. Together, PCA and DRN reduce the information-loss and improve accuracy. PCA also improves computational efficiency. An algorithm has been presented. The model was tested on three standardized datasets comprising CT scan images, fully annotated by expert clinicians. Ablation studies with and without PCA confirm that the use of PCA improves the mean accuracy from 89.54% to 99.89%. Comparison with other leading models shows that reducing low-variance features improved the computational efficiency by more than 60%, while achieving 99.89% mean accuracy with 0.0007 standard deviation. Our model outperforms accuracy achieved by the leading models and exhibits a classification time of 2.9 seconds on a laptop for each test-case.

Identification of faults/cracks through computer-based techniques is a growing trend these days. Any highly responsive system can be characterized by two key features: quick detection and being highly accurate, by leveraging modern techniques and by efficient utilization of resources. Bone fracture is the result of an excess external force which is beyond the threshold of the bone. Canny Edge detection is an image processing method used to detect the bone fracture through efficient use of automated fracture detection and it overwhelms the noise removal problem. Nowadays there are several methodologies available for edge detection like: Canny, Log, Prewitt, and Robert. However, these techniques are not useful to detect minor details during analysis due to its inability to perform multiresolution analysis. The other key problem of these techniques is that even though they work fine with high resolution and high-quality images they cannot work as well with noisy images because of their inherent lack of ability to differentiate between edges and the noise components. The method we are proposing overcomes over these problems using CNN algorithm. The results from the simulations we observed that the proposed method is a better option to perform edge detection at aggregate scales. The method proposed has also proved to be robust enough to extract the necessary information and do the processing needed and handle noise better than the currently available edge detectors.

This dissertation posits a metaphysical framework, termed Informational Platonism, that resolves the long-standing schism between the objective, seemingly deterministic world described by physics and the subjective, creative, and meaning-driven world of conscious experience. It argues that this dichotomy is illusory, arising from a misunderstanding of the fundamental nature of reality. The proposed solution is a monistic system in which reality is conceived as a single, complete, and atemporal informational structure of a fundamentally geometric and relational nature. The physical, unfolding universe is an instantiation of a subset of this total potentiality. Within this dynamic instantiation, complexity arises through emergence, culminating in consciousness-a localized pattern that perceives, processes, and thereby actualizes the universe's latent information. This framework redefines free will not as a contra-causal force but as a navigational capacity to discover and substantiate pre-existing pathways within the informational landscape. This framework reconciles determinism and free will, positing a Universe that is predetermined in its potential yet allows for meaningful choice in its actualization. Free will is thus redefined not as an act of creation, but selection. Quantum mechanics, rather than contradicting this model, is presented as its most direct evidence, describing the probabilistic nature of information prior to its actualization. The implications of this system are profound, offering an objective basis for morality, a teleological vector for the cosmos as a process of self-discovery, and a re-contextualization of all knowledge and art as acts of discovery rather than creation. It also means that whether or not we expand our understanding into the microscope or telescope, geometry is all we will ever find.

This article presents an integrated approach to the role of entanglement entropy as a quantitative measure of the information shared between two mirror universes coupled at the topological interface of a Klein bottle, according to the Kosmos Theory. This interface represents the holographic mental field, called Cosmological Consciousness. We explore the relationship between quantum entanglement, memory, and consciousness, following the hypotheses of Penrose and Hameroff, and its generalization in the Kosmos model for the emergence of the Unitary Phenomenal Self (UPS). Thus, a physical-mathematical foundation is proposed for consciousness as a cosmic phenomenon emerging from the holographic and entangled structure of the universe.

Wavelet/Total-Variation (WATV) denoising approach in the domain of the Stationary Bionic Wavelet Transform (SBWT). It consists firstly in applying the SBWT to the noisy ECG signal for obtaining two noisy coefficients named wtb1 and wtb2 which are respectively details and approximation coefficients. For estimating the level of noise altering the signal, named σ, we use wtb1. This noise is an additive Gaussian white noise. The thresholding of wtb1 is secondly performed employing the soft thresholding and a denoised coefficient wtd1 is obtained. This thresholding requires the use of a certain threshold, thr which is computed using σ. Thedenoising of wtb2 is performed using WATV denoising method and we obtain a denoised coefficient, wtd2. This WATV denoising method also uses 𝜎. The denoised ECG signal is finally obtained by applying the inverse of SBWT (SBWT -1 ) to wtd1 and wtd2. The proposed technique performance is justified by the results obtained from the computations of Signal to Noise Ratio (SNR), Minimum Square Error (MSE), Mean Absolute Error (MAE), Peak-SNR (PSNR) and Cross-Correlation (CC).

In this paper, is proposed a novel technique of Electrocardiogram (ECG) denoising. It is based on the application of Wavelet/Total-Variation (WATV) denoising approach in the domain of the Stationary Bionic Wavelet Transform (SBWT). It consists firstly in applying the SBWT to the noisy ECG signal for obtaining two noisy coefficients namedwtb1 and wtb2 which are respectively details and approximation coefficients. For estimating the level of noise altering the signal, namedσ, we use wtb1. This noise is an additive Gaussian white noise. The thresholding ofwtb1 is secondly performed employing the soft thresholding and a denoised coefficient wtd1 is obtained. This thresholding requires the use of a certain threshold, thr which is computed using σ. Thedenoising ofwtb2 is performed using WATV denoising method and we obtain a denoised coefficient, wtd2. This WATV denoising method also uses σ. The denoised ECG signal is finally obtained by applying the inverse of SBWT (SBWT-1) to wtd1 and wt...

Traditionally, Blind Speech Separation techniques are computationally expensive as they update the demixing matrix at every time frame index, making them impractical to use in many Real-Time applications. In this paper, a robust data driven twomicrophone sound source localization method is used as a criterion to reduce the computational complexity of the Independent Vector Analysis (IVA) Blind Speech Separation (BSS) method. IVA is used to separate convolutedly mixed speech and noise sources. The practical feasibility of the proposed method is proved by implementing it on a smartphone device to separate speech and noise in Real-World scenarios for Hearing-Aid applications. The experimental results with objective and subjective tests reveal the practical usability of the developed method in many real-world applications.