Require Complex Computation Research Articles

A SIAR transmitting waveform design utilizing positive and negative random carrier frequency coding technique for coupling reduction in circular array

The Synthetic Impulse and Aperture Radar (SIAR) is an advanced radar system which utilizes different orthogonal carrier frequencies. As a four-dimensional (4-D) radar, SIAR offers numerous advantages such as protection against anti-radiation missiles (ARM), detection of stealth targets, low probability of interception (LPI), and tracking of multiple targets. However, one of the key challenges facing these radars is the coupling between range and angle measurements, where errors in range measurements can impact angle measurements and vice versa. To tackle this issue, radar designers commonly develop transmitting waveforms based on carrier frequency coding to minimize the coupling. Various methods, including random frequency coding, sequential frequency coding, and positive and negative sequential frequency coding, have been proposed to reduce the coupling between range and angle measurements for two-dimensional (2-D) linear arrays. This article focuses on extending these ideas to circular arrays (3D radar) and introduces a method known as positive and negative random carrier frequency coding. We take advantage of random code and the concept of positive-negative frequency coding. Additionally, mathematical equations and simulations demonstrate that the coupling between azimuth angle and elevation angle is independent of the frequency coding type and remains consistent across all conditions. Through our research, we demonstrate that the proposed method effectively reduces coupling by approximately 40% more than other frequency coding methods. In the proposed method, we combine output of two consecutive periods by an approach similar to what happens in pulse integration in radar, so we don't require complex computations or new hardware. This method has another advantage such as pulse-to-pulse frequency agility, enhancing protection against ARM through virtual displacement of transmitters. Circular arrangements ensure simultaneous coverage of the entire space.

A discriminatory groups-based supervised band selection technique for hyperspectral image classification

ABSTRACT Dimensionality reduction is a prevalent preprocessing step when analysing hyperspectral images (HSIs). It uses band selection (BS) and feature extraction (FE) techniques, but the former can retain the physical meaning and interpretability of the HSI datasets. Hence, these techniques are preferred to select a proper subset of bands without transformation. This letter introduces a straightforward supervised BS technique using discriminatory groups (DGs) that does not require complex computations. In this technique, total DGs are achieved equal to the spectral dimension of the HSI dataset, and each holds bands similar to the number of classes. The constituent bands of the DGs may present more than once, but the total number of bands in the group remains unchanged. The obtained DGs are ranked using high- to low-discrimination fractions, which indicate the uniqueness of the respective DG. The highest value that a DG can attain represents the group’s ability to distinguish among all classes (the maximum number of classes). Hence, the proposed technique has been termed ‘maximum class discriminatory group (MCDG)’. The discrimination fraction works as the control parameter for the selection of bands. Subsequently, the unique bands are selected from the ranked DGs and used for HSI classification. The proposed MCDG technique has an articulate and quick implementation without requiring any initialization of bands and outperforms various state-of-the-art methods while experimenting using real HSI datasets.

Mobile Sensoring Data Verification via a Pairing-Free Certificateless Signature Secure Approach against Novel Public Key Replacement Attacks

To achieve flexible sensing coverage with low deployment costs, mobile users need to contribute their equipment as sensors. Data integrity is one of the most fundamental security requirements and can be verified by digital signature techniques. In the mobile crowdsensing (MCS) environment, most sensors, such as smartphones, are resource-limited. Therefore, many traditional cryptographic algorithms that require complex computations cannot be efficiently implemented on these sensors. In this paper, we study the security of certificateless signatures, in particular, some constructions without pairing. We notice that there is no secure pairing-free certificateless signature scheme against the super adversary. We also find a potential attack that has not been fully addressed in previous studies. To handle these two issues, we propose a concrete secure construction that can withstand this attack. Our scheme does not rely on pairing operations and can be applied in scenarios where the devices’ resources are limited.

