ALBERT

Description: With the growth of the urban population, the rapid development of smart cities has become the focus of urban regional development. Smart medical care is an indispensable part of smart city construction, which promotes the development of the medical industry. However, the security of data and timely service are the current problems faced by intelligent medical systems. Based on the public key encryption with filtered equality test and identity-based cryptography, an identity-based encryption with the filtered equality test (IBE-FET) is proposed for smart healthcare, in which a data receiver can use the private key and the message set to generate a warrant and send it to the cloud server. A cloud server can verify the equality between ciphertexts without decryption and check whether the encrypted message belongs to the same message set. Furthermore, the security analysis shows that the proposed scheme satisfies one-way security against the chosen identity and ciphertext attack in the random oracle model under the computational bilinear Diffie-Hellman assumption. The performance comparison shows that the scheme is feasible and practical in real life.

Description: The sharing of electronic health records (EHR) in cloud servers is an increasingly important development that can improve the efficiency of medical systems. However, there are several concerns focusing on the issues of security and privacy in EHR system. The EHR data contains the EHR owner’s sensitive personal information, if these data are obtained by a malicious user, it will not only cause the leakage of patient’s privacy, but also affect the doctor’s diagnosis. It is a very challenging problem for the EHR owner fully controls over own EHR data as well as preserves the privacy of himself. In this paper, we propose a new privacy-preserving access control (PPAC) scheme for EHR. To achieve fine-grained access control of the EHR data, we utilize the attribute-based signcryption (ABSC) mechanism to signcrypt data based on the access policy for the linear secret sharing schemes. Employing the cuckoo filter to hide the access policy, it could protect the EHR owner’s privacy information. In addition, the security analysis shows that the proposed scheme is provably secure under the decisional bilinear Diffie-Hellman exponent assumption and the computational Diffie-Hellman exponent assumption in the standard model. Furthermore, the performance analysis indicates that the proposed scheme achieves low costs of communication and computation compared with the related schemes, meanwhile preserves the EHR owner’s privacy. Therefore, the proposed scheme is better suited to EHR system.

Description: Sensors, Vol. 18, Pages 1573: PCPA: A Practical Certificateless Conditional Privacy Preserving Authentication Scheme for Vehicular Ad Hoc Networks Sensors doi: 10.3390/s18051573 Authors: Yang Ming Xiaoqin Shen Vehicle ad hoc networks (VANETs) is a promising network scenario for greatly improving traffic efficiency and safety, in which smart vehicles can communicate with other vehicles or roadside units. For the availability of VANETs, it is very important to deal with the security and privacy problems for VANETs. In this paper, based on certificateless cryptography and elliptic curve cryptography, we present a certificateless signature with message recovery (CLS-MR), which we believe are of independent interest. Then, a practical certificateless conditional privacy preserving authentication (PCPA) scheme is proposed by incorporating the proposed CLS-MR scheme. Furthermore, the security analysis shows that PCPA satisfies all security and privacy requirements. The evaluation results indicate that PCPA achieves low computation and communication costs because there is no need to use the bilinear pairing and map-to-point hash operations. Moreover, extensive simulations show that PCPA is feasible and achieves prominent performances in terms of message delay and message loss ratio, and thus is more suitable for the deployment and adoption of VANETs.

Description: Raman spectroscopy visualization is a challenging task due to the interference of complex background noise and the number of selected measurement points. In this paper, a super-resolution image reconstruction algorithm for Raman spectroscopy is studied to convert raw Raman data into pseudo-color super-resolution imaging. Firstly, the Raman spectrum data of a single measurement point is measured multiple times to calculate the mean value to remove the random background noise, and innovatively introduce the Retinex algorithm and the median filtering algorithm which improve the signal-to-noise ratio. The novel method of using deep neural network performs a super-resolution reconstruction operation on the gray image. An adaptive guided filter that automatically adjusts the filter radius and penalty factor is proposed to highlight the contour of the cell, and the super-resolution reconstruction of the pseudo-color image of the Raman spectrum is realized. The average signal-to-noise ratio of the reconstructed pseudo-color image sub-band reaches 14.29 db, and the average value of information entropy reaches 4.30 db. The results show that the Raman-based cell pseudo-color image super-resolution reconstruction algorithm is an effective tool to effectively remove noise and high-resolution visualization. The contrast experiments show that the pseudo-color image Kullback–Leiber (KL) entropy of the color image obtained by the method is small, the boundary is obvious, and the noise is small, which provide technical support for the development of sophisticated single-cell imaging Raman spectroscopy instruments.

Description: In the displacement measurement of the wafer stage in lithography machines, signal quality is affected by the relative angular position between the encoder head and the grating. In this study, a two-degree-of-freedom fiber-coupled heterodyne grating interferometer with large operating range of rotation is presented. Fibers without fiber couplers are utilized to receive the interference beams for high-contrast signals under the circumstances of large angular displacement and ZEMAX ray tracing software simulation and experimental validation have been carried out. Meanwhile, a reference beam generated inside the encoder head is adopted to suppress the thermal drift of the interferometer. Experimental results prove that the proposed grating interferometer could realize sub-nanometer displacement measurement stability in both in-plane and out-of-plane directions, which is 0.246 nm and 0.465 nm of 3σ value respectively within 30 s.

