Miracle-dz/UniFAR-FTUs huggingface dataset:
Iterative Memoryless Non-linear Estimators of Correlation for Complex-Valued Gaussian Processes That Exhibit Robustness to Impulsive Noise

The autocorrelation function is a commonly used tool in statistical time series analysis. Under the assumption of Gaussianity, the sample autocorrelation function is the standard method used to estimate this function given a finite number of observations. Non-Gaussian, impulsive observation noise following probability density functions with thick tails, which often occurs in practice, can bias this estimator, rendering classical time series analysis methods ineffective. This work examines the robustness of two estimators of correlation based on memoryless nonlinear functions of observations, the Phase-Phase Correlator (PPC) and the Medianof-Ratios Estimator (MRE), which are applicable to complex-valued Gaussian random processes. These estimators are very fast and easy to implement in current processors. We show that these estimators are robust from a bias perspective when complex-valued Gaussian processes are contaminated with impulsive noise at the expense of statistical efficiency at the assumed Gaussian distribution. Additionally, iterative versions of these estimators named the IMRE and IPPC are developed, realizing an improved bias performance over their noniterative counterparts and the well-known robust Schweppe-type Generalized M-estimator utilizing a Huber cost function (SHGM). An impulsive noise suppression technique is developed using basis pursuit and a priori atom weighting derived from the newly developed iterative estimators. This new technique is proposed as an alternative to the robust filter cleaner, a Kalman filter-like approach that relies on linear prediction residuals to identity and replace corrupted observations. It does not have the same initialization issues as the robust filter cleaner. Robust spectral estimation methods are developed using these new estimators and impulsive noise suppression techniques. Results are obtained for synthetic complex-valued Guassian processes and real-world digital television signals collected using a software defined radio.
Skorohod Integral at Vacuum State on Guichardet-Fock Spaces

In this paper, we define expectation of f∈F, i.e. E(f)=f(? ), according to Wiener-Ito-Segal isomorphic relation between Guichardet-Fock space F and Wienerspace W. Meanwhile, we derive a formula for the expectation of random Hermite polynomial in Skorohod integral on Guichardet- Fock spaces. In particular, we prove that the anticipative Girsanov identities under the condition E(Hx(δ(x),‖x‖2)),n≥1 on Guichardet-Fock spaces.
Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems

Some time ago, the continuous ranked probability score (CRPS) was proposed as a new verification tool for (probabilistic) forecast systems. Its focus is on the entire permissible range of a certain (weather) parameter. The CRPS can be seen as a ranked probability score with an infinite number of classes, each of zero width. Alternatively, it can be interpreted as the integral of the Brier score over all possible threshold values for the parameter under consideration. For a deterministic forecast system the CRPS reduces to the mean absolute error. In this paper it is shown that for an ensemble prediction system the CRPS can be decomposed into a reliability part and a resolution/uncertainty part, in a way that is similar to the decomposition of the Brier score. The reliability part of the CRPS is closely connected to the rank histogram of the ensemble, while the resolution/ uncertainty part can be related to the average spread within the ensemble and the behavior of its outliers. The usefulness of such a decomposition is illustrated for the ensemble prediction system running at the European Centre for Medium-Range Weather Forecasts. The evaluation of the CRPS and its decomposition proposed in this paper can be extended to systems issuing continuous probability forecasts, by realizing that these can be interpreted as the limit of ensemble forecasts with an infinite number of members.
Neurostereology: Unbiased Stereology Of Neural Systems

Stereological methods provide researchers with unparalleled quantitative data from tissue samples and allow for well-evidenced research advances in a broad range of scientific fields. Presenting a concise introduction to the methodology and application of stereological research in neuroscience, Neurostereology provides a fuller understanding of the use of these methods in research and a means for replicating successful scientific approaches. Providing sound footing for future research, Neurostereology is a useful tool for basic and clinical researchers and advanced students looking to integrate these methods into their research.
A comprehensive review on ReRAM-based accelerators for deep learning

The current success of AI-artificial intelligence is mainly due to the ubiquity of deep learning network in numerous applications such as language processing, object detection, image classification etc. Consequently, many research works are developing memristor-based accelerators to improve the effectiveness of existing deep learning networks. One of the design aspects of accelerators is to achieve computation near-memory to overcome the memory wall and von Neumann bottleneck. The requirements of deep neural networks to perform complex computations are increasing faster than the improvement in recent CMOS- based hardware. To achieve this, researchers have investigated a variety of crossbar topologies based on non-volatile memory to attain complex operations and increase on-chip storage density. This paper presents a comprehensive review of accelerators designed using non-volatile memories. The key challenge with the NVM-based crossbar is the extensive range of non-idealities at the device and circuit levels. Many research works have proposed a framework for mitigating non-idealities. This paper also reviews accelerators designed for modelling non-idealities in the NVM crossbar.
Enhanced No2 Gas Sensing Using Surface Plasmon Resonance Sensor Based on MXene and Black Phosphorus

