arXiv subjects

Abdul Jabbar

Publications and source records attributed to Abdul Jabbar.

12 recordsLinked to original sources

arXiv2026

Dynamic Metasurface Antenna Enabled Real-Time 60 GHz Wireless Video Streaming: A System-Level Demonstration with USRPs and mmWave Front-Ends

This paper presents a real-time 60 GHz wireless video transmission system enabled by a millimeter-wave (mmWave) Dynamic Metasurface Antenna (DMA) and offers a 60 GHz transceiver solution. The end-to-end communication system is established using off-the-shelf mmWave up/down-conversion modules (EK1HMC6350, HMC6300, and HMC6301) with a connectorized DMA-based transmit front-end and universal software radio peripheral (USRP) for digital baseband processing. The proposed framework eliminates the need for costly high-frequency signal generators and spectrum analyzers, thus simplifying implementation and validation while reducing operational costs. A real-time HD video stream at 720p resolution (1280 x 720) is transmitted and successfully retrieved over a wireless link using QPSK modulation, achieving a data rate of about 3 Mbps with high reliability. The demonstrated platform can support various 60 GHz standard physical-layer protocols, such as IEEE 802.11ad/ay and 802.15.3c. This work highlights the potential of programmable metasurface antennas as a compact, energy-efficient alternative to legacy phased arrays for next-generation 6G and beyond mmWave/sub-THz wireless communication systems, small-cell outdoor backhaul links, and RF sniffing and channel sounding.

eess.SP↗

arXiv2026

A Frequency-Controlled Comparison of Tick- and Minute-Based Information Bars for Cryptocurrency Markets

This paper provides a controlled comparison of six information bar types (dollar, volume, volatility, range, Renko, and hybrid bars) constructed from both raw Binance aggTrade tick data and one-minute OHLCV bars for the BTCUSDT USDT-margined perpetual futures market over a six-year period spanning January 2020 to December 2025, and evaluated against fixed-interval time-bar baselines. Both pipelines share a common adaptive EMA calibration framework; the tick pipeline additionally uses strictly tick-native activity signals, isolating data resolution as the sole experimental variable. Results across eight statistical quality criteria reveal that the tick advantage is bar-type-specific and most pronounced in bar types whose activity signals are most sensitive to intra-minute price dynamics: tick Renko bars achieve the smallest random-walk deviation recorded ($|\mathrm{VR}(4){-}1| = 0.020$, lag-1 autocorrelation $= 0.002$), and tick volatility bars reduce serial dependence by 69\% relative to the minute baseline ($|\mathrm{VR}(4){-}1|: 0.028$ versus $0.089$). In the multi-regime six-year sample, normality improvements are regime-dependent and secondary: the extreme market events of 2020--2022 inflate fat tails across all bar types, and Ljung-Box independence is rejected for all series at the sample sizes studied. A matched-frequency robustness analysis shows that the apparent tick underperformance on distributional criteria is largely a sampling-frequency artefact: when tick series are coarsened to the minute pipeline's bar count, frequency-matched tick dollar bars lead on all six criteria and matched tick volatility bars attain LB $p = 0.51$, recovering serial independence that the raw oversampled series rejects.

q-fin.ST↗

arXiv2026

Automated detection of pediatric congenital heart disease from phonocardiograms using deep and handcrafted feature fusion

Congenital heart disease (CHD) is the most common type of birth defect, impacting about 1% of live births worldwide. Echocardiography, the gold-standard diagnostic method, is costly and inaccessible in low-resource settings. Diagnosis is delayed due to limited skilled experts, whose ability to interpret pathological patterns varies significantly, causing inter- and intra-clinician variability. Therefore, we present a new method for a more accessible diagnostic modality, the digital stethoscope, to detect CHDs. Our method is based on deep feature fusion, integrating deep and handcrafted features for the automated early detection of CHDs. For this work, Phonocardiography (PCG) recordings were obtained from 751 pediatric subjects (Age:1 month- 16 years) in Bangladesh, ranging from infants to adults at four auscultation locations: mitral valve (MV), aortic valve (AV), pulmonary valve (PV), and tricuspid valve (TV). These recordings were labeled based on confirmed diagnoses by cardiologists as either cases of CHD or non-CHD. The results demonstrated that our proposed model achieved an accuracy of 92%, a sensitivity of 91%, and a specificity of 91%, based on a patient-wise split of 70% training, 20% validation, and 10% testing. Furthermore, the Area Under the Receiver Operating Characteristic curve (AUROC) of 96%, and an F1-score of 92%. This model promises efficient real-time remote detection of CHDs as a cost-effective screening tool for low-resource settings.

