publications

2025

1. Letter

   PULSE: Physics-Aware Temporal Embedding Learning for Domain Adaptive Wireless Sensing

   Rifat Zabin, and Md. Golam Rabiul Alam

   2025

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   We present PULSE, a lightweight domain-adaptive sensing framework that learns the physics-aware temporal embeddings, extracted from Wi-Fi channel frequency response (CFR). Unlike the existing approaches where the CFR is directly used as the input the tensor for Learning model, PULSE extracts temporal descriptors and embeds them through a light-weight 1D convolutional network that jointly learns discriminative representations and sensing semantics. The framework achieves over 99% accuracy while reducing 85% of the input tensor dimensionality and maintaining low inference latency, demonstrating suitability for real-time edge inference. Furthermore, through supervised contrastive pretraining and few-shot adaptation, PULSE generalizes effectively to unseen domains using only 5 s worth of labeled data, outperforming the state-of-the-art frameworks. We have extensively evaluated PULSE sesning framework with publicly available dataset of 20 different activities collected over multiple, subjects and propagation environment. For reproducibility, we pledge to share our code base at: https://github.com/rifatzabin/PULSE.

   @article{},
     doi = {10.13140/RG.2.2.36558.11845/1},
     title = {PULSE: Physics-Aware Temporal Embedding Learning for Domain Adaptive Wireless Sensing},
     author = {Zabin, Rifat and Alam, Md. Golam Rabiul},
     journal = {},
     volume = {},
     number = {},
     pages = {},
     year = {2025},
     publisher = {}
   }

2. Conference

   SignNet-Nano: Efficient Sign Language Recognition for Real-Time Edge Deployment

   Rifat Zabin, Md. Riasat Tanjim Hossain, Khandaker Foysal Haque, and 1 more author

   COMPAS, 2025

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   Real-time American Sign Language (ASL) alphabet recognition can enable inclusive communication on next-generation edge devices like Virtual Reality (VR) headsets and smart glasses. However, existing models are often too computationally demanding for such platforms, resulting in fast battery depletion and degraded performance. To address this challenge, we introduce SignNet-Nano, an ultra-lightweight Convolutional Neural Network (CNN), tailored specifically for efficient ASL alphabet recognition on constrained edge hardware. The proposed model integrates Depthwise Separable (DS) convolutions, Squeeze-and-Excitation (SE) attention, and Global Average Pooling (GAP) to achieve high accuracy with a compact architecture comprising only < 20K parameters and < 0.1M B model size. Unlike prior work, SignNet-Nanois explicitly designed with deployment efficiency as a primary goal, enabling real-time inference with minimal performance degradation while requiring significantly less energy, latency, computation, and memory usage than State-of-the-Art (SOTA) lightweight edge models. We profile inference performance in terms of Floating Point Operations (FLOPs), inference latency, Frames Per Second (FPS), and memory footprint on a diverse set of platforms, including three edge devices— Jetson Nano, Jetson Xavier NX, and Raspberry Pi 4— as well as three high-performance systems— Apple Mac M3 (MPS back-end), NVIDIA RTX 4070 GPU, and an Intel Core i7 12th Gen CPU. Experimental results show that SignNet-Nanoachieves classification accuracy within 1% of the best-performing baseline while reducing inference time (i.e., latency) and FLOPs by up to 73.7% and 97.1%, respectively, while improving the energy efficiency by up to 79.3%.

   @article{~,
     doi = {},
     title = {SignNet-Nano: Efficient Sign Language Recognition for Real-Time Edge Deployment},
     author = {Zabin, Rifat and Hossain, Md. Riasat Tanjim and Haque, Khandaker Foysal and K.M, Rumman},
     journal = {COMPAS},
     volume = {},
     number = {},
     pages = {},
     year = {2025},
     publisher = {IEEE}
   }

3. Journal

   FedMed: Communication-Efficient Federated Learning for Medical Imaging with Entropy-Weighted Aggregation

   Rifat Zabin, Khandaker Foysal Haque, and Ahmed Abdelgawad

   2025

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   Federated learning (FL) offers a transformative paradigm for privacy-preserving medical Artificial Intelligence (AI) by enabling collaborative model training without compromising client confidentiality. However, conventional FL frameworks face critical challenges, including performance degradation due to data heterogeneity and high communication overhead due to frequent federated rounds that limit scalability in real-world healthcare environments. This study introduces FedMed, a novel FL framework designed to jointly enhance model robustness and communication efficiency. FedMed introduces two key innovations: (1) an entropy-weighted aggregation mechanism that enhances global robustness by prioritizing high-confidence client updates; and (2) a selective weight transmission strategy that reduces communication overhead by allowing clients to upload updates only when they provide meaningful improvements. We evaluate FedMed on two distinct medical imaging tasks — COVID-19 detection from chest X-ray images and brain tumor classification from MRI scans — using popular CNN backbones, including VGG-16, ResNet-18, and EfficientNet. FedMed improves global model accuracy by up to 6.9% and reduces communication overhead by up to 61% compared to standard FedAvg. These results highlight FedMed ’s ability to improve accuracy while substantially reducing communication costs, confirming its effectiveness for real-world, resource-constrained medical imaging applications. The implementation and dataset partitions are publicly available to encourage further research.

