Time--to--Digital Converter (TDC)--Based Resonant Compute--in--Memory for INT8 CNNs with Layer--Optimized SRAM Mapping
Dhandeep Challagundla, Ignatius Bezzam, Riadul Islam
In recent years, Compute-in-memory (CiM) architectures have emerged as a promising solution for deep neural network (NN) accelerators. Multiply-accumulate~(MAC) is considered a {\textit de facto} unit operation in NNs. By leveraging the inherent parallel processing capabilities of CiM, NNs that require numerous MAC operations can be executed more efficiently. This is further facilitated by storing the weights in SRAM, reducing the need for extensive data movement and enhancing overall computational speed and efficiency. Traditional CiM architectures execute MAC operations in the analog domain, employing an Analog-to-Digital converter (ADC) to convert the analog MAC values into digital outputs. However, these ADCs introduce significant increase in area and power consumption, as well as introduce non-linearities. This work proposes a resonant time-domain compute-in-memory (TDC-CiM) architecture that eliminates the need for an ADC by using a time-to-digital converter (TDC) to digitize analog MAC results with lower power and area cost. A dedicated 8T SRAM cell enables reliable bitwise MAC operations, while the readout uses a 4-bit TDC with pulse-shrinking delay elements, achieving 1 GS/s sampling with a power consumption of only 1.25 mW. In addition, a weight stationary data mapping strategy combined with an automated SRAM macro selection algorithm enables scalable and energy-efficient deployment across CNN workloads. Evaluation across six CNN models shows that the algorithm reduces inference energy consumption by up to 8x when scaling SRAM size from 32~KB to 256~KB, while maintaining minimal accuracy loss after quantization. The feasibility of the proposed architecture is validated on an 8~KB SRAM memory array using TSMC 28~nm technology. The proposed TDC-CiM architecture demonstrates a throughput of 320~GOPS with an energy efficiency of 38.46~TOPS/W.
DMAVA: Distributed Multi-Autonomous Vehicle Architecture Using Autoware
Zubair Islam, Mohamed El-Darieby
Simulating and validating coordination among multiple autonomous vehicles remains challenging, as many existing simulation architectures are limited to single-vehicle operation or rely on centralized control. This paper presents the Distributed Multi-Autonomous Vehicle Architecture (DMAVA), a simulation architecture that enables concurrent execution of multiple independent vehicle autonomy stacks distributed across multiple physical hosts within a shared simulation environment. Each vehicle operates its own complete autonomous driving stack while maintaining coordinated behavior through a data-centric communication layer. The proposed system integrates ROS 2 Humble, Autoware Universe, AWSIM Labs, and Zenoh to support high data accuracy and controllability during multi-vehicle simulation, enabling consistent perception, planning, and control behavior under distributed execution. Experiments conducted on multiple-host configurations demonstrate stable localization, reliable inter-host communication, and consistent closed-loop control under distributed execution. DMAVA also serves as a foundation for Multi-Vehicle Autonomous Valet Parking, demonstrating its extensibility toward higher-level cooperative autonomy. Demo videos and source code are available at: https://github.com/zubxxr/distributed-multi-autonomous-vehicle-architecture.
On faithfulness and DP-transformations generated by arithmetic Cantor series expansions
Grygoriy Torbin, Yuliia Voloshyn
The paper is devoted to the study of conditions for the Hausdorff-Besicovitch faithfulness of the family of cylinders generated by Cantor series expansions. We show that there exist subgeometric Cantor series expansions for which the corresponding families of cylinders are not faithful for the Hausdorff-Besicovitch dimension on the unit interval. On the other hand we found a rather wide subfamily of subgeometric Cantor series expansions generating faithful families of cylinders. We also study conditions for the Hausdorff-Besicovitch dimension preservation on [0;1] by probability distribution functions of random variables with independent symbols of arithmetic Cantor series expansions.
The Influence of Sustainability Report Disclosure and Environmental Performance on Company Financial Performance
Ria Andriani
This study aims to examine the influence of sustainability report disclosure and environmental performance on company financial performance. As environmental concerns and corporate social responsibility gain importance, companies are increasingly expected to demonstrate accountability through transparent reporting and sustainable practices. The research employs a quantitative approach using secondary data from annual reports and sustainability reports of companies listed on the Indonesia Stock Exchange. Financial performance is measured using indicators such as Return on Assets (ROA) and Return on Equity (ROE), while environmental performance is assessed based on PROPER ratings. The findings indicate that both sustainability report disclosure and environmental performance have a significant and positive impact on the financial performance of companies. This suggests that companies that are more transparent in disclosing sustainability practices and perform well environmentally tend to gain higher trust from stakeholders, which contributes to improved financial outcomes.
