Low Resource Consumption Research Articles

Secure Bluetooth Communication in Smart Healthcare Systems: A Novel Community Dataset and Intrusion Detection System.

Smart health presents an ever-expanding attack surface due to the continuous adoption of a broad variety of Internet of Medical Things (IoMT) devices and applications. IoMT is a common approach to smart city solutions that deliver long-term benefits to critical infrastructures, such as smart healthcare. Many of the IoMT devices in smart cities use Bluetooth technology for short-range communication due to its flexibility, low resource consumption, and flexibility. As smart healthcare applications rely on distributed control optimization, artificial intelligence (AI) and deep learning (DL) offer effective approaches to mitigate cyber-attacks. This paper presents a decentralized, predictive, DL-based process to autonomously detect and block malicious traffic and provide an end-to-end defense against network attacks in IoMT devices. Furthermore, we provide the BlueTack dataset for Bluetooth-based attacks against IoMT networks. To the best of our knowledge, this is the first intrusion detection dataset for Bluetooth classic and Bluetooth low energy (BLE). Using the BlueTack dataset, we devised a multi-layer intrusion detection method that uses deep-learning techniques. We propose a decentralized architecture for deploying this intrusion detection system on the edge nodes of a smart healthcare system that may be deployed in a smart city. The presented multi-layer intrusion detection models achieve performances in the range of 97-99.5% based on the F1 scores.

Optimisation of the Construction Plan of Assembled Concrete Structures in Other Courtyard Buildings Based on Grey Clustering

The assembled concrete structure is a common construction solution in the current building industry, and it is debatable how to further improve the construction efficiency of the pointed claw assembled concrete structure. The study proposes a grey clustering method based on the improved OWA operator as a means to obtain more effective construction evaluation indicators. In performance tests, it was shown that the grey clustering method under the OWA operator optimisation is more capable of computing features for large amounts of data than traditional grey clustering, with an execution time of less than 200s. In the application tests of the construction scheme, the study showed that the proposed optimisation scheme has a more effective safety evaluation, shorter construction period and lower resource consumption. These results show that the use of grey clustering is effective in optimising the construction of assembled concrete structures, and that the optimised solutions show better reduction in construction time and resource utilisation than traditional construction solutions, so that the rational use of grey clustering in assembled buildings will help the construction industry to achieve greater efficiency in construction.

A Low-Latency RDP-CORDIC Algorithm for Real-Time Signal Processing of Edge Computing Devices in Smart Grid Cyber-Physical Systems.

Smart grids are being expanded in scale with the increasing complexity of the equipment. Edge computing is gradually replacing conventional cloud computing due to its low latency, low power consumption, and high reliability. The CORDIC algorithm has the characteristics of high-speed real-time processing and is very suitable for hardware accelerators in edge computing devices. The iterative calculation method of the CORDIC algorithm yet leads to problems such as complex structure and high consumption of hardware resource. In this paper, we propose an RDP-CORDIC algorithm which pre-computes all micro-rotation directions and transforms the conventional single-stage iterative structure into a three-stage and multi-stage combined iterative structure, thereby enabling it to solve the problems of the conventional CORDIC algorithm with many iterations and high consumption. An accuracy compensation algorithm for the direction prediction constant is also proposed to solve the problem of high ROM consumption in the high precision implementation of the RDP-CORDIC algorithm. The experimental results showed that the RDP-CORDIC algorithm had faster computation speed and lower resource consumption with higher guaranteed accuracy than other CORDIC algorithms. Therefore, the RDP-CORDIC algorithm proposed in this paper may effectively increase computation performance while reducing the power and resource consumption of edge computing devices in smart grid systems.

Configurable Image Rectification and Disparity Refinement for Stereo Vision

Stereo matching is an essential topic in computer vision. However, the high complexity and massive computation restrict its applicability in real-time. This brief presents a pixel-level pipeline coprocessor for configurable binocular stereo rectification, region-optimized semi-global matching (SGM), left-right consistency checking (LRC), and disparity refinement. The contributions are summarized as follows: 1) Instead of storing the entire image of a large number of lines, the buffer size can be significantly reduced by the adjustment range computed according to the pre-calibrated coefficients. 2) An approximate divider with Newton iterations can reach the precision of 10−7 for high accuracy rectification with low latency. 3) A four-layer parallel two-stage pipeline is leveraged to address the computational bottleneck of the SGM, i.e., cost aggregation. 4) The pseudo-dual-port SRAMs and stacks mechanism solve the problem of inverse pixel flow in hole filling with low resource consumption. Finally, the proposed architecture is implemented on an Intel Stratix-IV FPGA and can process 144 frames ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1024\mathbf {\times }768$ </tex-math></inline-formula> -pixel) per second at a maximum working frequency of 113.13 MHz.

