Variable Bandwidth Research Articles

Pyramid-like magnetic carbon composites device toward tunable and adaptive radar-visible compatible properties

Equipped with intelligent adjustable electromagnetic-visible light compatibility characteristics is of significant importance in military stealth fields. In this work, a tunable microwave absorption and visible light change metamaterial based on polylactic acid (PLA) thermal stimulation was fabricated using 4D printing technology with hydroxylated carbon nanotubes (CNTOH) and carbonyl iron powder (CIP) as active ingredients. The printing filament was synthesized, which exhibited excellent absorption performance with a bandwidth of 5.83 GHz and sufficient shape recovery ability. On this basis, a temperature-dominated shape memory variation cone device was also manufactured and the visible light color-changing coating was sprayed. The microwave absorption performance is adjusted according to the petal angle, achieving a bandwidth variation of 5.9–9.8 GHz. This work confirms the potential exploratory value of the designed shape memory polymer in the field of microwave absorption, and provides unlimited possibilities for designing intelligent controlled stealth materials.

A low power low phase noise wide frequency range PLL

This paper presents a low-noise, low-power, wide output frequency range phase-locked loop (PLL) for WLAN/WiFi transceivers. By employing a dual-symmetric CMOS cross-coupled pair differential inductor voltage-controlled oscillator (VCO), the design achieves low phase noise. In addition, an improved phase frequency detector (PFD) and a programmable low-mismatch charge pump (CP) with feedback compensation bias control are used to mitigate bandwidth and noise variations caused by different reference frequencies. The improved charge pump PLL (CPPLL) is designed in 65 nm CMOS process, and the chip layout occupies an area of 0.28 mm2. Post-layout simulation results indicate that the PLL has a tuning range of 4.6 GHz to 6 GHz, a phase noise of 111.7 dBc/Hz at 1 MHz offset at 5 GHz, a total power consumption of 7.14 mW, and a lock time of about 9μs.

Evaluating the Impact of Mid-Link Bandwidth Constraints on Video Streaming Performance Using NS2

A detailed simulation study of video streaming over a simple best-effort network topology is conducted using Network Simulator 2 (NS2). The simulation involves transmitting a video stream across a simple network topology where the central link's data rate varies to simulate different cases. The network topology consists of : node 0 (sender), node 3 (receiver), and nodes 1 and 2 as intermediate nodes. The study aims to examine the impact of mid-link bandwidth variations on packet loss during video transmission. Specifically, the mid-link connecting nodes experience bandwidth reductions to 10, 8, and 5 Mbps. By adjusting the mid-link bandwidth, we simulate scenarios that typically cause packet drops and measure the resulting packet loss for each bandwidth configuration. The simulation results provide insights into how bandwidth constraints affect video streaming performance, emphasizing the correlation between reduced mid-link capacity and increased packet drop rates. The results indicate that maintaining the video encoding constant while reducing the mid-link bandwidth to half of its required capacity results in an approximately 30% rise in dropped packets and a fivefold increase in end-to-end delay. This significant packet loss severely degrades the video quality, highlighting the importance of adequate mid-link bandwidth for maintaining high-quality transmission.

Entropy‐driven Progressive Compression of 3D Point Clouds

Abstract3D point clouds stand as one of the prevalent representations for 3D data, offering the advantage of closely aligning with sensing technologies and providing an unbiased representation of a measured physical scene. Progressive compression is required for real‐world applications operating on networked infrastructures with restricted or variable bandwidth. We contribute a novel approach that leverages a recursive binary space partition, where the partitioning planes are not necessarily axis‐aligned and optimized via an entropy criterion. The planes are encoded via a novel adaptive quantization method combined with prediction. The input 3D point cloud is encoded as an interlaced stream of partitioning planes and number of points in the cells of the partition. Compared to previous work, the added value is an improved rate‐distortion performance, especially for very low bitrates. The latter are critical for interactive navigation of large 3D point clouds on heterogeneous networked infrastructures.

A Metamaterials-Based Absorber Used for Switch Applications with Dynamically Variable Bandwidth in Terahertz Regime.