Islam, Economics as a science
Examining the Virtue of Justice in Spousal Relationships from a Psychological and Religious Perspective
Fatma Baynal
Although justice is one of the most fundamental concepts in human history, there is no consensus on its definition. However, justice can be interpreted as the granting of rights to their holders and the application of values and social rules. Justice is given importance in every social unit, including the family. In Turkey, 2025 has been declared the Year of the Family, and some goals have been set to protect the family. The subject of this research is the psychological and religious/spiritual analysis of the virtue of justice in the relationships of spouses with each other and with their children in the family. This study aims to analyze the relationship between spouses psychologically and religiously within the framework of the virtue of justice. For this purpose, a literature review was conducted on this topic. The semi-structured interview method, one of the qualitative research methods, was used in the study. The semi-structured interview method, one of the qualitative research methods, was applied in the study. Interviews were conducted with a total of 26 people, 13 working women and 13 working men. Criterion sampling method, one of the purposeful sampling methods, was used in the research. The data were analyzed by the thematic analysis method. At the end of the research, it was found that the virtue of justice between spouses is effective in maintaining healthy relationships between spouses. It has been determined that justice has significant effects on social communication, responsibilities, relations with the environment; psychological coping with adversities, psychological resilience, and psychological well-being in the family. For individuals with religious beliefs, it was found that belief in justice in the family was effective in experiencing and transferring spiritual values and coping with conflicts. When injustice is experienced between spouses, it was observed that individuals were negatively affected psychologically. It has been revealed that religious belief has a positive psychological effect on individuals as a consolation mechanism against injustices experienced in the family. However, it was found that the fact that the spouse is a religious individual does not mean that he/she will always ensure justice in the family; the family of origin can be effective in teaching justice to individuals. For working women, it was stated that when an egalitarian approach is not exhibited in terms of duties within the family, both physical and psychological difficulties are experienced. In addition, it has been revealed that individuals can prioritize love over justice, especially in terms of children; even if they are subjected to injustice, the love between the spouses continues, or the relationship continues due to the child element. However, to ensure justice and strengthen the bond of love in the marital union, it was observed that dividing work, such as housework and childcare, equally between the spouses is crucial. In addition, spouses being together more and having healthy communication with each other may be effective in ensuring emotional justice. The results of the study are limited to the data collected from a small group of participants. Expanding scientific studies on marriage and justice and conducting these studies in institutions such as family counseling centers may positively affect marital stability and satisfaction. In addition, the majority of the participants stated that they experienced injustice at work. Therefore, it would be useful to conduct future studies on work-life, marriage, and justice.
Islam. Bahai Faith. Theosophy, etc.
Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction
Nazmul Islam, Mohammad Zulkernine
Machine learning (ML) in Internet of Vehicles (IoV) applications enhanced intelligent transportation, autonomous driving capabilities, and various connected services within a large, heterogeneous network. However, the increased connectivity and massive data exchange for ML applications introduce significant privacy challenges. Privacy-preserving machine learning (PPML) offers potential solutions to address these challenges by preserving privacy at various stages of the ML pipeline. Despite the rapid development of ML-based IoV applications and the growing data privacy concerns, there are limited comprehensive studies on the adoption of PPML within this domain. Therefore, this study provides a comprehensive review of the fundamentals, recent advancements, and the challenges of integrating PPML into IoV applications. We first review existing surveys of various PPML techniques and their integration into IoV across different scopes. We then categorize IoV applications into three key domains and analyze the privacy challenges in leveraging ML in these application domains. Building on these fundamentals, we review recent advancements in integrating various PPML techniques within IoV applications, discussing their frameworks, key features, and performance in terms of privacy, utility, and efficiency. Finally, we identify current challenges and propose future research directions to enhance privacy and reliability in IoV applications.
