As big data and data mining technology advance, research on the collection and analysis of medical data on the internet of medical things (IoMT) has gained increasing attention. Medical institutions often collect users’ signs and symptoms from their devices for analysis. However, the process of data collection may pose a risk of privacy leakage without a trusted third party. To address this issue, we propose a medical data collection based on local differential privacy and Count Sketch (MDLDP). The algorithm first uses a random sampling technique to select only one symptom for perturbation by a single user. The perturbed data is then uploaded using Count Sketch. The third-party aggregates the user-submitted data to estimate the frequencies of the symptoms and the mean extent of their occurrence. This paper theoretically demonstrates that the designed algorithm satisfies local differential privacy and unbiased estimation. We also evaluated the algorithm experimentally with existing algorithms on a real medical dataset. The results show that the MDLDP algorithm has good utility for key-value type medical data collection statistics in the IoMT. Full article

A conformal cylindrical phased array antenna excited with composite right/left-handed (CRLH) phase shifters is proposed. The phase tuning of the CRLH phase shifter is achieved by embedding novel magneto-static field-responsive micron-sized particles in its structure. It is shown that through the tiny magnet activation of these novel magneto-static particles at appropriate locations along the length of CRLH stub and inter-digital fingers, variable phase shifts are obtained. The proposed particle-based CRLH phase shifter operates in C-band (5–6) GHz with a low insertion loss and phase error. The 1 × 4 cylindrical phased array is excited with the four unit cells of the proposed particle-embedded CRLH transmission line phase shifters to scan the main beam at desired scan angles. A prototype of a 1 × 4 cylindrical phased array excited with the particle-based CRLH phase shifters was fabricated, and the results show that the simulated results are in close agreement with the measured results. The conformal cylindrical array with the proposed particle-based CRLH phase shifters has great potential for use in printed and flexible electronics design where commercially available phase shifters have a definite drawback. Full article

Most existing traffic sign detection models suffer from high computational complexity and superior performance but cannot be deployed on edge devices with limited computational capacity, which cannot meet the direct needs of autonomous vehicles for detection model performance and efficiency. To address the above concerns, this paper proposes an improved SEDG-Yolov5 traffic sign detection method based on knowledge distillation. Firstly, the Slicing Aided Hyper Inference method is used as a local offline data augmentation method for the model training. Secondly, to solve the problems of high-dimensional feature information loss and high model complexity, the inverted residual structure ESGBlock with a fused attention mechanism is proposed, and a lightweight feature extraction backbone network is constructed based on it, while we introduce the GSConv in the feature fusion layer to reduce the computational complexity of the model further. Eventually, an improved response-based objectness scaled knowledge distillation method is proposed to retrain the traffic sign detection model to compensate for the degradation of detection accuracy due to light-weighting. Extensive experiments on two challenging traffic sign datasets show that our proposed method has a good balance on detection precision and detection speed with 2.77M parametric quantities. Furthermore, the inference speed of our method achieves 370 FPS with TensorRT and 35.6 FPS with ONNX at FP16-precision, which satisfies the requirements for real-time sign detection and edge deployment. Full article

Vehicle-to-everything services are in the implementation phase, and automakers agree that V2X would improve the safety-critical applications already deployed. 3GPP Release 12 introduces LTE-V for V2V and V2I services. The LTE-V is extended to C-V2X to support V2N. Because of the challenge of high mobility in the V2X system, cutting-edge technologies, such as SDN and small cell in 5G networks, pave the way to the next generation of vehicular networks. SDN is a network technology concept that divides the data and control planes. The OpenFlow protocol is used for communication between the control layer and the network layer in SDN. Different from wireless traditional cellular base stations, small cells are lower-power cell sites that are deployed every few blocks. Small cells can transmit data using mid- and high-band spectrums. Small cell-linked road side unit (RSU) is considered a key enabling technology because it has the capability to create a logical cluster platform residing at the edge of the network, which provides high computation performance. Accordingly, we consider a novel distributed software-defined small cell-linked road side unit vehicular network architecture (diSRsVN). Based on diSRsVN, logical software-defined on-board wireless vehicle, and topology discovery over diSRsVN are presented. The proposed architecture is evaluated under an omnet++ network simulator. The simulation results show the effectiveness of the proposed architecture, which improves the packet delivery ratio and minimizes end-to-end delay. Full article

