Showing papers in "IEEE Transactions on Consumer Electronics in 2022"

eChain: A Blockchain-Enabled Ecosystem for Electronic Device Authenticity Verification

Abstract: Counterfeit electronic devices can cause a significant revenue loss and brand value damage to the original component manufacturers (OCM). In addition, they can instigate serious safety and security issues in critical military and space applications. These devices can be injected by untrusted entities in the supply chain, such as outsourced foundries, distributors, PCB assemblers, and system integrators. Existing methods for device authenticity verification are either destructive, require an advanced electrical test or physical inspection infrastructure. Furthermore, the existing database query-based verification systems by OCMs provide an illusion of authenticity verification by looking for a device record in their online system. In reality, a customer may have bought a cloned or recycled copy of an electronic device and may find a valid record in the OCM verification system. This paper presents a blockchain-centric solution to address these limitations to verify electronic devices. A detailed study is presented to transform an existing supply chain into a trustworthy distributed ledger framework called eChain (electronic Chain). eChain generates device provenance records from blockchain that users can utilize to classify authentic and counterfeit ICs. A fully functional prototype of eChain is developed to demonstrate the feasibility and efficacy of the proposed solution.

Accurate Current Sharing and Voltage Regulation in Hybrid Wind/Solar Systems: An Adaptive Dynamic Programming Approach

Abstract: Renewable energy is an advisable choice to reduce fuel consumption and $\rm CO_{2}$ emission. Therein, wind energy and solar energy are the most promising contributors to reach this goal. Although the hybrid wind/solar system has been widely studied, the real-time current sharing based on their maximum capacities is rarely achieved in terms of seconds. Based on this, this paper proposes an accurate current sharing and voltage regulation approach in hybrid wind/solar systems, which is based on distributed adaptive dynamic programming approach. Firstly, the equivalent wind/solar model is built, which is an indispensable preprocessing to achieve the complementary between wind energy and solar energy. Therein, the wind energy and solar energy can output relative current according to their respective capacity ratio, which ensure the maximum utilization ratio of renewable energy source. Furthermore, current sharing and voltage regulation problem is switched into optimal control problem. Under this effect, each source agent aims to obtain the optimal control variable and achieve accurate current sharing/voltage regulation. Moreover, an adaptive dynamic programming approach based on Bellman principle is proposed. It can achieve accurate current sharing and voltage regulation. Finally, the simulation results are provided to illustrate the performance of the proposed adaptive dynamic programming approach.

iGLU 3.0: A Secure Noninvasive Glucometer and Automatic Insulin Delivery System in IoMT

Abstract: Consumer technologies have changed human life through various products for smart healthcare management. The evaluation of Internet-of-Medical-Things (IoMT) has provided the closed loop control system for point of care mechanism. The hardware security of medical devices has drawn the attention where any security breach could have catastrophic impact. The paper discusses iGLU 3.0 which includes security model of glucose measurement device along with insulin pump of IoMT framework. The novel glucose-insulin model has been proposed for glucose control of diabetes patient. The physical unclonable function (PUF) based security solution is developed for non-invasive glucometer iGLU and insulin pump for safe insulin secretion. PUF based Hardware-Assisted Security (HAS) is helpful to mitigate challenges which are present in automatic insulin delivery with iGLU.

Dual Locality-Based Flash Translation Layer for NAND Flash-Based Consumer Electronics

Abstract: NAND flash memory shows prominent performance, so it has been used as storage devices of consumer electronics, such as the smart phones and tablet personal computers. As the storage management software of NAND flash memory, the page-level flash translation layer (PLFTL) owns very high I/O access performance for consumer electronics. As an improved version of PLFTL, the demand-based PLFTL selectively keeps active mapping entries in the DRAM (Dynamic Random Access Memory) and the demand-based PLFTL mainly considers the temporal locality of workloads. However, the spatial locality also appears in many workloads. To exploit the temporal locality and spatial locality of workloads, a novel dual locality-based FTL (DL-FTL) is proposed in this paper. DL-FTL uses the sequential cache mapping state table (S-CMST) and sequential physical address cache mapping table (SPA-CMT) to process the sequential requests. To decrease the update counts of translation pages, the mapping entries that are evicted from S-CMST will be written back to NAND flash memory using a batch update strategy. The experimental results show that our proposed DL-FTL raises the cache hit ratio by up to 66.39% and reduces the system response time by up to 21.64% on average, compared with the demand-based PLFTL.

