Power Bandwidth Research Articles

Mechanical Expertise Management for the Information Management inScaling Systems with Deep Learning Process

Dynamic scaling information management refers to the adaptive process of adjusting resources and managing data in real time to meet varying demands in computational and storage environments, particularly in cloud computing and data-intensive applications. This approach ensures optimal performance and resource utilization by automatically allocating or deallocating computing power, storage capacity, and network bandwidth based on current workloads and system performance metrics. Key components include monitoring tools that continuously assess resource usage, predictive analytics to anticipate future demands, and automated orchestration systems that execute scaling actions without human intervention. This paper introduces Predicted Dynamic Scaling in Information System Development (PDS-ISD) as a novel framework for talent cultivation within ISD education. By synthesizing empirical observations and theoretical models, PDS-ISD offers a predictive tool to forecast talent cultivation outcomes based on key educational factors. Through a comprehensive review of literature, the paper explores the theoretical foundations and practical applications of PDS-ISD in ISD education. Additionally, simulated results demonstrate the effectiveness of PDS-ISD in predicting talent outcomes across various scenarios. The findings highlight the importance of factors such as curriculum design, instructor expertise, student readiness, and project demands in shaping talent cultivation outcomes. The paper concludes with implications for practice and future research directions to further enhance the predictive capabilities and applicability of PDS-ISD in ISD education. Additionally, simulated results demonstrate the effectiveness of PDS-ISD in predicting talent outcomes across various scenarios, with an average accuracy of 85%. The findings highlight the importance of factors such as curriculum design, instructor expertise, student readiness, and project demand in shaping talent cultivation outcomes

A 132–170 GHz high-gain driving amplifier utilizing asymmetric broadside coupled line in 40-nm CMOS

This paper presents a driving amplifier (DA) utilizing the proposed asymmetric broadside coupled line. In contrast to conventional coupled line, the balun devised with proposed asymmetric coupled line and enhanced wideband balance compensation technique is utilized, thereby achieving wideband impedance matching and mitigating insertion loss. Additionally, the utilization of the lossy over-neutralization technique substantially enhances the power gain of amplifiers operating in the upper millimeter-wave band (150 GHz–300 GHz). The DA is fabricated utilizing a 40-nm CMOS process without aluminum layers. The fabricated amplifier demonstrates a compact total area of 0.135 mm2 (0.031 λ2), with a core area of 0.027 mm2 (0.0061 λ2). It achieves a high gain of 19.4 dB, high power of 11.05 dBm, and high power-added efficiency (PAE) of 11.05% at 160 GHz. Moreover, both the 3-dB power gain bandwidth and the 3-dB saturated power bandwidth extend from 132 GHz to 170 GHz, covering a bandwidth of 38 GHz. This configuration underscores the robust performance of the DA in the upper millimeter-wave band.

Design of voice command recognition chip based on heterogeneous acceleration

Abstract Speech, as one of the earliest forms of communication used by humans, can effectively convey information. However, the current deep neural network models for speech recognition are generally large in scale and can only be deployed in the cloud, which imposes high deployment environment requirements and power consumption, thereby limiting their implementation on embedded devices. In the context of end-to-end speech recognition, a series of challenges are encountered, including power consumption constraints, computing power limitations, network dependencies, privacy protection, bandwidth restrictions, and communication delays. To address these issues, this paper proposes the design of an end-to-end voice command recognition chip based on deep neural networks specifically for recognizing voice commands in specific scenarios. This chip achieves low power consumption and minimal delay in recognition. Additionally, we introduce a weighted, overloadable chip architecture to enable seamless scene migration, ultimately aiming to resolve the aforementioned challenges.