Bayesian analysis of late-time tails in spin-aligned eccentric binary black hole mergers
Tousif Islam, Guglielmo Faggioli, Gaurav Khanna
We present a comprehensive analysis of late-time tails in gravitational radiation from merging spin-aligned eccentric binary black holes, using high-accuracy point-particle black hole perturbation theory simulations. We simulate the late-time evolution of 15 binary black hole mergers with mass ratio $q = 1000$, dimensionless spins $χ= [-0.9, -0.6, 0.0, 0.6, 0.9]$ and eccentricity at the last stable orbit $e_{\rm LSO} = [0.8, 0.9, 0.95]$. We track the tail amplitudes and exponents up to a retarded time coordinate $t = 9000M$ after merger for the six spin-weighted spherical harmonic modes $(2,1)$, $(2,2)$, $(3,2)$, $(3,3)$, $(4,3)$, and $(4,4)$ employing both frequentist and Bayesian approaches. We note that the tails are increasingly pronounced for binaries with high eccentricity $e_{\rm LSO}$ and large negative spin $χ$. We find that the overall late-time exponents closely approach their predicted asymptotic values ($p=-\ell-4$ for Weyl curvature scalar $ψ_{4,\ell m}$ where $\ell$ is the spin-weighted spherical harmonic index), while estimates restricted to the latest portion of the data exactly recover them. We further verify numerically that modes with the same spherical index $\ell$ share identical tail exponents, while variations in $m$ do not affect the tail behavior. Our analysis framework is publicly available through the gwtails Python package.
A Workflow for Map Creation in Autonomous Vehicle Simulations
Zubair Islam, Ahmaad Ansari, George Daoud
et al.
The fast development of technology and artificial intelligence has significantly advanced Autonomous Vehicle (AV) research, emphasizing the need for extensive simulation testing. Accurate and adaptable maps are critical in AV development, serving as the foundation for localization, path planning, and scenario testing. However, creating simulation-ready maps is often difficult and resource-intensive, especially with simulators like CARLA (CAR Learning to Act). Many existing workflows require significant computational resources or rely on specific simulators, limiting flexibility for developers. This paper presents a custom workflow to streamline map creation for AV development, demonstrated through the generation of a 3D map of a parking lot at Ontario Tech University. Future work will focus on incorporating SLAM technologies, optimizing the workflow for broader simulator compatibility, and exploring more flexible handling of latitude and longitude values to enhance map generation accuracy.
Understanding and Leveraging the Expert Specialization of Context Faithfulness in Mixture-of-Experts LLMs
Jun Bai, Minghao Tong, Yang Liu
et al.
Context faithfulness is essential for reliable reasoning in context-dependent scenarios. However, large language models often struggle to ground their outputs in the provided context, resulting in irrelevant responses. Inspired by the emergent expert specialization observed in mixture-of-experts architectures, this work investigates whether certain experts exhibit specialization in context utilization, offering a potential pathway toward targeted optimization for improved context faithfulness. To explore this, we propose Router Lens, a method that accurately identifies context-faithful experts. Our analysis reveals that these experts progressively amplify attention to relevant contextual information, thereby enhancing context grounding. Building on this insight, we introduce Context-faithful Expert Fine-Tuning (CEFT), a lightweight optimization approach that selectively fine-tunes context-faithful experts. Experiments across a wide range of benchmarks and models demonstrate that CEFT matches or surpasses the performance of full fine-tuning while being significantly more efficient.
An Improved Ensemble-Based Machine Learning Model with Feature Optimization for Early Diabetes Prediction
Md. Najmul Islam, Md. Miner Hossain Rimon, Shah Sadek-E-Akbor Shamim
et al.
Diabetes is a serious worldwide health issue, and successful intervention depends on early detection. However, overlapping risk factors and data asymmetry make prediction difficult. To use extensive health survey data to create a machine learning framework for diabetes classification that is both accurate and comprehensible, to produce results that will aid in clinical decision-making. Using the BRFSS dataset, we assessed a number of supervised learning techniques. SMOTE and Tomek Links were used to correct class imbalance. To improve prediction performance, both individual models and ensemble techniques such as stacking were investigated. The 2015 BRFSS dataset, which includes roughly 253,680 records with 22 numerical features, is used in this study. Strong ROC-AUC performance of approximately 0.96 was attained by the individual models Random Forest, XGBoost, CatBoost, and LightGBM.The stacking ensemble with XGBoost and KNN yielded the best overall results with 94.82\% accuracy, ROC-AUC of 0.989, and PR-AUC of 0.991, indicating a favourable balance between recall and precision. In our study, we proposed and developed a React Native-based application with a Python Flask backend to support early diabetes prediction, providing users with an accessible and efficient health monitoring tool.
Student Teams-Achievement Division (STAD) To Increase Students' Social and Spiritual Intelligence
St. Wardah Hanafie Das, Abdul Halik, Muhammad Naim
et al.
