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Kerber, Thomas, Kiayias, Aggelos, Kohlweiss, Markulf, Zikas, Vassilis.  2019.  Ouroboros Crypsinous: Privacy-Preserving Proof-of-Stake. 2019 IEEE Symposium on Security and Privacy (SP). :157–174.
We present Ouroboros Crypsinous, the first formally analyzed privacy-preserving proof-of-stake blockchain protocol. To model its security we give a thorough treatment of private ledgers in the (G)UC setting that might be of independent interest. To prove our protocol secure against adaptive attacks, we introduce a new coin evolution technique relying on SNARKs and key-private forward secure encryption. The latter primitive-and the associated construction-can be of independent interest. We stress that existing approaches to private blockchain, such as the proof-of-work-based Zerocash are analyzed only against static corruptions.
Ponuma, R, Amutha, R, Haritha, B.  2018.  Compressive Sensing and Hyper-Chaos Based Image Compression-Encryption. 2018 Fourth International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB). :1-5.

A 2D-Compressive Sensing and hyper-chaos based image compression-encryption algorithm is proposed. The 2D image is compressively sampled and encrypted using two measurement matrices. A chaos based measurement matrix construction is employed. The construction of the measurement matrix is controlled by the initial and control parameters of the chaotic system, which are used as the secret key for encryption. The linear measurements of the sparse coefficients of the image are then subjected to a hyper-chaos based diffusion which results in the cipher image. Numerical simulation and security analysis are performed to verify the validity and reliability of the proposed algorithm.

Tian, Yun, Xu, Wenbo, Qin, Jing, Zhao, Xiaofan.  2018.  Compressive Detection of Random Signals from Sparsely Corrupted Measurements. 2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC). :389-393.

Compressed sensing (CS) integrates sampling and compression into a single step to reduce the processed data amount. However, the CS reconstruction generally suffers from high complexity. To solve this problem, compressive signal processing (CSP) is recently proposed to implement some signal processing tasks directly in the compressive domain without reconstruction. Among various CSP techniques, compressive detection achieves the signal detection based on the CS measurements. This paper investigates the compressive detection problem of random signals when the measurements are corrupted. Different from the current studies that only consider the dense noise, our study considers both the dense noise and sparse error. The theoretical performance is derived, and simulations are provided to verify the derived theoretical results.

Shiddik, Luthfi Rakha, Novamizanti, Ledya, Ramatryana, I N Apraz Nyoman, Hanifan, Hasya Azqia.  2019.  Compressive Sampling for Robust Video Watermarking Based on BCH Code in SWT-SVD Domain. 2019 International Conference on Sustainable Engineering and Creative Computing (ICSECC). :223-227.

The security and confidentiality of the data can be guaranteed by using a technique called watermarking. In this study, compressive sampling is designed and analyzed on video watermarking. Before the watermark compression process was carried out, the watermark was encoding the Bose Chaudhuri Hocquenghem Code (BCH Code). After that, the watermark is processed using the Discrete Sine Transform (DST) and Discrete Wavelet Transform (DWT). The watermark insertion process to the video host using the Stationary Wavelet Transform (SWT), and Singular Value Decomposition (SVD) methods. The results of our system are obtained with the PSNR 47.269 dB, MSE 1.712, and BER 0.080. The system is resistant to Gaussian Blur and rescaling noise attacks.

Zhou, Guorui, Zhu, Xiaoqiang, Song, Chenru, Fan, Ying, Zhu, Han, Ma, Xiao, Yan, Yanghui, Jin, Junqi, Li, Han, Gai, Kun.  2018.  Deep Interest Network for Click-Through Rate Prediction. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. :1059-1068.

Click-through rate prediction is an essential task in industrial applications, such as online advertising. Recently deep learning based models have been proposed, which follow a similar Embedding&MLP paradigm. In these methods large scale sparse input features are first mapped into low dimensional embedding vectors, and then transformed into fixed-length vectors in a group-wise manner, finally concatenated together to fed into a multilayer perceptron (MLP) to learn the nonlinear relations among features. In this way, user features are compressed into a fixed-length representation vector, in regardless of what candidate ads are. The use of fixed-length vector will be a bottleneck, which brings difficulty for Embedding&MLP methods to capture user's diverse interests effectively from rich historical behaviors. In this paper, we propose a novel model: Deep Interest Network (DIN) which tackles this challenge by designing a local activation unit to adaptively learn the representation of user interests from historical behaviors with respect to a certain ad. This representation vector varies over different ads, improving the expressive ability of model greatly. Besides, we develop two techniques: mini-batch aware regularization and data adaptive activation function which can help training industrial deep networks with hundreds of millions of parameters. Experiments on two public datasets as well as an Alibaba real production dataset with over 2 billion samples demonstrate the effectiveness of proposed approaches, which achieve superior performance compared with state-of-the-art methods. DIN now has been successfully deployed in the online display advertising system in Alibaba, serving the main traffic.