Lightweight Channel Estimation Networks for OFDM Systems

Channel estimation using neural networks has proven to be a breakthrough technology in the communications field. However, to achieve good performance, the existing studies mostly focus on building a complex neural network with a large number of layers, which consumes a lot of storage space and computing resources. In this letter, a lightweight channel estimation Transformer (LCET) is proposed for an orthogonal frequency division multiplexing (OFDM) system with pilots, based on the application of a convolutional neural network (CNN) and Transformer. The network uses a lightweight feature extraction CNN (LFEC) to extract channel features and then feeds the processed features into a lightweight-adjusted Transformer (LAT) for channel estimation. Specifically, LFEC extracts deep channel features at a low computational cost and retains the high frequency information. The LAT solves the problem of long-range feature information loss with low resource consumption. The experimental results revealed that LCET performed better than the traditional least squares estimation algorithm and state-of-the-art learning network.

Decompression Load Analysis in Large Passenger Planes with Windshield Cracked

To prevent the possible accident of a large passenger plane due to rapid decompression, transient load analysis is of vital importance in the assessment of structure strength and also an important clause of airworthiness standard. A 0-D isentropic model and a 1-D model based on the characteristic line are developed to simulate the rapid decompression process of the cockpit-cabin model due to a cracked windshield. The accuracy of these models is presented by comparing them with experiments and 3-D CFD simulations. Then, the 1-D model is applied to study the influence of cabin and cockpit volume, windshield and decompression panel area, compartments, and environment pressure on the decompression load. The non-dimensional decompression time and the non-dimensional decompression load are developed to evaluate the decompression characteristics, and the correlation equations are established. The relative deviation between the results of the correlation equation fit and the results of the one-dimensional simulation is less than 3%. This work provides a new engineering method for structure strength design and decompression load analysis with high accuracy and low resource consumption.

Adaptable Process Design as a Key for Sustainability Upgrades in Wastewater Treatment: Comparative Study on the Removal of Micropollutants by Advanced Oxidation and Granular Activated Carbon Processing at a German Municipal Wastewater Treatment Plant

Micropollutants have been increasingly detected at low concentrations in surface waters and may have harmful effects on humans, organisms, and the environment. As wastewater treatment plants are one of the main sources of micropollutants, conventional wastewater treatment methods and plants (mainly one to three cleaning stages) must be improved through an advanced (fourth) treatment stage. The optimal fourth treatment stage should be determined based not only on removal efficiencies but also on a holistic sustainability assessment that further considers the process’s adaptability, economic, environmental, and social parameters. The ability of a tertiary wastewater treatment plant to remove organic pollutants was investigated over four months using two different advanced treatment methods: (1) an advanced oxidation process (AOP) (using UV + H2O2) and (2) granular activated carbon (GAC). The resulting average micropollutant removal efficiencies were 76.4 ± 6.2% for AOP and 90.0 ± 4.6% for GAC. As the GAC became saturated, it showed a decreasing performance from 97.6% in week one to 80.7% in week 13, after 2184 bed volumes were processed. For the AOP, adjusting the UV and H2O2 doses results in higher removal efficiencies. With 40 ppm H2O2 and 10 kJ/m2 UV, a removal of 97.1% was achieved. Furthermore, the flexibility and adaptability of the AOP process to adjust to real-time water quality, along with a lower resource consumption and waste disposal, make it a more promising technology when comparing the sustainability aspects of the two methods.

Plant-based production and characterization of a promising Fc-fusion protein against microgravity-induced bone density loss.