Digital watermarking technology is widely used in today’s copyright protection, data monitoring, and data tracking. Digital watermarking attack techniques are designed to corrupt the watermark information contained in the watermarked image (WMI) so that the watermark information cannot be extracted effectively or correctly. While traditional digital watermarking attack technology is more mature, it is capable of attacking the watermark information embedded in the WMI. However, it is also more damaging to its own visual quality, which is detrimental to the protection of the original carrier and defeats the purpose of the covert attack on WMI. To advance watermarking attack technology, we propose a new covert watermarking attack network (CWAN) based on a convolutional neural network (CNN) for removing low-frequency watermark information from WMI and minimizing the damage caused by WMI through the use of deep learning. We import the preprocessed WMI into the CWAN, obtain the residual feature images (RFI), and subtract the RFI from the WMI to attack image watermarks. At this point, the WMI’s watermark information is effectively removed, allowing for an attack on the watermark information while retaining the highest degree of image detail and other features. The experimental results indicate that the attack method is capable of effectively removing the watermark information while retaining the original image’s texture and details and that its ability to attack the watermark information is superior to that of most traditional watermarking attack methods. Compared with the neural network watermarking attack methods, it has better performance, and the attack performance metrics are improved by tens to hundreds of percent in varying degrees, indicating that it is a new covert watermarking attack method. Full article

Analog integrated circuit sizing is notoriously difficult to automate due to its complexity and scale; thus, it continues to heavily rely on human expert knowledge. This work presents a machine learning-based design automation methodology comprising pre-defined building blocks such as current mirrors or differential pairs and pre-computed look-up tables for electrical characteristics of primitive devices. Modeling the behavior of primitive devices around the operating point with neural networks combines the speed of equation-based methods with the accuracy of simulation-based approaches and, thereby, brings quality of life improvements for analog circuit designers using the $g_{\mathrm{m}}/I_{\mathrm{d}}$ method. Extending this procedural automation method for human design experts, we present a fully autonomous sizing approach. Related work shows that the convergence properties of conventional optimization approaches improve significantly when acting in the electrical domain instead of the geometrical domain. We, therefore, formulate the circuit sizing task as a sequential decision-making problem in the alternative electrical design space. Our automation approach is based entirely on reinforcement learning, whereby abstract agents learn efficient design space navigation through interaction and without expert guidance. These agents’ learning behavior and performance are evaluated on circuits of varying complexity and different technologies, showing both the feasibility and portability of the work presented here. Full article

Hybrid energy storage systems (HESS) composed of a battery and ultracapacitor (UC) provide a feasible solution to the economy of electric vehicles (EVs). To fully exploit the potential of HESSs, a power distribution strategy that can split power between the battery and UC in HESSs plays an important role. Therefore, a novel power distribution strategy and its online application are proposed in this paper. First, a new and simple power distribution model of HESSs is proposed, and the model parameters are optimized offline through particle swarm optimization (PSO). Then, a driving condition recognizer based on a neural network is introduced, and the online application of the strategy is realized by combining offline global optimization and online recognition. Compared with the traditional rule-based strategy, the strategy proposed reduces the average fluctuation of the battery current by 52.53% and the average amplitude of the battery current by 11.51%. Meanwhile, it can be seen from the results that the strategy proposed is very close to the offline PSO-based strategy proposed and exhibits good performance under all driving cycles. Full article

Realizing stable and reliable monitoring of a distribution network voltage environment can obtain real-time power parameter information and ensure the normal and safe operation of transmission lines, which is of great research significance and engineering value. Based on the distributed capacitance relationship between sensor and transmission line, an equivalent circuit capacitance voltage dividing model is proposed, and the relevant factors affecting the stability of the voltage dividing ratio are analyzed. The self-calibration principle of noncontact AC voltage measurement is proposed based on the system identification theory. The noncontact sensing structure is designed, a sensor probe prototype is fabricated, and a back-end conditioning circuit is designed to realize the overall measurement system. Finally, the validity of the measurement model is verified by simulation and experiment, and a measurement platform is built which proves the feasibility of the self-calibration method for noncontact voltage measurement. The experimental results show that the error is less than ±2%. This method can correctly restore the measured voltage waveform, has good linearity, and can realize wireless data transmission, which provides a new idea for the voltage measurement method of a distribution network. Full article

