Rakesh Reddy Yakkati

• Viswanathuni, Venkata Raghava Shashank; Yakkati, Rakesh Reddy; Yeduri, Sreenivasa Reddy & Cenkeramaddi, Linga Reddy (2025). WiCNNAct: Wi-Fi-based Human Activity Recognition utilizing Deep Learning on the Edge Computing Devices. IEEE Access. 13, p. 104173–104183. doi: 10.1109/ACCESS.2025.3579649. Full text in Research Archive Show summary

  Nowadays, human activity recognition plays an essential role in the application of human-computer interaction. Comprehensive systems, however, mostly rely on wearables, video cameras, and ambient sensors, which might be expensive and difficult to deploy or cause privacy issues. Wi-Fi-based wireless human sensing utilizes channel state information to detect human presence and movements, leveraging changes in signal properties caused by humans. This cost-effective approach utilizes existing Wi-Fi infrastructure and can operate in obscured or obstructed environments. However, signal interference and environmental conditions can affect accuracy and reliability. To address these challenges, a WiCNNAct for human activity recognition is proposed in this paper. The proposed approach utilizes the channel state information (CSI) measurements (complex values) from Wi-Fi and processes the different combinations of the real, imaginary, and absolute values using multi-channel 1D convolutional neural networks (1D-CNN). After conducting preliminary investigations, we validated various combinations of multi-channel 1D-CNNs and identified three methods for accurate activity recognition: a three-channel (real, imaginary, and absolute) setup, two channels (real and imaginary/real and absolute/imaginary and absolute), single channel (real/imaginary/absolute). The proposed three-channel Wi-Fi-Net underwent verification with 10-fold testing validation and achieved an overall accuracy of 98.29% with a standard deviation of 0.33%. The model is deployed on various edge computing devices, including Raspberry Pi, to assess real-time deployment feasibility. In this work, 1D-CNN is chosen based on its ability to automatically extract spatial and temporal features, reducing manual feature engineering. Experimental validation identifies the optimal setup for robust activity recognition.

• Tellefsen, Anders; Yakkati, Rakesh Reddy & Cenkeramaddi, Linga Reddy (2024). Vehicle Type Classification using Lightweight CNN from Aerial Images for Traffic Management Applications, 2024 IEEE 19th Conference on Industrial Electronics and Applications - ICIEA. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350360868. doi: 10.1109/ICIEA61579.2024.10664890. Full text in Research Archive
• Ellore, Ram Chandra Kamavaram; Yakkati, Rakesh Reddy; Yeduri, Sreenivasa Reddy; Boppu, Srinivas & Cenkeramaddi, Linga Reddy (2024). GSM-based Mobile Handset Identification Using RF Fingerprints and Deep Learning on the Edge Computing Devices. IEEE Sensors Journal. ISSN 1530-437X. doi: 10.1109/JSEN.2024.3464742. Full text in Research Archive
• M., Durga Prasad Varma; Yeduri, Sreenivasa Reddy; Yakkati, Rakesh Reddy; Boddu, Anudeep Bhaskar & Cenkeramaddi, Linga Reddy (2024). Wireless Technology Identification Using Continuous Wavelet Transform and Deep Learning. IEEE Sensors Journal. ISSN 1530-437X. doi: 10.1109/JSEN.2024.3467681. Full text in Research Archive
• Nathala, Sai Sree; Yakkati, Rakesh Reddy; Dayal, Aveen; Manikandan, M. Sabarimalai; Zhou, Jing & Cenkeramaddi, Linga Reddy (2024). Vessel Type Classification utilizing Underwater Acoustic Data and Deep Learning, 2024 IEEE 19th Conference on Industrial Electronics and Applications - ICIEA. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350360868. doi: 10.1109/ICIEA61579.2024.10665252. Full text in Research Archive
• Nathala, Sai Sree; Yakkati, Rakesh Reddy; Breland, Daniel Skomedal; Yeduri, Sreenivasa Reddy; Manikandan, M. Sabarimalai & Jha, Ajit [Show all 8 contributors for this article] (2024). A Deep CNN-based Hand Gestures Recognition using High-Resolution Thermal Imaging, 2024 IEEE 19th Conference on Industrial Electronics and Applications - ICIEA. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350360868. doi: 10.1109/ICIEA61579.2024.10665263. Full text in Research Archive
• Yakkati, Rakesh Reddy; Boddu, Anudeep Bhaskar; Bethi, Pardhasaradhi; Yeduri, Sreenivasa Reddy & Cenkeramaddi, Linga Reddy (2024). Low Probability of Intercept Radar Signal Classification using Multi-Channel 1D-CNN, 2024 10th International Conference on Communication and Signal Processing (ICCSP). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350353068. p. 536–541. doi: 10.1109/ICCSP60870.2024.10543688. Full text in Research Archive
• Yakkati, Rakesh Reddy; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2024). Lightweight CNN Classifier for Multipath and Non-Multipath Human Activity Scenes of Radar Spectrograms, 2024 20th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350387445. p. 92–97. doi: 10.1109/WiMob61911.2024.10770439. Full text in Research Archive
• Puduru, Vamsi Krishna; Yakkati, Rakesh Reddy; Bethi, Pardhasaradhi; Babu, Korra Sathya & Cenkeramaddi, Linga Reddy (2024). Real-Time Detection of Spot Jamming Attacks in mmWave Radar Systems Using a Lightweight CNN. IEEE Sensors Letters. 8(12). doi: 10.1109/LSENS.2024.3480815. Full text in Research Archive

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• Miryala, Neha; Yakkati, Rakesh Reddy; Puranapanda, Sarada; Yeduri, Sreenivasa Reddy & Cenkeramaddi, Linga Reddy (2025). Lightweight CNN for Real-Time Nature Sound Classification on Edge Devices. Full text in Research Archive
• Ellore, Ram Chandra Kamavaram; Yakkati, Rakesh Reddy; Bethi, Pardhasaradhi; Zhou, Jing & Cenkeramaddi, Linga Reddy (2025). A Lightweight CNN for Real-Time Domestic Animal Sound Classification on IoT Devices Using Time-Frequency Imaging. Full text in Research Archive
• Nathala, Sai Sree; Yakkati, Rakesh Reddy; Dayal, Aveen; Manikandan, M. Sabarimalai; Zhou, Jing & Cenkeramaddi, Linga Reddy (2024). Vessel Type Classification utilizing Underwater Acoustic Data and Deep Learning. Full text in Research Archive
• Tellefsen, Anders; Yakkati, Rakesh Reddy & Cenkeramaddi, Linga Reddy (2024). Vehicle Type Classification using Lightweight CNN from Aerial Images for Traffic Management Applications. Full text in Research Archive
• Nathala, Sai Sree; Yakkati, Rakesh Reddy; Breland, Daniel Skomedal; Yeduri, Sreenivasa Reddy; Manikandan, M. Sabarimalai & Jha, Ajit [Show all 8 contributors for this article] (2024). A Deep CNN-based Hand Gestures Recognition using High-Resolution Thermal Imaging. Full text in Research Archive
• Yakkati, Rakesh Reddy; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2024). Lightweight CNN Classifier for Multipath and Non-Multipath Human Activity Scenes of Radar Spectrograms. Full text in Research Archive

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