GENERATION OF PSEUDORANDOM SEQUENCES ON MICROCONTROLLERS WITH LIMITED COMPUTATIONAL RESOURCES, ENTROPY SOURCES, AND STATISTICAL PROPERTIES TESTING

Traditional encryption algorithms cannot be implemented on Internet of Things (IoT) devices due to their constrained computational resources. This necessitates the search and development of cryptographic solutions for securing data processed and transmitted by such devices. When encrypting data on devices with limited computational resources, simple encryption algorithms based on elementary bitwise operations, such as bitwise modulo-2 addition (XOR), can be utilized since these operations execute in a single processor cycle and do not require complex computations. However, a drawback of such operations is their invertibility—knowing the encryption key enables easy decryption by applying the same operation to the ciphertext. Ensuring the reliability of such ciphers requires continuous generation of random encryption keys.This work explores the functionality of the linear congruential method for generating sequences of random numbers. Several entropy sources available on microcontrollers are presented for the initial generator value, along with proposed algorithms for collecting initial data from these sources. The use of noise from unconnected pins of the analog-to-digital converter is suggested as the primary entropy source, while the uninitialized area of the microcontroller’s random-access memory serves as an additional source. A method for generating random sequences using the specified entropy sources is implemented and the algorithm’s performance is evaluated, specifically the key characteristic—randomness of the encryption key. The NIST STS 800-22 test suite is employed for evaluation. In all tests, the random sequence generation algorithm demonstrated results confirming the hypothesis that the sequence can be considered random.

A simple chaotic base encryption scheme for securing OFDM-PON communications

Abstract Optical communications and their security have been got a great attention. In this work, a simple, low cost and secure optical system integrating orthogonal frequency division multiplexing-based passive optical network (OFDM-PON) and chaotic theory is proposed. In the proposed system, a novel scheme of quadrature amplitude modulation (QAM) encryption method is adopted. For privacy of image and video encryption, the process of encryption consists of two processes and a 3D chaotic system is employed. The chaotic system is used to generate the encryption sequences. In the first stage, the first generated chaotic sequence is XORed with the incoming data. While in the second stage, the QAM symbol is remapped using the other two generated chaotic sequences. Generally, the proposed system is designed to provide secure data, videos, and images transmission. The proposed system is simulated and evaluated with a transmission of a 10 Gb/s along 50 km single-mode fiber (SMF). The performance of the proposed system showed that there is a performance enhancement in terms of Peak to average power ratio (PAPR) and bit error rate (BER). The BER of unauthorized user is around 0.6147, while the PAPR is reduced to approximately half. The proposed system is simple and doesn’t require complex computations. It requires simple analog multiplexers for its implementation.

Energy-Efficient Online Path Planning of Multiple Drones Using Reinforcement Learning

Drones, typically unmanned aerial vehicles (UAVs), have many purposes. However, simultaneous operation of multiple drones is challenging, considering the real-time interactions and the environment; the drone must avoid collision with the other drones or obstacles. The proposed Advanced TD3 model performs energy-efficient path planning at the edge-level drone. We modify the twin-delayed deep deterministic policy gradient (TD3), which is the state-of-the-art policy gradient reinforcement learning. The frame stacking technique considers the continuous action space of the drone to the TD3 model. During the training, we gradually increase the observation range of agents for fast and stable convergence. We train the modified TD3 model through Offline RL to reduce the overhead for the RL model training. Drones mount the converged RL model on their onboard computer. The Advanced TD3 model in the drones selects an energy-efficient path without the overhead of the training process of the RL model in real-time, considering external factors such as wind or another drone. The total energy consumption of drones in flight along with online path planning is approximately 106% of the total energy consumption of drones that follow offline path planning, even though the trained TD3 model does not require complex computations for real-time execution.

Spark‐based parallel processing whale optimization algorithm

SummarySwarm intelligence meta‐heuristic optimization algorithms for optimizing engineering applications have become increasingly popular. The whale optimization algorithm (WOA) is a recent and effective swarm intelligence optimization algorithm that mimics humpback whales' behaviors when optimizing a problem. Applying the algorithm to achieve optimal solutions has shown good results compared to most meta‐heuristic optimization algorithms. However, complex applications might require the processing of large‐scale computations, which results in down‐scaling computational throughput of WOA. Apache Spark, a well‐known parallel data processing framework, is the most recent distributed computing framework and has been proven to be the most efficient. In this article, we propose a WOA implementation on top of Apache Spark, represented as SBWOA, to enhance its computational performance while providing higher scalability of the algorithm for handling more complex problems. Compared with the recently reported MapReduce WOA (MR‐WOA), and serial implementation of WOA, our approach achieves significant enhancements with respect to computational performance for the highest population size with the maximum number of iterations. SBWOA successfully handles higher‐complexity problems which require complex computations.