   @article{,
     doi = {},
     title = {FedMed: Communication-Efficient Federated Learning for Medical Imaging with Entropy-Weighted Aggregation},
     author = {Zabin, Rifat and Haque, Khandaker Foysal and Abdelgawad, Ahmed},
     journal = {},
     volume = {},
     number = {},
     pages = {},
     year = {2025},
     publisher = {}
   }

2024

1. Journal

   PredXGBR: A Machine Learning Framework for Short-Term Electrical Load Prediction

   Rifat Zabin, Khandaker Foysal Haque, and Ahmed Abdelgawad

   Electronics, 2024

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   The growing demand for consumer-end electrical load is driving the need for smarter management of power sector utilities. In today’s technologically advanced society, efficient energy usage is critical, leaving no room for waste. To prevent both electricity shortage and wastage, electrical load forecasting becomes the most convenient way out. However, the conventional and probabilistic methods are less adaptive to the acute, micro, and unusual changes in the demand trend. With the recent development of artificial intelligence (AI), machine learning (ML) has become the most popular choice due to its higher accuracy based on time-, demand-, and trend-based feature extractions. Thus, we propose an Extreme Gradient Boosting (XGBoost) regression-based model—PredXGBR-1, which employs short-term lag features to predict hourly load demand. The novelty of PredXGBR-1 lies in its focus on short-term lag autocorrelations to enhance adaptability to micro-trends and demand fluctuations. Validation across five datasets, representing electrical load in the eastern and western USA over a 20-years period, shows that PredXGBR-1 outperforms a long-term feature-based XGBoost model, PredXGBR-2, and state-of-the-art recurrent neural network (RNN) and long short-term memory (LSTM) models. Specifically, PredXGBR-1 achieves a mean absolute percentage error (MAPE) between 0.98 and 1.2% and an 𝑅2 value of 0.99, significantly surpassing PredXGBR-2’s 𝑅2 of 0.61 and delivering up to 86.8% improvement in MAPE compared to LSTM models. These results confirm the superior performance of PredXGBR-1 in accurately forecasting short-term load demand.

   @article{zabin2024predxgbr,
     doi = {10.3390/electronics13224521},
     title = {PredXGBR: A Machine Learning Framework for Short-Term Electrical Load Prediction},
     author = {Zabin, Rifat and Haque, Khandaker Foysal and Abdelgawad, Ahmed},
     journal = {Electronics},
     volume = {13},
     number = {22},
     pages = {4521},
     year = {2024},
     publisher = {MDPI}
   }

2023

1. Conference

   PredXGBR: A Machine Learning Based Short-Term Electrical Load Forecasting Architecture

   Rifat Zabin, Labanya Barua, and Tofael Ahmed

   In Proceedings of International Conference on Information and Communication Technology for Development, 2023

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   The increase of consumer end load demand is leading to a path to the smart handling of power sector utility. In recent era, the civilization has reached to such a pinnacle of technology that there is no scope of energy wastage. Consequently, questions arise on power generation sector. To prevent both electricity shortage and wastage, electrical load forecasting becomes the most convenient way out. Artificial Intelligent, Conventional and Probabilistic methods are employed in load forecasting. However the conventional and probabilistic methods are less adaptive to the acute, micro and unusual change of the demand trend. With the recent development of Artificial intelligence, machine learning has become the most popular choice due to its higher accuracy based on time, demand and trend based feature extractions. Even though machine learning based models have got the potential, most of the contemporary research works lack in precise and factual feature extractions which results in lower accuracy and higher convergence time. Thus the proposed model takes into account the extensive features derived from both long and short time lag based auto-correlation. Also, for an accurate prediction from these extracted features two Extreme Gradient Boosting (XGBoost) Regression based models: (i) PredXGBR-1 and (ii) PredXGBR-2 have been proposed with definite short time lag feature to predict hourly load demand. The proposed model is validated with five different historical data record of various zonal area over a twenty years of-2 time span. The average accuracy (𝑅2 ) of PredXGBR-1 and PredXGBR-2 are 61.721% and 99.0982% with an average MAPE (error) of 8.095% and 0.9101% respectively.

