[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

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This is a book review of 'The Micro-Doppler Effect in Radar' by V. C. Chen. Artech House, 16 Sussex Street, London, SW1V 4RW, UK. 2011. 290pp + diskette. Illustrated. £90. ISBN 978-1-60807-057-2.

'The Micro-Doppler Effect in Radar' by V.C. Chen

The authors acknowledge the generous support of King Abdullah University of Science and Technology (KAUST). The authors thank Kelly Rader for proofreading this manuscript.

Recent Progress on Flexible Capacitive Pressure Sensors: From Design and Materials to Applications

High/ultra-high speed data connections are currently being developed, and by the year 2020, it is expected that the 5th generation networking (5GN) should be much smarter. It would provide great quality of service (QoS) due to low latency, less implementation cost and high efficiency in data processing. These networks could be either a point-to-point (P2P) communication link or a point-to-multipoint (P2M) communication link, which, P2M is also known as multi-casting that addresses multiple subscribers. The P2M systems usually have diverse nodes (also called as ‘Things’) according to services and levels of security required. These nodes need an uninterrupted network inter-connectivity as well as a cloud platform to manage data sharing and storage. However, the Internet of Things (IoT), with real-time applications like in smart cities, wearable gadgets, medical, military, connected driver-less cars, etc., includes massive data processing and transmission. Nevertheless, integrated circuits (ICs) deployed in IoT based infrastructures have strong constraints in terms of size, cost, power consumption and security. Concerning the last aspect, the main challenges identified so far are resilience of the deployed infrastructure, confidentiality, integrity of exchanged data, user privacy and authenticity. Therefore, well secured and effective cryptographic algorithms are needed that cause small hardware footprints, i.e. Lightweight Cryptography (LWC), also with the provision of robustness, long range transfer of encrypted data and acceptable level of security.In this paper, the implementation, challenges and futuristic applications of LWC algorithms for smart IoT devices have been discussed, especially the performance of Long-Range Wide Area Network (LoRaWAN) which is an open standard that defines the communication protocol for Low-Power Wide Area Network (LPWAN) technology.

Next Generation Lightweight Cryptography for Smart IoT Devices: : Implementation, Challenges and Applications

In this paper, we present a new approximation method for non-linear activation functions including tanh and sigmoid functions using stochastic computing (SC) logic based on the piecewise-linear approximation (PWL) for the full range of [-1, 1]. SC implementations with PWL approximation expansions for non-linear functions are based on a 90nm CMOS process. The implementation results shown that the proposed SC circuits can provide better performance compared with the previous methods such as the well-known Maclaurin expansions based, Bernstein polynomial based and finite-state-machine (FSM) based implementations. The implementation results are also presented and discussed.

An Efficient Hardware Implementation of Activation Functions Using Stochastic Computing for Deep Neural Networks

The design of ultra-low power advanced encryption standard (AES) encryption cores for emerging wireless networks and Internet of things systems by combining optimised architectures, a simple clock gating technique and an advanced 65 nm silicon on thin buried oxide (SOTB) CMOS process is presented. The implementation results show that the proposed 2-Sbox AES encryption core requires the smallest number of clock cycles and achieves the lowest power consumption of 0.4 µW/MHz which is 3.3× lower than that of the best previous presented AES encryption core, with a very small area overhead. Moreover, the proposed 1-Sbox AES encryption core consumes very low hardware resources of 2.4 kgates gate equivalent.

Design of ultra-low power AES encryption cores with silicon demonstration in SOTB CMOS process

This paper presents a compact, ultra-low power AES-CCM authenticated encryption IP core for WBANs by combining a low area 8-bit AES encryption core, iterative structure and other optimized circuits. The proposed AES-CCM IP core can be used for the message security at the MAC level, e.g. message encryption and authentication, based on AES forward cipher function with a 128-bit key for counter and cipher block chaining modes of operations. The implementation results show that the proposed AES-CCM IP core achieves a very high resource efficiency and ultra-low power consumption while meeting the requirement of operation speed in WBANs.

A compact, ultra-low power AES-CCM IP core for wireless body area networks

This paper presents an efficient ASIC implementation of the low area 8-bit AES encryption core using an optimized SBox for wireless networks. The proposed AES core supports 128- bit key length and 128-bit data blocks. The implementation results in a 90nm CMOS standard library show that the proposed AES encryption core has the maximum clock frequency of 452.5 MHz and higher resource usage efficiency compared with other designs.

http://ieeexplore.ieee.org/iel7/7390329/7394221/07394268.pdf

An ASIC implementation of low area AES encryption core for wireless networks

In this letter, a multi-task deep convolutional neural network, namely MoDANet, is proposed to perform modulation classification and DOA estimation simultaneously. In particular, the network architecture is designed with multiple residual modules, which tackle the vanishing gradient problem. The multi-task learning (MTL) efficiency of MoDANet was evaluated with different variants of Y-shaped connection and fine-tuning some hyper-parameters of the deep network. As a result, MoDANet with one shared residual module using more filters, larger filter size, and longer signal length can achieve better performance of modulation classification and DOA estimation, but those might result in higher computational complexity. Therefore, choosing these parameters to attain a good trade-off between accuracy and computational cost is important, especially for resource-constrained devices. The network is investigated with two typical propagation channel models, including Pedestrian A and Vehicular A, to show the effect of those channels on the efficiency of the network. Remarkably, our work is the first DL-based MTL model to handle two unrelated tasks of modulation classification and DOA estimation.

Van-Phuc Hoang

Papers

An Efficient Hardware Implementation of Activation Functions Using Stochastic Computing for Deep Neural Networks

Design of ultra-low power AES encryption cores with silicon demonstration in SOTB CMOS process

A compact, ultra-low power AES-CCM IP core for wireless body area networks

An ASIC implementation of low area AES encryption core for wireless networks

MoDANet: Multi-Task Deep Network for Joint Automatic Modulation Classification and Direction of Arrival Estimation