Boundary Guided Semantic Learning for Real-Time COVID-19 Lung Infection Segmentation System

Abstract: The coronavirus disease 2019 (COVID-19) continues to have a negative impact on healthcare systems around the world, though the vaccines have been developed and national vaccination coverage rate is steadily increasing. At the current stage, automatically segmenting the lung infection area from CT images is essential for the diagnosis and treatment of COVID-19. Thanks to the development of deep learning technology, some deep learning solutions for lung infection segmentation have been proposed. However, due to the scattered distribution, complex background interference and blurred boundaries, the accuracy and completeness of the existing models are still unsatisfactory. To this end, we propose a boundary guided semantic learning network (BSNet) in this paper. On the one hand, the dual-branch semantic enhancement module that combines the top-level semantic preservation and progressive semantic integration is designed to model the complementary relationship between different high-level features, thereby promoting the generation of more complete segmentation results. On the other hand, the mirror-symmetric boundary guidance module is proposed to accurately detect the boundaries of the lesion regions in a mirror-symmetric way. Experiments on the publicly available dataset demonstrate that our BSNet outperforms the existing state-of-the-art competitors and achieves a real-time inference speed of 44 FPS. The code and results of our BSNet can be found from the link of https://github.com/rmcong/BSNet.

Low-Complexity Intra Coding in Versatile Video Coding

Abstract: Versatile Video Coding (VVC) was finalized in 2020 and offered promising coding efficiency with a bitrate reduction of about 50% for same video quality as High Efficiency Video Coding. However, its high encoding complexity is a heavy burden on real-time applications. In particular, the very high complexity in intra coding can be a big barrier into market entry. This paper presents an efficient low-complexity intra coding scheme, which employs downsampling and upsampling processes. The downsampling is simply performed by reducing the resolution of an original video in both horizontal and vertical directions. In the upsampling, convolutional neural network based super-resolution is used to increase the resolution of the reconstructed video. In addition, this paper thoroughly analyzes the performance and complexity of all intra coding tools in VVC. Experimental results demonstrate that the significantly high reduction of the encoding complexity can be achieved with acceptable video quality.

Realizing Robust, Lightweight Strong PUFs for Securing Smart Grids

Abstract: Improving the reliability of energy distribution systems is a major concern to multiple parties as they are not only critical infrastructure themselves, but also affect other connected infrastructure. Smart Grids have been proposed to leverage the advantages of Internet of Things (IoT) to allow smarter management and faster recovery of energy distribution systems against disruptions. However, Smart Grid applications require a reliable, lightweight and fast authentication system to realize their potential in a secure manner. In this work, we propose a Strong PUF (physically unclonable function) system that can meet the stringent resource and performance constraints imposed by a Smart Grid operational environment. Our results indicate that the proposed mechanism produces a Strong PUF with close to ideal normalized uniqueness of 50 % and an accuracy of 50 % to modeling attacks using the best known machine learning algorithms. Additionally, our scheme has a low hardware overhead cost of $750~ \mu m^{2}$ in 45 nm technology and a sub-1 ms key generation time, ensuring the entire system is fast and lightweight.

From HEVC to VVC: The First Development Steps of a Practical Intra Video Encoder

Abstract: Versatile Video Coding (VVC/H.266) is an emerging successor to the widespread High Efficiency Video Coding (HEVC/H.265) and is shown to double the coding efficiency for the same subjective visual quality. Nevertheless, VVC still adopts the similar hybrid video coding scheme as HEVC and thereby sets the scene for reusing many HEVC coding tools and techniques as is or with minor modifications. This paper explores the feasibility of developing a practical software VVC intra encoder from our open-source Kvazaar HEVC encoder. The outcome of this work is called uvg266 VVC intra encoder that is distributed under the same permissive 3-clause BSD license as Kvazaar. uvg266 inherits the optimized coding flow of Kvazaar and all upgradable Kvazaar intra coding tools, but it also introduces basic VVC intra coding tools not available in HEVC. To the best of our knowledge, this is the first work to describe the implementation details of upgrading an HEVC encoder to a VVC encoder. The rapid development time with promising coding performance make our proposal a viable approach over the encoder development from scratch.

Design and Implementation of Gray-Coded Bit-Plane Based Reconfigurable Motion Estimation Architecture Using Binary Content Addressable Memory for Video Encoder

Abstract: Motion Estimation (ME) is the most power consuming module in the video encoder due to its high computational complex operations. So designing an efficient ME hardware without losing coding performance is a major challenge. This paper proposes a low-bit-depth ME technique based on Gray-Coded bit-planes and its hardware implementation using Binary Content Addressable Memory (BCAM). The proposed method significantly reduces the computational burden due to its low-bit-depth representation. The novel BCAM based ME hardware provides faster results because of its on-chip memory computation without compromising other performance parameters. It can process 8K @53.71 fps operated at maximum frequency of 155 MHz with 152.78K NAND equivalent gate count using 90 nm technology library.