For sensitivity improvement, a Kretschmann-configured SPR gas sensor with a monolayer of MXene (Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub>), a bimetal layer of silver (Ag)–gold (Au), and 40 layers of black phosphorous (BP) is proposed. A sodium fluoride (NaF) glass prism is used here as a coupling substrate. The performance of the sensor is examined using the angular interrogation technique. BP surface is used for the attachment of gas molecules due to its unique binding properties for gas molecules. It is observed that the proposed gas sensor shows the maximum sensitivity of 248°/refractive index unit (RIU) at <inline-formula> <tex-math notation="LaTeX">$R_{\mathrm {min}}$ </tex-math></inline-formula> value of <inline-formula> <tex-math notation="LaTeX">$6.5\times 10^{-4}$ </tex-math></inline-formula> (a.u.) and a remarkable figure of merit. Numerical results show that the use of multilayer BP improves the sensor performance over a conventional gas sensor. In addition, the capability of the proposed gas sensor to detect gases ranging from helium to carbon dioxide has been demonstrated.
Efficient Medical Diagnosis Hybrid System based on RF-DNN Mixed Model for Skin Diseases Classification

Accurate and efficient diagnosis is essential for effective treatment and management of these diseases. The current diagnostic methods rely mostly on visual inspection by dermatologists, which can be subjective and time-consuming. Therefore, there is a need for an automated and accurate system for skin disease diagnosis. A hybrid system has the potential to improve the diagnostic accuracy and efficiency of skin disease classification. The proposed research presents an efficient medical diagnosis hybrid system that combines a Random Forest model and a Deep Neural Network for the classification of skin diseases. The system aims to improve diagnostic accuracy and efficiency by utilizing the strengths of both algorithms, such as their ability to handle large datasets, provide fast and accurate predictions, and analyze images of the patient's skin. The system is composed of two parts, a Random Forest classifier and a DNN classifier, and is evaluated on a dataset of skin disease images, achieving an accuracy of 96.8%. To optimize efficiency, the DNN is trained on a subset of data where the Random Forest model is less confident, and the system is able to identify important features for skin disease classification. The benefits of this hybrid system are clear, including increased accuracy and reliability, reduced time and cost associated with diagnosis, and its potential for continued use in the future.
Programmable Active Scans Controlled by Passive Traffic Inference for IoT Asset Characterization

The proliferation of Internet-of-things (IoT) assets has expanded the attack surface of enterprise networks exposed to malicious actors. Therefore, obtaining visibility into connected assets and their behavioral characteristics is increasingly becoming essential to security teams in better managing their network and connected assets. Scheduled vulnerability scans are widely used by enterprises to manage traditional information technology (IT) assets. However, resource-constraint IoT assets may not always withstand disruptive active scans. Passive traffic inference tools have recently emerged for continuous network detection and response capabilities that can be safely applied to IoT and IT networks. Both active and passive approaches come with advantages and limitations in the insights they provide versus measurement and computing costs. This paper attempts to systematically and dynamically leverage the combined capabilities offered by these two approaches. Our contributions are twofold. (1) We highlight capabilities (richness of insights, response time, and temporal utility) and quantity costs (overhead traffic and computing resources) across five active scanning tools (open-source and commercial) and a commercial passive inference tool by applying them to our testbed consisting of 12 commercial IoT devices; and, (2) We develop “pScan”, a programmable packet emitter with open APIs that is dynamically controlled to perform contextualized scans on target IoT assets via SNMP, mDNS, and SSDP packets, as well as banner grabbing and custom probing via TCP connections. We show on our testbed how pScan integrated with the commercial passive inference tool helps to maximize the insights into the characteristics of IoT assets and their utility at significantly reduced costs. We contribute pScan as open source.
Novel CNN Accelerator Design With Dual Benes Network Architecture

We presented a novel hardware architecture that uses dual Benes networks to accelerate Convolutional Neural Network (CNN) algorithms. This architecture can reduce the need for high-speed buses and maintain a high-speed connection between execution units and memories. Also, this design can work with multiple neural network models by changing the Benes network configuration only due to the reorderability of the non-blocking networks. The proposed architecture can save the time of re-implementing the hardware design. Thus, this architecture can act as a general CNN accelerator. We successfully implemented our CNN accelerator on the Xilinx Virtex UltraScale+ VU19P and evaluated it by running the CNN model LeNet-5.
An Optimized Wireless Video Transmission in a Smart Agricultural Solutions

