A Mobile Cloud-Based eHealth Scheme

Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid pace. Similarly, the field of health informatics is also considered as an extremely important field. This work observes the collaboration between these two fields to solve the traditional problem of extracting Electrocardiogram signals from trace reports and then performing analysis. The developed system has two front ends, the first dedicated for the user to perform the photographing of the trace report. Once the photographing is complete, mobile computing is used to extract the signal. Once the signal is extracted, it is uploaded into the server and further analysis is performed on the signal in the cloud. Once this is done, the second interface, intended for the use of the physician, can download and view the trace from the cloud. The data is securely held using a password-based authentication method. The system presented here is one of the first attempts at delivering the total solution, and after further upgrades, it will be possible to deploy the system in a commercial setting.     

An Optimized Resource Scheduling Strategy for Hadoop Speculative Execution Based on Non-cooperative Game Schemes

Hadoop is a well-known parallel computing system for distributed computing and large-scale data processes. “Straggling” tasks, however, have a serious impact on task allocation and scheduling in a Hadoop system. Speculative Execution (SE) is an efficient method of processing “Straggling” Tasks by monitoring real-time running status of tasks and then selectively backing up “Stragglers” in another node to increase the chance to complete the entire mission early. Present speculative execution strategies meet challenges on misjudgement of “Straggling” tasks and improper selection of backup nodes, which leads to inefficient implementation of speculative executive processes. This paper has proposed an Optimized Resource Scheduling strategy for Speculative Execution (ORSE) by introducing non-cooperative game schemes. The ORSE transforms the resource scheduling of backup tasks into a multi-party non-cooperative game problem, where the tasks are regarded as game participants, whilst total task execution time of the entire cluster as the utility function. In that case, the most benefit strategy can be implemented in each computing node when the game reaches a Nash equilibrium point, i.e., the final resource scheduling scheme to be obtained. The strategy has been implemented in Hadoop-2.x. Experimental results depict that the ORSE can maintain the efficiency of speculative executive processes and improve fault-tolerant and computation performance under the circumstances of Normal Load, Busy Load and Busy Load with Skewed Data.     

解调分析的一种优化方法

在分析当前的解调方法原理和实现方法的基础上，针对宽带解调方法将不包括故障的两时域相加信号，也以频率之差作为调制信号解出而引起误诊断的特点［４］，提出将带通滤波与希尔伯特变换和细化重抽样有机结合起来的解调算法。提高了解调分析的速度和精度，克服了希尔伯特变换应用的局限性。     

An Immunization Scheme for Ransomware

In recent years, as the popularity of anonymous currencies such as Bitcoin has made the tracking of ransomware attackers more difficult, the amount of ransomware attacks against personal computers and enterprise production servers is increasing rapidly. The ransomware has a wide range of influence and spreads all over the world. It is affecting many industries including internet, education, medical care, traditional industry, etc. This paper uses the idea of virus immunity to design an immunization solution for ransomware viruses to solve the problems of traditional ransomware defense methods (such as anti-virus software, firewalls, etc.), which cannot meet the requirements of rapid detection and immediate prevention of new outbreaks attacks. Our scheme includes two parts: server and client. The server provides an immune configuration file and configuration file management functions, including a configuration file module, a cryptography algorithm module, and a display module. The client obtains the immunization configuration file from server in real time, and performs the corresponding operations according to the configuration file to make the computer have an immune function for a specific ransomware, including an update module, a configuration file module, a cryptography algorithm module, a control module, and a log module. This scheme controls mutexes, services, files and registries respectively, to destroy the triggering conditions of the virus and finally achieve the purpose of immunizing a computer from a specific ransomware.     

