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This letter presented a pencil-on-paper-based ecofriendly capacitive touchpad fabricated through a low-cost lab- and solvent-free fabrication process. Economical, readily available, and biodegradable materials, such as pencil and paper, have been used for sensor fabrication. A touchpad based on five such sensors has been developed and evaluated by using it as a user control interface to on/off LEDs and displaying results on LCD and mobile app. The graphite sensors in the user interface have been shielded from the direct interaction of humans and the environment using changeable cling film to make the interface reliable and reusable. The interface can be used in different conditions and users by changing the shielding film. Simultaneously, the system's functionality will remain consistent and functioned similarly to the conventional touch sensors and buttons. These recyclable and easily arrayed touch sensor-based keypads can contribute to future developments in disposable and lightweight interfaces for human–machine interface, medical and healthcare applications, smart packaging, security monitoring, and consumer products.

Due to the increasing size of s (s), their design and optimization phases (i.e., ) grow increasingly complex. At design time, a large design space needs to be explored to find an implementation that fulfills all specifications and then optimizes metrics like energy, area, delay, reliability, etc. At run time, a large configuration space needs to be searched to find the best set of parameters (e.g., voltage/frequency) to further optimize the system. Both spaces are infeasible for exhaustive search typically leading to heuristic optimization algorithms that find some trade-off between design quality and computational overhead. ML can build powerful models that have successfully been employed in related domains. In this survey, we categorize how () may be used and is used for design-time and run-time optimization and exploration strategies of s. A meta-study of published techniques unveils areas in that are well-explored and underexplored with, as well as trends in the employed algorithms. We present a comprehensive categorization and summary of the state of the art on for. Finally, we summarize remaining challenges and promising open research directions.

This paper presents a new active electrode design for electroencephalogram (EEG) and electrocardiogram (ECG) sensors based on inertial measurement units to remove motion artefacts during signal acquisition. Rather than measuring motion data from a single source for the entire recording unit, inertial measurement units are attached to each individual EEG or ECG electrode to collect local movement data. This data is then used to remove the motion artefact by using normalised least mean square adaptive filtering. Results show that the proposed active electrode design can reduce motion contamination from EEG and ECG signals in chest movement and head swinging motion scenarios. However, it is found that the performance varies, necessitating the need for the algorithm to be paired with more sophisticated signal processing to identify scenarios where it is beneficial in terms of improving signal quality. The new instrumentation hardware allows data driven artefact removal to be performed, providing a new data driven approach compared to widely used blind-source separation methods, and helps enable in the wild EEG recordings to be performed.

This paper presents an innovative Through-Load element aimed at characterization applications at mm-wave frequencies. The proposed structure can behave as a Through connection or as a 50- $\Omega $ load depending on a DC control voltage. Among other potential applications, this system can be used to implement a transfer switch or an attenuator. A demonstrator was fabricated and measured in the STM 55-nm BiCMOS technology. Over a wide bandwidth, from 55 GHz up to 170 GHz, experimental measurements demonstrate a maximum 1.6-dB of insertion loss when behaving as a Through connection and a minimum 14-dB of insertion loss when behaving as a 50- $\Omega $ load. In both cases, the return loss is better than 10 dB. The insertion loss at 90 GHz is 0.6 dB for the Through connection and 20 dB for the 50- $\Omega $ load connection.

This paper presents a highly sensitive phase-variation sensor for dielectric constant measurements based on a capacitively-loaded periodic slow-wave transmission line. Such line is implemented in microstrip technology, and the loading capacitors are formed by closely spaced rectangular patches. The sensing area of the device is delimited by the region occupied by the capacitive patches, where the material under test (MUT) must be located. The presence of the MUT modifies the coupling capacitance between adjacent patches, thereby producing a variation in the electrical length, or phase of the transmission line, which is the output variable. A detailed analysis of the equivalent circuit model of the unit cell, useful for design purposes, is carried out in the paper. Based on such analysis, a prototype device sensor is designed and fabricated. It is demonstrated in the paper that the sensitivity of the proposed sensor is by far superior to the one achieved in an ordinary meandered line with similar sensing area. Thus, the proposed slow-wave structure constitutes a good alternative to meandered lines for the implementation of phase-variation sensors with simultaneously high sensitivity and compact size.

In this paper, a long-term workload management problem for multi-server edge computing with server collaboration is studied. In the considered model, mobile users computation-intensive tasks are generated dynamically over the time and offloaded to associated edge servers according to pre-determined subscription agreements. Upon receiving the subscribed workload, each edge server can then decide to whether participate in server collaboration for enabling workload re-allocation (i.e., workload exchange) with other heterogeneously configured edge servers. Unlike most of the existing work, this paper takes into account both competitions and collaborations among strategic edge servers in sharing their computing capacities. To achieve the equilibrium for each edge server in minimizing its expected cost (including energy consumption, delay, transmission, configuration and pricing costs), a joint optimization is formulated for determining i) its amount of workload to undertake, ii) compensation price charged from peers, and iii) computing speed to adopt. To efficiently solve this problem, we propose a novel cooperative queueing game approach, which integrates a convex optimization, a core cost sharing scheme and a mapping rule. Theoretical analyses and extensive simulations are conducted to evaluate the performance of the proposed solution, and demonstrate its superiority over counterparts.

This paper aims to predict a set of apps a user will open on her mobile device in the next time slot. Such an information is essential for many smartphone operations, e.g., app pre-loading and content pre-caching, to improve user experience. However, it is hard to build an explicit model that accurately captures the complex environment context and predicts a set of apps at one time. This paper presents a deep reinforcement learning framework, named as DeepAPP, which learns a model-free predictive neural network from historical app usage data. Meanwhile, an online updating strategy is designed to adapt the predictive network to the time-varying app usage behavior. To transform DeepAPP into a practical deep reinforcement learning system, several challenges are addressed by developing a context representation method for complex contextual environment, a general agent for overcoming data sparsity and a lightweight personalized agent for minimizing the prediction time. Extensive experiments on a large-scale anonymized app usage dataset reveal that DeepAPP provides high accuracy (precision 70.6% and recall of 62.4%) and reduces the prediction time of the state-of-the-art by 6.58 times. A field experiment of 29 participants demonstrates DeepAPP can effectively reduce launch time of apps.