Tai, Kai Sheng, Sharan, Vatsal, Bailis, Peter, Valiant, Gregory.  2018.  Sketching Linear Classifiers over Data Streams. Proceedings of the 2018 International Conference on Management of Data. :757-772.

We introduce a new sub-linear space sketch—the Weight-Median Sketch—for learning compressed linear classifiers over data streams while supporting the efficient recovery of large-magnitude weights in the model. This enables memory-limited execution of several statistical analyses over streams, including online feature selection, streaming data explanation, relative deltoid detection, and streaming estimation of pointwise mutual information. Unlike related sketches that capture the most frequently-occurring features (or items) in a data stream, the Weight-Median Sketch captures the features that are most discriminative of one stream (or class) compared to another. The Weight-Median Sketch adopts the core data structure used in the Count-Sketch, but, instead of sketching counts, it captures sketched gradient updates to the model parameters. We provide a theoretical analysis that establishes recovery guarantees for batch and online learning, and demonstrate empirical improvements in memory-accuracy trade-offs over alternative memory-budgeted methods, including count-based sketches and feature hashing.

Deng, Lijin, Piao, Yan, Liu, Shuo.  2018.  Research on SIFT Image Matching Based on MLESAC Algorithm. Proceedings of the 2Nd International Conference on Digital Signal Processing. :17-21.

The difference of sensor devices and the camera position offset will lead the geometric differences of the matching images. The traditional SIFT image matching algorithm has a large number of incorrect matching point pairs and the matching accuracy is low during the process of image matching. In order to solve this problem, a SIFT image matching based on Maximum Likelihood Estimation Sample Consensus (MLESAC) algorithm is proposed. Compared with the traditional SIFT feature matching algorithm, SURF feature matching algorithm and RANSAC feature matching algorithm, the proposed algorithm can effectively remove the false matching feature point pairs during the image matching process. Experimental results show that the proposed algorithm has higher matching accuracy and faster matching efficiency.

Feng, Chenwei, Wang, Xianling, Zhang, Zewang.  2018.  Data Compression Scheme Based on Discrete Sine Transform and Lloyd-Max Quantization. Proceedings of the 3rd International Conference on Intelligent Information Processing. :46-51.

With the increase of mobile equipment and transmission data, Common Public Radio Interface (CPRI) between Building Base band Unit (BBU) and Remote Radio Unit (RRU) suffers amounts of increasing transmission data. It is essential to compress the data in CPRI if more data should be transferred without congestion under the premise of restriction of fiber consumption. A data compression scheme based on Discrete Sine Transform (DST) and Lloyd-Max quantization is proposed in distributed Base Station (BS) architecture. The time-domain samples are transformed by DST according to the characteristics of Orthogonal Frequency Division Multiplexing (OFDM) baseband signals, and then the coefficients after transformation are quantified by the Lloyd-Max quantizer. The simulation results show that the proposed scheme can work at various Compression Ratios (CRs) while the values of Error Vector Magnitude (EVM) are better than the limits in 3GPP.

Huang, Lilian, Zhu, Zhonghang.  2018.  Compressive Sensing Image Reconstruction Using Super-Resolution Convolutional Neural Network. Proceedings of the 2Nd International Conference on Digital Signal Processing. :80-83.

Compressed sensing (CS) can recover a signal that is sparse in certain representation and sample at the rate far below the Nyquist rate. But limited to the accuracy of atomic matching of traditional reconstruction algorithm, CS is difficult to reconstruct the initial signal with high resolution. Meanwhile, scholar found that trained neural network have a strong ability in settling such inverse problems. Thus, we propose a Super-Resolution Convolutional Neural Network (SRCNN) that consists of three convolutional layers. Every layer has a fixed number of kernels and has their own specific function. The process is implemented using classical compressed sensing algorithm to process the input image, afterwards, the output images are coded via SRCNN. We achieve higher resolution image by using the SRCNN algorithm proposed. The simulation results show that the proposed method helps improve PSNR value and promote visual effect.