Single-image dehazing plays a significant preprocessing role in machine vision tasks. As the dark-channel-prior method will fail in the sky region of the image, resulting in inaccurately estimated parameters, and given the failure of many methods to address a large band of haze, we propose a simple yet effective method for single-image dehazing based on an improved bright prior and dark channel prior. First, we use the Otsu method by particle swarm optimization to divide the hazy image into sky regions and non-sky regions. Then, we use the improved bright channel prior and dark channel prior to estimate the parameters in the physical model. Second, we propose a weighted fusion function to efficiently fuse the parameters estimated by two priors. Finally, the clear image is restored through the physical model. Experiments illustrate that our method can solve the problem of the invalidation of the dark channel prior in the sky region well and achieve high-quality image restoration, especially for images with limited haze. Full article

Cyberattacks always remain the major threats and challenging issues in the modern digital world. With the increase in the number of internet of things (IoT) devices, security challenges in these devices, such as lack of encryption, malware, ransomware, and IoT botnets, leave the devices vulnerable to attackers that can access and manipulate the important data, threaten the system, and demand ransom. The lessons from the earlier experiences of cyberattacks demand the development of the best-practices benchmark of cybersecurity, especially in modern Smart Environments. In this study, we propose an approach with a framework to discover malware attacks by using artificial intelligence (AI) methods to cover diverse and distributed scenarios. The new method facilitates proactively tracking network traffic data to detect malware and attacks in the IoT ecosystem. Moreover, the novel approach makes Smart Environments more secure and aware of possible future threats. The performance and concurrency testing of the deep neural network (DNN) model deployed in IoT devices are computed to validate the possibility of in-production implementation. By deploying the DNN model on two selected IoT gateways, we observed very promising results, with less than 30 kb/s increase in network bandwidth on average, and just a 2% increase in CPU consumption. Similarly, we noticed minimal physical memory and power consumption, with 0.42 GB and 0.2 GB memory usage for NVIDIA Jetson and Raspberry Pi devices, respectively, and an average 13.5% increase in power consumption per device with the deployed model. The ML models were able to demonstrate nearly 93% of detection accuracy and 92% f1-score on both utilized datasets. The result of the models shows that our framework detects malware and attacks in Smart Environments accurately and efficiently. Full article

The fifth generation (5G) of mobile communication technology poses lots of questions while introducing significant improvements compared with previous generations. The most sensitive question is related to the safety of human exposure. The aim of present work was to analyze, with a few chosen examples, two of the most significant features of 5G emissions: the extreme spatial variability of the exposure and the nonlinear dynamics characteristics of the temporal variability of the exposure. Two models of patch antenna arrays operating at 3.7 GHz with varying beam forming and beam steering capabilities were considered for an analysis of the specific absorption rate of electromagnetic energy deposition in tissues of a head model. This allowed clear emphasis on the influence of the antenna geometry and feeding peculiarities on the spatial variability of exposure. The second approach implemented the original idea of following the nonlinear recurrence behavior of exposure in time, and underlined the time variability characteristics of emissions with a real-life mobile phone running different 5G applications. Time series of the emitted electric-field strengths were recorded by means a real-time spectrum analyzer and two near-field probes differently positioned in the beam. The presence of laminar emissions, chaotic emissions, determinism and recurrence in the exposures prove the potential for recurrence quantification in predicting time variability features of 5G exposure. Overall, the impact of 5G signals on living bodies, with the highest possible man-made spatial and temporal variability, may have very unpredictable bio-medical consequences. Full article