A broadband absorber based on metamaterials of graphene and vanadium dioxide (VO2) is proposed and investigated in the terahertz (THz) regime, which can be used for switch applications with a dynamically variable bandwidth by electrically and thermally controlling the Fermi energy level of graphene and the conductivity of VO2, respectively. The proposed absorber turns 'on' from 1.5 to 5.4 THz, with the modulation depth reaching 97.1% and the absorptance exceeding 90% when the Fermi energy levels of graphene are set as 0.7 eV, and VO2 is in the metallic phase. On the contrary, the absorptance is close to zero and the absorber turns 'off' with the Fermi energy level setting at 0 eV and VO2 in the insulating phase. Furthermore, other four broadband absorption modes can be achieved utilizing the active materials graphene and VO2. The proposed terahertz absorber may benefit the areas of broadband switch, cloaking objects, THz communications and other applications.

Theoretical proposal for a broadband on-chip multistage quantum amplifier

A broadband on-chip multistage quantum amplifier (MQA) for reading out multiple superconducting qubits is proposed. The bandwidth of quantum amplifier is enhanced by concatenating amplifiers with modular nonreciprocal elements, which are superconducting isolators and circulators based on tunable inductor bridge. The circuit model of MQA is built and simulated. The variation of bandwidth, gain and gain-bandwidth product (GBP) of MQA with the number of stages and bandpass of the constitutive amplifiers are simulated. It is revealed that the bandwidth can be as large as ∼3.2 GHz with a gain of 20 dB at 4–8 GHz frequency range. For a 4-stage MQA composed of four quantum amplifiers with 20 dB gain and 0.3 GHz BW-pass, the bandwidth is 2.14 GHz at 20 dB gain, which is quite cost-efficient. Due to its non-reciprocity, MQA can effectively prevent signals from reflecting to quantum processors. In addition, MQA breaks the limitation of GBP and is easy to integrate with superconducting circuits. The MQA would play a crucial role in the high-fidelity readout of multiple qubits in large-scale superconducting quantum computers.

Distance-Enhanced Hybrid Hierarchical Modulation and QAM Modulation Schemes for UAV Terahertz Communications

Unmanned aerial vehicles (UAVs) are extensively employed in pursuit, rescue missions, and agricultural applications. These operations require substantial data and video transmission, demanding significant spectral resources. The ultra-broad bandwidth of 0.1–10 THz in the Terahertz (THz) frequency range is essential for future UAV-based wireless communications. However, the available bandwidth in the THz frequency spectrum varies with transmission distance. To enhance spectral efficiency over this variable bandwidth, we propose using hierarchical modulation (HM) in the overlapped spectrum and traditional quadrature amplitude modulation (QAM) in the non-overlapped spectrum for closer users. Furthermore, we analyze the single-user case and utilize the block-coordinated descent (BCD) method to jointly optimize the modulation order, subcarrier bandwidth, and sub-band power to improve the system sum rate. Finally, considering the mobility and randomness of UAV users, we design a modulation switching rule to dynamically adjust to changes in distance as users move, thereby enhancing data rates. Simulation results demonstrate superior performance in data rate and design complexity compared to existing methods such as hierarchical bandwidth modulation (HBM) and HM schemes.

Medical Data for the Nadaraya-Watson Kernel Method in Nonparametric Regression Models Based on Hyperbolic Secant Function

Nonparametric kernel estimates used in this work aim to compare different treatment options by examining the recorded medical data. The following use of the suggested strategy depends on a kernel function and a parameter called bandwidth. The Nadaraya-Watson kernel (NWK) estimation is a necessary nonparametric kernel estimator used in regression models. A new Nadaraya-Watson regression estimate depends on the hyperbolic secant kernel (HSK) with fixed bandwidth (FNW) and Variable Bandwidth (VNW) is proposed.. We calculated some properties of the unknown regression function estimator, including bias, variance, optimal bandwidth, and a global measure of error criterion mean square error. Finally, simulation and three real data sets are used to evaluate its performance. Results from simulation and real data showed that the VNW using HSK is more effective than the FNW based on Average Mean Square Error Criterion. Also, Nadaraya-Watson using HSK function is more effective than Nadaraya-Watson using the Gaussian kernel density function.

Improving the hearing aid system using optimized variable bandwidth filter based on wolf optimization

Improving the hearing aid system using optimized variable bandwidth filter based on wolf optimization

Primary outcomes of the enhanced, EHR-facilitated cancer symptom control (E2C2) cluster-randomized, stepped wedge, pragmatic trial.