This research discusses STAD in Cooperative Learning strategy to enhance students' social intelligence. It focuses on how to apply the cooperative learning strategy, social intelligence, strengths, and weaknesses, which aims to determine the process of implementing the unified learning model from beginning to end and describe the social intelligence of students after its implementation. The author used classroom action research with a qualitative descriptive approach to complement this study. Research on the disclosure of problems as they existed was data analysis using field research, observation, interviews, and documentation using data analysis, namely, data presentation, data reduction, and withdrawal. The results of this study indicated that the implementation of PAI learning on the STAD-type cooperative learning strategy in SMPN 4 Simbang class VII-A was well-implemented, effective, efficient, fun, and exciting. Besides, it had a positive impact on students ' learning. There were several advantages to implementing cooperative learning strategies, including fun, self-confidence, responsibility, interest in Education, and creative thinking. Some of these factors were interconnected or related, needed, and complemented each other. Then, the shortcomings in implementing cooperative learning strategies included excessive pleasure, less skill, and limited learning resources and media.
Theory and practice of education, Islam
A High-Temperature Thermocouple Development by Additive Manufacturing: Tungsten-Nickel (W-Ni) and Molybdenum (Mo) Integration with Ceramic Structures
Azizul Islam, Aayush Alok, Vamsi Borra
et al.
Additive manufacturing holds more potential to enable the development of ceramic-based components. Ceramics offer high resistance to heat, high fracture toughness, and are extremely corrosion resistant. Thus, ceramics are widely used in sectors such as the aerospace industry, automotive, microelectronics, and biomedicine. Using various additive manufacturing platforms, ceramics with complex and uniquely designed geometry can be developed to suit specific applications. This project aims at innovating high-temperature thermocouples by embedding conductive metal pastes into a ceramic structure. The paste used includes tungsten, molybdenum, and antimony. The metal pastes are precisely extruded into a T-shaped trench inside the ceramic matrix. Following specific temperature ranges, the ceramic matrix is sintered to improve the properties of the material. The sensors produced can function at extremely high temperatures and are thereby suitable for high-temperature environments. Comparative testing of the 3D sintered sensors with conventional temperature sensors shows high correlation between the two classes of sensors. The resulting R-squared value of 0.9885 is satisfactory which implies the reliability and accuracy of 3D sintering sensors are satisfactory in temperature sensing applications.
en
physics.chem-ph, cond-mat.mtrl-sci
A review of faithfulness metrics for hallucination assessment in Large Language Models
Ben Malin, Tatiana Kalganova, Nikoloas Boulgouris
This review examines the means with which faithfulness has been evaluated across open-ended summarization, question-answering and machine translation tasks. We find that the use of LLMs as a faithfulness evaluator is commonly the metric that is most highly correlated with human judgement. The means with which other studies have mitigated hallucinations is discussed, with both retrieval augmented generation (RAG) and prompting framework approaches having been linked with superior faithfulness, whilst other recommendations for mitigation are provided. Research into faithfulness is integral to the continued widespread use of LLMs, as unfaithful responses can pose major risks to many areas whereby LLMs would otherwise be suitable. Furthermore, evaluating open-ended generation provides a more comprehensive measure of LLM performance than commonly used multiple-choice benchmarking, which can help in advancing the trust that can be placed within LLMs.