A Generalized Iterative Map for Analysis of Protein Sequences.

The similarities comparison of biological sequences is an important task in bioinformatics. The methods of the similarities comparison for biological sequences are divided into two classes: sequence alignment method and alignment-free method. The graphical representation of biological sequences is a kind of alignment-free method, which constitutes a tool for analyzing and visualizing the biological sequences. In this article, a generalized iterative map of protein sequences was suggested to analyze the similarities of biological sequences. Based on the normalized physicochemical indexes of 20 amino acids, each amino acid can be mapped into a point in 5D space. A generalized iterative function system was introduced to outline a generalized iterative map of protein sequences, which can not only reflect various physicochemical properties of amino acids but also incorporate with different compression ratios of the component of a generalized iterative map. Several properties were proved to illustrate the advantage of the generalized iterative map. The mathematical description of the generalized iterative map was suggested to compare the similarities and dissimilarities of protein sequences. Based on this method, similarities/dissimilarities were compared among ND5 protein sequences, as well as ND6 protein sequences of ten different species. By correlation analysis, the ClustalW results were compared with our similarity/dissimilarity results and other graphical representation results to show the utility of our approach. The comparison results show that our approach has better correlations with ClustalW for all species than other approaches and illustrate the effectiveness of our approach. Two examples show that our method not only has good performances and effects in the similarity/dissimilarity analysis of protein sequences but also does not require complex computation.

MIoT: Metamorphic IoT Platform for On-Demand Hardware Replacement in Large-Scaled IoT Applications.

As the Internet of Things (IoT) is becoming more pervasive in our daily lives, the number of devices that connect to IoT edges and data generated at the edges are rapidly increasing. On account of the bottlenecks in servers, due to the increase in data, as well as security and privacy issues, the IoT paradigm has shifted from cloud computing to edge computing. Pursuant to this trend, embedded devices require complex computation capabilities. However, due to various constraints, edge devices cannot equip enough hardware to process data, so the flexibility of operation is reduced, because of the limitations of fixed hardware functions, relative to cloud computing. Recently, as application fields and collected data types diversify, and, in particular, applications requiring complex computation such as artificial intelligence (AI) and signal processing are applied to edges, flexible processing and computation capabilities based on hardware acceleration are required. In this paper, to meet these needs, we propose a new IoT platform, called a metamorphic IoT (mIoT) platform, which can various hardware acceleration with limited hardware platform resources, through on-demand transmission and reconfiguration of required hardware at edges instead of via transference of sensing data to a server. The proposed platform reconfigures the edge’s hardware with minimal overhead, based on a probabilistic value, known as callability. The mIoT consists of reconfigurable edge devices based on RISC-V architecture and a server that manages the reconfiguration of edge devices based on callability. Through various experimental results, we confirmed that the callability-based mIoT platform can provide the hardware required by the edge device in real time. In addition, by performing various functions with small hardware, power consumption, which is a major constraint of IoT, can be reduced.

Multicore development environment for embedded processor in arduino IDE

Internet of things (IoT) technology has found more applications that require complex computation while still preserving power. Embedded processors as the core of the IoT system approaches the need for computation by employing a parallel processor system, namely MPSoC. While various MPSoCs hardware is widely available, there is limited software support form of user-friendly libraries and development platform. There is a need for such a platform to facilitate both the study and development of parallel embedded software. arduino as the widely used embedded development platform is yet to officially support multicore programming. This work proposes an arduino-based development environment that supports multicore programming while maintaining arduino’s simple program structure, targeted at specific low-power MPSoC, the RUMPS401. The environment is fully functional, and while it targets only specific MPSoC, the proposed environment can easily be adopted to other MPSoCs with similar structures with minimal modification.

Torque ripple suppression of permanent magnet synchronous machines by minimal harmonic current injection

Reducing torque ripples in a permanent magnet (PM) synchronous machine (PMSM) system has been an essential requirement in some high-performance applications. To suppress the torque ripples caused by time harmonics and spatial harmonics, an approach based on injecting minimum harmonic currents is proposed here. The proposed method extracts torque ripples from speed fluctuations, and the gradient descent optimisation (GDO) is utilised to determine the injected harmonic currents which are the commands of the harmonic current regulators. Using the torque ripple minimisation and the injected harmonic current minimisation as the two objectives, the GDO not only minimises the torque ripples of the PMSM system but also guarantees that the magnitude of the injected harmonic currents is minimal. The proposed method is simple to implement in the hardware and does not require complex computation. Its effectiveness is verified by experimental results.