Smart agriculture requires field sensing, especially video and photo transmissions, to detect severe issues in this field. Although the most suitable communication infrastructure is wireless transmission, the agriculture field suffers from severe diffraction and scattering of the wirelessly propagated waves. This paper proposes an optimization technique for improving wireless transmission in the agricultural environment by utilizing the most appropriate wireless impairment mitigation methods. The proposed technique adopts a joint wireless transmission optimization approach for both the client and the network sides. The amendment in the wireless transmission from the client-side perspective utilized the concurrent load balance over the heterogeneous wireless connections. While the refinement of the heterogeneous wireless network achieved better coverage by minimizing the number of propagating nodes by optimizing the propagating nodes' placement within the agricultural plot. The simulation, experiment, and results proved the effectiveness of the proposed solution for achieving better wireless video transmission performance.
MIMO-NOMA and MIMO-OMA: Outage Probability Analysis and BER comparative study

5G wireless technology is available to many more users than other cellular networks. For users, there are several ways to connect to base stations, including Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Orthogonal Multiple Access (OMA), and Non-orthogonal multiple access (NOMA). This last one is considered the best option for 5G, which enables sharing the same resources (e.g., frequency, time, and code domain resources) with multiple users. Furthermore, NOMA permits massive connections and increases the efficiency of the spectrum network by using strategies for radio resource optimization and interference control that involves successive interference cancellation (SIC). There are two types of users: strong and weak. A high data rate is required by strong users, while a low one is sufficient for weak users. Throughout this paper, we compare the rate achievable capacity, outage probability (OP), and Bit Error Rate (BER) indicatively with transmit power, of those two users' type. [Moreover, we also compare the outage probability per fair and fixed power allocation (PA), applied respectively to NOMA and OMA. This study centers on creating a MATLAB framework for systems using non-orthogonal multiple access (NOMA). All schemes are run in MATLAB Version R2021a. The obtained results concluds that NOMA is the better option. Furthermore, we suggest MIMO as an alternative solution to increase performance and decrease the outage probability. In addition, we compare MIMO-NOMA and MIMO-OMA in terms of performance for the two types of users to demonstrate the advantages of the MIMO-NOMA design.
Gated Deep Reinforcement Learning With Red Deer Optimization for Medical Image Classification

One of the most complex areas of image processing is image classification, which is heavily relied upon in clinical care and educational activities. However, conventional models have reached their limits in effectiveness and require extensive time and effort to extract and choose classification variables. In addition, the large volume of medical image data being produced makes manual procedures ineffective and prone to errors. Deep learning has shown promise for many classification problems. In this study, a deep learning-based classification model is developed to decrease misclassifications and handle large amounts of data. The Adaptive Guided Bilateral Filter is used to filter images, and texture and edge attributes are gathered using the Spectral Gabor Wavelet Transform. The Black Widow Optimization method is used to choose the best features, which are then input into the Red Deer Optimization-enhanced Gated Deep Reinforcement Learning network model for classification. The brain tumor MRI dataset was used to test the model on the MATLAB platform, and the results showed an accuracy of 98.8%.
UCSL: Toward Unsupervised Common Subspace Learning for Cross-Modal Image Classification

The emerging research line of cross-modal learning focuses on the issue of transferring feature representation manner learned from limited multimodal data with labelings to the testing phase with partial modalities. This is essentially common and practical in the remote sensing (RS) community when only modal-incomplete data are in users’ hands due to inevitable imaging or access restrictions under large-scale observation scenarios. However, most of the existing cross-modal learning methods have been designed with exclusive reliance on labeling, which can be either limited or noisy due to their costly production. To address this issue, we explore, in this article, the possibility to learn cross-modal feature representation in an unsupervised fashion. By integrating the multimodal data into a fully recombined matrix form, we propose the following: 1) the use of common subspace representation as the regression target instead of conventionally adopted binary labels and 2) the orthogonality and manifold alignment regularization terms to shrink the solution space while preserving the pairwise manifold correlations. Through this manner, the modality-specific and mutual latent representations in this common subspace as well as their corresponding projections can be learned simultaneously, and their optimums can be efficiently reached through a nearly one-step computation with the help of eigendecomposition. Finally, we show the superiority of our method through extensive image classification experiments on three multimodal datasets with four remotely sensed modalities involved (i.e., hyperspectral (HS), multispectral, synthetic aperture radar, and light detection and ranging (LiDAR) data). The code and dataset will be made freely available at https://github.com/jingyao16/UCSL to encourage the reproduction of our method and further use.
Development of an Institutional Digital Research Archive for Adamson University