Sun, Jie, Yu, Jiancheng, Zhang, Aiqun, Song, Aijun, Zhang, Fumin.  2018.  Underwater Acoustic Intensity Field Reconstruction by Kriged Compressive Sensing. Proceedings of the Thirteenth ACM International Conference on Underwater Networks & Systems. :5:1-5:8.

This paper presents a novel Kriged Compressive Sensing (KCS) approach for the reconstruction of underwater acoustic intensity fields sampled by multiple gliders following sawtooth sampling patterns. Blank areas in between the sampling trajectories may cause unsatisfying reconstruction results. The KCS method leverages spatial statistical correlation properties of the acoustic intensity field being sampled to improve the compressive reconstruction process. Virtual data samples generated from a kriging method are inserted into the blank areas. We show that by using the virtual samples along with real samples, the acoustic intensity field can be reconstructed with higher accuracy when coherent spatial patterns exist. Corresponding algorithms are developed for both unweighted and weighted KCS methods. By distinguishing the virtual samples from real samples through weighting, the reconstruction results can be further improved. Simulation results show that both algorithms can improve the reconstruction results according to the PSNR and SSIM metrics. The methods are applied to process the ocean ambient noise data collected by the Sea-Wing acoustic gliders in the South China Sea.

Cui, Wenxue, Jiang, Feng, Gao, Xinwei, Zhang, Shengping, Zhao, Debin.  2018.  An Efficient Deep Quantized Compressed Sensing Coding Framework of Natural Images. Proceedings of the 26th ACM International Conference on Multimedia. :1777-1785.

Traditional image compressed sensing (CS) coding frameworks solve an inverse problem that is based on the measurement coding tools (prediction, quantization, entropy coding, etc.) and the optimization based image reconstruction method. These CS coding frameworks face the challenges of improving the coding efficiency at the encoder, while also suffering from high computational complexity at the decoder. In this paper, we move forward a step and propose a novel deep network based CS coding framework of natural images, which consists of three sub-networks: sampling sub-network, offset sub-network and reconstruction sub-network that responsible for sampling, quantization and reconstruction, respectively. By cooperatively utilizing these sub-networks, it can be trained in the form of an end-to-end metric with a proposed rate-distortion optimization loss function. The proposed framework not only improves the coding performance, but also reduces the computational cost of the image reconstruction dramatically. Experimental results on benchmark datasets demonstrate that the proposed method is capable of achieving superior rate-distortion performance against state-of-the-art methods.

Braverman, Mark, Kol, Gillat.  2018.  Interactive Compression to External Information. Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing. :964-977.

We describe a new way of compressing two-party communication protocols to get protocols with potentially smaller communication. We show that every communication protocol that communicates C bits and reveals I bits of information about the participants' private inputs to an observer that watches the communication, can be simulated by a new protocol that communicates at most poly(I) $\cdot$ loglog(C) bits. Our result is tight up to polynomial factors, as it matches the recent work separating communication complexity from external information cost.

Huang, Xuping.  2018.  Mechanism and Implementation of Watermarked Sample Scanning Method for Speech Data Tampering Detection. Proceedings of the 2Nd International Workshop on Multimedia Privacy and Security. :54-60.

The integrity and reliability of speech data have been important issues to probative use. Watermarking technologies supplies an alternative solution to guarantee the the authenticity of multiple data besides digital signature. This work proposes a novel digital watermarking based on a reversible compression algorithm with sample scanning to detect tampering in time domain. In order to detect tampering precisely, the digital speech data is divided into length-fixed frames and the content-based hash information of each frame is calculated and embedded into the speech data for verification. Huffman compression algorithm is applied to each four sampling bits from least significant bit in each sample after pulse-code modulation processing to achieve low distortion and high capacity for hiding payload. Experimental experiments on audio quality, detection precision and robustness towards attacks are taken, and the results show the effectiveness of tampering detection with a precision with an error around 0.032 s for a 10 s speech clip. Distortion is imperceptible with an average 22.068 dB for Huffman-based and 24.139 dB for intDCT-based method in terms of signal-to-noise, and with an average MOS 3.478 for Huffman-based and 4.378 for intDCT-based method. The bit error rate (BER) between stego data and attacked stego data in both of time-domain and frequency domain is approximate 28.6% in average, which indicates the robustness of the proposed hiding method.