Analytical model of the linear inflow to a horizontal well with hydraulic fractures in low-permeability reservoirs

At present, increasing attention has been concentrated on low-permeability deposits whose development is tightly related with technologies of horizontal drilling and hydraulic fracturing of the reservoir. However, the simultaneous presence of a horizontal section of the well and hydraulic fractures in the reservoir impedes the process of flow of reservoir liquids and gases through the porous medium and complicates simulation of such flows. In the present study a new analytical model of the linear inflow to a horizontal well with hydraulic fractures in low-permeability reservoirs is proposed. This is a generalizing model which takes into account both situations when there exists a linear flow in the direction perpendicular to the borehole of a horizontal well in the distant sections of the reservoir and when there is a linear flow in the direction parallel to the borehole of a horizontal well. The use of the model is not restricted by the shape of the flow domain. The new model proposed has a wide application field and does not require complex computations.

FLOWER, an innovative Fuzzy Lower-than-Best-Effort transport protocol

We present a new delay-based transport protocol named FLOWER, that aims at providing a Lower-than-Best-Effort (LBE) service. The objective is to propose an alternative to the Low Extra Delay Background Transport (LEDBAT) widely deployed within the official BitTorrent client. Indeed, besides its intra-fairness problem, known as latecomer unfairness, LEDBAT can be too aggressive against TCP, making it ill suited for providing LBE services over certain networks such as constrained wireless networks. By using a fuzzy controller to modulate the sending rate, FLOWER aims to solve LEDBAT issues while fulfilling the role of a LBE protocol. FLOWER operates to a modification of the standard LEDBAT protocol implementation by replacing its proportional controller by a fuzzy controller. Thanks to this modification, our simulation results show that FLOWER can carry LBE traffic in network scenarios where LEDBAT cannot while solving the latecomer unfairness problem. The presented algorithm is simple to implement and does not require complex computation that would prevent its deployment. Finally, we show that FLOWER remains compliant when used over an AQM-based network and remains LBE while not increasing the bufferbloat.

OpenCL-based optimization methods for utilizing forward DCT and quantization of image compression on a heterogeneous platform

Recent computer systems and handheld devices are equipped with high computing capability, such as general purpose GPUs (GPGPU) and multi-core CPUs. Utilizing such resources for computation has become a general trend, making their availability an important issue for the real-time aspect. Discrete cosine transform (DCT) and quantization are two major operations in image compression standards that require complex computations. In this paper, we develop an efficient parallel implementation of the forward DCT and quantization algorithms for JPEG image compression using Open Computing Language (OpenCL). This OpenCL-based parallel implementation utilizes a multi-core CPU and a GPGPU to perform DCT and quantization computations. We demonstrate the capability of this design via two proposed working scenarios. The proposed approach also applies certain optimization techniques to improve the kernel execution time and data movements. We developed an optimal OpenCL kernel for a particular device using device-based optimization factors, such as thread granularity, work-items mapping, workload allocation, and vector-based memory access. We evaluated the performance in a heterogeneous environment, finding that the proposed parallel implementation was able to speed up the execution time of the DCT and quantization by factors of 7.97 and 8.65, respectively, obtained from 1024 × 1024 and 2084 × 2048 image sizes in 4:4:4 format.

A comparative study of neuro fuzzy and recurrent neuro fuzzy model-based controllers for real-time industrial processes

Nonlinearities in system dynamics and the multivariable nature of processes offer a stiff challenge in designing predictive controllers that improve process performance in industries. This investigation presents a recurrent neuro fuzzy network (RNFN) model for a nonlinear multivariable system in process industries and a methodology to design model-predictive controllers (MPCs) using the proposed model. The RNFN model combines the learning features of artificial neural networks with human cognition capabilities of fuzzy systems. Therefore, RNFN leads to a modelling framework that has the ability not only to learn the model parameters, but also makes decision on operating region of the nonlinear model depending on the input–output data. Furthermore, the recurrent structure and the introduction of a memory unit between the fuzzy inference and fuzzification layer enhance the prediction capability due to the use of past input–output data, making the model more suitable for designing predictive controllers. Next, the MPC design methodology that exploits the advantages of the RNFN model to optimize the control moves is presented. The proposed MPC uses the gradient descent algorithm to minimize the control moves as against the traditional state-space approaches that require complex computations and solvers. Therefore, implementing the proposed MPC in embedded hardware becomes easier. The proposed modelling framework and the MPC design methodology are illustrated using experiments on a laboratory-scale quadruple tank. Our experiments show that the proposed RNFN-based MPC performs better than the neuro fuzzy network-based MPC for both servo and regulatory responses.