Joint Regression Network and Window Function-Based Piecewise Neural Network for Cuffless Continuous Blood Pressure Estimation Only Using Single Photoplethesmogram

Abstract: The blood pressure (BP) is generally measured using a cuff based sphygmomanometer. However, it is inconvenient to be used. Recently, an alternative solution only using the photoplethesmograms (PPGs) was proposed. In this case, the continuous BP estimation could be performed. First, the features were extracted from the PPGs. Then, a regression network was employed to estimate the BP values. Nevertheless, the accuracy of this approach was not so high. In order to improve the estimation accuracy, this paper proposes to cascade a two layer piecewise neural network to the output of the existing regression network to correct the estimation error. In particular, the overall system is a three layer network. The first layer of the network is the existing regression network. It generates the initial estimated BP values. The second layer of the network consists of the window functions. It segments the range of the BP values into various regions for the further processing. The final layer of the network performs the estimation correction. The performance of our proposed network is evaluated via two practical datasets and three common regression networks including the three layer artificial neural network (ANN) based regression network, the random forest (RF) based regression network and the support vector regression (SVR) based network. For the first dataset, our proposed method with the RF model and the piecewise neural network achieves the systolic BP (SBP) estimation error and the diastolic BP (DBP) estimation error at $3.01{\pm }2.22$ mmHg with the correlation coefficient at 0.926 and $4.43{\pm }3.37$ mmHg with the correlation coefficient at 0.935, respectively. On the other hand, the conventional RF model without the piecewise neural network achieves the SBP estimation error and the DBP estimation error at $5.34{\pm }4.08$ mmHg with the correlation coefficient at 0.740 and $5.89{\pm }4.98$ mmHg with the correlation coefficient at 0.840, respectively. For the second dataset, our proposed method with the RF model and the piecewise neural network achieves the SBP estimation error and the DBP estimation error at $7.91{\pm }8.06$ mmHg with the correlation coefficient at 0.876 and $3.47{\pm }5.59$ mmHg with the correlation coefficient at 0.859, respectively. On the other hand, the conventional RF model without the piecewise neural network achieves the SBP estimation error and the DBP estimation error at $9.77{\pm }9.01$ mmHg with the correlation coefficient at 0.805 and $7.08{\pm }5.55$ mmHg with the correlation coefficient at 0.612, respectively. It can be seen that our proposed network yields the estimated BP values highly correlated to the reference BP values. Also, our proposed method yields the higher accuracies compared to the existing networks. This demonstrates the effectiveness of our proposed network.

Boundary Guided Semantic Learning for Real-Time COVID-19 Lung Infection Segmentation System

Abstract: The coronavirus disease 2019 (COVID-19) continues to have a negative impact on healthcare systems around the world, though the vaccines have been developed and national vaccination coverage rate is steadily increasing. At the current stage, automatically segmenting the lung infection area from CT images is essential for the diagnosis and treatment of COVID-19. Thanks to the development of deep learning technology, some deep learning solutions for lung infection segmentation have been proposed. However, due to the scattered distribution, complex background interference and blurred boundaries, the accuracy and completeness of the existing models are still unsatisfactory. To this end, we propose a boundary guided semantic learning network (BSNet) in this paper. On the one hand, the dual-branch semantic enhancement module that combines the top-level semantic preservation and progressive semantic integration is designed to model the complementary relationship between different high-level features, thereby promoting the generation of more complete segmentation results. On the other hand, the mirror-symmetric boundary guidance module is proposed to accurately detect the boundaries of the lesion regions in a mirror-symmetric way. Experiments on the publicly available dataset demonstrate that our BSNet outperforms the existing state-of-the-art competitors and achieves a real-time inference speed of 44 FPS. The code and results of our BSNet can be found from the link of https://github.com/rmcong/BSNet .