Bertino, Elisa, Nabeel, Mohamed.  2018.  Securing Named Data Networks: Challenges and the Way Forward. Proceedings of the 23Nd ACM on Symposium on Access Control Models and Technologies. :51-59.

Despite decades of research on the Internet security, we constantly hear about mega data breaches and malware infections affecting hundreds of millions of hosts. The key reason is that the current threat model of the Internet relies on two assumptions that no longer hold true: (1) Web servers, hosting the content, are secure, (2) each Internet connection starts from the original content provider and terminates at the content consumer. Internet security is today merely patched on top of the TCP/IP protocol stack. In order to achieve comprehensive security for the Internet, we believe that a clean-slate approach must be adopted where a content based security model is employed. Named Data Networking (NDN) is a step in this direction which is envisioned to be the next generation Internet architecture based on a content centric communication model. NDN is currently being designed with security as a key requirement, and thus to support content integrity, authenticity, confidentiality and privacy. However, in order to meet such a requirement, one needs to overcome several challenges, especially in either large operational environments or resource constrained networks. In this paper, we explore the security challenges in achieving comprehensive content security in NDN and propose a research agenda to address some of the challenges.

Pulungan, Farid Fajriana, Sudiharto, Dodi Wisaksono, Brotoharsono, Tri.  2018.  Easy Secure Login Implementation Using Pattern Locking and Environmental Context Recognition. 2018 International Conference on Applied Engineering (ICAE). :1-6.
Smartphone has become the tool which is used daily in modern human life. Some activities in human life, according to the usage of the smartphone can be related to the information which has a high privilege and needs a privacy. It causes the owners of the smartphone needs a system which can protect their privacy. Unfortunately, the secure the system, the unease of the usage. Hence, the system which has an invulnerable environment but also gives the ease of use is very needful. The aspect which is related to the ease of use is an authentication mechanism. Sometimes, this aspect correspondence to the effectiveness and the efficiency. This study is going to analyze the application related to this aspect which is a lock screen application. This lock screen application uses the context data based on the environment condition around the user. The context data used are GPS location and Mac Address of Wi-Fi. The system is going to detect the context and is going to determine if the smartphone needs to run the authentication mechanism or to bypass it based on the analysis of the context data. Hopefully, the smartphone application which is developed still can provide mobility and usability features, and also can protect the user privacy even though it is located in the environment which its context data is unknown.
Cui, Hongyan, Chen, Zunming, Xi, Yu, Chen, Hao, Hao, Jiawang.  2019.  IoT Data Management and Lineage Traceability: A Blockchain-based Solution. 2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops). :239–244.
The Internet of Things is stepping out of its infancy into full maturity, requiring massive data processing and storage. Unfortunately, because of the unique characteristics of resource constraints, short-range communication, and self-organization in IoT, it always resorts to the cloud or fog nodes for outsourced computation and storage, which has brought about a series of novel challenging security and privacy threats. For this reason, one of the critical challenges of having numerous IoT devices is the capacity to manage them and their data. A specific concern is from which devices or Edge clouds to accept join requests or interaction requests. This paper discusses a design concept for developing the IoT data management platform, along with a data management and lineage traceability implementation of the platform based on blockchain and smart contracts, which approaches the two major challenges: how to implement effective data management and enrich rational interoperability for trusted groups of linked Things; And how to settle conflicts between untrusted IoT devices and its requests taking into account security and privacy preserving. Experimental results show that the system scales well with the loss of computing and communication performance maintaining within the acceptable range, works well to effectively defend against unauthorized access and empower data provenance and transparency, which verifies the feasibility and efficiency of the design concept to provide privacy, fine-grained, and integrity data management over the IoT devices by introducing the blockchain-based data management platform.
Wu, Songrui, Li, Qi, Li, Guoliang, Yuan, Dong, Yuan, Xingliang, Wang, Cong.  2019.  ServeDB: Secure, Verifiable, and Efficient Range Queries on Outsourced Database. 2019 IEEE 35th International Conference on Data Engineering (ICDE). :626–637.
Data outsourcing to cloud has been a common IT practice nowadays due to its significant benefits. Meanwhile, security and privacy concerns are critical obstacles to hinder the further adoption of cloud. Although data encryption can mitigate the problem, it reduces the functionality of query processing, e.g., disabling SQL queries. Several schemes have been proposed to enable one-dimensional query on encrypted data, but multi-dimensional range query has not been well addressed. In this paper, we propose a secure and scalable scheme that can support multi-dimensional range queries over encrypted data. The proposed scheme has three salient features: (1) Privacy: the server cannot learn the contents of queries and data records during query processing. (2) Efficiency: we utilize hierarchical cubes to encode multi-dimensional data records and construct a secure tree index on top of such encoding to achieve sublinear query time. (3) Verifiability: our scheme allows users to verify the correctness and completeness of the query results to address server's malicious behaviors. We perform formal security analysis and comprehensive experimental evaluations. The results on real datasets demonstrate that our scheme achieves practical performance while guaranteeing data privacy and result integrity.
Pei, Xin, Li, Xuefeng, Wu, Xiaochuan, Zheng, Kaiyan, Zhu, Boheng, Cao, Yixin.  2019.  Assured Delegation on Data Storage and Computation via Blockchain System. 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC). :0055–0061.
With the widespread of cloud computing, the delegation of storage and computing is becoming a popular trend. Concerns on data integrity, security, user privacy as well as the correctness of execution are highlighted due to the untrusted remote data manipulation. Most of existing proposals solve the integrity checking and verifiable computation problems by challenge-response model, but are lack of scalability and reusability. Via blockchain, we achieve efficient and transparent public verifiable delegation for both storage and computing. Meanwhile, the smart contract provides API for request handling and secure data query. The security and privacy issues of data opening are settled by applying cryptographic algorithms all through the delegations. Additionally, any access to the outsourced data requires the owner's authentication, so that the dat transference and utilization are under control.
Dijkhuis, Sander, van Wijk, Remco, Dorhout, Hidde, Bharosa, Nitesh.  2018.  When Willeke Can Get Rid of Paperwork: A Lean Infrastructure for Qualified Information Exchange Based on Trusted Identities. Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age. :89:1-89:10.