Microgravity-induced bone loss is a main obstacle for long term space missions as it is difficult to maintain bone mass when loading stimuli is reduced. With a typical bone mineral density loss of 1.5% per month of microgravity exposure, the chances for osteoporosis and fractures may endanger astronauts’ health. Parathyroid Hormone or PTH (1–34) is an FDA approved treatment for osteoporosis, and may reverse microgravity-induced bone loss. However, PTH proteins requires refrigeration, daily subcutaneous injection, and have a short shelf-life, limiting its use in a resource-limited environment, like space. In this study, PTH was produced in an Fc-fusion form via transient expression in plants, to improve the circulatory half-life which reduces dosing frequency and to simplify purification if needed. Plant-based expression is well-suited for space medicine application given its low resource consumption and short expression timeline. The PTH-Fc accumulation profile in plant was established with a peak expression on day 5 post infiltration of 373 ± 59 mg/kg leaf fresh weight. Once the PTH-Fc was purified, the amino acid sequence and the binding affinity to its target, PTH 1 receptor (PTH1R), was determined utilizing biolayer interferometry (BLI). The binding affinity between PTH-Fc and PTH1R was 2.30 × 10−6 M, similar to the affinity between PTH (1–34) and PTH1R (2.31 × 10−6 M). Its function was also confirmed in a cell-based receptor stimulation assay, where PTH-Fc was able to stimulate the PTH1R producing cyclic adenosine monophosphate (cAMP) with an EC50 of (8.54 ± 0.12) x 10−9 M, comparable to the EC50 from the PTH (1–34) of 1.49 × 10−8 M. These results suggest that plant recombinant PTH-Fc exhibits a similar binding affinity and potency in a PTH1R activation assay compared to PTH. Furthermore, it can be produced rapidly at high levels with minimal resources and reagents, making it ideal for production in low resource environments such as space.

LbSP: Load-Balanced Secure and Private Autonomous Electric Vehicle Charging Framework With Online Price Optimization

Nowadays, autonomous electric vehicles (AEVs) are increasingly popular due to low resource consumption, low pollutant emission, and high efficiency. In practice, Vehicle-to-Grid (V2G) networks supply energy power to EVs to ensure the usage of EVs. However, there are still certain security and privacy concerns in V2G connections, such as identity impersonation and message manipulation. Additionally, the widespread usage of EVs brings significant pressure on the power grid, leading to undesirable effects like voltage deviations if EVs’ charging is not well coordinated. In this article, to tackle these issues, we design a novel load-balanced secure and private EV charging framework named load-balanced secure and private framework (LbSP) for secure, private, and efficient EV charging with a minimal negative effect on the existing power grid. It assures reliable and efficient charging services by a lightweighted encryption technique. Also, it balances the energy consumption of power grids via an online pricing strategy that minimizes load variance by optimizing energy prices in real time. Moreover, it preserves users’ privacy while not affecting online pricing using an advanced differential privacy technique. Furthermore, LbSP deploys on an edge-cloud structure for fast response and more precise pricing, where clouds balance overall load consumption by online price optimization while edges gather data for clouds and respond to charging requests from EVs. The evaluation results show that the proposed framework ensures secure and private EV charging, balances energy load consumption, and preserves users’ privacy.

Improved Method of Blockchain Cross-Chain Consensus Algorithm Based on Weighted PBFT.

Aiming to solve the problems of low fault tolerance, low throughput, and high delay in traditional methods, an improved method of the blockchain cross-chain consensus algorithm based on weighted PBFT is proposed. This article constructs a blockchain cross-chain exchange model based on cluster centers and divides the nodes in the blockchain system into consensus service nodes, cross-chain exchange nodes, and application nodes to improve the performance of consensus computing services. On this basis, according to the weighted PBFT consensus mechanism, the blockchain consensus environment is set up, and the distribution of nodes in the consensus domain and the blockchain signature scheme are obtained. Therefore, the blockchain cross-chain consensus optimization algorithm is designed to reduce throughput and delay and optimize the consensus effect. The experimental results show that the proposed method can effectively improve the shortcomings of traditional methods, with high throughput and low latency, and strong security. It shows that it is a low resource consumption and secure consensus method.

Semantic segmentation method of underwater images based on encoder-decoder architecture.