Design and ASIC-Implementation of Hardware-Efficient Cooperative Spectrum-Sensor for Data Fusion-Based Cognitive Radio Network

Abstract: This paper presents hardware-friendly algorithm for Gini-index (GI) cooperative-spectrum-sensing (CSS) algorithm for the data-fusion based cooperative cognitive-radio network. It simplifies the complex computations of sample-covariance-matrix (SCM) elements and test-statistics value of the conventional GI-based CSS algorithm. It delivers excellent detection performance under the realistic scenario of non-uniform dynamical noise and signal-power. Based on GI-based CSS algorithm, three different VLSI architectures are proposed for the cooperative spectrum sensor (CSR): CSR-VLAR1, CSR-VLAR2, and CSR-VLAR3. Here, CSR-VLAR1 is the first-time reported CSR-architecture for the conventional GI-based CSS algorithm. Subsequently, CSR-VLAR2 represents hardware-architecture of the proposed hardware-friendly GI-based CSS algorithm. Eventually, additional architectural optimization has been applied to CSR-VLAR2 that is transformed into the most hardware-efficient VLSI-architecture of CSR, referred as CSR-VLAR3, which is ASIC chip-fabricated in UMC 130 nm-CMOS technology node. Furthermore, both CSR-VLAR1 and CSR-VLAR2 are synthesized and post-layout simulated in the same technology node. Our ASIC-chip of CSR-VLAR3 occupies 0.35 mm2 of core-area and consumes 8.31 mW of total power at 88.8 MHz of maximum clock frequency, when the supply voltage is 1.2 V. Our CSR ASIC-chip has been functionally verified with the aid of real-world signals, using USRPs and FPGAs based test-setup of cooperative cognitive-radio network. Measured results of our design are compared with reported implementations where the proposed CSR is $4.52\times $ hardware-efficient and $2.8\times $ power-efficient than the state-of-the-art CSR-implementations. Thus, our work addresses the key challenge of designing hardware-efficient CSR that delivers excellent detection performance in the real-world scenario.

The Energy Management of Multiport Energy Router in Smart Home

Abstract: Although smart home has received wide attention in recent years, numerous scholars focus more on energy optimization strategy than energy dispatch hardware device (named energy router). Meanwhile, this energy router should have several features, i.e., high renewable energy utilization, energy multi-port and low volume. Thus, this paper designs a nine-port energy router regarding smart home and proposes a multimode hierarchical management strategy for this energy router. First, for the multi-port demand of wind, solar, storage and utilization, this paper presents a nine-port energy router to improve the renewable energy consumption and power supply flexibility. In addition, to reduce the volume of the energy router, a non-isolated AC/DC hybrid topology is constructed through embedding the integrated power electronic converters, which achieves the miniaturization of the energy router. In order to improve the renewable energy utilization rate, the decentralized module control is proposed for the components of energy router to provide the voltage and frequency support for system, and realizes the power sharing of distributed generations (DGs). Furthermore, the power exchange control with three-mode switching is proposed to guarantee the global energy flow balance under complex conditions. Eventually, the feasibility of the energy router is verified by the simulation and experiments.

Hardware-Friendly Multiple Transform Selection Module for the VVC Standard

Abstract: The H.266/versatile video coding (VVC) standard is the most recent ITU/ISO video coding standard finalized in July 2020. VVC includes several new coding tools at different levels of the coding scheme. These coding tools enable a significant bitrate saving of up to 50% for the same subjective video quality than its predecessor H.265/high efficiency video coding (HEVC). Among these tools, we can cite the multiple transform selection (MTS) which selects at the encoder horizontal and vertical transforms among three trigonometrical transforms, including discrete cosine transform (DCT) type II, discrete sine transform (DST) type VII and DCT type VIII. Unlike the DCT-II, the DST-VII does not have efficient fast algorithmic implementation. Moreover, the MTS increases the memory usage required to store the coefficients of the three transforms. Consequently, this paper targets an efficient approximation of the DST-VII kernel based on the DCT-II and adjustment stage. The approximation of the DST-VII is modeled as an integer optimization problem jointly minimizing the error and the orthogonality of the approximate DST-VII under sparsity constraint of the adjustment stage. The sparse nonlinear optimizer (SNOPT) solver with an additional relaxation constraint is used to solve the problem and find the best sparse adjustment band matrices for different transform sizes. The DCT-VIII is then computed from the approximate DST-VII with pre/post processing operations involving only sign changes and input/output reordering. The proposed approximation provides a significant reduction in both arithmetic operations and memory usage. Moreover, it preserves the coding gain brought by the MTS under the VVC reference software. These advantages make our solution suitable for energy-efficient hardware H.266//VVC encoders and decoders deployed on consumer electronic devices.