This paper is the second part of a two-part survey series on large-scale global optimization. The first part covered two major algorithmic approaches to large-scale optimization, namely decomposition methods and hybridization methods such as memetic algorithms and local search. In this part we focus on sampling and variation operators, approximation and surrogate modeling, initialization methods, and parallelization. We also cover a range of problem areas in relation to large-scale global optimization, such as multi-objective optimization, constraint handling, overlapping components, the component imbalance issue, and benchmarks, and applications. The paper also includes a discussion on pitfalls and challenges of current research and identifies several potential areas of future research.

Citation recommendation is important in the environment of scholarly big data, where finding relevant papers has become more difficult because of information overload. Applying traditional collaborative filtering (CF) to citation recommendation is rather challenging due to the cold start problem and the lack of paper ratings. To address these two challenges, in this paper, we propose a Collaborative filtering with Network representation learning framework for Citation Recommendation dubbed as CNCRec, which is a hybrid user-based CF considering both paper content and network topology. It aims at recommending citations in heterogeneous academic information networks. CNCRec creates the paper rating matrix based on attributed citation network representation learning, where the attributes are topics extracted from the paper text information. Meanwhile, the learned representations of attributed collaboration network is utilized to improve the selection of nearest neighbors. By harnessing the power of network representation learning, CNCRec is able to make full use of the whole citation network topology compared with previous context-aware network-based models. Extensive experiments on both DBLP and APS datasets show that the proposed method outperforms state-of-the-art methods in terms of precision, recall, and Mean Reciprocal Rank. Moreover, CNCRec can better solve the data sparsity problem compared with other CF-based baselines.

On-board embedded software developed for spaceflight systems (space software) must adhere to stringent software quality assurance procedures. For example, verification and validation activities are typically performed and assessed by third party organizations. To further minimize the risk of human mistakes, space agencies, such as the European Space Agency (ESA), are looking for automated solutions for the assessment of software testing activities, which play a crucial role in this context. Over the years, mutation analysis has shown to be a promising solution for the automated assessment of test suites; it consists of measuring the quality of a test suite in terms of the percentage of injected faults leading to a test failure. A number of optimization techniques, addressing scalability and accuracy problems, have been proposed to facilitate the industrial adoption of mutation analysis. However, to date, two major problems prevent space agencies from enforcing mutation analysis in space software development. In this paper, we enhance mutation analysis optimization techniques to enable their applicability to embedded software and propose a pipeline that successfully integrates them to address scalability and accuracy issues in this context, as described above. Further, we report on the largest study involving embedded software systems in the mutation analysis literature. Our research is part of a research project funded by ESA ESTEC involving private companies (GomSpace Luxembourg and LuxSpace) in the space sector. These industry partners provided the case studies reported in this paper; they include an on-board software system managing a microsatellite currently on-orbit, a set of libraries used in deployed cubesats, and a mathematical library certified by ESA., Accepted for publication on IEEE TRANSACTIONS ON SOFTWARE ENGINEERING

To achieve higher throughputs in cellular networks, 3GPP proposes to use unlicensed frequency bands and develops technologies - the latest one is NR-U - allowing a cellular base station to transmit in unlicensed bands, which are already occupied by Wi-Fi networks. To enable fair channel sharing between two technologies, the base station uses a sort of CSMA/CA with binary exponential backoff similar to Wi-Fi. However, the base station can start data transmission only at strictly periodic time moments. Many papers propose sending a reservation signal between the end of the backoff procedure and such a moment to prevent nearby devices from accessing the channel. However, this approach significantly reduces Wi-Fi performance. The paper proposes a novel method CR-LBT of transmitting a reservation signal that greatly decreases channel resource waste caused by collisions and improves channel resource sharing fairness. With developed analytical models and simulations, it is shown that CR-LBT may simultaneously increase the throughput of both NR-U and Wi-Fi networks. The effect is more noticeable for the Wi-Fi network, the throughput of which may rise three times compared with the traditional method of sending the reservation signal. Finally, the influence of various factors on CR-LBT performance is studied.

This Special Issue is a selection of the best articles presented at the 18th IEEE International NEWCAS Conference (NEWCAS) 2020 that was held in Montreal, Canada, on June 16–19, 2020. As an Interregional flagship conference of the IEEE Circuits and Systems Society (CASS), this conference covers a wide spectrum of topics, research, and practice in the fields of circuits and systems, and offers an international forum for exchanging ideas and results.

We present Arianna+, a framework to design networks of ontologies for representing knowledge enabling smart homes to perform human activity recognition online. In the network, nodes are ontologies allowing for various data contextualisation, while edges are general-purpose computational procedures elaborating data. Arianna+ provides a flexible interface between the inputs and outputs of procedures and statements, which are atomic representations of ontological knowledge. Arianna+ schedules procedures on the basis of events by employing logic-based reasoning, i.e., by checking the classification of certain statements in the ontologies. Each procedure involves input and output statements that are differently contextualised in the ontologies based on specific prior knowledge. Arianna+ allows to design networks that encode data within multiple contexts and, as a reference scenario, we present a modular network based on a spatial context shared among all activities and a temporal context specialised for each activity to be recognised. In the paper, we argue that a network of small ontologies is more intelligible and has a reduced computational load than a single ontology encoding the same knowledge. Arianna+ integrates in the same architecture heterogeneous data processing techniques, which may be better suited to different contexts. Thus, we do not propose a new algorithmic approach to activity recognition, instead, we focus on the architectural aspects for accommodating logic-based and data-driven activity models in a context-oriented way. Also, we discuss how to leverage data contextualisation and reasoning for activity recognition, and to support an iterative development process driven by domain experts., The paper has been accepted for publication in the IEEE Transactions on Cybernetics journal on April 2021 and with DOI 10.1109/TCYB.2021.3073539. It is an extension of arXiv:1707.03988v1 and it is related to the arXiv:1809.08208v1 article. It contains 20 pages, 6 figures, 4 tables and 2 Appendices

This paper focuses on designing high-data-rate wireless communications for drone networks in the mmWave and THz frequency bands. MmWave/THz-band communications has been envisioned as a key technology to achieve ultra broadband wireless links through beamforming in 5G and beyond networks. However, a main challenge with these frequency bands is that the narrow-beam directional wireless links can be easily disconnected because of the beam misalignment in mobile environments. To address this challenge, in this paper we design a new beam control scheme called LeBeam, with the objective of maximizing the expected capacity of the mmWave/THz-band links by determining the optimal beamwidth dynamically under drone mobility uncertainties. In LeBeam, an ESN is adopted to capture the mobility uncertainties of the drones dynamically and predict the optimal beamwidth based on the first- and second-order moments of the drone mobility. The ESN has been trained based on real drone flight traces. The performance of LeBeam is evaluated over UBSim, a newly developed trace-driven Universal Broadband Simulator for integrated aerial and ground wireless networking.