With the exploration and development of marine resources, deep learning is more and more widely used in underwater image processing. However, the quality of the original underwater images is so low that traditional semantic segmentation methods obtain poor segmentation results, such as blurred target edges, insufficient segmentation accuracy, and poor regional boundary segmentation effects. To solve these problems, this paper proposes a semantic segmentation method for underwater images. Firstly, the image enhancement based on multi-spatial transformation is performed to improve the quality of the original images, which is not common in other advanced semantic segmentation methods. Then, the densely connected hybrid atrous convolution effectively expands the receptive field and slows down the speed of resolution reduction. Next, the cascaded atrous convolutional spatial pyramid pooling module integrates boundary features of different scales to enrich target details. Finally, the context information aggregation decoder fuses the features of the shallow network and the deep network to extract rich contextual information, which greatly reduces information loss. The proposed method was evaluated on RUIE, HabCam UID, and UIEBD. Compared with the state-of-the-art semantic segmentation algorithms, the proposed method has advantages in segmentation integrity, location accuracy, boundary clarity, and detail in subjective perception. On the objective data, the proposed method achieves the highest MIOU of 68.3 and OA of 79.4, and it has a low resource consumption. Besides, the ablation experiment also verifies the effectiveness of our method.

DITES: A Lightweight and Flexible Dual-Core Isolated Trusted Execution SoC Based on RISC-V.

A Trusted Execution Environment (TEE) is an efficient way to secure information. To obtain higher efficiency, the building of a dual-core system-on-chip (SoC) with TEE security capabilities is the hottest topic. However, TEE SoCs currently commonly use complex processor cores such as Rocket, resulting in high resource usage. More importantly, the cryptographic unit lacks flexibility and ignores secure communication in dual cores. To address the above problems, we propose DITES, a dual-core TEE SoC based on a Reduced Instruction Set Computer-V (RISC-V). At first, we designed a fully isolated multi-level bus architecture based on a lightweight RISC-V processor with an integrated crypto core supporting Secure Hashing Algorithm-1 (SHA1), Advanced Encryption Standard (AES), and Rivest–Shamir–Adleman (RSA), among which RSA can be configured to five key lengths. Then, we designed a secure boot based on Chain-of-Trust (CoT). Furthermore, we propose a hierarchical access policy to improve the security of inter-core communication. Finally, DITES is deployed on a Kintex 7 Field-Programmable-Gate-Array (FPGA) with a power consumption of 0.297 W, synthesized using TSMC 90 nm. From the results, the acceleration ratios of SHA1 and RSA1024 decryption/encryption can reach 75 and 1331/1493, respectively. Compared to exiting TEE SoCs, DITES has lower resource consumption, higher flexibility, and better security.

Error Detection and Correction of Mismatch Errors in M-Channel TIADCs Based on Genetic Algorithm Optimization

In order to achieve higher system performance, a digital calibration technique for the sub-channel mismatches of time-interleaved ADCs (TIADCs) is proposed in this paper. The sine-fit-based estimation algorithm is introduced to estimate the channel mismatches and a calibration algorithm is proposed to compensate for the mismatches. Subsequently, the genetic algorithm (GA) is firstly utilized to detect the mismatch errors of the outputs of sub-channels after frequency domain filtering. The detected offset error and gain error are then corrected by performing the calibration algorithm, and the time errors are corrected by fractional delay filters based on Farrow structure. The spurious-free dynamic range (SFDR) is enhanced from 19.69 dB to 108.12 dB, and the signal to noise and distortion ratio (SNDR) is enhanced from 16.02 dB to 97.63 dB. The proposed technique is further validated on the FPGA. Compared with existing calibration techniques, the proposed technique has the advantages of simple algorithm structure, low hardware resource consumption, and high calibration accuracy and can be applied to the calibration of high-resolution (18-bit) TIADCs for low-frequency inputs.

Lightweight Architecture for Elliptic Curve Scalar Multiplication over Prime Field

In this paper, we present a novel lightweight elliptic curve scalar multiplication architecture for random Weierstrass curves over prime field Fp. The elliptic curve scalar multiplication is executed in Jacobian coordinates based on the Montgomery ladder algorithm with (X,Y)-only common Z coordinate arithmetic. At the finite field operation level, the adder-based modular multiplier and modular divider are optimized by the pre-calculation method to reduce the critical path while maintaining low resource consumption. At the group operation level, the point addition and point doubling methods in (X,Y)-only common Z coordinate arithmetic are modified to improve computation parallelism. A compact scheduling method is presented to improve the architecture’s performance, which includes appropriate scheduling of finite field operations and specific register connections. Compared with existing works, our design is implemented on the FPGA platform without using DSPs or BRAMs for higher portability. It utilizes 6.4~6.5k slices in Kintex-7, Virtex-7, and ZYNQ FPGA and executes an elliptic curve scalar multiplication for a field size of 256-bit in 1.73 ms, 1.70 ms, and 1.80 ms, respectively. Additionally, our design is resistant to timing attacks, simple power analysis attacks, and safe-error attacks. This architecture outperforms most state-of-the-art lightweight designs in terms of area-time products.

