Research Article Lifting-BasedFractionalWaveletFilter:Energy-EfficientDWT ArchitectureforLow-CostWearableSensors MohdTausif , 1 EkramKhan , 2 MohdHasan , 2 andMartinReisslein 3 1 Faculdade de Engenharia, Departamento de Inform´ atica, Universidade da Beira Interior, Covilhã, Portugal 2 Department of Electronic Engineering, Zakir Husain College of Engineering & Technology, Aligarh Muslim University, Aligarh 202002, India 3 School of Electrical Computer and Energy Engineering, Arizona State University, Goldwater Center, East Tyler Mall 650, MC 5706, Tempe, AZ 85287-5706, USA Correspondence should be addressed to Martin Reisslein; reisslein@asu.edu Received 20 April 2020; Revised 18 November 2020; Accepted 28 November 2020; Published 16 December 2020 Academic Editor: Constantine Kotropoulos Copyright©2020MohdTausifetal.isisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ispaperproposesandevaluatestheLFrWF,anovellifting-basedarchitecturetocomputethediscretewavelettransform(DWT) ofimagesusingthefractionalwaveletfilter(FrWF).Inordertoreducethememoryrequirementoftheproposedarchitecture,only one image line is read into a buffer at a time. Aside from an LFrWF version with multipliers, i.e., the LFrWF m , we develop a multiplier-less LFrWF version, i.e., the LFrWF ml , which reduces the critical path delay (CPD) to the delay T a of an adder. e proposed LFrWF m and LFrWF ml architectures are compared in terms of the required adders, multipliers, memory, and critical pathdelaywithstate-of-the-artDWTarchitectures.Moreover,theproposedLFrWF m andLFrWF ml architectures,alongwiththe state-of-the-art FrWF architectures (with multipliers (FrWF m ) and without multipliers (FrWF ml )) are compared through implementationonthesameFPGAboard.eLFrWF m requires22%lesslook-uptables(LUT),34%lessflip-flops(FF),and50% lesscomputecycles(CC)andconsumes65%lessenergythantheFrWF m .Also,theproposedLFrWF ml architecturerequires50% lessCCandconsumes43%lessenergythantheFrWF ml .us,theproposedLFrWF m andLFrWF ml architecturesappearsuitable for computing the DWT of images on wearable sensors. 1.Introduction 1.1. Motivation. e availability of low-cost small-sized cameras attached to wearable sensors and portable imaging devices has opened up a wide range of imaging-oriented applications, including assisted living, smart healthcare, traffic monitoring, virtual sports experiences, and posture recognition [1–12]. An interconnection of visual sensor nodes (sensor nodes with attached camera) is known as visual sensor network (VSN) [13, 14] or as wireless multi- media sensor network (WMSN) [15, 16]. Wearable visual sensors may also be a part of the Internet of things (IoT) [17–21]. Low-cost IoT wearable sensors [22] enable a wide range of activities for the benefit of society, e.g., hazard avoidancesystemsforworkersafety[23],navigationaidsfor visually impaired individuals [24], activity monitoring [25], smart irrigation [26], and sports [27]. In many visual applications of wearable sensors and portable imaging devices, images captured by the camera need to be transmitted wirelessly to a body-worn or nearby hub device. e wearable sensors and portable imaging devices have limited resources, and the wireless links have narrow bandwidth [28], making it impossible to directly sendtheraw(uncompressed)images.us,thereisaneedto compresstheimagesbeforetransmission[29].erefore,an imagecoderisneededinordertocompresstheimages.Inan image coder, an image is generally first transformed using thediscretecosinetransform(DCT)[30]ordiscretewavelet transform (DWT) [31, 32] and then it is quantized and entropy coded. e DWT, which is also used in JPEG 2000 Hindawi Advances in Multimedia Volume 2020, Article ID 8823689, 13 pages https://doi.org/10.1155/2020/8823689