The field of cryptography has endeavored to solve numerous security problems. However, a common premise of many of those problems is that the encryptor always generates the ciphertext correctly. Around 10 years ago, this premise was not a problem. However, due to the rapid development and the use of the cloud, which has introduced various access policies and functionalities to provide higher security, it is not correct to assume that this premise is always applied. A "Fake Policy Attack", which we introduce in this paper, is an attack that incorrectly sets the access policy of the ciphertext against the system rules so that users who do not meet the rules can decrypt the ciphertext. In other words, it is an attack that ignores the rules of the system and eventually breaks the security and leaks information. This attack can be more critical for the application environments that require strong security not to leak any related information about ciphertext. In this paper, we demonstrate the possible threat of the Fake Policy Attack by providing two relevant examples. Then, we propose a scheme called Policy Authenticable ABE (PA-ABE) to resolve this issue. We provide a formal security analysis of the proposed scheme and performance evaluation results based on our implementation.

Many applications for the automated diagnosis of plant disease have been developed based on the success of deep learning techniques. However, these applications often suffer from overfitting, and the diagnostic performance is drastically decreased when used on test datasets from new environments. In this paper, we propose LeafGAN, a novel image-to-image translation system with own attention mechanism. LeafGAN generates a wide variety of diseased images via transformation from healthy images, as a data augmentation tool for improving the performance of plant disease diagnosis. Thanks to its own attention mechanism, our model can transform only relevant areas from images with a variety of backgrounds, thus enriching the versatility of the training images. Experiments with five-class cucumber disease classification show that data augmentation with vanilla CycleGAN cannot help to improve the generalization, i.e., disease diagnostic performance increased by only 0.7% from the baseline. In contrast, LeafGAN boosted the diagnostic performance by 7.4%. We also visually confirmed the generated images by our LeafGAN were much better quality and more convincing than those generated by vanilla CycleGAN. The code is available publicly at: https://github.com/IyatomiLab/LeafGAN., Accepted as a regular paper in the IEEE Transactions on Automation Science and Engineering (T-ASE)

Objective: This paper assesses the diagnostic performance of deep learning methods for tumour detection in breast microwave sensing (BMS). Methods: A convolutional neural network (CNN) was used to predict the presence of a cancerous lesion in data from experimental scans of MRI-derived phantoms. An experimental dataset containing data from 1257 scans was used. The CNN was compared to a similarly sized dense neural network (DNN) and logistic regression classifier. Results: The CNN was able to exploit the sinogram data structure to achieve diagnostic performance significantly better than random classification, while neither the DNN nor logistic regression classifiers could generalize to unseen test data. The area under the curve of the receiver operating characteristic curve of the CNN classifier was estimated to be between (78 3)% and (90 3)%, where the upper estimate was obtained when the testing set was constrained to consist of phantoms with breast volumes that are within the volume bounds of the training set and when the tumour was located at the same vertical position as the system antennas. Conclusion: The results obtained in this investigation demonstrate the potential of combining deep learning and BMS systems for breast cancer detection. Significance: This paper provides an estimate of the diagnostic performance of an air-based BMS system using deep learning methods for automatic tumour detection on a sizable experimental dataset. The performance was found to be comparable to that of AI-assisted mammography and marks a first step toward larger-scale investigations in BMS.

In recent years driving violations have increased in big and crowded cities specially in developing countries. This has resulted in escalation of traffic congestions and number of accidents, making them difficult to handle by a central managing authority. New technologies can be used in order to mitigate this problem. Hence, in this paper, we propose a novel concept, called self-fining vehicle, using Blockchain infrastructure on top of the Vehicular Edge Computing, VEC. Self-fining vehicle is a vehicle that detects and records driving violations, issues tickets and pays fines without the need for centralized management system. Violations can be coded into OBU and detected by vehicular communications. The conceptual architecture of secure self-fining vehicle is proposed and discussed in this paper. The proposed architecture improves violation data security, processing speed and communication delay using encryption, authentication, Blockchain and edge computing, enhancing quality of service and consumers‘ quality of experience. The performance analysis of the proposed architecture proves its efficiency in terms of latency in service delivery.

The concept of the fifth generation (5G) mobile network system has emerged in recent years as telecommunication operators and service providers look to upgrade their infrastructure and delivery modes to meet the growing demand. Concepts such as softwarization, virtualization, and machine learning will be key components as innovative and flexible enablers of such networks. In particular, paradigms such as software-defined networks, software-defined perimeter, cloud & edge computing, and network function virtualization will play a major role in addressing several 5G networks' challenges, especially in terms of flexibility, programmability, scalability, and security. In this work, the role and potential of these paradigms in the context of V2X communication is discussed. To do so, the paper starts off by providing an overview and background of V2X communications. Then, the paper discusses in more details the various challenges facing V2X communications and some of the previous literature work done to tackle them. Furthermore, the paper describes how softwarization, virtualization, and machine learning can be adapted to tackle the challenges of such networks.

IEC61850 is the mainstream of the development for substation automation. This paper presents a practical consideration and analysis for implementing a secure sampled measured value (SeSV) message in substation automation system. Due to the lack of security features in the standard, IEC Working Group 15 of Technical Committee 57 published IEC62351 on security for IEC61850 profiles. However, the use of authentication methods for SV based on IEC62351 standards are still not integrated, and computational capabilities and performance are not validated and tested with commercial grade equipment. Hence, this paper shows the performance of security feature enabled SeSV packets transmitted between protection and control devices by appending a message authentication code (MAC) to the extended IEC61850 packets. A prototype implementation on a low cost commodity embedded system has proved that the MAC-enabled SV message can fully secure the process bus communication in the digital substation with negligible time delay.