Point Cloud Generation Using Deep Adversarial Local Features for Augmented and Mixed Reality Contents

Abstract: We present a generative model-based point cloud generation method using deep adversarial local features. The proposed generative adversarial network (GAN) can reduce computational load and increase the accuracy in three-dimensional (3D) acquisition, reconstruction, and rendering processes. To train the proposed GAN, we first optimize the latent space using an autoencoder to extract local features. The training process provides an accurate estimation of local context from the latent variables and robust point cloud generation. The main contribution of this work is a novel deep learning-based 3D point cloud generation, which significantly reduces computational load to render augmented reality (AR) and mixed reality (MR) contents. Additional contribution in the deep learning field is twofold: i) The autoencoder in the proposed network avoids the vanishing gradient problem using hierarchically linked features in different layers, and ii) the complexity of the network is significantly reduced by removing the transformation network that estimates the affine transformation matrix of the point cloud.

Crowd Counting by Using Top-k Relations: A Mixed Ground-Truth CNN Framework

Abstract: Crowd counting has important applications in the environments of smart cities, such as intelligent surveillance. In this paper, we propose a novel convolutional neural network (CNN) framework for crowd counting with mixed ground-truth, called top- $k$ relation-based network (TKRNet). Specifically, the estimated density maps generated in a coarse-to-fine manner are treated as coarse locations for crowds so as to assist our TKRNet to regress the scattered point-annotated ground truth. Moreover, an adaptive top- $k$ relation module (ATRM) is proposed to enhance feature representations by leveraging the top- $k$ dependencies between the pixels with an adaptive filtering mechanism. Specifically, we first compute the similarity between two pixels so as to select the top- $k$ relations for each position. Then, a weight normalization operation with an adaptive filtering mechanism is proposed to make the ATRM adaptively eliminate the influence from the low correlation positions in the top- $k$ relations. Finally, a weight attention mechanism is introduced to make the ATRM pay more attention to the positions with high weights in the top- $k$ relations. Extensive experimental results demonstrate the effectiveness of our proposed TKRNet on several public datasets in comparison to state-of-the-art methods.

Deep-Learned Perceptual Quality Control for Intelligent Video Communication

Abstract: With the development of video technology, a large amount of video data generated from video conferences, sports events, live broadcasts and network classes flows into our daily lives. However, ultra-high-definition video transmission is still a challenge due to the limited network bandwidth and instability, which further affects the quality of video service closely linked with consumer electronic video display. To address this challenge, we propose a deep-learned perceptual quality control approach, which can significantly improve the video quality and visual experience at the same bandwidth. The proposed scheme mainly involves saliency region extraction, perceptual-based bits allocation, and video enhancement. Firstly, we exploit a multi-scale deep convolutional network module to predict the static saliency map that semantically highlights the salient regions. Secondly, we develop a recurrent neural network model to extract the dynamic saliency regions. Finally, a three-level rate allocation scheme is developed based on the resulted saliency guidance, which is more reasonable by taking into account the visual characteristics of human eyes. Experimental results on a large dataset show that our method achieves an average gain of 1.5dB on the salient regions without introducing an extra bandwidth burden, which significantly improves the visual experience and paves the way to intelligent video communication.

The Energy Management of Multiport Energy Router in Smart Home

Abstract: Although smart home has received wide attention in recent years, numerous scholars focus more on energy optimization strategy than energy dispatch hardware device (named energy router). Meanwhile, this energy router should have several features, i.e., high renewable energy utilization, energy multi-port and low volume. Thus, this paper designs a nine-port energy router regarding smart home and proposes a multimode hierarchical management strategy for this energy router. First, for the multi-port demand of wind, solar, storage and utilization, this paper presents a nine-port energy router to improve the renewable energy consumption and power supply flexibility. In addition, to reduce the volume of the energy router, a non-isolated AC/DC hybrid topology is constructed through embedding the integrated power electronic converters, which achieves the miniaturization of the energy router. In order to improve the renewable energy utilization rate, the decentralized module control is proposed for the components of energy router to provide the voltage and frequency support for system, and realizes the power sharing of distributed generations (DGs). Furthermore, the power exchange control with three-mode switching is proposed to guarantee the global energy flow balance under complex conditions. Eventually, the feasibility of the energy router is verified by the simulation and experiments.

Pinning Control of Complex Dynamical Networks

Abstract: This article introduces the notion of pinning control for complex dynamical networks regarding their stabilization, synchronization and control. Specifically, it will first review the concept of network pinning control and then address the fundamental issues of network stabilizability, synchronizability and controllability. Basic ideas will be explained, technical derivations will be outlined, and important theoretical problems will be briefly discussed. It will show that the self-contained theoretical framework of pinning control technology is promising for practical applications in network science and engineering.