An Adjust Duty Cycle Method for Optimized Congestion Avoidance and Reducing Delay for WSNs

With the expansion of the application range and network scale of wireless sensor networks in recent years, WSNs often generate data surges and delay queues during the transmission process, causing network paralysis, even resulting in local or global congestion. In this paper, a dynamically Adjusted Duty Cycle for Optimized Congestion based on a real-time Queue Length (ADCOC) scheme is proposed. In order to improve the resource utilization rate of network nodes, we carried out optimization analysis based on the theory and applied it to the adjustment of the node’s duty cycle strategy. Using this strategy to ensure that the network lifetime remains the same, can minimize system delay and maximize energy efficiency. Firstly, the problems of the existing RED algorithm are analyzed. We introduce the improved SIG-RED algorithm into the ADCOC mechanism. As the data traffic changes, the RED protocol cannot automatically adjust the duty cycle. A scheduler is added to the buffer area manager, referring to a weighted index of network congestion, which can quickly determine the status of network congestion. The value of the weighting coefficient W is adjusted by the Bayesian method. The scheduler preferably transmits severely urgent data, alleviating the memory load. Then we combined improved data fusion technology and information gain methods to adjust the duty cycle dynamically. By simulating the algorithm, it shows that it has faster convergence speed and smaller queue jitter. Finally, we combine the adjusted congestion weight and the duty cycle growth value to adjust the data processing rate capability in the real-time network by dynamically adjusting it to adapt to bursts of data streams. Thus, the frequency of congestion is reduced to ensure that the system has higher processing efficiency and good adaptability.     

An Efficient Ciphertext-Policy Attribute-Based Encryption Scheme with Policy Update

Ciphertext-policy attribute-based encryption (CP-ABE) is a promising cryptographic solution to the problem for enforcing fine-grained access control over encrypted data in the cloud. However, when applying CP-ABE to data outsourcing scenarios, we have to address the challenging issue of policy updates because access control elements, such as users, attributes, and access rules may change frequently. In this paper, we propose a notion of access policy updatable ciphertext-policy attribute-based encryption (APU-CP-ABE) by combining the idea of ciphertext-policy attribute-based key encapsulation and symmetric proxy re-encryption. When an access policy update occurs, data owner is no longer required to download any data for re-encryption from the cloud, all he needs to do is generate a re-encryption key and produce a new encapsulated symmetric key, and then upload them to the cloud. The cloud server executes re-encryption without decryption. Because the re-encrypted ciphertext is encrypted under a completely new key, users cannot decrypt data even if they keep the old symmetric keys or parts of the previous ciphertext. We present an APU-CP-ABE construction based on Syalim et al.’s[Syalim, Nishide and Sakurai (2017)] improved symmetric proxy re-encryption scheme and Agrawal et al.’s [Agrawal and Chase (2017)] attribute-based message encryption scheme. It requires only 6 bilinear pairing operations for decryption, regardless of the number of attributes involved. This makes our construction particularly attractive when decryption is time-critical.     

An Efficient Scheme for Data Pattern Matching in IoT Networks

The Internet has become an unavoidable trend of all things due to the rapid growth of networking technology, smart home technology encompasses a variety of sectors, including intelligent transportation, allowing users to communicate with anybody or any device at any time and from anywhere. However, most things are different now. Background: Structured data is a form of separated storage that slows down the rate at which everything is connected. Data pattern matching is commonly used in data connectivity and can help with the issues mentioned above. Aim: The present pattern matching system is ineffective due to the heterogeneity and rapid expansion of large IoT data. The method requires a lot of manual work and has a poor match with real-world applications. In the modern IoT context, solving the challenge of automatic pattern matching is complex. Methodology: A three-layer mapping matching is proposed for heterogeneous data from the IoT, and a hierarchical pattern matching technique. The feature classification matching, relational feature clustering matching, and mixed element matching are all examples of feature classification matching. Through layer-by-layer matching, the algorithm gradually narrows the matching space, improving matching quality, reducing the number of matching between components and the degree of manual participation, and producing a better automatic mode matching. Results: The algorithm's efficiency and performance are tested using a large number of data samples, and the results show that the technique is practical and effective. Conclusion: the proposed algorithm utilizes the instance information of the data pattern. It deploys three-layer mapping matching approach and mixed element matching and realizes the automatic pattern matching of heterogeneous data which reduces the matching space between elements in complex patterns. It improves the efficiency and accuracy of automatic matching.     