By leveraging the Internet, cloud computing allows users to have on-demand access to large pools of configurable computing resources. PaaS (Platform as a Service), IaaS (Infrastructure as a Service), and SaaS (Software as a Service) are three basic categories for the services provided by cloud the computing environments. Quality of service (QoS) metrics like reliability, availability, performance, and cost determine which resources and services are available in a cloud computing scenario. Provider and the user-specified performance characteristics, such as, rejection rate, throughput, response time, financial cost, and energy consumption, form the basis for QoS. To fulfil the needs of its customers, cloud computing must ensure that its services are given with the appropriate quality of service QoS. A “A legally enforceable agreement known as a “Service Level Agreement” (SLA) between a service provider and a customer that outlines service objectives, quality of service requirements, and any associated financial penalties for falling short. We, therefore, presented “A Proactive Resource Supply based Run-time Monitoring of SLA in Cloud Computing”, which allows for the proactive management of SLAs during run-time via the provisioning of cloud services and resources. Within the framework of the proposed work, SLAs are negotiated between cloud users and providers at run-time utilizing SLA Manager. Resources are proactively allocated via the Resource Manager to cut down on SLA violations and misdetection costs. As metrics of performance, we looked at the frequency with which SLAs were broken and the money lost due to false positives. We compared the proposed PRP-RM-SLA model’s simulated performance to the popular existing SLA-based allocation strategy SCOOTER. According to simulation data, the suggested PRP-RM-SLA model is 25% more effective than the current work SCOOTER at reducing SLA breaches and the cost of misdetection. Full article

A hybrid method combining the adaptive cross approximation method (ACA) and the Chebyshev approximation technique (CAT) is presented for fast wideband BCS prediction of arbitrary-shaped 3D targets based on non-cooperative radiation sources. The incident and scattering angles can be computed by using their longitudes, latitudes and altitudes according to the relative positions of the satellite, the target and the passive bistatic radar. The ACA technique can be employed to reduce the memory requirement and computation time by compressing the low-rank matrix blocks. By exploiting the CAT into ACA, it is only required to calculate the currents at several Chebyshev–Gauss frequency sampling points instead of direct point-by-point simulations. Moreover, a wider frequency band can be obtained by using the Maehly approximation. Three numerical examples are presented to validate the accuracy and efficiency of the hybrid ACA-CAT method. Full article

The important electrochemical measurements of reduced graphene oxide-titanium oxide (rGO)/TiO₂) electrodes for the application of a glucose sensor are reported in the proposed work. Investigating the sensitivity, stability, and reproducibility of sensor electrodes that were made and used to evaluate the concentration of glucose in the serum is one of the novel aspects of this work. This study presents the use of citrus limetta (sweet lime) fruit peel waste to synthesize a green reduction of graphene oxide (rGO). The rGO/TiO₂ composite obtained using the microwave heating method is applied for measuring the structural and morphological properties by various means. A conducting fluorine-tin oxide substrate is used to modify the enzymeless glucose sensor electrode. The electrochemical measurements of rGO/TiO₂ sensor electrodes are carried out using the technique of cyclic voltammetry. The rGO/TiO₂ sensor electrode exhibits a high sensitivity of 1425 µA/mM cm² towards glucose concentration in the range of 0.1 to 12 mM. The sensor was found to be extremely stable and repeatable with a response time of 5 s along with a minimum detection limit of 0.32 μM of glucose. The rGO/TiO₂ sensor shows relative standard deviation (RSD) of 1.14%, 1.34%, and 1.3% which reveals its excellent stability, repeatability, and reproducibility respectively. The sensor was used for glucose level detection in natural blood serum and shows an RSD of 1.88%. which is in good agreement with the commercial glucose sensor values. Full article

Companies seek to promote a swift digitalization of their business processes and new disruptive features to gain an advantage over their competitors. This often results in a wider attack surface that may be exposed to exploitation from adversaries. As budgets are thin, one of the most popular security solutions CISOs choose to invest in is Network-based Intrusion Detection Systems (NIDS). As anomaly-based NIDS work over a baseline of normal and expected activity, one of the key areas of development is the training of deep learning classification models robust enough so that, given a different network context, the system is still capable of high rate accuracy for intrusion detection. In this study, we propose an anomaly-based NIDS using a deep learning stacked-LSTM model with a novel pre-processing technique that gives it context-free features and outperforms most related works, obtaining over 99% accuracy over the CICIDS2017 dataset. This system can also be applied to different environments without losing its accuracy due to its basis on context-free features. Moreover, using synthetic network attacks, it has been shown that this NIDS approach can detect specific categories of attacks. Full article