As a frequent participant in eSociety, Willeke is often preoccupied with paperwork because there is no easy to use, affordable way to act as a qualified person in the digital world. Confidential interactions take place over insecure channels like e-mail and post. This situation poses risks and costs for service providers, civilians and governments, while goals regarding confidentiality and privacy are not always met. The objective of this paper is to demonstrate an alternative architecture in which identifying persons, exchanging information, authorizing external parties and signing documents will become more user-friendly and secure. As a starting point, each person has their personal data space, provided by a qualified trust service provider that also issues a high level of assurance electronic ID. Three main building blocks are required: (1) secure exchange between the personal data space of each person, (2) coordination functionalities provided by a token based infrastructure, and (3) governance over this infrastructure. Following the design science research approach, we developed prototypes of the building blocks that we will pilot in practice. Policy makers and practitioners that want to enable Willeke to get rid of her paperwork can find guidance throughout this paper and are welcome to join the pilots in the Netherlands.

Lu, Zhaojun, Wang, Qian, Qu, Gang, Liu, Zhenglin.  2018.  BARS: A Blockchain-Based Anonymous Reputation System for Trust Management in VANETs. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :98–103.
The public key infrastructure (PKI) based authentication protocol provides the basic security services for vehicular ad-hoc networks (VANETs). However, trust and privacy are still open issues due to the unique characteristics of vehicles. It is crucial for VANETs to prevent internal vehicles from broadcasting forged messages while simultaneously protecting the privacy of each vehicle against tracking attacks. In this paper, we propose a blockchain-based anonymous reputation system (BARS) to break the linkability between real identities and public keys to preserve privacy. The certificate and revocation transparency is implemented efficiently using two blockchains. We design a trust model to improve the trustworthiness of messages relying on the reputation of the sender based on both direct historical interactions and indirect opinions about the sender. Experiments are conducted to evaluate BARS in terms of security and performance and the results show that BARS is able to establish distributed trust management, while protecting the privacy of vehicles.
Dreier, Jannik, Hirschi, Lucca, Radomirovic, Sasa, Sasse, Ralf.  2018.  Automated Unbounded Verification of Stateful Cryptographic Protocols with Exclusive OR. 2018 IEEE 31st Computer Security Foundations Symposium (CSF). :359-373.

