2,248 research outputs found
Deepfakes Recognition with Physiological Signals
No full textA Master of Science thesis in Electrical Engineering by Muhammad Riyyan Khan entitled, “Deepfakes Recognition with Physiological Signals”, submitted in April 2024. Thesis advisor is Dr. Usman Tariq and thesis co-advisors are Dr. Hasan Al-Nashash and Dr. Abhinav Dhall. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE
Modeling and minimization of FWM effects in DWDM-based long-haul optical communication systems
No full textOptical communication systems (OCSs) mainly represent the backbone of modern long-haul communication networks because of low loss transmission over long distances and ultra-high capacity. However high data-rate transmission through optical fiber suffers from deterioration due to nonlinear impairments, such as four-wave mixing (FWM) in particular. At high launch power levels, which are required for the long-haul transmission over hundreds of km, these nonlinear effects become more severe which imposes a challenge to achieve satisfactory transmission performance. In this paper, a theoretical model for the FWM effects and its mitigation is presented and validated through simulation results. Moreover, two other nonlinear effects, polarization mode dispersion and nonlinear dispersion variations are also investigated for various values of launch power level. The transmission performance of the proposed OCS model is evaluated on the basis of bit error rate, optical signal-to-noise ratio and quality factor using different transmission channel parameters such as effective area, nonlinear refractive index, nonlinear dispersion, and linear dispersion.
Enumeration and Identification of Active Users for Grant-Free NOMA Using Deep Neural Networks
Full text linkIn next-generation mobile radio systems, multiple access schemes will support a massive number of uncoordinated devices exhibiting sporadic traffic, transmitting short packets to a base station. Grant-free non-orthogonal multiple access (NOMA) has been introduced to provide services to a large number of devices and to reduce the communication overhead in massive machine-type communication (mMTC) scenarios. In grant-free communication, there is no coordination between the device and base station (BS) before the data transmission; therefore, the challenging task of active users detection (AUD) must be conducted at the BS. For NOMA with sparse spreading, we propose a deep neural network (DNN)-based approach for AUD called active users enumeration and identification (AUEI). It consists of two phases: firstly, a DNN is used to estimate the number of active users; then in the second phase, another DNN identifies them. To speed up the training process of the DNNs, we propose a multi-stage transfer learning technique. Our numerical results show a remarkable performance improvement of AUEI in comparison to previously proposed approaches
Haematological Alterations in Common Carp (Cyprinus carpio) Infected by Saprolegnia spp.
Full text linkThe effect of Saprolegniasis on hematological parameters of Cyprinus carpio was studied in River Indus at Swabi, Khyber Pakhtunkhwa, Pakistan. The results showed that Saprolegniasis significantly decreased the total erythrocytes count, packed cell volume, and hemoglobin content, while the white blood cells and mean corpuscular volume were significantly increased in the infected fish as compared to the healthy fish. Mean corpuscular hemoglobin was found higher while mean corpuscular hemoglobin concentration was found lower in infected fish. Saprolegnia triggers a strong inflammatory response in its host by suppressing fish immunity. The Pearson linear correlation analysis showed a significant correlation for several parameters (P<0.05). It is obvious that Saprolegniasis seriously damage the population of freshwater fishes
Blind User Activity Detection for Grant-Free Random Access in Cell-Free mMIMO Networks
No full textCell-free massive MIMO (CF-mMIMO) networks have recently emerged as a promising solution to tackle the challenges arising from next-generation massive machine-type communications. In this paper, a fully grant-free deep learning (DL)-based method for user activity detection in CF-mMIMO networks is proposed. Initially, the known non-orthogonal pilot sequences are used to estimate the channel coefficients between each user and the access points. Then, a deep convolutional neural network is used to estimate the activity status of the users. The proposed method is 'blind', i.e., it is fully data-driven and does not require prior large-scale fading coefficients estimation. Numerical results show how the proposed DL-based algorithm is able to merge the information gathered by the distributed antennas to estimate the user activity status, yet outperforming a state-of-the-art covariance-based method
Composted Sugarcane By‐product Press Mud Cake Supports Wheat Growth and Improves Soil Properties