PHOTONIC INTEGRATED CIRCUITS: GIANT LEAP IN COMPUTING TECHNOLOGY

There is a continued demand for increase in computing requirements from sensor, smartphones, to servers. However, the technology improvements in clock speed, power, and memory bandwidth from traditional electronic integrated circuits (EIC) has started to saturate. This has triggered further research in exploration of photon integrated circuits (PIC) that utilize photons (or particles of light) as opposed to electrons to form a functioning circuit. Research suggests that by integrating the benefits of EIC and PIC, one is able to harness the best of both worlds. The inherent advantages of photonics technology such as speed, energy-efficiency, and density, coupled with the advances in material technology, make them suitable to address the challenge of designing heterogeneous chips. This paper presents the fundamentals of the photonic technology, applicability to solving digital computing problems, ability to integrate into PICs, and its capability to complement electronic integrated circuits for powering the computing demands of emerging applications.

Enhancing security offloading performance in NOMA heterogeneous MEC networks using access point selection and meta-heuristic algorithm

The research delves into the intricate domain of security offloading within the context of non-orthogonal multiple access (NOMA) heterogeneous mobile edge computing (het-MEC) networks operating over Rayleigh fading channels. The investigation centers on a system model comprising a single antenna-equipped edge user, denoted as U, which strategically offloads computational tasks to two distinct heterogeneous wireless access points (APs): the far AP (AP1) and the near one (AP2), employing NOMA techniques. Notably, the research accounts for a passive eavesdropper (E) intending to intercept the U−AP2 transmission. A four-phase protocol is proposed to ensure the security offloading process, namely SAPS, which leverages wireless access point selection (APS) and physical layer security (PLS) techniques. The focus extends to derive a closed-form expression for a novel critical system performance metric: the secrecy successful computation probability (SSCP). Furthermore, an algorithm based on Ant Colony Optimization (ACO) within the continuous domain is introduced, which aims to enhance the SSCP by intelligently determining system parameters. The impact of critical factors such as transmit power, power allocation coefficient, bandwidth, CPU frequency, and task division ratio under the SAPS scheme is explored and compared to the conventional approach using pure NOMA. Remarkably, the algorithm in the proposed scheme demonstrates up to a 3% performance improvement. The validity and accuracy of the study findings are verified through Monte-Carlo simulations. The work contributes significantly to advancing secure offloading strategies in NOMA-based MEC networks, offering valuable insights for practical deployment and optimization.

Zebra: A cluster-aware blockchain consensus algorithm

The Consensus algorithm is the core of the permissioned blockchain, it directly affects the performance and scalability of the system. Performance is limited by the computing power and network bandwidth of a single leader node. Most existing blockchain systems adopt mesh or star topology. Blockchain performance decreases rapidly as the number of nodes increases. To solve this problem, we first design the n-k cluster tree and corresponding generation algorithm, which supports rapid reconfiguration of nodes. Then we propose the Zebra consensus algorithm, which is a cluster tree-based consensus algorithm. Compared to the PBFT, it has higher throughput and supports more nodes under the same hardware conditions. However, the tree network topology enhances scalability while also increasing latency among nodes. To reduce transaction latency, we designed the Pipeline-Zebra consensus algorithm that further improves the performance of blockchain systems in a tree network topology through parallel message propagation and block validation. The message complexity of the algorithm is O(n). Experimental results show that the performance of the algorithm proposed in this paper can reach 2.25 times that of the PBFT algorithm, and it supports four times the number of nodes under the same hardware.

Performance of Cloud Computing Resources Allocations SGLA Model Compared to ARIMA

Solutions for cloud computing are growing in popularity as a means for businesses to streamline operations, save expenses and increase productivity. The benefit of cloud services is that they let customers access on-demand apps and services from a shared pool of programmable computer resources and store data offsite. Cloud computing resource allocation requires sophisticated tools and methodologies for optimal utilization. These problems include load balancing, efficient resource management and compliance with legal and regulatory requirements. The majority of businesses are switching to cloud services and advising their customers to use internet services. However, effective resource allocation is critical for improving performance and lowering costs in this area. Due to unpredictable network traffic in cloud computing, resource allocation is challenging, which causes customers to complain about application timeouts, delayed system times and higher bandwidth use during peak hours. This entails allocating resources to various users and programs, such as memory, processing power, storage, and network bandwidth. In this regard, this study compares the performance ensemble method, which is stepwise Gaussian Linear Autoregressive (SGLA), and the individual method which is autoregressive integrated moving average (ARIMA). The Matlab tool is used for simulation and evaluation of the results. The results show SGLA prediction accuracy increased to an average of 98.9%, and ARIMA prediction showed an accuracy of 75.5%. In this regard, the ensemble method performed better than individual methods using the same datasets. The study recommends the ensemble method for the prediction and allocation of resources in cloud computing.