Exclusive-or (XOR) operations are common in cryptographic protocols, in particular in RFID protocols and electronic payment protocols. Although there are numerous applications, due to the inherent complexity of faithful models of XOR, there is only limited tool support for the verification of cryptographic protocols using XOR. The Tamarin prover is a state-of-the-art verification tool for cryptographic protocols in the symbolic model. In this paper, we improve the underlying theory and the tool to deal with an equational theory modeling XOR operations. The XOR theory can be freely combined with all equational theories previously supported, including user-defined equational theories. This makes Tamarin the first tool to support simultaneously this large set of equational theories, protocols with global mutable state, an unbounded number of sessions, and complex security properties including observational equivalence. We demonstrate the effectiveness of our approach by analyzing several protocols that rely on XOR, in particular multiple RFID-protocols, where we can identify attacks as well as provide proofs.

Padon, Oded.  2018.  Deductive Verification of Distributed Protocols in First-Order Logic. 2018 Formal Methods in Computer Aided Design (FMCAD). :1-1.

Formal verification of infinite-state systems, and distributed systems in particular, is a long standing research goal. In the deductive verification approach, the programmer provides inductive invariants and pre/post specifications of procedures, reducing the verification problem to checking validity of logical verification conditions. This check is often performed by automated theorem provers and SMT solvers, substantially increasing productivity in the verification of complex systems. However, the unpredictability of automated provers presents a major hurdle to usability of these tools. This problem is particularly acute in case of provers that handle undecidable logics, for example, first-order logic with quantifiers and theories such as arithmetic. The resulting extreme sensitivity to minor changes has a strong negative impact on the convergence of the overall proof effort.

Mahale, Anusha, B.S., Kariyappa.  2019.  Architecture Analysis and Verification of I3C Protocol. 2019 3rd International Conference on Electronics, Communication and Aerospace Technology (ICECA). :930-935.

In VLSI industry the design cycle is categorized into Front End Design and Back End Design. Front End Design flow is from Specifications to functional verification of RTL design. Back End Design is from logic synthesis to fabrication of chip. Handheld devices like Mobile SOC's is an amalgamation of many components like GPU, camera, sensor, display etc. on one single chip. In order to integrate these components protocols are needed. One such protocol in the emerging trend is I3C protocol. I3C is abbreviated as Improved Inter Integrated Circuit developed by Mobile Industry Processor Interface (MIPI) alliance. Most probably used for the interconnection of sensors in Mobile SOC's. The main motivation of adapting the standard is for the increase speed and low pin count in most of the hardware chips. The bus protocol is backward compatible with I2C devices. The paper includes detailed study I3C bus protocol and developing verification environment for the protocol. The test bench environment is written and verified using system Verilog and UVM. The Universal Verification Methodology (UVM) is base class library built using System Verilog which provides the fundamental blocks needed to quickly develop reusable and well-constructed verification components and test environments. The Functional Coverage of around 93.55 % and Code Coverage of around 98.89 % is achieved by verification closure.

Luo, Qiming, Lv, Ang, Hou, Ligang, Wang, Zhongchao.  2018.  Realization of System Verification Platform of IoT Smart Node Chip. 2018 IEEE 3rd International Conference on Integrated Circuits and Microsystems (ICICM). :341-344.

With the development of large scale integrated circuits, the functions of the IoT chips have been increasingly perfect. The verification work has become one of the most important aspects. On the one hand, an efficient verification platform can ensure the correctness of the design. On the other hand, it can shorten the chip design cycle and reduce the design cost. In this paper, based on a transmission protocol of the IoT node, we propose a verification method which combines simulation verification and FPGA-based prototype verification. We also constructed a system verification platform for the IoT smart node chip combining two kinds of verification above. We have simulated and verificatied the related functions of the node chip using this platform successfully. It has a great reference value.

Hu, Yayun, Li, Dongfang.  2019.  Formal Verification Technology for Asynchronous Communication Protocol. 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C). :482-486.

For aerospace FPGA software products, traditional simulation method faces severe challenges to verify product requirements under complicated scenarios. Given the increasing maturity of formal verification technology, this method can significantly improve verification work efficiency and product design quality, by expanding coverage on those "blind spots" in product design which were not easily identified previously. Taking UART communication as an example, this paper proposes several critical points to use formal verification for asynchronous communication protocol. Experiments and practices indicate that formal verification for asynchronous communication protocol can effectively reduce the time required, ensure a complete verification process and more importantly, achieve more accurate and intuitive results.