Deep-Learned Perceptual Quality Control for Intelligent Video Communication

Abstract: With the development of video technology, a large amount of video data generated from video conferences, sports events, live broadcasts and network classes flows into our daily lives. However, ultra-high-definition video transmission is still a challenge due to the limited network bandwidth and instability, which further affects the quality of video service closely linked with consumer electronic video display. To address this challenge, we propose a deep-learned perceptual quality control approach, which can significantly improve the video quality and visual experience at the same bandwidth. The proposed scheme mainly involves saliency region extraction, perceptual-based bits allocation, and video enhancement. Firstly, we exploit a multi-scale deep convolutional network module to predict the static saliency map that semantically highlights the salient regions. Secondly, we develop a recurrent neural network model to extract the dynamic saliency regions. Finally, a three-level rate allocation scheme is developed based on the resulted saliency guidance, which is more reasonable by taking into account the visual characteristics of human eyes. Experimental results on a large dataset show that our method achieves an average gain of 1.5dB on the salient regions without introducing an extra bandwidth burden, which significantly improves the visual experience and paves the way to intelligent video communication.

VVC Search Space Analysis Including an Open, Optimized Implementation

Abstract: Versatile Video Coding (VVC) is a new video coding standard finalized in July 2020. During the standard development much attention was paid to keeping the decoding complexity increase as small as possible, with more permissive approach being taken with regard to the encoding. The VVC reference software VTM in random access configuration requires around double the time to decode and $8\times $ the time to encode a video, compared to High Efficiency Video Coding (HEVC) reference software HM. With this runtime increase, an objective bitrate reduction of around 40% is achieved. In this paper we analyze the encoding complexity increase of VVC over HEVC. We abstract the implementation-based aspects, including low-level software optimizations and introduce an empirical measure to quantify the extent of encoder search space given a specific search algorithm. Based on the measure, we compare the search space of HM and VTM, but also of the open and optimized VVC encoder implementation VVenC, showing the potential for search space reduction and its impact on compression performance. Overall, it can be seen that while VVC’s search space is quite large in VTM, it can be efficiently limited either by including early termination strategies or by disabling VVC coding tools.

Secured Convolutional Layer IP Core in Convolutional Neural Network Using Facial Biometric

Abstract: This paper presents a novel methodology to design a secured custom reusable intellectual property (IP) core for the convolutional layer of convolutional neural network (CNN). Since the reusable IP cores used in system-on-chips (SoCs) of consumer electronics (CE) systems are susceptible to the hardware threat of IP counterfeiting. Therefore, this paper also presents the security of the proposed convolutional layer reusable IP core against the threat of IP counterfeiting using facial biometrics. This enables the integration of secured reusable IP cores in the SoCs of CE systems, thereby ensuring the safety of end consumers. In the proposed approach, the convolutional layer IP core is designed through high-level synthesis (HLS) process and secured by embedding secret biometric security information into the design during register allocation phase of the HLS process. The qualitative and quantitative analysis of the proposed approach exhibits significantly lower probability of coincidence (Pc) (up to 47% less) and higher tamper tolerance (1.93E+25) than recent approaches. Further, it offers robust security with zero design overhead.

Remote Device Management via Smart Contracts

Abstract: With the continued scaling of transistor feature size, the cost of IC development has been escalating. The economics of semiconductor IC development favors high volume manufacturing, while high volume cannot be attained without developing an IC that serves many applications. Some of these applications are in low-margin Internet of Things (IoT) devices, where an SoC cannot command a high price. Consequently, without the ability to customize IC features after production, its lowest-priced application will determine an IC’s price. This motivates the manufacturers to develop chips with provisions for post-manufacturing IC customization. This paper explores how an IC can be programmed repeatedly and securely using blockchain-based smart contracts to allow users to upgrade or rent features. The availability of such a system could, for example, allow a buyer to upgrade her processor from a low-end to a high-end part by making an additional payment to the manufacturer. Implementing such a system will require remote device management capabilities. Remote device management presents unique design considerations, such as, necessity for transparency of the actions that a device takes on behalf of a user; the requirement of a trusted arbiter; and provision for management of these devices beyond the intended lifespan. To overcome the challenges of transparency, longevity, and the necessity of a trusted arbiter, we propose embedded smart contracts in concert with a blockchain. Our proposed smart contract takes the device feature configuration request as input and outputs the corresponding configuration. To support remote, secure, and authorized updates, we propose an on-die hardware module which communicates with the smart contract and enforces its functionalities. This was prototyped using a programmable system-on-chip working in concert with Ethereum blockchain. The prototype demonstrates the feasibility and practicality of the proposed solution.