Structural modal parameter identification with the Power-Exponential window function

In view of the demand for accurate modal identification, and based on the characteristics of free vibration response, this paper introduces a new window function for Fourier Transform called the Power–Exponential window. The Power–Exponential window addresses the characteristics of free vibration response. It significantly enhances the accuracy of modal identification by improving the spectral properties of structural response. The proposed window function consists of exponential and power terms. This study focuses on the additional damping and frequency-domain differentiation introduced by the Power–Exponential window function. The exponential term weakens the boundary effect related to the time-domain truncation and suppresses the spectral leakage. Moreover, it can be interpreted in clear physical terms as providing additional damping to the signal. The power term in the window function corresponds to frequency domain differentiation, and it alleviates the spectral broadening that arises due to the additional damping. Furthermore, the analytical expression for the response spectrum confirms that the Power–Exponential window not only aligns the peak response frequency with the damped natural frequency but also establishes an explicit linear relationship between the actual structural damping ratio and the identification result from the half power bandwidth method. Both contribute to an improved accuracy and usability of certain frequency-domain modal identification methods. The influence of the Power–Exponential window parameters on modal parameter identification is analyzed, and the optimal selection principle and suggested parameter values are proposed. Finally, numerical simulations and an experimental frame model test are conducted to verify the accuracy and validity of modal parameter identification based on the Power–Exponential window.

Modelling and characterisation of a magnetically coupled piezoelectric beam for energy harvesting gear meshing motion

Gear transmission systems are crucial components for transmitting power and motion in a host of engineering applications. Recently, the potential to embed sensors into transmission components has attracted significant attention for accurate condition monitoring of system health. As a result, embedded sensors must operate in a safe and stable manner, whilst being able to provide a continuous power-supply and ensure operational autonomy. In this work, a magnetically coupled beam-type piezoelectric energy harvester is developed for energy harvesting of rotational centrifugal forces and individual gear meshing excitation events. A new coupled electromechanical dynamic model is developed to explain the working principle and response of the harvester when excited by a combination of gear meshing excitation events, a centrifugal force, and a magnetic force. Since gear meshing events are observed to lead to an increased hardening nonlinearity of the energy harvester, and a decrease in power output, a novel variable-section cantilever structure was developed. Our detailed theoretical analysis demonstrates that the novel variable stiffness structure improves both the power output and bandwidth, with excellent agreement with experimental measurements. This work provides new theoretical insights into the application of magnetically coupled piezoelectric energy harvesters for self-powered sensing systems for critical gear transmission systems.

A TabPFN-based intrusion detection system for the industrial internet of things

The industrial internet of things (IIoT) has undergone rapid growth in recent years, which has resulted in an increase in the number of threats targeting both IIoT devices and their connecting technologies. However, deploying tools to counter these threats involves tackling inherent limitations, such as limited processing power, memory, and network bandwidth. As a result, traditional solutions, such as the ones used for desktop computers or servers, cannot be applied directly in the IIoT, and the development of new technologies is essential to overcome this issue. One approach that has shown potential for this new paradigm is the implementation of intrusion detection system (IDS) that rely on machine learning (ML) techniques. These IDSs can be deployed in the industrial control system or even at the edge layer of the IIoT topology. However, one of their drawbacks is that, depending on the factory’s specifications, it can be quite challenging to locate sufficient traffic data to train these models. In order to address this problem, this study introduces a novel IDS based on the TabPFN model, which can operate on small datasets of IIoT traffic and protocols, as not in general much traffic is generated in this environment. To assess its efficacy, it is compared against other ML algorithms, such as random forest, XGBoost, and LightGBM, by evaluating each method with different training set sizes and varying numbers of classes to classify. Overall, TabPFN produced the most promising outcomes, with a 10–20% differentiation in each metric. The best performance was observed when working with 1000 training set samples, obtaining an F1 score of 81% for 6-class classification and 72% for 10-class classification.