Description: Soil nitrate–nitrogen (NO3−-N) is one of the primary factors used to control nitrogen topdressing application during the crop growth period. The ion-selective electrode (ISE) is a promising method for rapid lower-cost in-field detection. Due to the simplification of sample preparation, the accuracy and stability of ISE-based in-field detection is doubted. In this paper, a self-designed prototype system for on-site soil NO3−-N detection was developed. The procedure of spinning centrifugation was used to avoid interference from soil slurry suspension. A modified Nernstian prediction model was quantitatively characterized with outputs from both the ISE and the soil moisture sensor. The measurement accuracy of the sensor fusion model was comparable with the laboratory ISE detections with standard sample pretreatment. Compared with the standard spectrometric method, the average absolute error (AE) and root-mean-square error (RMSE) were found to be less than 4.7 and 6.1 mg/L, respectively. The on-site soil testing efficiency was 4–5 min/sample, which reduced the operation time by 60% compared with manual sample preparation. The on-site soil NO3−-N status was dynamically monitored for 42 consecutive days. The declining peak of NO3−-N was observed. In all, the designed ISE-based detection system demonstrated a promising capability for the dynamic on-site monitoring of soil macronutrients.

Description: Visual tracking is a fundamental vision task that tries to figure out instances of several object classes from videos and images. It has attracted much attention for providing the basic semantic information for numerous applications. Over the past 10 years, visual tracking has made a great progress, but huge challenges still exist in many real-world applications. The facade of a target can be transformed significantly by pose changing, occlusion, and sudden movement, which possibly leads to a sudden target loss. This paper builds a hybrid tracker combining the deep feature method and correlation filter to solve this challenge, and verifies its powerful characteristics. Specifically, an effective visual tracking method is proposed to address the problem of low tracking accuracy due to the limitations of traditional artificial feature models, then rich hiearchical features of Convolutional Neural Networks are used to make the multi-layer features fusion improve the tracker learning accuracy. Finally, a large number of experiments are conducted on benchmark data sets OBT-100 and OBT-50, and show that our proposed algorithm is effective.

Description: Significant variation in impedance under a wide range of loads increases the difficulty of frequency tracking and vibration control in high-power piezoelectric systems (HPPSs). This paper proposed a wide operating range driving and control scheme for HPPSs. We systematically analyzed the impedance characteristics and deduced the load optimization frequency. In order to provide sufficient drive capability, the inverter combined with an LC matching circuit is configured. With the aid of a transformer ratio arm bridge (TRAB) combined with a proposed pulse-based phase detector (PBPD), the proposed scheme can control the vibration amplitude and keep parallel resonance status under a wide range of loads. Experiments conducted under actual operating conditions verify the feasibility of the proposed scheme under the modal resistance range from 7.40 to 500 Ω and the vibration range from 20% to 100%. Moreover, with the aid of a laser displacement sensor, our scheme is verified to have a vibration amplitude control accuracy better than 2% over a tenfold load variation. This research could be helpful for the driving and control of HPPSs operating in a wide range.

Description: Wi-Fi network has an open nature so that it needs to face greater security risks compared to wired network. The MAC address represents the unique identifier of the device, and is easily obtained by an attacker. Therefore MAC address randomization is proposed to protect the privacy of devices in a Wi-Fi network. However, implicit identifiers are used by attackers to identify user’s device, which can cause the leakage of user’s privacy. We propose device identification based on 802.11ac probe request frames. Here, a detailed analysis on the effectiveness of 802.11ac fields is given and a novel device identification method based on deep learning whose average f1-score exceeds 99% is presented. With a purpose of preventing attackers from obtaining relevant information by the device identification method above, we design a novel defense mechanism based on stream cipher. In that case, the original content of probe request frame is hidden by encrypting probe request frames and construction of probe request is reserved to avoid the finding of attackers. This defense mechanism can effectively reduce the performance of the proposed device identification method whose average f1-score is below 30%. In general, our research on attack and defense mechanism can preserve device privacy better.

Description: Rice is one of the three major crops in the world and is the major crop in Asia. Climate change and water resource shortages may result in decreases in rice yields and possible food shortage crises. In this study, water-saving farming management was tested, and IOT field water level monitoring was used to regulate water inflow automatically. Plant height (PH) is an important phenotype to be used to determine difference in rice growth periods and yields using water-saving irrigation. An unmanned aerial vehicle (UAV) with an RGB camera captured sequential images of rice fields to estimate rice PH compared with PH measured on site for estimating rice growth stages. The test results, with two crop harvests in 2019, revealed that with adequate image calibration, the correlation coefficient between UAV-PH and field-PH was higher than 0.98, indicating that UAV images can accurately determine rice PH in the field and rice growth phase. The study demonstrated that water-saving farming is effective, decreasing water usage for the first and second crops of 2019 by 53.5% and 21.7%, respectively, without influencing the growth period and final yield. Coupled with an automated irrigation system, rice farming can be adaptive to water shortage situations.