| 1. |
- Ben Dhaou, I., et al.
(författare)
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Low-latency hardware architecture for cipher-based message authentication code
- 2017
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Ingår i: 2017 IEEE International Symposium on Circuits and Systems (ISCAS). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781467368520
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Konferensbidrag (refereegranskat)abstract
- Cipher-based message authentication code, CMAC, is a NIST approved standard for checking message integrity and authentication. This work presents a low-latency AES architecture for CMAC. The architecture uses intensive parallel processing per round and takes advantage of the BRAM present in modern FPGA. Experimental results show that for typical IoT application, the proposed architecture has a latency of 10 clock cycles, consumes 1355 slices, 2 BRAMs and achieves a throughput of 3.8Gbps.
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| 2. |
- Javed, Nimra, et al.
(författare)
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Directly Printable Moisture Sensor Tag for Intelligent Packaging
- 2016
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Ingår i: IEEE Sensors Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 1530-437X .- 1558-1748. ; 16:16, s. 6147-6148
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Tidskriftsartikel (refereegranskat)abstract
- A compact, flexible 24-b dual-polarized chip-less radio frequency identification tag with a size of 20.6mm x 19.9mm is realized. The tag structure is optimized and analyzed for Taconic, Kapton HN and organic substrate. The prototype fabricated on HP photopaper with silver nanoparticles-based conductive ink is exhibiting a behavior of moisture sensor. The proposed moisture sensor tag has a bandwidth of 13.5GHz. The direct printability of moisture sensor tag makes it suitable for intelligent packaging and various low-cost applications.
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| 3. |
- Kondoro, Aron, et al.
(författare)
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Trends of using blockchain technology in the smart grid
- 2021
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Ingår i: Proceedings of 2021 Global Congress on Electrical Engineering, GC-ElecEng 2021. - : IEEE. ; , s. 102-108
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Konferensbidrag (refereegranskat)abstract
- Recently, the traditional power grid has been evolving into a new type of intelligent system known as smart grid. The new smart grid uses information and communication technologies to automate and optimize the power generation and distribution process. Despite improvements, there are still challenges that cannot be solved using existing technologies. Blockchain is an emerging technology with unique features to solve some of remaining challenges in smart grids. In this paper, we review the recent developments in this area. We describe the existing smart grid challenges, explore blockchain features suitable for smart grids, and highlight notable existing successful implementations. The review shows several applications such as peer to peer energy trading and autonomous asset management that have been enabled by blockchain technology. It also highlights challenges such as performance and regulations that might hinder the future use of blockchains in smart grids.
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| 4. |
- Sahebi, G., et al.
(författare)
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A reliable weighted feature selection for auto medical diagnosis
- 2017
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Ingår i: Proceedings - 2017 IEEE 15th International Conference on Industrial Informatics, INDIN 2017. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538608371 ; , s. 985-991
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Konferensbidrag (refereegranskat)abstract
- Feature selection is a key step in data analysis. However, most of the existing feature selection techniques are serial and inefficient to be applied to massive data sets. We propose a feature selection method based on a multi-population weighted intelligent genetic algorithm to enhance the reliability of diagnoses in e-Health applications. The proposed approach, called PIGAS, utilizes a weighted intelligent genetic algorithm to select a proper subset of features that leads to a high classification accuracy. In addition, PIGAS takes advantage of multi-population implementation to further enhance accuracy. To evaluate the subsets of the selected features, the KNN classifier is utilized and assessed on UCI Arrhythmia dataset. To guarantee valid results, leave-one-out validation technique is employed. The experimental results show that the proposed approach outperforms other methods in terms of accuracy and efficiency. The results of the 16-class classification problem indicate an increase in the overall accuracy when using the optimal feature subset. Accuracy achieved being 99.70% indicating the potential of the algorithm to be utilized in a practical auto-diagnosis system. This accuracy was obtained using only half of features, as against an accuracy of66.76% using all the features.
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