Optimizing Communication System Performance through Hybrid Modulation: A Comprehensive Analysis and Guideline

There is a clear need for dependable high-speed communication systems in light of 5G's recent rapid development. The enormous data that recently developed immersive media generate need to be transmitted, and traditional modulation techniques are unable to provide a suitable level of communication. Consequently, to improve communication system performance, hybrid modulation is needed. This paper examines the communication process and conducts a performance analysis to establish a reasonable method for choosing a hybrid modulation scheme. The constellation diagram, power spectral density, bandwidth efficiency, eye diagram, and bit error rate are the main components that determine a communication system's performance. Based on the analysis, the author concludes that to achieve a better-performing communication system, there must be a compromise between these performance factors. Specific recommendations are provided regarding performance indicators listed in the article, such as the constellation diagram, power spectrum density, bandwidth efficiency, eye diagram, and bit error rate. This work aims to address these issues to provide new researchers with guidance when selecting a hybrid modulation approach and to suggest a line of inquiry in the increasingly complicated and time-consuming age of large data volumes.

Study of Fixed Point Message Scheduling Algorithm for In-Vehicle Ethernet

With the rapid development of advanced driver assistance systems (ADASs) and autonomous driving technology, in-vehicle networks are facing huge challenges in real-time operation and data loss. Traditional vehicle bus network systems such as LIN, CAN, and FlexRay are insufficient to meet the real-time requirements of intelligent connected vehicles. In-vehicle Ethernet meets the requirements of high reliability, low electromagnetic radiation, low power consumption, bandwidth allocation, low latency, and real-time synchronization of intelligent connected vehicles. In-vehicle Ethernet has become one of the trends in the next generation of in-vehicle network architecture. This research focuses on the delay problem existing in the real-time data transmission process of in-vehicle Ethernet, and innovatively proposes a fixed point message scheduling algorithm (FPMS) based on time-sensitive network (TSN) technology. By building an experimental platform based on the CANoe simulation tool, the high-efficiency message transmission performance of the fixed point message scheduling algorithm was verified. Experimental results show that the fixed point message scheduling algorithm proposed in this study improves message transmission efficiency by 66%, laying a solid foundation for improving the real-time and reliability performance of in-vehicle Ethernet.

Unidimensional and Bidimensional Beam Expander Effects on the Output Power and Spectral Bandwidth of a Dye Laser

Unidimensional and Bidimensional Beam Expander Effects on the Output Power and Spectral Bandwidth of a Dye Laser

A Small Power Margin and Bandwidth Expansion Allow Data Transmission during Rainfall despite Large Attenuation: Application to GeoSurf Satellite Constellations at mm–Waves

The traditional approach of considering the probability distribution of rain attenuation leads to provide very large power margin (overdesign) in data channels. We have extended a method which, with a small power margin, bandwidth expansion and variable symbol rate, avoids overdesign and can transfer the same data volume as if the link were in clear–sky conditions. It is characterized only by the link mean efficiency, suitably defined. It is useful only if: (a) data must be up– and downloaded when it is raining; (b) real–time communication is not required. We have applied it to the links of GeoSurf satellite constellations (in which, at any latitude of ground stations, propagation paths are at the local zenith) by simulating rain attenuation time series at 80 GHz (mm–wave)–the new frontier of satellite frequencies–with the Synthetic Storm Technique, from rain–rate time series recorded on–site, at sites located in different climatic regions. The power margin to be implemented at 80 GHz ranges from 2.0 dB to 7.4 dB–well within the current technology–regardless the instantaneous rain attenuation. The clear–sky bandwidth is expanded 1.75 to 2.80 times, a factor not large per se, but it may challenge current technology if the clear–sky bandwidth is already large.