Realization of a Real-Time Image Denoising System for Dashboard Camera Applications

Abstract: Noise interference during the acquisition of digital images can severely degrade image quality, particularly for images captured under low-light conditions; however, the removal of image noise requires sophisticated digital image processing systems. This study presents a hardware-based solution to real-time image denoising using an existing algorithm designed for the removal of mixed impulse noise (salt-and-pepper and random-valued impulse noise), while preserving image edge details and image borders, without the need for additional computation time or memory capacity. Note that mixed impulse noise is typical of most real-world situations, such as the video noise associated with dashboard cameras. The proposed design was implemented using 180 nm complementary metal-oxide-semiconductor (CMOS) technology, consuming only 21.7 mW when operated at 200 MHz. This operating frequency allows the proposed chip to process noisy video streams with resolution of $1920\mathbf {\times }1080$ at 60 frames per second in real time. In terms of image restoration, the proposed algorithm achieved image quality on par with that achieved using software simulation. We also demonstrated the efficacy of the proposed scheme in denoising noisy video images from a dashboard camera.

Fortified-NoC: A Robust Approach for Trojan-Resilient Network-on-Chips to Fortify Multicore-Based Consumer Electronics

Abstract: Consumer electronics hardware is designed and manufactured following a global supply chain which opens doors to their security challenges. Even with the advanced methods of formal verification and coverage analysis, there is still a chance of hiding malicious hardware/software which can degrade performance, leak data, or even stop functionalities. In this work, we present security solution of Network on Chip (NoC) based Multiprocessor System-on-Chip (MPSoC) consumer electronics (CE) systems such as set-top boxes and autonomous vehicles. Most of the existing methods targeted for such systems focus on protection in Network Interfaces (NI) and other software solutions rather than routers against Hardware Trojans (HT) which can be embedded in NoC by a rogue designer. In this work, we propose a 3-tier methodology leading to “Fortified-NoC” to secure the data and resources against different kinds of threats. A Trojan cognizant routing algorithm (TCRA) is proposed which limits the HTs to a particular router that contains them. Data shuffling with Trojan detectability is also used to mislead and identify the HTs. We validated the proposed approach using various experiments. Our proposed method is capable of mitigating the Trojan attacks such as data leakage, performance degradation, denial of service and live locking of data packets at the cost of a little latency and, some extra hardware. It is able to recover more than 80% of lost packets, improve the throughput by $1.3\times $ against performance degrading Trojan attacks.

Adaptive Loop Filter Hardware Design for 4K ASIC VVC Decoders

Abstract: Versatile video coding (VVC) is the next generation video coding standard released in July 2020. VVC introduces new coding tools enhancing the coding efficiency compared to its predecessor high efficiency video coding (HEVC). These new tools have a significant impact on the VVC software decoder with a complexity estimated to two times HEVC decoder complexity. In particular, the adaptive loop filter (ALF) introduced in VVC as an in-loop filter increases both the decoding complexity and memory usage. These concerns need to be carefully addressed regarding the design of an efficient hardware implementation of a VVC decoder. In this paper, we present an efficient hardware implementation of the ALF tool for VVC decoder. The proposed solution establishes a novel scanning order between Luma and Chroma components that reduces significantly the ALF memory. The design takes advantage of all ALF features and establishes an unified hardware module for all ALF filters. The design uses 26 regular multipliers in a pipelined architecture with a fixed throughput of 2 pixels/cycle and fixed system latency regardless of the selected filter. This design operates at 600 MHz frequency enabling to decode on ASIC platform a 4K video at 30 frames per second in 4:2:2 chroma sub-sampling format.

2-Phase Adiabatic Logic for Low-Energy and CPA-Resistant Implantable Medical Devices