   @inproceedings{10.1007/978-981-19-7528-8_42,
     author = {Zabin, Rifat and Barua, Labanya and Ahmed, Tofael},
     title = {PredXGBR: A Machine Learning Based Short-Term Electrical Load Forecasting Architecture},
     booktitle = {Proceedings of International Conference on Information and Communication Technology for Development},
     year = {2023},
     publisher = {Springer Nature Singapore},
     address = {Singapore},
     pages = {535--546},
     doi = {https://doi.org/10.1007/978-981-19-7528-8_42},
   }

2. Conference

   Energy Consumption Optimization of Zigbee Communication: An Experimental Approach with XBee S2C Module

   Rifat Zabin, and Khandaker Foysal Haque

   In Proceedings of International Conference on Information and Communication Technology for Development: ICICTD, 2023

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   Zigbee is a short-range wireless communication standard that is based on IEEE 802.15.4 and is vastly used in both indoor and outdoor Internet of Things (IoT) applications. One of the basic constraints of Zigbee and similar wireless sensor networks (WSN) standards is limited power source as in most of the cases they are battery powered. Thus, it is very important to optimize the energy consumption to have a good network lifetime. Even though tuning the power transmission level to a lower value might make the network more energy efficient, it also hampers the network performances very badly. This work aims to optimize the energy consumption by finding the right balance and trade-off between the transmission power level and network performance through extensive experimental analysis. Packet delivery ratio (PDR) is taken into account for evaluating the network performance. This work also presents a performance analysis of both the encrypted and unencrypted Zigbee with the stated metrics in a real-world testbed, deployed in both indoor and outdoor scenarios. The major contribution of this work includes (i) to optimize the energy consumption by evaluating the most optimized transmission power level of Zigbee where the network performance is also good in terms of PDR (ii) identifying and quantizing the trade-offs of PDR, transmission power levels, current and energy consumption (iii) creating an indoor and outdoor Zigbee testbed based on commercially available Zigbee module XBee S2C to perform any sort of extensive performance analysis.

   @inproceedings{zabin2023energy,
     doi = {https://doi.org/10.1007/978-981-19-7528-8_41},
     title = {Energy Consumption Optimization of Zigbee Communication: An Experimental Approach with XBee S2C Module},
     author = {Zabin, Rifat and Haque, Khandaker Foysal},
     booktitle = {Proceedings of International Conference on Information and Communication Technology for Development: ICICTD},
     pages = {521--534},
     year = {2023},
     organization = {Springer}
   }

2020

1. Conference

   An IoT based efficient waste collection system with smart bins

   Khandaker Foysal Haque, Rifat Zabin, Kumar Yelamarthi, and 2 more authors

   In 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), 2020

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   Waste collection and management is an integrated part of both city and village life. Lack of optimized and efficient waste collection system vastly affect public health and costs more. The prevailing traditional waste collection system is neither optimized nor efficient. Internet of Things (IoT) has been playing a great role in making human life easier by making systems smart, adequate and self-sufficient. Thus, this paper proposes an IoT based efficient waste collection system with smart bins. It does real-time monitoring of the waste bins and determines which bins are to emptied in every cycle of waste collection. The system also presents an enhanced navigation system that shows the best route to collect wastes from the selected bins. Four waste bins are assumed in the city of Mount Pleasant, Michigan at random location. The proposed system decreases the travel distance by 30.76% on an average in the assumed scenario, compared to the traditional waste collection system. Thus it reduces the fuel cost and human labor making the system optimized and efficient by enabling real-time monitoring and enhanced navigation

   @inproceedings{haque2020iot,
     title = {An IoT based efficient waste collection system with smart bins},
     author = {Haque, Khandaker Foysal and Zabin, Rifat and Yelamarthi, Kumar and Yanambaka, Prasanth and Abdelgawad, Ahmed},
     booktitle = {2020 IEEE 6th World Forum on Internet of Things (WF-IoT)},
     pages = {1--5},
     year = {2020},
     publisher = {IEEE},
     doi = {10.1109/WF-IoT48130.2020.9221251},
   }

2019

1. Conference

   Analysis of Grid Integrated PV System as Home RES with Net Metering Scheme

   Nazmus Saqib, Khandaker Foysal Haque, Rifat Zabin, and 1 more author

   In 2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST), 2019

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   To meet the increased demand of electricity, PV system is being used as home RES (Renewable Energy Source) throughout the world. In this paper, a grid integrated PV system has been proposed with net metering scheme. A home of 149 sq. meter in Dhaka city is considered whose average daily load is 11.27 kWh/day with an annual peak load of 1.21 kW. According to DESCO (Dhaka Electric Supply Company Limited), for the span of last one year (July, 2017-July, 2018) the monthly electricity usage of this home varies from 401-600 units (kWh) with a Cost of Energy (COE) of $0.1. Simulation and analysis of the proposed system shows that the Cost of Energy (COE) and Net present Cost (NPC) of the proposed system can be reduced to a great extent with the application of net metering scheme which also improves the renewable fraction of the system.

   @inproceedings{8644098,
     author = {Saqib, Nazmus and Haque, Khandaker Foysal and Zabin, Rifat and Preonto, Sayed Nahian},
     booktitle = {2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)},
     title = {Analysis of Grid Integrated PV System as Home RES with Net Metering Scheme},
     year = {2019},
     volume = {},
     number = {},
     pages = {395-399},
     publisher = {IEEE},
     doi = {10.1109/ICREST.2019.8644098}
   }