Efficient Energy Optimized Faithful Adder with Parallel Carry Generation

Approximate computing has received significant attention in the design of portable CMOS hardware for error-tolerant applications. This work proposes an approximate adder that to optimize area delay and achieve energy efficiency using Parallel Carry (PC) generation logic. For ‘n’ bits in input, the proposed algorithm use approximate addition for least n/2 significant bits and exact addition for most n/2 significant bits. A simple OR logic with no carry propagation is used to implement the approximate part. In the exact part, addition is performed using 4-bit adder blocks that implement PC at block level to reduce node capacitance in the critical path. Evaluations reveal that the maximum error of the proposed adder confines not more than 2^n/2. As an enhancement of the proposed algorithm, we use the Error Recovery (ER) module to reduce the average error. Synthesis results of Proposed-PC (P-PC) and Proposed-PCER (P-PCER) adders with n-16 in 180nm Application Specific Integrated Circuit (ASIC) PDK technology revealed 44.2% & 41.7% PDP reductions and 43.4% & 40.7% ADP reductions, respectively compared to the latest best approximate design compared. The functional and driving effectiveness of proposed adders are examined through digital image processing applications.     

Decision Support System for Evaluating Impact of Product Carbon Labeling Scheme

In this paper a decision support system for systematically evaluating the impact of labeling products with their carbon footprints is developed and applied to prioritize products for carbon labeling in a large supermarket chain in the UK. Carbon labels may change consumers' behavior and encourage suppliers to implement carbon-reduction solutions. Those changes may, however, lead to unintended risks. To handle the challenges of uncertainties in the evaluation, the Evidential Reasoning approach and the Intelligent Decision System software for multi-criteria decision analysis are applied to support the process. The system developed can be applied to assessing the impact of sustainable development policies to maximize their benefits and minimize their risks.     

Algorithmic Scheme for Concurrent Detection and Classification of Printed Circuit Board Defects

An ideal printed circuit board (PCB) defect inspection system can detect defects and classify PCB defect types. Existing defect inspection technologies can identify defects but fail to classify all PCB defect types. This research thus proposes an algorithmic scheme that can detect and categorize all 14-known PCB defect types. In the proposed algorithmic scheme, fuzzy c-means clustering is used for image segmentation via image subtraction prior to defect detection. Arithmetic and logic operations, the circle hough transform (CHT), morphological reconstruction (MR), and connected component labeling (CCL) are used in defect classification. The algorithmic scheme achieves 100% defect detection and 99.05% defect classification accuracies. The novelty of this research lies in the concurrent use of CHT, MR, and CCL algorithms to accurately detect and classify all 14-known PCB defect types and determine the defect characteristics such as the location, area, and nature of defects. This information is helpful in electronic parts manufacturing for finding the root causes of PCB defects and appropriately adjusting the manufacturing process. Moreover, the algorithmic scheme can be integrated into machine vision to streamline the manufacturing process, improve the PCB quality, and lower the production cost.     

An Efficient Certificateless Aggregate Signature Scheme Designed for VANET

The Vehicular Ad-hoc Network (VANET) is the fundamental of smart transportation system in the future, but the security of the communication between vehicles and vehicles, between vehicles and roadside infrastructures have become increasingly prominent. Certificateless aggregate signature protocol is used to address this security issue, but the existing schemes still have many drawbacks in terms of security and efficiency: First, many schemes are not secure, and signatures can be forged by the attacker; Second, even if some scheme are secure, many schemes use a large number of bilinear pairing operation, and the computation overhead is large. At the same time, the length of the aggregated signature also increases linearly with the increase of user numbers, resulting in a large communication overhead. In order to overcome the above challenges, we propose a new certificateless aggregate signature scheme for VANET, and prove the security of the scheme under the random oracle model. The new scheme uses pseudonym to realize the conditional privacy protection of the vehicle’s information. The new scheme does not use bilinear pairing operation, and the calculation efficiency is high. At the same time, the length of the aggregate signature of the new scheme is constant, thereby greatly reducing the communication and storage overhead. The analysis results demonstrate that the new scheme is not only safer, but also superior in performance to the recent related schemes in computation overhead and communication cost.     