Caching is an efficient way to reduce network traffic congestion during peak hours by storing some content at the users' local caches. For the shared-link network with end-user-caches, Maddah-Ali and Niesen proposed a two-phase coded caching strategy. In practice, users may communicate with the server through intermediate relays. This paper studies the tradeoff between the memory size $M$ and the network load $R$ for networks where a server with $N$ files is connected to $H$ relays (without caches), which in turn are connected to $K$ users equipped with caches of $M$ files. When each user is connected to a different subset of $r$ relays, i.e., $K = \binom{H}{r}$, the system is referred to as a {\it combination network with end-user-caches}. In this work, converse bounds are derived for the practically motivated case of {\it uncoded} cache contents, that is, bits of the various files are directly pushed into the user caches without any coding. In this case, once the cache contents and the user demands are known, the problem reduces to a general index coding problem.This paper shows that relying on a well-known "acyclic index coding converse bound" results in converse bounds that are not tight for combination networks with end-user-caches. A novel converse bound that leverages the network topology is proposed, which is the tightest converse bound known to date. As a result of independent interest, an inequality that generalizes the well-known sub-modularity of entropy is derived. Several novel caching schemes are proposed, based on the Maddah-Ali and Niesen cache placement. The proposed schemes are proved: (i) to be (order) optimal for some $(N,M,H,r)$ parameters regimes under the constraint of uncoded cache placement, and (ii) to outperform the state-of-the-art schemes in numerical evaluations., 22 pages, 3 figures; accepted by IEEE TIT

As a powerful architecture for large-scale computation, cloud computing has revolutionized the way that computing infrastructure is abstracted and utilized. Coupled with the challenges caused by Big Data, the rocketing development of cloud computing boosts the complexity of system management and maintenance, resulting in weakened trustworthiness of cloud services. To cope with this problem, a compelling method, i.e., Support Vector Data Description (SVDD), is investigated in this paper for detecting anomalous performance metrics of cloud services. Although competent in general anomaly detection, SVDD suffers from unsatisfactory false alarm rate and computational complexity in time series anomaly detection, which considerably hinders its practical applications. Therefore, this paper proposes a relaxed form of linear programming SVDD (RLPSVDD) and presents important insights into parameter selection for practical time series anomaly detection in order to monitor the operations of cloud services. Experiments on the Iris dataset and the Yahoo benchmark datasets validate the effectiveness of our approaches. Furthermore, the comparison of RLPSVDD and the methods obtained from Twitter, Numenta, Etsy and Yahoo, shows the overall preference for RLPSVDD in time series anomaly detection.

Numerous machine learning (ML), and more recently, deep learning (DL) based approaches, have been proposed to tackle scalability issues in electronic design automation, including those in integrated circuit (IC) test. This paper examines state-of-the-art DL for IC test and highlights two critical unaddressed challenges. The first challenge involves identifying fit-for-purpose statistical metrics to train and evaluate ML model performance and usefulness in IC test. Our work shows that current metrics do not reflect how well ML models have learned to generalize and perform in the domain-specific context. From this insight, we propose and evaluate alternative metrics that better capture a model’s likely usefulness in the IC test problem. The second challenge is to choose an appropriate input abstraction so as to enable an ML model to learn robust and reliable features. We investigate how well DL for IC test techniques generalize by exploring their robustness to perturbations that alter a netlist’s structure but do not alter its functionality. This paper provides insights into challenges via empirical evaluation of the state-ofthe- art and offers guidance for future work.

This paper introduces a methodology to use the thermal side channel as a proxy for the behavior of embedded processors to detect changes in this behavior in a cyber-physical system. Such changes may be due to software attacks, hardware attacks, and altered processors. Since control system processes are periodic computations, the thermal side channel signals exhibit a temporal pattern. This enables detection of altered code and changed device characteristics. We present a machine learning approach to estimate the activity of the embedded device from the time sequence of thermal images and show the extent that deviations from expected behavior can be detected. The approach is validated on a testbed of a multi-core processor running a periodic computational code. The infrared imager directly collects thermal imagery from the processor, which is cooled from the backside. While an external infrared imager is used in this study, it is desirable to deploy a finite number of on-chip temperature sensors. This paper shows that integrating on-chip temperature sensors allows robust real-time monitoring of the processor behavior. Finally, we also offer a machine learning approach to find the optimal locations of the on-chip sensors to aid detection.

Offloading computation based on mobile edge computing paradigms can augment the computational capabilities of resource-scarce mobile devices. However, the capacity limitations of edge servers constrain the performance improvement achieved through computation offloading. In this paper, we consider a three-tier computation offloading schema with multiple users, edge servers and cloud servers. Computation can be offloaded from mobile devices to edge servers or can be further offloaded to remote cloud servers if necessary. Since a number of mobile devices connected to edge servers will share a common wireless communication network, which may contain both uplink and downlink channels, the assignment of bandwidth assignment the channels also constrains the performance improvement of computation offloading. In this paper, the problems of how to determine the offloading strategy and of how to assign the bandwidth are jointly studied and formulated as a programming problem to minimize the average application response time. We analyze the joint problem and further transform it into a piecewise convex programming problem. We propose an efficient algorithm that can find the optimal solution. Extensive experiments demonstrate that our algorithm significantly outperforms previous algorithms. The experimental results also show that the performance of our algorithm is highly robust.

An increasing number of cloud services are operated globally, where the service data are frequently replicated across geographically distributed datacenters to improve service quality and reliability. Such replication generates many one-to-many bulk data transfers over inter-datacenter networks from one datacenter to many receiver datacenters. To provide end-users with guaranteed services, these data transfers are usually required to be completed within designated deadlines. Despite the exponential growth in data demand, there has been little work on guaranteeing deadlines for one-to-many transfers, which is the subject of this paper. This paper proposes a centralized admission control coupled with a scheduling algorithm, named deAdline-Guaranteed transfEr (AGE), to guarantee the deadline of admitted data transfers and utilize the network capacity efficiently. The key idea is to flexibly select the source datacenter for receiver datacenters and allow the remaining receivers to obtain a replica from either the original source or the other receivers that have already received a copy. By jointly allocating the source for receivers and the bandwidth and routing paths for every data transfer, AGE maximizes the number of deadline-satisfied transfers. Our simulations show that compared to the state-of-the-art, AGE guarantees the deadline for up to 70% more transfers, achieves at least 2× higher network throughput, and reduces the completion time up to 80%.