Key Challenges of Cloud Computing Resource Allocation in Small and Medium Enterprises

Although cloud computing offers many benefits, such as flexibility, scalability, and profitability, some small and medium enterprises (SMEs) are still unable to fully utilize cloud resources, such as memory, computing power, storage, and network bandwidth. This reduces their productivity and increases their expenses. Therefore, the central objective of this paper was to examine the key challenges related to the allocation of cloud computing resources in small and medium enterprises. The method used for this study is based upon qualitative research using 12 interviews with 12 owners, managers, and experts in cloud computing in four countries: the United States of America, the United Kingdom, India, and Pakistan. Our results, based on our empirical data, show 11 key barriers to resource allocation in cloud computing that are classified based on the technology, organization, and environment (TOE) framework. Theoretically, this research contributes to the body of knowledge concerning cloud computing technology and offers valuable understanding of the cloud computing resource allocation approaches employed by small and medium enterprises (SMEs). In practice, this research is useful to aid SMEs in implementing successful and sustainable strategies for allocating cloud computing resources.

A new design approach of Rat-Race coupler based compact GaN HEMT power amplifier towards flat high efficiency over broadband

This paper presents a high efficiency Rat-Race Coupler based compact GaN HEMT power amplifier (PA) design over broadband for high power transmitter in wireless communication. The rat-race coupler integrated PA Compact design is proposed for the first time as per author best knowledge. The design methodology used a higher order two open stubs and a rat-race coupler (RRC) at input/output sections to control harmonics impedances. The RRC is used to enhance the i/o power, and efficiency over broadband, which provides a good insertion loss, and consumes the least power and non-crucial impedance bandwidth for the normalized frequency band of interest. As a proof of concept, a PA is fabricated using a monolithic microwave integrated circuit (MMIC) 0.15 µm gallium nitride high electron mobility transistor (GaN HEMT) process. The measured result shows that the designed PA achieves a flat power added efficiency (PAE) of 65 % − 74 %, output power (Pout) of 44.8 dBm − 46 dBm, and drain efficiency (DE) of 72 % − 85 %, over a record wide frequency of 1.8 GHz − 3.6 GHz, which is the highest one among all reported harmonic tuned PAs.

Range-Doppler-Time Tensor Processing for Deep-Space Satellite Characterization Using Narrowband Radar

There is growing demand for the high-fidelity characterization of satellites in Geosynchronous Earth Orbit (GEO) to support Space Domain Awareness (SDA). This is particularly true for newly launched satellites, where it is necessary for satellite providers to ascertain whether components have deployed properly. Conventional wideband radar systems are capable of imaging satellites provided that (i) they have sufficient power aperture and bandwidth, and (ii) they observe enough target aspect change to generate a resolved image. While wideband radars are used routinely for characterizing satellites in Low-Earth Orbit (LEO), powerful radars with sensitivity sufficient for large GEO ranges (36,000 km or greater) are lacking. Thus, researchers often rely on more widely available high-power narrowband tracking radars for GEO characterization. In this paper, we present a novel range-Doppler-time (RDT) tensor processing technique for GEO characterization with narrowband radar. This technique encapsulates the strengths of previously proposed methods for narrowband-radar characterization at GEO, providing a generalized approach that can be applied in a variety of settings. The technique generates fully resolved 2D images of rotating GEO satellites in low-bandwidth scenarios. In cases where aspect change is limited, the technique provides detailed Doppler information for enhanced satellite status monitoring. This work presents a comprehensive quantitative analysis of the technique that considers the impact of key parameters on characterization performance. Simulated radar data, and radar data collected in a compact range on a scaled satellite model, are used to evaluate the technique.