Multi-VMs Intrusion Detection for Cloud Security Using Dempster-shafer Theory

Cloud computing provides easy and on-demand access to computing resources in a configurable pool. The flexibility of the cloud environment attracts more and more network services to be deployed on the cloud using groups of virtual machines (VMs), instead of being restricted on a single physical server. When more and more network services are deployed on the cloud, the detection of the intrusion likes Distributed Denial-of-Service (DDoS) attack becomes much more challenging than that on the traditional servers because even a single network service now is possibly provided by groups of VMs across the cloud system. In this paper, we propose a cloud-based intrusion detection system (IDS) which inspects the features of data flow between neighboring VMs, analyzes the probability of being attacked on each pair of VMs and then regards it as independent evidence using Dempster-Shafer theory, and eventually combines the evidence among all pairs of VMs using the method of evidence fusion. Unlike the traditional IDS that focus on analyzing the entire network service externally, our proposed algorithm makes full use of the internal interactions between VMs, and the experiment proved that it can provide more accurate results than the traditional algorithm.     

A Proxy Re-Encryption with Keyword Search Scheme in Cloud Computing

With the widespread use of cloud computing technology, more and more users and enterprises decide to store their data in a cloud server by outsourcing. However, these huge amounts of data may contain personal privacy, business secrets and other sensitive information of the users and enterprises. Thus, at present, how to protect, retrieve, and legally use the sensitive information while preventing illegal accesses are security challenges of data storage in the cloud environment. A new proxy re-encryption with keyword search scheme is proposed in this paper in order to solve the problem of the low retrieval efficiency of the encrypted data in the cloud server. In this scheme, the user data are divided into files, file indexes and the keyword corresponding to the files, which are respectively encrypted to store. The improved scheme does not need to re-encrypt partial file cipher-text as in traditional schemes, but re-encrypt the cipher-text of keywords corresponding to the files. Therefore the scheme can improve the computational efficiency as well as resist chosen keyword attack. And the scheme is proven to be indistinguishable under Hash Diffie-Hellman assumption. Furthermore, the scheme does not need to use any secure channels, making it more effective in the cloud environment.     

强反射复杂表面随机缺陷自动检测系统

系统采用分光照明方式,设计均匀散射光源,通过图像增强预处理,灰度分析,图像二值化,特征提取与图像识别, 自动分选出表面有缺陷的工件。硬软件包括:光学系统;接收系统;控制电路和Visual C 、汇编语言程序等。系统有效解决了强反射光、复杂表面和随机缺陷带来的检测困难,检测精度可达97.3%。     

A Novel Edge Computing Based Area Navigation Scheme

The area navigation system, discussed in this paper, is composed of ground responders and a navigation terminal and can position a high-velocity aircraft and measure its velocity. This navigation system is silent at ordinary times. It sends out a request signal when positioning is required for an aircraft, and then the ground responders send a signal for resolving the aircraft. Combining the direct sequence spread spectrum and frequency hopping, the concealed communication mode is used in the whole communication process, with short communication pulses as much as possible, so the system has strong concealment and anti-interference characteristics. As the transmission delay is apparent in the response communication mode, there is a big error where the common navigation algorithm is used for the positioning and velocity measurement of a high-velocity aircraft. In this paper, a new algorithm is proposed, which can eliminate the influence of response communication delay on the positioning of a high-velocity moving target to realize the precise positioning and velocity measurement of a high-velocity aircraft.     

An Advanced Quantum-Resistant Signature Scheme for Cloud Based on Eisenstein Ring

Signature, widely used in cloud environment, describes the work as readily identifying its creator. The existing signature schemes in the literature mostly rely on the Hardness assumption which can be easily solved by quantum algorithm. In this paper, we proposed an advanced quantum-resistant signature scheme for Cloud based on Eisenstein Ring (ETRUS) which ensures our signature scheme proceed in a lattice with higher density. We proved that ETRUS highly improve the performance of traditional lattice signature schemes. Moreover, the Norm of polynomials decreases significantly in ETRUS which can effectively reduce the amount of polynomials convolution calculation. Furthermore, storage complexity of ETRUS is smaller than classical ones. Finally, according to all convolution of ETRUS enjoy lower degree polynomials, our scheme appropriately accelerate 56.37% speed without reducing its security level.     