In recent years, plenty of metrics have been proposed to identify networks that are free of gradient explosion and vanishing. However, due to the diversity of network components and complex serial-parallel hybrid connections in modern DNNs, the evaluation of existing metrics usually requires strong assumptions, complex statistical analysis, or has limited application fields, which constraints their spread in the community. In this paper, inspired by the Gradient Norm Equality and dynamical isometry, we first propose a novel metric called Block Dynamical Isometry, which measures the change of gradient norm in individual block. Because our Block Dynamical Isometry is norm-based, its evaluation needs weaker assumptions compared with the original dynamical isometry. To mitigate the challenging derivation, we propose a highly modularized statistical framework based on free probability. Our framework includes several key theorems to handle complex serial-parallel hybrid connections and a library to cover the diversity of network components. Besides, several sufficient prerequisites are provided. Powered by our metric and framework, we analyze extensive initialization, normalization, and network structures. We find that Gradient Norm Equality is a universal philosophy behind them. Then, we improve some existing methods based on our analysis, including an activation function selection strategy for initialization techniques, a new configuration for weight normalization, and a depth-aware way to derive coefficients in SeLU. Moreover, we propose a novel normalization technique named second moment normalization, which is theoretically 30% faster than batch normalization without accuracy loss. Last but not least, our conclusions and methods are evidenced by extensive experiments on multiple models over CIFAR10 and ImageNet., Comment: Under review as a regular paper in TPAMI

The next-generation of wireless networks will enable many machine learning (ML) tools and applications to efficiently analyze various types of data collected by edge devices for inference, autonomy, and decision making purposes. However, due to resource constraints, delay limitations, and privacy challenges, edge devices cannot offload their entire collected datasets to a cloud server for centrally training their ML models or inference purposes. To overcome these challenges, distributed learning and inference techniques have been proposed as a means to enable edge devices to collaboratively train ML models without raw data exchanges, thus reducing the communication overhead and latency as well as improving data privacy. However, deploying distributed learning over wireless networks faces several challenges including the uncertain wireless environment (e.g., dynamic channel and interference), limited wireless resources (e.g., transmit power and radio spectrum), and hardware resources (e.g., computational power). This paper provides a comprehensive study of how distributed learning can be efficiently and effectively deployed over wireless edge networks. We present a detailed overview of several emerging distributed learning paradigms, including federated learning, federated distillation, distributed inference, and multi-agent reinforcement learning. For each learning framework, we first introduce the motivation for deploying it over wireless networks. Then, we present a detailed literature review on the use of communication techniques for its efficient deployment. We then introduce an illustrative example to show how to optimize wireless networks to improve its performance. Finally, we introduce future research opportunities. In a nutshell, this paper provides a holistic set of guidelines on how to deploy a broad range of distributed learning frameworks over real-world wireless communication networks.

A hydraulic cylinder is a mechanical actuator that is widely used in different industries such as construction, manufacturing, aerospace and offshore oil and gas. Seal wear in hydraulic cylinders results in hydraulic fluid leakage or contamination of the hydraulic fluid. Untimely failure of a hydraulic cylinder increases the maintenance cost and reduces productivity. Therefore, condition monitoring of the hydraulic cylinder is necessary to understand the current state of equipment. In literature, there have been numerous documented attempts to perform condition monitoring of hydraulic cylinders using different methods, based on fluid properties, pressure, vibration and acoustic emission. However, there have been limited attempts to present a state-of-the-art review of condition monitoring of hydraulic cylinders. This paper presents an overview of the methods used for the condition monitoring of hydraulic cylinders, including the detection of different failure modes using different sensors, the separability of fault conditions using sensor-based features, and the ability to pick up incipient faults by sensor-based features. This information is required for new readers in this area of expertise. This paper summarizes different condition monitoring methods that have been used from its early implementation to very recent dates, aiming at clarifying recent advances and identifying challenges in the research of the condition monitoring of hydraulic cylinders. It is anticipated that the information presented in this review paper will be beneficial for new researchers and provide directions for future research in the area of condition monitoring of hydraulic cylinders.

This paper describes a compact and low cost optically fed antenna for using as a receiver in 5G cellular networks and in distributed antenna systems. It compares the performance of two different topologies of patch antennas tightly integrated with vertical-cavity surface-emitting lasers (VCSELs), without the need for an amplifier stage or matching circuit. Opto-microwave (OM) gain of −21.5 dB at 5.15 GHz for the Microstrip patch antenna (MPA), and −24.2 dB at 4.8 GHz for the Grounded Coplanar patch antenna (GCPA) are achieved. The MPA architecture is quite original as it removes the need for RF feed lines through a direct injection of the signal from the patch to the VCSEL. The paper evaluates the different contributions to the total gain due to the VCSEL responsivity, the coupling between the optical fiber and the VCSEL, the wire-bonding losses and the antenna gain, with the goal of optimizing the steps at which major losses occur.

"Knowledge is power" is an old adage that has been found to be true in today's information age. Knowledge is derived from having access to information. The ability to gather information from large volumes of data has become an issue of relative importance. Big Data Analytics (BDA) is the term coined by researchers to describe the art of processing, storing and gathering large amounts of data for future examination. Data is being produced at an alarming rate. The rapid growth of the Internet, Internet of Things (IoT) and other technological advances are the main culprits behind this sustained growth. The data generated is a reflection of the environment it is produced out of, thus we can use the data we get out of systems to figure out the inner workings of that system. This has become an important feature in cybersecurity where the goal is to protect assets. Furthermore, the growing value of data has made big data a high value target. In this paper, we explore recent research works in cybersecurity in relation to big data. We highlight how big data is protected and how big data can also be used as a tool for cybersecurity. We summarize recent works in the form of tables and have presented trends, open research challenges and problems. With this paper, readers can have a more thorough understanding of cybersecurity in the big data era, as well as research trends and open challenges in this active research area.

Objective: Longitudinal neuroimaging data have been widely used to predict clinical scores for automatic diagnosis of Alzheimer’s Disease (AD) in recent years. However, incomplete temporal neuroimaging records of the patients pose a major challenge to use these data for accurately diagnosing AD. In this paper, we propose a novel method to learn an enriched representation for imaging biomarkers, which simultaneously captures the information conveyed by both the baseline neuroimaging records of all the participants in a studied cohort and the progressive variations of the available follow-up records of every individual participant. Methods: Taking into account that different participants usually take different numbers of medical records at different time points, we develop a robust learning objective that minimizes the summations of a number of not-squared $\ell _2$ -norm distances, which, though, is difficult to efficiently solve in general. Thus we derive a new efficient iterative algorithm with rigorously proved convergence. Results: We have conducted extensive experiments using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Clear performance gains have been achieved when we predict different cognitive scores using the enriched biomarker representations learned by our new method. We further observe that the top selected biomarkers by our proposed method are in perfect accordance with the known knowledge in existing clinical AD studies. Conclusion: All these promising experimental results have demonstrated the effectiveness of our new method. Significance: We anticipate that our new method is of interest to biomedical engineering communities beyond AD research and have open-sourced the code of our method online. 1 1 The code package of this paper have been made publicly available online at https://github.com/lyujian/Improved-Prediction-of-Cognitive-Outcomes