Dynamic change Forecast of the Saline-Alkali Farmland Based on the Non-Equal Time Interval Grey Model

Salinization has become one of the most important questions about farmland in China. How to monitor and forecast the salinization trend of the farmland and to carry out valid prevention and cure measures is important for farmland protection in China. The grey model based on the grey system theory is different from the traditional regression forecast analysis. It doesn't need a large number of original data and doesn't require complex computation. It can avoid the fatal weakness of insufficient data and avoid the subjective factors on the forecast precision. By the mechanism of the grey model, the internal rules of the change of the saline-alkali farmland can be scientifically forecasted. It is usually executed with the mode of GM (1, 1). However, the GM (1,1) requires the equal time interval original data sequence. The saline-alkali degree always can't be monitored with the equal time interval. So, a new model by integrating the GM (1,1) with the least square method is proposed. With the new model, the or...

Synthetic analog computation in living cells

A central goal of synthetic biology is to achieve multi-signal integration and processing in living cells for diagnostic, therapeutic and biotechnology applications. Digital logic has been used to build small-scale circuits, but other frameworks may be needed for efficient computation in the resource-limited environments of cells. Here we demonstrate that synthetic analog gene circuits can be engineered to execute sophisticated computational functions in living cells using just three transcription factors. Such synthetic analog gene circuits exploit feedback to implement logarithmically linear sensing, addition, ratiometric and power-law computations. The circuits exhibit Weber's law behaviour as in natural biological systems, operate over a wide dynamic range of up to four orders of magnitude and can be designed to have tunable transfer functions. Our circuits can be composed to implement higher-order functions that are well described by both intricate biochemical models and simple mathematical functions. By exploiting analog building-block functions that are already naturally present in cells, this approach efficiently implements arithmetic operations and complex functions in the logarithmic domain. Such circuits may lead to new applications for synthetic biology and biotechnology that require complex computations with limited parts, need wide-dynamic-range biosensing or would benefit from the fine control of gene expression.

SENSITIVITY-INDEPENDENT DIFFERENTIAL PRIVACY VIA PRIOR KNOWLEDGE REFINEMENT

We propose a new mechanism to implement differential privacy. Unlike the usual mechanism based on adding a noise whose magnitude is proportional to the sensitivity of the query function, our proposal is based on the refinement of the user's prior knowledge about the response. Our mechanism is shown to have several advantages over noise addition: it does not require complex computations, and thus it can be easily automated; it lets the user exploit her prior knowledge about the response to achieve better data quality; and it is independent of the sensitivity of the query function (although this can be a disadvantage if the sensitivity is small). Furthermore, we give a general algorithm for knowledge refinement and we show some compounding properties of our mechanism for the case of multiple queries; also, we build an interactive mechanism on top of knowledge refinement and we show that it is safe against adaptive attacks. Finally, we give a quality assessment for the responses to individual queries.

An Adaptive Neural-Fuzzy Approach for Object Detection in Dynamic Backgrounds for Surveillance Systems

Object detection is a fundamental aspect in surveillance systems. Although several works aimed at detecting objects in video sequences have been reported, many are not tolerant to dynamic background or require complex computation in addition to manual parameter adjustments. This paper proposes an adaptive object detection method to work in dynamic backgrounds without human intervention. The proposed method is based on a neural-fuzzy model. The neural stage, based on a one-to-one self-organizing map (SOM) architecture, deals with the dynamic background for object detection as well as shadow elimination. The fuzzy inference Sugeno system mimics human behavior to automatically adjust the main parameters involved in the SOM detection model, making the system independent of the scenario. Results of the model over real video scenes show its robustness. These findings are comparable to the results obtained with human intervention to define the parameters of the model. A quantitative comparison with methods reported in the literature is also provided to show the performance of the system.

Assessing Coronary Risk Assessment: What's Next?

Assessing Coronary Risk Assessment: What's Next?