Kâtip Çelebi’ye Göre Mârifetullah
Hatice Toksöz
Bu çalışmanın konusu, İslam düşünce geleneğinin önemli bir meselesi olan mârifetullah’tır. Çalışmada Osmanlı Devleti’nin 17. yüzyılında yetişmiş önde gelen bilginlerinden biri olan Kâtip Çelebi’ye (ö. 1067/1657) göre mârifetullah meselesinin araştırılması amaçlanmıştır. Kâtip Çelebi, hem aklî ve dinî ilimlerde oldukça yetkin hem de döneminin bilim bilimsel gelişmelerinin farkında olan bir şahsiyettir. Düşünür, başta Keşfü’z-zûnûn olmak üzere çeşitli eserlerinde Tanrı’nın varlığı ve birliğinin ispatı, ilâhî sıfatlar ve benzeri konular hakkında telif edilmiş literatür hakkında mukayeseli bilgi vermektedir. Ayrıca Kâtip Çelebi’nin mârifetullah konusuna ilişkin İslam düşünce geleneğindeki literatürü değerlendirmekle birlikte, eklektik yorumlarıyla meseleye önemli katkılar sağladığı görülmektedir. Bu sebeple İslam metafizik düşüncesinin önemli bir tartışma meselesi olan isbât-ı vâcib ve mârifetullah konusuna dair Kâtip Çelebi’nin iddia, yorum ve katkılarının belirlenmesi İslam düşünce geleneğinin 17. yüzyıldaki gelişim ve sürekliliğinin anlaşılması açısından oldukça önemlidir. Kâtip Çelebi, insanın fıtrî olarak öğrenme arzusu bulunduğunu belirtmektedir. Ona göre insanda var olan bu öğrenme arzusu, onun aklıyla bilgi elde etmesine imkân vermektedir. İnsan kazandığı bilgilerle metafizik hakikatleri idrak edip en yüce mutluluğa (es-sa’âdetü’l-kusvâ) ulaşma yollarını öğrenebilmektedir. Böylece insan mârifetullah hakkında bilgi sahibi olma imkânı elde etmektedir. Kâtip Çelebi’ye göre mârifetullah, bir insanın ulaşabileceği en yüksek bilgi seviyesidir. Düşünür, insanın bu en yüksek bilgi seviyesine ancak metafizik ilmini öğrenmesiyle mümkün olduğunu belirtmektedir. Çünkü metafizik ilmi, hem Tanrı, O’nun varlığı ve birliği, ilâhî sıfatlar ve ilâhî fiillerin bilgisinin nasıl kazanılabileceği hem de insanın gayesi olan ebedi mutluluk ve bu mutluluğa nasıl ulaşılabileceği hakkında bilgi vermektedir. Çelebi, İslam düşünce geleneğinde, bilhassa müteahhir dönemde hakkında müstakil risalelerin telif edilmek suretiyle kozmopolit bir kelam-felsefe meselesi haline dönüşmüş olan mârifetullah’a ulaştıran iki farklı yöntem ortaya konulduğunu ifade etmiştir. Bu yöntemlerden biri nazar ve istidlâl; diğeri keşf ve müşahededir. Ona göre her ne kadar iki yol birbirinden farklı gibi görünse de her iki yol da aynı hakikate ulaştırmaktadır. Çünkü bu yollardan birinden hareket edilerek diğerine ulaşabilme imkânı söz konusudur. Görünüşte birbirinden farklıymış gibi olan her iki yolun yolcusu da tıpkı iki denizin birleştiği bir yer gibi, aynı hakikate ulaşabilmektedir. Dolayısıyla Kâtip Çelebi, her iki yolun birbirini tamamladığını düşünmekte ve her iki yöntemin birlikte kullanılması suretiyle insanın Tanrı’yı tanıyabileceğini belirtmektedir. Bu bağlamda çalışmada Kâtip Çelebi’nin zikrettiği mârifetullah’a ulaştıran iki yönteme ilişkin yorum, katkı ve eleştirileri detaylı bir şekilde incelenecektir.
Philosophy. Psychology. Religion, Islam. Bahai Faith. Theosophy, etc.
Epistemologis Tafsir Tematik: Menuju Tafsir Al-Qur’an Yang Holistik
Fajri Kamil, Pathur Rahman, Sulaiman Mohammad Nur
et al.
The aim of this research is to discuss the Epistemological Efforts of Thematic Interpretation in Holistic Interpretation of the Qur'an. This research falls under the category of qualitative research, therefore the researcher only conducts a literature review. The first step involves gathering literature sources, both primary and secondary sources. The next step in this research is to provide a description of the literature sources that discuss the history, meaning, principles, and steps of thematic interpretation. The conclusion of this research is obtained through an analysis or epistemological approach to the method of thematic interpretation. The results and discussions of this research reveal that the discourse on the thematic method among scholars and scientists has led to the development of this method through the collaboration of modern scientific knowledge in the field of interpretation. This collaborative interpretation is referred to as integrative thematic interpretation, which enhances the existing classification of thematic interpretation. Ideally, all classifications of thematic interpretation can be interwoven into an integrative formulation of thematic interpretation, thus resulting in a holistic thematic interpretation of the Qur'an. Holistic thematic interpretation of the Qur'an is an epistemological effort to establish the validity of interpretation as a divine truth and as a rational and sensory truth that can be justified apriori, aposteriori, and pragmatically.
Keywords: qur’an; thematic interpretation; epistemological; holistic.