cs.LG↗

arXiv2026

Fully 3D-Printed Wideband Metasurface Folded Reflectarray Antenna

This article presents a fully 3D-printed wideband metasurface folded reflectarray antenna (MFRA) operating in the millimeter-wave n257 band. The proposed MFRA integrates a novel polarization-rotating reflective metasurface (RMS), a compact embedded horn feed, and a polarization-selective metasurface polarization grid (MPG), all fabricated using a low-cost in-house 3D-printed method. Unlike conventional PCB-based FRAs constrained to planar unit-cell geometries, the proposed anisotropic meta-element design exploits full three-dimensional dielectric control by tailoring varying unit-cell heights. This volumetric tuning, combined with the spatial distribution of the meta-elements, enables phase compensation exceeding $400^{\circ}$ across the aperture, supporting robust wideband performance. An MFRA prototype is in-house fabricated and experimentally validated. Measured results agree well with simulations, achieving a $-10$ dB impedance bandwidth of 20.7\% (26--32 GHz) and a peak realized gain of 31.1 dBi at 28.2 GHz. The antenna exhibits sidelobe levels below $-20$ dB, cross-polarization below $-30$ dB, and a compact height-to-diameter ratio of 0.20. Stable pencil beams with an average HPBW of $3.7^{\circ}$ are maintained across the operating band. To further validate the robustness of the proposed in-house designed MFRA, a commercially manufactured RMS was also obtained, whose measured performance shows excellent agreement with the in-house 3D-printed version, confirming a cost-effective rapid-prototyping antenna solution. The proposed MFRA is a cost-effective solution for beyond 5G and 6G high-gain point-to-point mmWave wireless applications, such as fixed wireless access, near field communication, and beam focusing.

eess.SP↗

arXiv2025

Analysis of Frequency-Diverse and Dispersion Effects in Dynamic Metasurface Antenna for Holographic Sensing and Imaging

Dynamic metasurface antennas (DMAs) represent a novel approach to programmable and affordable electromagnetic wave manipulation for enhanced wireless communications, sensing, and imaging applications. Nevertheless, current DMA designs and models are usually quasi-narrowband, neglecting the versatile frequency-diverse manifestation and its utilization. This work demonstrates the frequency-diversity and dispersion operations of a representative DMA structure at the millimeter-wave band. We demonstrate flexible dispersion manipulation through dynamic holographic reconfigurability of the meta-atoms in a DMA. This effect can create distinct radiation patterns across the operating frequency band, achieving flexible frequency diversity with enhanced scanning range within a compact, reconfigurable platform. It eliminates the need for wideband systems or complex phase-shifting networks while offering an alternative to frequency-scanned static beams of traditional leaky-wave antennas. The results establish fundamental insights into modelling and utilization of dispersive effects of DMAs in next-generation near-field and far-field holographic sensing and computational holographic imaging applications.

eess.SP↗

arXiv2025

Millimeter-Wave Communication Testbed Using Digital Coding Dynamic Metasurface Antenna: Practical Design and Implementation