No full textRestoring soil fertility is essential to sustain crop production in order to meet the needs of the ever-blooming population. In this light, the present investigation was carried on the same soil for two consecutive years (2014-15 and 2015-16) in Punjab, Pakistan, to determine the influence of press mud compost (PMC) and mineral fertilizers (NPK) on wheat growth, yield and soil properties. The experiment was composed of an unfertilized control and five inter-exchanging combinations of NPK and PMC (100:0, 75:25, 50:50, 25:75, 0:100). 100% PMC (900 kg ha-1) was intermediate in wheat growth and yield between unfertilized and 100% NPK, this latter being the recommended dose of mineral nutrients (120, 100 and 60 kg ha-1 of the respective N, P2O5, and K2O). The 50:50 combinations of NPK and PMC determined the best growth and final yield (+19% vs. 100% NPK), despite an approximately 40% lower nutrient supply with respect to 100% NPK. Soils traits bulk density, pH, organic matter, total N, and available nutrients P and K significantly improved with 100% PMC. Based on the ANOVA, the 50:50 combinations of NPK and PMC was no worse than 100% PMC in bulk density, available P and K, and it was a good compromise between 100% NPK and 100% PMC in organic matter content. Therefore, conjunctive use of PMC and NPK fertilizers appeared a good choice to improve wheat productivity and soil properties. Additionally, the use of PMC will lower the reliance on mineral fertilizers while restoring soil fertility and assuring environmental protection
Recent developments and challenges in biomass cookstove
Full text linkThe growing demand for more efficient cooking methods has been fueled by the rapid advancements in biomass utilization. Considerable progress has been made in the development of a biomass cookstove that is both highly thermally efficient and produces less pollution. This review provides a comprehensive overview of the current status and advancements in biomass cookstove technologies. It explores various types of biomass cookstoves, with a particular focus on advanced models available in the market. The paper explores the recent advancements, highlighting the effectiveness of ceramic materials in combustion chambers for reducing emissions, and the impact of introducing swirl vanes and hybrid air injection systems on fuel consumption and overall performance. The review also discusses the important design strategies and limitations that affect the efficiency of these cookstoves. In addition, it acknowledges the considerable challenges in the field, such as design limitations, maintenance, and performance testing variations. Given recent advancements in biomass cookstove technologies, this review identifies important areas for future research. Although there have been significant research in the field of biomass cookstove, there are still gaps in the literature, particularly when it comes to complex heat transfer mechanism. These gaps in knowledge emphasize the need for further investigation to develop more practical and efficient cooking technologies
Deflection of coupled elasticity–electrostatic bimorph PVDF material: theoretical, FEM and experimental verification
No full textPiezoelectric materials have wide applications in the field of mechanical, aerospace and civil engineering because of its
voltage dependent actuation. Piezoelectric material goes through voltage generation whenever deflection is induced in it
and vice versa. Piezoelectric bimorph beam has been widely used for sensing and actuating. In the actuation mode, an
electric field is applied across the beam thickness, one layer contracts while the other expands. This results in the bending
of the entire structure and tip deflection. In the sensing mode, the bimorph is used to measure an external load by
monitoring the piezoelectric induced electrode voltages. In this research work, a 2D bimorph piezoelectric actuator model
having two layers made of polyvinylidene fluoride (PVDF) material was developed to examine the inverse piezoelectric
effect. Finite element analysis (FEA) was carried out on specially designed actuator model by using MATLAB Partial
Differential Equation (PDE) ToolboxTM. Theoretical analysis has been carried out to measure the tip deflection under
applied electric field. The laboratory test was performed to investigate the deformation behavior of piezoelectric actuator. It
is observed that, more the electric field applied, more the material would be deformed in a particular direction. The
experimental results are in good agreement with numerical results
AFP-LSE: Antifreeze Proteins Prediction Using Latent Space Encoding of Composition of k-Spaced Amino Acid Pairs
No full textSpecies living in extremely cold environments resist the freezing conditions through antifreeze proteins (AFPs). Apart from being essential proteins for various organisms living in sub-zero temperatures, AFPs have numerous applications in different industries. They possess very small resemblance to each other and cannot be easily identified using simple search algorithms such as BLAST and PSI-BLAST. Diverse AFPs found in fishes (Type I, II, III, IV and antifreeze glycoproteins (AFGPs)), are sub-types and show low sequence and structural similarity, making their accurate prediction challenging. Although several machine-learning methods have been proposed for the classification of AFPs, prediction methods that have greater reliability are required. In this paper, we propose a novel machine-learning-based approach for the prediction of AFP sequences using latent space learning through a deep auto-encoder method. For latent space pruning, we use the output of the auto-encoder with a deep neural network classifier to learn the non-linear mapping of the protein sequence descriptor and class label. The proposed method outperformed the existing methods, yielding excellent results in comparison. A comprehensive ablation study is performed, and the proposed method is evaluated in terms of widely used performance measures. In particular, the proposed method demonstrated a high Matthews correlation coefficient of 0.52, F-score of 0.49, and Youden's index of 0.81 on an independent test dataset, thereby outperforming the existing methods for AFP prediction.
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