Three-dimensional Morphable Models (3DMMs) are powerful statistical tools for representing the 3D shapes and textures of an object class. Here we present the most complete 3DMM of the human head to date that includes face, cranium, ears, eyes, teeth and tongue. To achieve this, we propose two methods for combining existing 3DMMs of different overlapping head parts: i. use a regressor to complete missing parts of one model using the other, ii. use the Gaussian Process framework to blend covariance matrices from multiple models. Thus we build a new combined face-and-head shape model that blends the variability and facial detail of an existing face model (the LSFM) with the full head modelling capability of an existing head model (the LYHM). Then we construct and fuse a highly-detailed ear model to extend the variation of the ear shape. Eye and eye region models are incorporated into the head model, along with basic models of the teeth, tongue and inner mouth cavity. The new model achieves state-of-the-art performance. We use our model to reconstruct full head representations from single, unconstrained images allowing us to parameterize craniofacial shape and texture, along with the ear shape, eye gaze and eye color., 18 pages, 18 figures, submitted to Transactions on Pattern Analysis and Machine Intelligence (TPAMI) on the 9th of October as an extension paper of the original oral CVPR paper : arXiv:1903.03785

Data integration from heterogeneous data sources in low-voltage (LV) power distribution grids will be valuable to distribution system operators (DSOs). The power measurements from customer premises need to be processed with other data such as grid topology, line parameters etc., to deploy smart grid applications (SGA) such as real-time grid monitoring and voltage regulation. The most challenging task for DSOs is to collect and integrate data from several sources as several entities are involved in the data management and access to databases are restricted. This paper presents an op E n common information ${M}$ odel (CIM) BA sed sma ${R}$ t grid application framewor ${K}$ ( EMBARK ) to address the above-mentioned challenge. EMBARK is developed to be an efficient, modular and scalable architecture for extracting relevant grid related information from various asset management databases. A novel data management functionality is a part of EMBARK that enables data-driven update of settings and parameters of the algorithms behind smart grid applications. The proposed approach is demonstrated and numerically validated using grid data from a medium-sized distribution grid operator in Denmark. The architecture developed and presented in this paper can support all the phases from planning to the actual smart grid operation, i.e., automatically building the models to perform load flows, grid impact studies, planning, asset management etc.

Large scale quantum computers will break classical public-key cryptography protocols by quantum algorithms such as Shor’s algorithm. Hence, designing quantum-safe cryptosystems to replace current classical algorithms is crucial. Luckily there are some post-quantum candidates that are assumed to be resistant against future attacks from quantum computers, and NIST is considering standardizing them. Among these candidates, lattice-based cryptography sounds more interesting than others due to the performance results as well as confidence in the security. There are few works in the literature evaluating the performance of lattice-based cryptography in hardware. In this paper, we focus on Cryptographic Suite for Algebraic Lattices (CRYSTALS) key exchange mechanisms known as Kyber and provide an instruction-set hardware architecture and implement on Xilinx Artix-7 FPGA for performance evaluation and testing. Our proposed architecture provides an efficient and high-performance set of components to perform polynomial sampling, number-theoretic transform (NTT), and point-wise multiplication to speed up lattice-based post-quantum cryptography (PQC). This architecture implemented on ASIC outperforms state-of-the-art implementations.

Uplink-downlink duality refers to the fact that under a sum-power constraint, the capacity regions of a Gaussian multiple-access channel and a Gaussian broadcast channel with Hermitian transposed channel matrices are identical. This paper generalizes this result to a cooperative cellular network, in which remote access-points are deployed as relays in serving the users under the coordination of a central processor (CP). In this model, the users and the relays are connected over noisy wireless links, while the relays and the CP are connected over noiseless but rate-limited fronthaul links. Based on a Lagrangian technique, this paper establishes a duality relationship between such a multiple-access relay channel and broadcast relay channel, under the assumption that the relays use compression-based strategies. Specifically, we show that under the same total transmit power constraint and individual fronthaul rate constraints, the achievable rate regions of the Gaussian multiple-access and broadcast relay channels are identical, when either independent compression or Wyner-Ziv and multivariate compression strategies are used. The key observations are that if the beamforming vectors at the relays are fixed, the sum-power minimization problems under the achievable rate and fronthaul constraints in both the uplink and the downlink can be transformed into either a linear programming or a semidefinite programming problem depending on the compression technique, and that the uplink and downlink problems are Lagrangian duals of each other. Moreover, the dual variables corresponding to the downlink rate constraints become the uplink powers; the dual variables corresponding to the downlink fronthaul constraints become the uplink quantization noises. This duality relationship enables an efficient algorithm for optimizing the downlink transmission and relaying strategies based on the uplink., Comment: Accepted in IEEE Transactions on Information Theory; 34 pages

With the development of the information and Internet technology, the public opinions with big data will rapidly emerge in an online-offline social network, and an inefficient management of public opinions often will lead to the security crisis for either firms or governments. To unveil the interaction mechanism among a large number of agents between the online and offline social networks, in this paper we propose the public opinion dynamics model in an online-offline social network context. Next, in the theory aspect we investigate the analytical conditions to form a consensus in the public opinion dynamics model. Furthermore, we conduct the extensive simulations to investigate how the online agents impact the dynamics of public opinion formation, and unfold that the online agents shorten the steady-state time, decrease the number of opinion clusters, and smoothen the opinion changes in the opinion dynamics. The increase in the size of the online agents often enhances these effects. The results in this paper can provide a basis for the management of the public opinions in the Internet age.