A Lightweight, Efficient, and Physically Secure Key Agreement Authentication Protocol for Vehicular Networks

In the contemporary era, Vehicular Ad Hoc Networks (VANETs) have emerged as a vital technology in intelligent transportation systems, substantially enhancing the overall travel experience by providing advanced services to vehicles while ensuring driver safety. Despite the notable improvements, the inherent complexity of VANETs presents persistent security challenges, encompassing issues such as privacy preservation for vehicles, message authentication, and constraints in computational power and network bandwidth. Various authentication protocols have been designed for VANETs. However, many of these protocols exhibit significant vulnerabilities, rendering them insecure and unreliable in the face of diverse security threats, such as denial of service, replay, forgery, and impersonation attacks. Moreover, some existing schemes encounter limitations, including high computational complexity and the introduction of additional communication overhead and computational costs. To tackle these concerns, we designed a lightweight and secure identity authentication protocol based on elliptic curve cryptography with the objective of furnishing an effective and secure data transmission mechanism across a public communication channel for the Internet of Vehicles. In addition, we introduce Physically Unclonable Functions (PUFs) to ensure physical layer security during the communication process. A detailed security analysis demonstrates that the proposed protocol is resilient against various attacks. Through a comparative analysis with existing relevant protocols, in scenarios with a high density of vehicles, the algorithm demonstrates significantly lower computational costs and communication overhead than the related protocols, indicating that the proposed protocol is lightweight and efficient. Consequently, the empirical findings indicate that our protocol surpasses others in terms of reliability, user convenience, and practicality for ensuring secure data transmission within VANETs.

Study of a vortex-induced vibration piezoelectric wind energy harvester based on the synergy of multi-degree-of-freedom technology and magnetic nonlinear technology

A novel multi-degree-of-freedom and magnetic piezoelectric wind energy harvester (MNPWEH) is proposed in this work to improve the working bandwidth and output power for the wind energy harvesting based on vortex-induced vibration. In the process of studying the presented collaborative strategy utilizing multi-degree-of-freedom and nonlinear force enhancement technologies, four response states of the harvester are observed and named as inter-well state, intra-well state, pulse state, and intermittent state. The key factors determining these four response states are firstly obtained through a series of numerical simulations and pre-experiments. Then, a thorough investigation into the influence of structural parameters on these four response states and the output performance of the system has been conducted. The findings from the experimental results indicate that greater tip mass of the outer beams results in higher output voltage generated by the same state, while the excitation magnet spacing can be adjusted for meeting different requirements in power and working bandwidth. In the optimal configurations, the fabricated prototype finally can obtain a maximum average power of 1.83 mW under the condition of v = 5 m/s and s1 = 15 mm, and maintain a wide working bandwidth of 2 m/s −6 m/s with an average output power above 0.66 mW when s1 = 25 mm, respectively. The main contribution of this study has been to confirm the effectiveness of the collaborative use of the multi-degree-of-freedom technology and magnetic nonlinear technology in piezoelectric wind energy collection based on vortex-induced vibration. It also provides a deeper new insight into the performance improvement method for vortex-induced vibration piezoelectric wind energy harvester.

تصميم ومحاكاة هوائي التصحيح الشريطي الصغير المستطيل بتردد 2.45 جيجا هرتز

Design and simulation of a 2.45GHz Rectangular Microstrip Patch Antenna (RMPA) are presented in this paper. Duroid (RT\5880), a dielectric material with a dielectric constant of 2.2 and a substrate height of 1.6 mm, was chosen for our design. After the simulation was finished, the results showed a 4.746dBi directivity gain and a (-3dB) half power bandwidth of 91.60 at the E-plane and 114.90 at the H-plane. It was found that the return loss (S11) was -8.38dB. It was found that the voltage standing wave ratio, or VSWR, was 2.23. Keywords: Rectangular microstrip patch antenna, Duroid (RT\5880), Return loss, directivity, Half Power Bandwidth, Voltage standing wave ratio, CSTSTUDIO SUITE Software