Abstract: Designing a low-energy and secure lightweight cryptographic coprocessor is the primary design constraint for modern wireless Implantable Medical Devices (IMDs). The lightweight cryptographic ciphers are the preferred cryptographic solution for low-energy encryption. This article proposes 2-SPGAL, the 2-phase sinusoidal clocking implementation of Symmetric Pass Gate Adiabatic Logic (SPGAL) for IMDs. The proposed 2-SPGAL is energy-efficient and secure against the Correlation Power Analysis (CPA) attack. The proposed 2-SPGAL was evaluated with the integration of synchronous resonant Power Clock Generators (PCGs): (i) 2N2P-PCG, and (ii) 2N-PCG. The case study implementation of one round of PRESENT-80 encryption using 2-SPGAL, with 2N2P-PCG integrated into the design, shows an average of 47.50% of energy saving compared to its CMOS counterpart, over the frequency range of 50 kHz to 250 kHz. The same 2-SPGAL based case study, with 2N-PCG integrated into the design, shows 51.18% of an average energy saving compared to its CMOS counterpart, over 50 kHz to 250 kHz. Further, the 2-SPGAL based PRESENT-80 one round shows an average energy saving of 16.62% and 28.90% respectively for 2N2P-PCG and 2N-PCG integrated into the design compared to existing 2-phase adiabatic logic called 2-EE-SPFAL. We also subjected PRESENT-80 design of 2-SPGAL and CMOS against CPA attack. The 2-SPGAL, with 2N2P-PCG and 2N-PCG, integrated into one round of PRESENT-80 design protects the encryption key. However, the encryption key was successfully revealed in one round of PRESENT-80 design using CMOS logic. Therefore, the proposed 2-SPGAL logic can be useful to design energy-efficient and CPA resilient Implantable Medical Devices (IMDs).

SIELNet: 3D Chaotic-map-based Secure Image Encryption using Customized Residual Dense Spatial Network

Abstract: The continuous development of Industry 5.0 technology has brought great convenience to people’s work and life. However, during digital data transmission and storage, the data may be accessed by unauthorized persons, resulting in privacy disclosure. Therefore, efficient data protection is always a high demand to solve this realistic problem. This work proposes a secure encryption algorithm, SIELNet, for colour images. First, we introduce a new three-dimensional chaotic map to encrypt colour images, obtaining the cipher images with a relationship to the plain images. We provide its excellent chaotic behaviour through standard randomness test. Secondly, we use a customized residual dense spatial network to perform the task of lossy image reconstruction from an encrypted, compressed image, which solves the constrained super-resolution task. Extensive experimental results on four public datasets demonstrate the superior performance of SIELNet against state-of-the-art techniques with excellent reconstruction quality. We believe the secure design of SIELNet can contribute to the favourable data integrity application of Industry 5.0.

An Innovative Architecture of Full-Digital Microphone Arrays Over A²B Network for Consumer Electronics

Abstract: Microphone arrays of various sizes and shapes are currently employed in consumer electronics devices such as speakerphones, smart TVs, smartphones, and headphones. In this paper, a full-digital, planar microphone array is presented. It makes use of digital Micro Electro-Mechanical Systems (MEMS) microphones, connected through the Automotive Audio Bus (A2B). A clock propagation model for A2B networks, developed in a previous work, was employed to estimate the effects of jitter and delay on microphone arrays. It will be shown that A2B allows for a robust data transmission, while ensuring deterministic latency and channels synchronization, thus overcoming the signal integrity issues which usually affect MEMS capsules. The microphone positioning is also discussed since it greatly affects the spatial accuracy of beamforming. Numerical simulations were performed on four regular geometries to identify the optimal layout in terms of number of capsules and beamforming directivity. An A2B planar array with equilateral triangle geometry and four microphones, three in the vertices and one in the center, was built. Experimental measurements were performed, obtaining an excellent matching with numerical simulations. Finally, the concept of an array of arrays (meta-array) is presented, designed by combining several triangular units and analyzed through numerical simulations.

Design Space Exploration of Practical VVC Encoding for Emerging Media Applications

Abstract: Versatile Video Coding (VVC/H.266) is the latest video coding standard designed for a broad range of next-generation media applications. This paper explores the design space of practical VVC encoding by profiling the Fraunhofer Versatile Video Encoder (VVenC). All experiments were conducted over five 2160p video sequences and their downsampled versions under the random access (RA) condition. The exploration was performed by analyzing the rate-distortion-complexity (RDC) of the VVC block structure and coding tools. First, VVenC was profiled to provide a breakdown of coding block distribution and coding tool utilization in it. Then, the usefulness of each VVC coding tool was analyzed for its individual impact on overall RDC performance. Finally, our findings were elevated to practical implementation guidelines: the highest coding gains come with the multi type tree (MTT) structure, adaptive loop filter (ALF), cross component linear model (CCLM), and bi-directional optical flow (BDOF) coding tools, whereas multi transform selection (MTS) and affine motion estimation are the primary candidates for complexity reduction. To the best of our knowledge, this is the first work to provide a comprehensive RDC analysis for practical VVC encoding. It can serve as a basis for practical VVC encoder implementation or optimization on various computing platforms.