Internet of Things (IoT) is considered as a key enabler of health informatics. IoT-enabled devices are used for in-hospital and in-home patient monitoring to collect and transfer biomedical data pertaining to blood pressure, electrocardiography (ECG), blood sugar levels, body temperature, etc. Among these devices, wearables have found their presence in a wide range of healthcare applications. These devices generate data in real-time and transmit them to nearby gateways and remote servers for processing and visualization. The data transmitted by these devices are vulnerable to a range of adversarial threats, and as such, privacy and integrity need to be preserved. In this paper, we present LightIoT, a lightweight and secure communication approach for data exchanged among the devices of a healthcare infrastructure. LightIoT operates in three phases: initialization, pairing, and authentication. These phases ensure the reliable transmission of data by establishing secure sessions among the communicating entities (wearables, gateways and a remote server). Statistical results exhibit that our scheme is lightweight, robust, and resilient against a wide range of adversarial attacks and incurs much lower computational and communication overhead for the transmitted data in the presence of existing approaches., This paper has been accepted for publication by the IEEE Transactions on Green Communications and Networking. The copyright is with the IEEE

In this paper, we consider an Internet-of-Things (IoT) network that supports two types of devices, namely static devices (SDs) and dynamic devices (DDs), with multiple channels. The notion of power-domain non-orthogonal multiple access (NOMA) is applied so that DDs can not only dynamically change channels, but also transmit signals with a high power to reduce collisions with SDs. Thus, learning algorithms are needed for DDs to select channels. To this end, multiarmed bandit (MAB) algorithms can be used. However, in general, it is difficult to analyze the performance of MAB algorithms in the setting due to the interaction of DDs. Thus, in this paper, instead of analyzing MAB algorithms, we formulate an evolutionary game of DDs in NOMA-based IoT networks and analyze the evolutionary game. It is shown that the game has a unique evolutionary stable state (ESS) in terms of the channel selection probability. The performance according to the ESS can be regarded as a baseline performance, because it is the best performance achieved by the DDs competing for multiple channel resources. Thus, the resulting baseline performance can be used to see if a learning algorithm including MAB can achieve a better performance by avoiding competition between DDs.

This paper studies a memoryless state-dependent multiple access channel (MAC) where two transmitters wish to convey a message to a receiver under the assumption of causal and imperfect channel state information at transmitters (CSIT) and imperfect channel state information at receiver (CSIR). In order to emphasize the limitation of transmitter cooperation between physically distributed nodes, we focus on the so-called distributed CSIT assumption, i.e., where each transmitter has its individual channel knowledge, while the message can be assumed to be partially or entirely shared a priori between transmitters by exploiting some on-board memory. Under this setup, the first part of the paper characterizes the common message capacity of the channel at hand for arbitrary CSIT and CSIR structure. The optimal scheme builds on Shannon strategies, i.e., optimal codes are constructed by letting the channel inputs be a function of current CSIT only. For a special case when CSIT is a deterministic function of CSIR, the considered scheme also achieves the capacity region of a common message and two private messages. The second part addresses an important instance of the previous general result in a context of a cooperative multi-antenna Gaussian channel under i.i.d. fading operating in frequency-division duplex mode, such that CSIT is acquired via an explicit feedback of perfect CSIR. The capacity of the channel at hand is achieved by distributed linear precoding applied to Gaussian codes. Surprisingly, we demonstrate that it is suboptimal to send a number of data streams bounded by the number of transmit antennas as typically considered in a centralized CSIT setup. Finally, numerical examples are provided to evaluate the sum capacity of the binary MAC with binary states as well as the Gaussian MAC with i.i.d. fading.

This paper proposes a Differential-Input Body Bias Sense Amplifier (DIBBSA) with an auto-offset mitigation feature suitable for low-voltage SRAMs where the differential bitline signals are applied to the sources as well as to the body of the critical sensing transistors. We simulated and fabricated the proposed DIBBSA architecture with various operational modes in 65-nm CMOS technology to analyze body biasing’s effectiveness in mitigating the offset. The standard deviation of offset ( $\boldsymbol {\sigma }_{\mathbf {OS}}$ ) was measured over 5120 SAs in 10 ICs. The iso-gate area reduction in $\boldsymbol {\sigma }_{\mathbf {OS}}$ for the proposed DIBBSA-FL and DIBBSA-PD modes resulted in 68.1% and 61.9% compared to conventional Current Latch SA (CLSA) and 24.1% and 18.1% compared to Voltage Latch SA (VLSA) at 0.4 V supply and 25 °C, respectively. Carried out measurements on 512 SAs in an IC show the minimum required differential input voltage across the temperature range of 0 °C to 75 °C at 0.4 V is achieved to be 48% lower compared to CLSA and 28% lower compared to VLSA by both the DIBBSA-FL and DIBBSA-PD modes.

In order to improve the effective transfer range and then enhance the interoperability of magnetic coupling wireless power transfer (MC-WPT) systems, an electromagnetically induced transparency (EIT)-like MC-WPT system is proposed in this paper, which can maintain a reasonable level of power and efficiency over a wide range of transfer distance. Firstly, the circuit model of the tri-coil MC-WPT system is presented along with several key concepts, including EIT mechanism and eigen parameters. Then the energy efficiency characteristic of the system in eigen state is investigated, based on which the energy efficiency improvement principle and corresponding parameter design condition of the EIT-like mechanism are derived. Finally, practical systems are designed, simulated and measured to show the accuracy and effectiveness of the proposed method. The simulation and experimental results show that, with the proposed method, the effective transfer range that takes into account both output power and transfer efficiency is greatly improved. The method is particularly suitable for the design of electric vehicle charging piles, based on which the effective charging of vehicles with different chassis heights on a charging pile can be achieved.

Practical application of deep learning based non-intrusive load monitoring (NILM) system requires the deep neural network model to generalize on new unseen data. Existing NILM solutions are not suitable for real-world application due to their poor disaggregation accuracy on new unseen data. In order to address this problem, this paper presents a NILM algorithm that uses data augmentation to generate synthetic data for training deep convolutional neural network models for each target appliance. Proposed data augmentation technique works by combining on and off-durations of a target appliance from various datasets, and forms a unified and comprehensive synthetic aggregate and sub-meter profiles. Apart from proposed algorithm, this paper also proposes an evaluation approach that relies on total predicted energy and ground-truth energy of an appliance to provide detailed insights about total overlapping energy, missing energy and extra energy predicted by the algorithm. Comparison results on our proposed evaluation approach showed that proposed disaggregation algorithm was able to predict energy that was 60% overlapping with ground truth energy and 36% energy was extra. Overall results showed that overlapping energy was 2.5 times more, and extra-predicted energy was 60% less than state-of-the-art algorithms in unseen test cases.

Microbenchmarking frameworks, such as Java's Microbenchmark Harness (JMH), allow developers to write fine-grained performance test suites at the method or statement level. However, due to the complexities of the Java Virtual Machine, developers often struggle with writing expressive JMH benchmarks which accurately represent the performance of such methods or statements. In this paper, we empirically study bad practices of JMH benchmarks. We present a tool that leverages static analysis to identify 5 bad JMH practices. Our empirical study of 123 open source Java-based systems shows that each of these 5 bad practices are prevalent in open source software. Further, we conduct several experiments to quantify the impact of each bad practice in multiple case studies, and find that bad practices often significantly impact the benchmark results. To validate our experimental results, we constructed seven patches that fix the identified bad practices for six of the studied open source projects, of which six were merged into the main branch of the project. In this paper, we show that developers struggle with accurate Java microbenchmarking, and provide several recommendations to developers of microbenchmarking frameworks on how to improve future versions of their framework.