Thermal Optimization of Additively Manufactured Lightweight Concrete Wall Elements with Internal Cellular Structure through Simulations and Measurements

Combining the additive manufacturing (AM) process of extrusion with lightweight concrete, mono-material but multi-functional elements with an internal cellular structure can be created to achieve good thermal performance of a wall at low resource consumption. The aim of this paper is to analyze and optimize the actual thermal performance of such a component. A sensitivity analysis and a parametric optimization were conducted based on a mathematical description of heat transfer in cellular structures. To investigate the thermal performance, 2D and 3D heat transfer simulations were used and validated by heat flux measurements on an existing prototype. A geometric optimization led to a further reduction of the U-value by up to 24%, reaching 0.58 W/m2 K. The ratio of solid material to air inside the cells (relative density) was identified as the main driver, in addition to cell diameter, cell height, and cell wall thickness. The comparison of analytical and numerical results showed high correspondence with deviations of 3–10%, and for the experimental results 25%. These remaining deviations can be traced back to simplifications of the theoretical models and discrepancies between as designed and as built. The presented approach provides a good basis for optimizing the thermal design of complex AM components by investigating practical thermal problems with the help of 2D and 3D simulations, and thus offers a great potential for further applications.

A medical assistant segmentation method for MRI images of osteosarcoma based on DecoupleSegNet

Nowadays, the most common primary bone tumor is osteosarcoma, which mostly occurs in teenagers. A common diagnosis method is currently that doctors manually diagnose osteosarcoma in magnetic resonance imaging (MRI) images because it is nonradioactive and has no biological damage to brain tissue and more obvious performance in soft tissue components such as tumors, blood vessels, and muscles in MRI images. However, this method is labor-intensive and time-consuming work, and cannot guarantee the accuracy of the diagnostic results. Existing osteosarcoma MRI image segmentation methods either aim to model the global context to improve the inner consistency of objects, or multiscale feature fusion to refine the detail of objects along their boundaries, which all ignore the interaction between the body of the object and the object boundary. Therefore, this paper proposes a novel segmentation method for osteosarcoma MRI images based on DecoupleSegNet, which explores the relationship between body feature and edge feature. It can assist doctors in diagnosing osteosarcoma and improve their work efficiency. First, we warp the feature of MRI images through learning a flow field so we can make the object more consistent. We then make further work to optimize the resulting body feature and residual edge feature through explicitly sampling pixels from different parts under decoupled supervision. Through these steps, we finally obtain the final feature map with fine boundaries from the MRI image of osteosarcoma. We take a test by using more than 80,000 osteosarcoma MRI images obtained from three hospitals in China. We find that compared with existing osteosarcoma MRI image segmentation methods, our proposed method achieves 90.51 Intersection of Union % with few parameters on the test, outperforming other models. In the test, we prove that our proposed method has better accuracy and lower resource consumption.

Analog/Digital Multiplierless Implementations for Nullcline-Characteristics-Based Piecewise Linear Hindmarsh-Rose Neuron Model

Multipliers are essential in implementing nonlinear neuron models, but they take huge implementation costs. Many multiplierless fitting schemes have been proposed to simplify the implementation of nonlinearities in neuron models. To optimize these schemes, this paper presents a nullcline-characteristics- based piecewise linear (NC-PWL) fitting scheme for multiplierless implementations of Hindmarsh-Rose (HR) neuron model. This NC-PWL fitting scheme uses as few line segments as possible to approximate the critical nonlinearity characteristics of the local nullclines. A NC-PWL HR neuron model that reproduces diverse firing patterns of the original one is successfully established. Using off-the-shelf low-cost components, an analog multiplierless circuit is designed for this fitting model and welded on print circuit board (PCB). Meanwhile, by logical shift method, a digital multiplierless circuit with low resource consumption is developed for this fitting model on field-programmable gate array (FPGA) platform. Experimental results of the analog and digital multiplierless hardware implementations verify the numerical simulations and show the simplicity and feasibility of the presented fitting scheme.