Personalized Prediction of Recurrent Stress Events Using Self-Supervised Learning on Multimodal Time-Series Data
Tanvir Islam, Peter Washington
Chronic stress can significantly affect physical and mental health. The advent of wearable technology allows for the tracking of physiological signals, potentially leading to innovative stress prediction and intervention methods. However, challenges such as label scarcity and data heterogeneity render stress prediction difficult in practice. To counter these issues, we have developed a multimodal personalized stress prediction system using wearable biosignal data. We employ self-supervised learning (SSL) to pre-train the models on each subject's data, allowing the models to learn the baseline dynamics of the participant's biosignals prior to fine-tuning the stress prediction task. We test our model on the Wearable Stress and Affect Detection (WESAD) dataset, demonstrating that our SSL models outperform non-SSL models while utilizing less than 5% of the annotations. These results suggest that our approach can personalize stress prediction to each user with minimal annotations. This paradigm has the potential to enable personalized prediction of a variety of recurring health events using complex multimodal data streams.
Robustness Stress Testing in Medical Image Classification
Mobarakol Islam, Zeju Li, Ben Glocker
Deep neural networks have shown impressive performance for image-based disease detection. Performance is commonly evaluated through clinical validation on independent test sets to demonstrate clinically acceptable accuracy. Reporting good performance metrics on test sets, however, is not always a sufficient indication of the generalizability and robustness of an algorithm. In particular, when the test data is drawn from the same distribution as the training data, the iid test set performance can be an unreliable estimate of the accuracy on new data. In this paper, we employ stress testing to assess model robustness and subgroup performance disparities in disease detection models. We design progressive stress testing using five different bidirectional and unidirectional image perturbations with six different severity levels. As a use case, we apply stress tests to measure the robustness of disease detection models for chest X-ray and skin lesion images, and demonstrate the importance of studying class and domain-specific model behaviour. Our experiments indicate that some models may yield more robust and equitable performance than others. We also find that pretraining characteristics play an important role in downstream robustness. We conclude that progressive stress testing is a viable and important tool and should become standard practice in the clinical validation of image-based disease detection models.
Sentiment analysis and opinion mining on E-commerce site
Fatema Tuz Zohra Anny, Oahidul Islam
Sentiment analysis or opinion mining help to illustrate the phrase NLP (Natural Language Processing). Sentiment analysis has been the most significant topic in recent years. The goal of this study is to solve the sentiment polarity classification challenges in sentiment analysis. A broad technique for categorizing sentiment opposition is presented, along with comprehensive process explanations. With the results of the analysis, both sentence-level classification and review-level categorization are conducted. Finally, we discuss our plans for future sentiment analysis research.
Multivariate Empirical Mode Decomposition of EEG for Mental State Detection at Localized Brain Lobes
Monira Islam, Tan Lee
In this study, the Multivariate Empirical Mode Decomposition (MEMD) approach is applied to extract features from multi-channel EEG signals for mental state classification. MEMD is a data-adaptive analysis approach which is suitable particularly for multi-dimensional non-linear signals like EEG. Applying MEMD results in a set of oscillatory modes called intrinsic mode functions (IMFs). As the decomposition process is data-dependent, the IMFs vary in accordance with signal variation caused by functional brain activity. Among the extracted IMFs, it is found that those corresponding to high-oscillation modes are most useful for detecting different mental states. Non-linear features are computed from the IMFs that contribute most to mental state detection. These MEMD features show a significant performance gain over the conventional tempo-spectral features obtained by Fourier transform and Wavelet transform. The dominance of specific brain region is observed by analysing the MEMD features extracted from associated EEG channels. The frontal region is found to be most significant with a classification accuracy of 98.06%. This multi-dimensional decomposition approach upholds joint channel properties and produces most discriminative features for EEG based mental state detection.
Morse-Novikov cohomology on foliated manifolds
Md. Shariful Islam
The idea of Lichnerowicz or Morse-Novikov cohomology groups of a manifold has been utilized by many researchers to study important properties and invariants of a manifold. Morse-Novikov cohomology is defined using the differential $d_ω=d+ω\wedge$, where $ω$ is a closed $1$-form. We study Morse-Novikov cohomology relative to a foliation on a manifold and its homotopy invariance and then extend it to more general type of forms on a Riemannian foliation. We study the Laplacian and Hodge decompositions for the corresponding differential operators on reduced leafwise Morse-Novikov complexes. In the case of Riemannian foliations, we prove that the reduced leafwise Morse-Novikov cohomology groups satisfy the Hodge theorem and Poincar{é} duality. The resulting isomorphisms yield a Hodge diamond structure for leafwise Morse-Novikov cohomology.