This paper presents an IC implementation of on-chip learning neural network accelerator using highly linear CMOS-compatible floating gate charge trap devices. A simple learning algorithm utilizing winner-take-all and competitive learning is proposed to design fast and power-efficient hardware. This algorithm was analyzed with behavioral model of emerging non-volatile memory via MATLAB. The linearity, symmetry, and cycle-to-cycle variation of multi-bit switching characteristic affects training accuracy. The proposed content-aware programming technique of modulated column line driver provides flexibility for real-time training while maintaining device linearity, despite having to update a different step for every unit cell and training. The prototype IC is embedded in the process-in-memory structure for energy efficient computing, in which cell arrays were divided into 4 sub-blocks to reduce I-R drop. The prototype IC fabricated using 180nm CMOS technology consumes 353.3pJ and 898.2pJ during inference and training mode, which corresponds 95.05TOPS/W and 38.03 TOPS/W, respectively. The fully integrated non-volatile AI IC with on-chip solution is demonstrated with throughput of 1343.2 GOPS.

Developing an algorithm for a visualization prototype often involves the direct comparison of different development stages and design decisions, and even minor modifications may dramatically affect the results. While existing development tools provide visualizations for gaining general insight into performance and structural aspects of the source code, they neglect the central importance of result images unique to graphical algorithms. In this paper, we present a novel approach that enables visualization programmers to simultaneously explore the evolution of their algorithm during the development phase together with its corresponding visual outcomes by providing an automatically updating meta visualization. Our interactive system allows for the direct comparison of all development states on both the visual and the source code level, by providing easy to use navigation and comparison tools. The on-the-fly construction of difference images, source code differences, and a visual representation of the source code structure further enhance the user's insight into the states' interconnected changes over time. Our solution is accessible via a web-based interface that provides GPU-accelerated live execution of C++ and GLSL code, as well as supporting a domain-specific programming language for scientific visualization., Comment: This paper appears in: IEEE Transactions on Visualization and Computer Graphics

This paper presents a comprehensive scheduling framework for residential demand response (DR) programs considering both the day-ahead and real-time electricity markets. In the first stage, residential customers determine the operating status of their responsive devices such as heating, ventilation, and air conditioning (HVAC) systems and electric water heaters (EWHs), while the distribution system operator (DSO) computes the amount of electricity to be purchased in the day-ahead electricity market. In the second stage, the DSO purchases insufficient (or sells surplus) electricity in the real-time electricity market to maintain the supply-demand balance. Due to its computational complexity and data privacy issues, the proposed model cannot be directly solved in a centralized manner, especially with a large number of uncertain scenarios. Therefore, this paper proposes a combination of stochastic programming (SP) and the alternating direction method of multipliers (ADMM) algorithm, called SP-ADMM, to decompose the original model and then solve each sub-problem in a distributed manner while considering multiple uncertain scenarios. The simulation study is performed on the IEEE 33-bus system including 121 residential houses. The results demonstrate the effectiveness of the proposed approach for large-scale residential DR applications under weather and consumer uncertainties.

International audience; Massive machine-type communications (mMTC) is one of the main services delivered by the fifth Generation (5G) mobile network. The traditional cellular architecture where all devices connect to the base station is not energy efficient. For this reason, the use of device-to-device (D2D) communications is considered to reduce the energy consumption of mMTC devices. The main idea is to use nearby user equipment (UE) as a relay and establish with it D2D communication. However, the relay selection process also consumes energy, and this consumption can be significant compared to the energy consumed during the data transmission phase. In this paper, we propose a distributed energy-efficient D2D relaying mechanism for mMTC applications. This mechanism favors the selection of the UEs with low path loss with the mMTC device. Through mathematical analysis and simulations, we show that our mechanism allows a reduction of the total energy consumption of mMTC devices (up to 75% compared to direct transmission) when they have an unfavorable link budget. Moreover, our mechanism achieves almost constant energy consumption for a large range of UE densities and distances between the mMTC device and the base station.

In recent years, the rapid growth of active consumers in the distribution networks transforms the modern power markets’ structure more independent, flexible, and distributed. Specifically, in the recent trend of peer-to-peer (P2P) transactive energy systems, the traditional consumers became prosumers (producer+consumer) who can maximize their energy utilization by sharing it with neighbors without any conventional arbitrator in the transactions. Although a distributed energy pricing scheme is inevitable in such systems to make optimal decisions, it is challenging to establish under the influence of non-linear physical network constraints with limited information. Therefore, this paper presents a distributed pricing strategy for P2P transactive energy systems considering voltage and line congestion management, which can be utilized in various power network topologies. This paper also introduces a new mutual reputation index as a product differentiation between the prosumers to consider their bilateral trading willingness. In this paper, a Fast Alternating Direction Method of Multipliers (F-ADMM) algorithm is realized instead of the standard ADMM algorithm to improve the convergence rate. The effectiveness of the proposed approach is validated through software simulations. The result shows that the algorithm is scalable, converges faster, facilitates easy implementation, and ensures maximum social welfare/profit.

In an 8B/10B mode giga-bit-per-second serial data transactions, the de-serialized data is sent to a comma detection and word alignment (CDWA) module to identify the word boundaries, which is a prerequisite in the high-speed transceivers such as PCIe, USB and JESD204B/C. In order to ensure that the comma code (/K/-code) can be correctly detected. Ten 10-bit comma detector cells are adopted in a typical CDWA module, which require a complex circuitry and an enormous power consumption. To overcome these limitations, a low-area and low-power CDWA circuit for JESD204B/C transceiver chip in 8B/10B mode has been proposed in this paper. The bit width of the detector cells can be truncated from 10 to 6 under the condition, that CDWA module can detect a complete comma code correctly. On one hand, the proposed CDWA module is verified with a FPGA development platform with the reduction of the hardware resources and power consumption to 31.72% and 20.11% respectively as compared to the typical structure available. On the other hand, a 10-Gbps transceiver chip with the proposed CDWA module is fabricated with a 55-nm CMOS process and the word alignment function of the proposed module is proved by the measurement results. The area of this transceiver chip including $2\times $ transmitting links and $2\times $ receiving links is 2.89 mm2, and the power consumption is 467.8 mW, under a maximum data transmission rate of 10 Gbps.