Automation of organizations using cloud technologies: security issues

The article reveals the principles of automation of organizations using cloud technologies through the prism without the peak aspect. The main stages of development of cloud technologies are characterized, the advantages of using these systems in everyday work are revealed (low consumption of financial resources, possibility of universal access to information, constant improvement of software and technologies). At the same time, attention is drawn to the main challenges facing users of cloud services: the need for a constant and uninterrupted Internet, the inability to recover information in case of loss. Security remains a major issue, therefore. The purpose of the article is to analyze the risks and possible ways to overcome them in the work of organizations with cloud technologies. It is established that the system that affects the reliable storage of information from customers can be exposed to such threats as cross-site-scripting, fishing, Trojans, viruses. This is due to the fact that, as a rule, consumers working with the cloud computing service, use an Internet browser. In order to optimize information security at this level, you need to use licensed anti-virus software packages, personal firewall tools, tools to encrypt data or information on disk, a properly configured Internet browser. It is proved that the security of virtualization mechanisms also affects the reliability of the virtual environment. For example, if the attack is organized on the monitor of virtual machines, the attacker may unnoticed by the security system, which are in the virtual machines can edit, copy or block the flow of information. Thus, if a hacker gains access to control capabilities, he will not only be able to edit data, but also steal it throughout the virtual network. Therefore, to avoid this situation, you need to protect the administration or servers of virtual machines. In addition, you need to create specialized information security tools that can monitor traffic inside the required server. At the same time, compliance with basic fire safety rules is also important for the safe operation of servers.

New Trends in Aviation and Medical Technology Enabled by Additive Manufacturing

In this publication, the potentials of additive manufacturing in the field of sustainability and individualization for aviation and medical technology are presented. Design approaches for each application field as well as examples in the fields are shown. In the field of aviation, structures can be manufactured so that they are load path optimized. This has a great lightweight potential and results in a low resource consumption. The examples contain the design of an aircraft cabin partition using the Direct Energy Deposition process and the optimization of load introduction points directly integrated into the sandwich core. Furthermore, in medical technology, additive manufacturing can be used to produce patient-specific models based on original medical imaging data, which can be used for training of medical treatments, quality assurance or for the validation of new developed medical devices. As examples a stroke simulation model containing a modular aortic model as well as functional stenose models are shown. Furthermore, the use of AM molds to generate a deformable bladder shell and a prostate phantom are described.

Multi-Scale Tumor Localization Based on Priori Guidance-Based Segmentation Method for Osteosarcoma MRI Images

Osteosarcoma is a malignant osteosarcoma that is extremely harmful to human health. Magnetic resonance imaging (MRI) technology is one of the commonly used methods for the imaging examination of osteosarcoma. Due to the large amount of osteosarcoma MRI image data and the complexity of detection, manual identification of osteosarcoma in MRI images is a time-consuming and labor-intensive task for doctors, and it is highly subjective, which can easily lead to missed and misdiagnosed problems. AI medical image-assisted diagnosis alleviates this problem. However, the brightness of MRI images and the multi-scale of osteosarcoma make existing studies still face great challenges in the identification of tumor boundaries. Based on this, this study proposed a prior guidance-based assisted segmentation method for MRI images of osteosarcoma, which is based on the few-shot technique for tumor segmentation and fine fitting. It not only solves the problem of multi-scale tumor localization, but also greatly improves the recognition accuracy of tumor boundaries. First, we preprocessed the MRI images using prior generation and normalization algorithms to reduce model performance degradation caused by irrelevant regions and high-level features. Then, we used a prior-guided feature abdominal muscle network to perform small-sample segmentation of tumors of different sizes based on features in the processed MRI images. Finally, using more than 80,000 MRI images from the Second Xiangya Hospital for experiments, the DOU value of the method proposed in this paper reached 0.945, which is at least 4.3% higher than other models in the experiment. We showed that our method specifically has higher prediction accuracy and lower resource consumption.