LBA12006 Background: Symptom burden and functional decline are prevalent, inconsistently treated, and associated with adverse health outcomes in patients with cancer. Symptom monitoring with electronic patient-reported outcome measures (ePROMs) has yielded mixed results due, in part, to care teams’ variable bandwidth and resourcing for symptom management. The collaborative care model (CCM) offers a validated means to address these issues and potentially improve clinical and health services outcomes. Capturing ePROMS through the electronic health record (EHR) for use in CCM delivery provides a potentially scalable approach to manage symptoms at the population level. Methods: E2C2 is a cluster-randomized, population-level, stepped wedge pragmatic trial that compares a bundled, EHR-facilitated, CCM-based intervention to improve control of SPPADE symptoms (Sleep interference, Pain, impaired Physical function, Anxiety, Depression, and Energy deficit/fatigue) with usual care. All patients, regardless of cancer type or stage, treated in the medical oncology clinics of a multi-state health system, were assigned to one of fifteen clusters. Control and intervention conditions monitored SPPADE symptoms with Epic EHR administered 11-point numerical rating scales (NRSs). Moderate and severe symptoms were defined as 4-6/10 and >7/10, respectively. The intervention added EHR clinician decision support; automated delivery of symptom self-management information; and options to address severe symptoms with a dedicated symptom care manager. The primary outcome was post-baseline SPPADE scores assessed using multivariate regression of six cluster-period mean SPPADE symptom scores against E2C2 exposure, fixed cluster and secular time effects. Results: From March 2019 to January 2023, 50,559 patients were assigned to E2C2 clusters and 40,295 completed at least one ePROM. At first assessment, participants’ mean age was 63.3 years; 58% were female; 26% were rural, and the prevalences of moderate or worse symptoms were fatigue 42%, impaired function 34%, sleep disturbance 34%, anxiety 27%, pain 26%, and depression 23%. The intervention significantly reduced mean composite SPPADE symptom scores, p <0.001, among all patients, as well as those with >1 moderate or worse symptom. All mean symptom scores were lower in the intervention group, with the largest effects detected among patients with fatigue -0.2 (-0.4, -0.07), anxiety -0.14 (-0.2, -0.03), and depression -0.1 (-0.2, -0.002). Conclusions: In this large cluster-randomized trial, an EHR-facilitated, bundled intervention that scaled CCM-based surveillance and management of SPPADE symptoms significantly reduced population-level symptom burden, particularly for fatigue, anxiety, and depression. This study provides real world evidence and the foundation for future national efforts aimed at controlling symptoms in patients with cancer. Clinical trial information: NCT03892967 .

A Reliable and Efficient I-f Startup Method of Sensorless Ultra-High-Speed SPMSM for Fuel Cell Air Compressors

Extended back electromotive force (EEMF)-based position sensorless field-oriented control (FOC) is widely utilized for ultra-high-speed surface-mounted permanent magnet synchronous motors (UHS-SPMSMs) driven fuel cell air compressors in medium-high speed applications. Unfortunately, the estimated position is imprecise due to too small EEMF under low speed operation. Hence, current-to-frequency (I-f) control is more suitable for startup. Conventional I-f methods rarely achieve the tradeoff between startup acceleration and load capacity, and the transition to sensorless FOC is mostly realized in the constant-speed stage, which is unacceptable for UHS-SPMSM considering the critical requirement of startup time. In this article, a new closed-loop I-f control approach is proposed to achieve fast and efficient startup. The frequency of reference current vector is corrected automatically based on the active power and the real-time motor torque, which contributes to damping effect for startup reliability. Moreover, an amplitude compensator of reference current vector is designed based on the reactive power, ensuring the maximum torque per ampere operation and higher efficiency. Furthermore, the speed PI controller is enhanced by variable bandwidth design for smoother sensorless transition. These theoretical advantages are validated through experiments with a 550 V, 35 kW UHS-SPMSM. The experimental results demonstrated the enhanced startup performance compared with conventional I-f control.