Dynamic Metasurface Antennas (DMAs) are transforming reconfigurable antenna technology by enabling energy-efficient, cost-effective beamforming through programmable meta-elements, eliminating the need for traditional phase shifters and delay lines. This breakthrough technology is emerging to revolutionize beamforming for next-generation wireless communication and sensing networks. In this paper, we present the design and real-world implementation of a DMA-assisted wireless communication platform operating in the license-free 60 GHz millimeter-wave (mmWave) band. Our system employs high-speed binary-coded sequences generated via a field-programmable gate array (FPGA), enabling real-time beam steering for spatial multiplexing and independent data transmission. A proof-of-concept experiment successfully demonstrates high-definition quadrature phase-shift keying (QPSK) modulated video transmission at 62 GHz. Furthermore, leveraging the DMA's multi-beam capability, we simultaneously transmit video to two spatially separated receivers, achieving accurate demodulation. We envision the proposed mmWave testbed as a platform for enabling the seamless integration of sensing and communication by allowing video transmission to be replaced with sensing data or utilizing an auxiliary wireless channel to transmit sensing information to multiple receivers. This synergy paves the way for advancing integrated sensing and communication (ISAC) in beyond-5G and 6G networks. Additionally, our testbed demonstrates potential for real-world use cases, including mmWave backhaul links and massive multiple-input multiple-output (MIMO) mmWave base stations.

eess.SP↗

arXiv2025

Congenital Heart Disease Classification Using Phonocardiograms: A Scalable Screening Tool for Diverse Environments

Congenital heart disease (CHD) is a critical condition that demands early detection, particularly in infancy and childhood. This study presents a deep learning model designed to detect CHD using phonocardiogram (PCG) signals, with a focus on its application in global health. We evaluated our model on several datasets, including the primary dataset from Bangladesh, achieving a high accuracy of 94.1%, sensitivity of 92.7%, specificity of 96.3%. The model also demonstrated robust performance on the public PhysioNet Challenge 2022 and 2016 datasets, underscoring its generalizability to diverse populations and data sources. We assessed the performance of the algorithm for single and multiple auscultation sites on the chest, demonstrating that the model maintains over 85% accuracy even when using a single location. Furthermore, our algorithm was able to achieve an accuracy of 80% on low-quality recordings, which cardiologists deemed non-diagnostic. This research suggests that an AI- driven digital stethoscope could serve as a cost-effective screening tool for CHD in resource-limited settings, enhancing clinical decision support and ultimately improving patient outcomes.

eess.AS↗

arXiv2024

Proactive Blockage Prediction for UAV assisted Handover in Future Wireless Network

The future wireless communication applications demand seamless connectivity, higher throughput, and low latency, for which the millimeter-wave (mmWave) band is considered a potential technology. Nevertheless, line-of-sight (LoS) is often mandatory for mmWave band communication, and it renders these waves sensitive to sudden changes in the environment. Therefore, it is necessary to maintain the LoS link for a reliable connection. One such technique to maintain LoS is using proactive handover (HO). However, proactive HO is challenging, requiring continuous information about the surrounding wireless network to anticipate potential blockage. This paper presents a proactive blockage prediction mechanism where an unmanned aerial vehicle (UAV) is used as the base station for HO. The proposed scheme uses computer vision (CV) to obtain potential blocking objects, user speed, and location. To assess the effectiveness of the proposed scheme, the system is evaluated using a publicly available dataset for blockage prediction. The study integrates scenarios from Vision-based Wireless (ViWi) and UAV channel modeling, generating wireless data samples relevant to UAVs. The antenna modeling on the UAV end incorporates a polarization-matched scenario to optimize signal reception. The results demonstrate that UAV-assisted Handover not only ensures seamless connectivity but also enhances overall network performance by 20%. This research contributes to the advancement of proactive blockage mitigation strategies in wireless networks, showcasing the potential of UAVs as dynamic and adaptable base stations.

eess.SP↗

arXiv2024

High Performance 5G FR-2 Millimeter-Wave Antenna Array for Point-to-Point and Point-to-Multipoint Operation: Design and OTA Measurements Using a Compact Antenna Test Range