A Novel DDoS Attack Detection Method Using Optimized Generalized Multiple Kernel Learning

Distributed Denial of Service (DDoS) attack has become one of the most destructive network attacks which can pose a mortal threat to Internet security. Existing detection methods cannot effectively detect early attacks. In this paper, we propose a detection method of DDoS attacks based on generalized multiple kernel learning (GMKL) combining with the constructed parameter R. The super-fusion feature value (SFV) and comprehensive degree of feature (CDF) are defined to describe the characteristic of attack flow and normal flow. A method for calculating R based on SFV and CDF is proposed to select the combination of kernel function and regularization paradigm. A DDoS attack detection classifier is generated by using the trained GMKL model with R parameter. The experimental results show that kernel function and regularization parameter selection method based on R parameter reduce the randomness of parameter selection and the error of model detection, and the proposed method can effectively detect DDoS attacks in complex environments with higher detection rate and lower error rate.     

图像式目标微小位移监测系统

提出了一种新颖的用于滑坡监测的多目标微位移图像监测系统。利用目标的微小形变反映在 CCD 获取的图像对应特征点位移的性质,运用光学重心等算法来确定目标是否超出安全警戒范围。结合步进电机和经纬仪对目标方位的精确定位能力并通过电台的控制,实现多点目标的遥控测量。系统的监测距离为 500m,实验达到的测量位移分辨率为 12mm,光斑重心坐标检测的重复精度优于 0.7μm。     

An Optimized Algorithm for CR-MIMO Wireless Networks

With the rapid development of wireless communication technology, the spectrum resources are increasingly strained which needs optimal solutions. Cognitive radio (CR) is one of the key technologies to solve this problem. Spectrum sensing not only includes the precise detection of the communication signal of the primary user (PU), but also the precise identification of its modulation type, which can then determine the a priori information such as the PU’ service category, so as to use this information to make the cognitive user (CU) aware to discover and use the idle spectrum more effectively, and improve the spectrum utilization. Spectrum sensing is the primary feature and core part of CR. Classical sensing algorithms includes energy detection, cyclostationary feature detection, matched filter detection, and so on. The energy detection algorithm has a simple structure and does not require prior knowledge of the PU transmitter signal, but it is easily affected by noise and the threshold is not easy to determine. The combination of multiple-input multiple-output (MIMO) with CR improves the spectral efficiency and multi-path fading utilization. To best utilize the PU spectrum while minimizing the overall transmit power, an iterative technique based on semidefinite programming (SDP) and minimum mean squared error (MMSE) is proposed. Also, this article proposed a new method for max-min fairness beamforming. When compared to existing algorithms, the simulation results show that the proposed algorithms perform better in terms of total transmitted power and signal-to-interference plus noise ratio (SINR). Furthermore, the proposed algorithm effectively improved the system performance in terms of number of iterations, interference temperature threshold and balance SINR level which makes it superior over the conventional schemes.     

An Efficient Greedy Traffic Aware Routing Scheme for Internet of Vehicles

A new paradigm of VANET has emerged in recent years: Internet of Vehicles (IoV). These networks are formed on the roads and streets between travellers who have relationships, interactions and common social interests. Users of these networks exchange information of common interest, for example, traffic jams and dangers on the way. They can also exchange files such as multimedia files. IoV is considered as part of the Internet of Things (IoT) where objects are vehicles, which can create a multitude of services dedicated to the intelligent transportation system. The interest is to permit to all connected vehicles to communicate with each other and/or with a central server, through other vehicles. Vehicle to Vehicle (V2V) communication is the main component, because the services encompassed in the IoV are based on the vehicles in question, such as transmitter, relay and receiver. This work is focusing on designing and developing a Quality of Service (QoS) routing scheme dedicated to IoV networks. Especially, we aim to improve the Greedy Traffic Aware Routing (GyTAR) protocol to support QoS in IoV networks. To evaluate the proposed approach in terms of QoS in the context of IoV networks, the performance metrics such as average end-to-end delay and packet delivery ratio are taken into consideration to analyse the network situation.