Replication and a lack of departmental collaboration are two issues with institutional research that are generally recognized. Institutions are creating innovation silos across offices and departments without access to pooled research information. It hinders innovation and evades the institution's research mission. The research intends to develop a digital archive for scholarly works of Adamson University to facilitate the storage and information sharing of research data across the school. User Management, Research Repository, Data Visualization, Creation of User Interfaces, and System Testing are the research's five main scopes. Respondents were surveyed by the researchers and grouped into three groups: students, academic staff, and research staff. Using a user acceptance survey based on ISO 25010 standard on software quality, it was determined that features of the developed system were acceptable for all user groups. Correlation analysis using Pearson and Spearman correlation was also done, to determine the consistency and relationship of the acceptance of each user group. The developed system and its user acceptance analysis show its acceptance of users on different levels and its effectivity on the current organization.
Enabling Energy Efficient Augmented Reality in Multi-UAV-UGV Systems

UAVs and UGVs are being used in various applications. In this PhD project, we propose to form a multi-UAV s-UGVs collaborative team and given the power of Augmented Reality which overlays digital information onto real-world views, we also propose to incorporate AR in this multi-robots system on the human side to help human better understand the robots and thus promote the collaboration between human and robots. We also identify several challenges for this system ranging from energy constraints to the design of the AR application. In this paper, we also present an empirical preliminary study to demonstrate the need for computation offloading followed by a discussion on building our own multi-UAVs-UGVs simulation. We treat these two studies as the foundation stone for our future studies.
Selective Data Offloading in Edge Computing for Two-Tier Classification With Local Domain Partitions

We consider a two-tier approach for the classification of user-generated data, where low-complexity decision algorithms are available on mobile devices, and a better assessment can be performed on a shared edge server to which the samples can be offloaded. While an overall accurate classification can be achieved by either massive offloading to the edge server alone or performing a computationally intense domain partitioning for local evaluation, both these solutions taken individually are excessively demanding. Importantly, the former strategy achieves higher accuracy, yet is very bandwidth-consuming, while the latter results in lower accuracy while reducing bandwidth usage. To cope with these challenges, we take a quantitative stance to investigate the benefit of combining these two strategies, i.e., performing most of the evaluations with a local decision over constrained domains, while at the same time offloading to the edge server a small fraction of the samples for which the classification is expected to be less accurate. If properly harmonized, such an approach is shown to lead to a sharp increase in classification accuracy, with overall limited resource usage, which makes it suitable for practical implementations.
Doppler Resiliency with Golay Sequences and MOCS in PMCW MIMO Radars

We tackle the detrimental impact of Doppler shifts on Golay complementary sequences (GCS) and mutually orthogonal complementary sets (MOCS). These sequences sets are attractive for binary PMCW MIMO radars since they can offer, under ideal circumstances, exactly zero range sidelobes and perfect orthogonality between simultaneously transmitting antennas for MIMO schemes. However, their use is precluded with moving targets because the phase rotations due to Doppler significantly degrade the range profiles and destroy the orthogo-nality. We first describe how GCS and MOCS can be arranged in a PMCW MIMO radar frame and then introduce a low-complexity method to mitigate the degradations due to Doppler. Our method does not require any knowledge about the targets and achieves very low sidelobes and near-ideal orthogonality over the full unambiguous Doppler interval. Using an automotive scenario at 79GHz, we show by simulation that our scheme achieves sidelobe levels and orthogonality leakages below -80dB with respect to the target level.
A Novel SVM Based CFS for Intrusion Detection in IoT Network

Due to the rapid growth of technologies, the internet of things become an important research topic, which can ensure collecting and transferring data over the network between connected objects without any human intervention. However, these connected objects are generally constrained by energy consumption and data security in terms of confidentiality, integrity and availability against attackers. This paper presents a solution for this problem, in this content, we propose an intelligent host-based intrusion detection system using machine learning. our approache based on Support vector machine (SVM) is implemented, we used correlation-based feature selection (CFS) technique to detect the pertinent features in the NSL-KDD dataset. The experimental results show that our approach has an accuracy as 99.09% in binary classification and 99.11% in multiclass classification which are performed better than most of previous approaches.
Energy Management Frameworks In HEMS: A Review

Home energy management systems (HEMSs) aids to diminish power demand in household appliances to optimize power usage pattern along by incorporation of renewable-energy based power-generation without altering the user quality of life (QoL) and comfort. The key objectives of HEMs are energy conservation, diminish Peak to Average Ratio (PAR), reduced cost of energy maximized user comfort. In this manuscript, we discuss the overview of HEMS, Infrastructures, Energy Management Scheme, challenges and issues associated to HEM and Demand Response (DR) Programs. It is evident that by appropriate implementation of HEM, the usage of domestic electricity turns out to be more reliable, smarter and efficient.