This paper presents the design and comprehensive measurements of a high-performance 8-element linear array and a compact high-gain 32-element planar antenna array covering the n257 (26.5--29.5 GHz) FR-2 millimeter-wave (mmWave) band. First, an 8-element series-fed linear array is designed with a fan-shaped pattern for 5G point-to-multipoint connectivity. Then a 4-way corporate-series feed network is designed for a high-gain 32-element compact and directive array for point-to-point mmWave connectivity. Comprehensive over-the-air (OTA) measurements are conducted using a state-of-the-art compact antenna test range (CATR) system, enabling precise characterization of radiation patterns across a 180^\circ span in the azimuth and elevation planes. The planar array achieves a peak measured gain of 18.45 dBi at 28.5 GHz, with half-power beamwidths ranging from 11^\circ--13^\circ (wide axis) and 23^\circ--27^\circ (narrow axis) across the band of interest. The sidelobe levels are below -10 dB in the desired band of interest. The measured results match well with the simulation results. The designed antenna array is applicable to various emerging 5G and beyond mmWave applications such as high data rate mmWave wireless backhaul, mmWave near-field focusing, high-resolution indoor radar systems, 28 GHz Local Multipoint Distribution Service (LMDS), as well as the characterization of mmWave path loss and channel sounding in diverse indoor environments.

eess.SP↗

arXiv2024

A Comprehensive Analysis of Machine Learning Models for Algorithmic Trading of Bitcoin

This study evaluates the performance of 41 machine learning models, including 21 classifiers and 20 regressors, in predicting Bitcoin prices for algorithmic trading. By examining these models under various market conditions, we highlight their accuracy, robustness, and adaptability to the volatile cryptocurrency market. Our comprehensive analysis reveals the strengths and limitations of each model, providing critical insights for developing effective trading strategies. We employ both machine learning metrics (e.g., Mean Absolute Error, Root Mean Squared Error) and trading metrics (e.g., Profit and Loss percentage, Sharpe Ratio) to assess model performance. Our evaluation includes backtesting on historical data, forward testing on recent unseen data, and real-world trading scenarios, ensuring the robustness and practical applicability of our models. Key findings demonstrate that certain models, such as Random Forest and Stochastic Gradient Descent, outperform others in terms of profit and risk management. These insights offer valuable guidance for traders and researchers aiming to leverage machine learning for cryptocurrency trading.

q-fin.TR↗

arXiv2023

Methods and advancement of content-based fashion image retrieval: A Review

Content-based fashion image retrieval (CBFIR) has been widely used in our daily life for searching fashion images or items from online platforms. In e-commerce purchasing, the CBFIR system can retrieve fashion items or products with the same or comparable features when a consumer uploads a reference image, image with text, sketch or visual stream from their daily life. This lowers the CBFIR system reliance on text and allows for a more accurate and direct searching of the desired fashion product. Considering recent developments, CBFIR still has limits when it comes to visual searching in the real world due to the simultaneous availability of multiple fashion items, occlusion of fashion products, and shape deformation. This paper focuses on CBFIR methods with the guidance of images, images with text, sketches, and videos. Accordingly, we categorized CBFIR methods into four main categories, i.e., image-guided CBFIR (with the addition of attributes and styles), image and text-guided, sketch-guided, and video-guided CBFIR methods. The baseline methodologies have been thoroughly analyzed, and the most recent developments in CBFIR over the past six years (2017 to 2022) have been thoroughly examined. Finally, key issues are highlighted for CBFIR with promising directions for future research.

cs.IR↗

arXiv2020

A Survey on Generative Adversarial Networks: Variants, Applications, and Training

The Generative Models have gained considerable attention in the field of unsupervised learning via a new and practical framework called Generative Adversarial Networks (GAN) due to its outstanding data generation capability. Many models of GAN have proposed, and several practical applications emerged in various domains of computer vision and machine learning. Despite GAN's excellent success, there are still obstacles to stable training. The problems are due to Nash-equilibrium, internal covariate shift, mode collapse, vanishing gradient, and lack of proper evaluation metrics. Therefore, stable training is a crucial issue in different applications for the success of GAN. Herein, we survey several training solutions proposed by different researchers to stabilize GAN training. We survey, (I) the original GAN model and its modified classical versions, (II) detail analysis of various GAN applications in different domains, (III) detail study about the various GAN training obstacles as well as training solutions. Finally, we discuss several new issues as well as research outlines to the topic.

cs.CV↗