Low-complexity reconfigurable FIR lowpass equalizers for polynomial channel models

This paper introduces realizations of a reconfigurable finite-impulse-response (FIR) filter for simultaneous equalization and lowpass filtering. The main advantage of the proposed solutions is computational complexity reduction compared to existing solutions for a given performance, which leads to reduced hardware complexity. The proposed structures employ properties of both a variable bandwidth (VBW) filter and a variable equalizer (VE) with variable coefficients. The overall transfer function of the proposed reconfigurable lowpass equalizer (RLPE) is a weighted linear combination of fixed subfilters where the weights are directly determined by the bandwidth and one or several parameters of the channel needed to be equalized. The paper provides design procedures based on minimax optimization and introduces a fast design method for the filter with several variable parameters that can substantially decrease the design time. Filter order estimation expressions as well as complexity expressions are presented for all proposed realizations. Design examples include comparison of the RLPE structures and a common approach of using a regular FIR equalization filter requiring online redesign when the bandwidth or channel characteristics are changed. It is shown that the number of general multiplications can be reduced up to 91% using the proposed RLPE.

ДО ПИТАННЯ ПОБУДОВИ АЛГОРИТМІВ ПЕРЕДАЧІ ПОТОКОВОГО ВІДЕО ДЛЯ МЕРЕЖ З НЕСТАБІЛЬНОЮ ПРОПУСКНОЮ ЗДАТНІСТЮ

Some questions of development of advanced video transmission algorithms that could robustly operate in the electronic warfare conditions are considered. The technology of adaptation of MJPEG compressed video stream for the networks with variable and unstable bandwidth is proposed. The new approach where connection control is separated from encoder basing on man-in-the-middle scenario is developed. In this case it became possible to preserve not only standard decoders on the client side, but the encoder code on the server side as well. The performance and reliability of the proposed MJPEG transmission algorithm was demonstrated in the field tests. The MJPEG transcoder based on developed algorithm demonstrated sufficiently stable video stream from the drone via WiFi network in the presence of impulse signal jamming.

Practical System Identification and Incremental Control Design for a Subscale Fixed-Wing Aircraft

An incremental differential proportional integral (iDPI) control law using eigenstructure assignment gain design is tested in flight on a subscale platform to validate its suitability for fixed-wing flight control. A kinematic relation for the aerodynamic side-slip angle rate is developed to apply a pseudo full state feedback. In order to perform the gain design and assessment, a plant model is estimated using flight test data from gyro, accelerometer, airspeed and surface deflection measurements during sine-sweep excitations. Transfer function models for the actuators and surface deflections are identified both in-flight and on the ground for several different actuators and control surfaces using hall sensor surface deflection measurements. The analysis reveals a large variation in bandwidth between the different types of servo motors. Flight test results are presented which demonstrates that the plant model estimates based on tests with good frequency excitation, high bandwidth actuators and surface deflection measurements can be used to reasonably predict the closed-loop dynamic behavior of the aircraft. The closed-loop flight test results of the iDPi control law show good performance and lays the groundwork for further development.

An efficient bandwidth-adaptive gradient compression algorithm for distributed training of deep neural networks

In distributed deep learning with data parallelism, communication bottleneck throttles the efficiency of model training. Recent studies adopt versatile gradient compression techniques, with communication sparsification standing out as an effective approach for reducing the number of gradients to be transmitted. However, the deployment of gradient sparsification is adversely influenced by the change of network environment in real systems, and existing methods either neglect bandwidth dynamics during training or experience drastic fluctuation of compression ratios. In this paper, we propose ACE, a novel adaptive gradient compression mechanism with high communication efficiency under bandwidth variation. ACE adapts the sparsification ratio to the average bandwidth in a time window, other than following its dynamics exactly. To accurately compute the compression ratio, we first profile the compression time and model a single iteration time consisting of communication, computation and compression operations. We then present a practical model to fit the needed training rounds till convergence, and formulate an optimization problem to compute the optimal sparsification ratio. We conduct experiments on different DNN models in different network environments and compare various methods in terms of convergence and model quality. The experimental results show that ACE achieves up to 9.39× and 1.28× training speedups over fixed and state-of-the-art adaptive compression methods.

E-Chain: Lightweight and Secure BIoT Voting Mechanism on Variable Bandwidth Networks

E-Chain: Lightweight and Secure BIoT Voting Mechanism on Variable Bandwidth Networks

Variable bandwidth via Wilson bases

We introduce a new concept of variable bandwidth that is based on the frequency truncation of Wilson expansions. For this model we derive sampling theorems, a complete reconstruction of f from its samples, and necessary density conditions for sampling. Numerical simulations support the interpretation of this model of variable bandwidth. In particular, chirps, as they arise in the description of gravitational waves, can be modeled in a space of variable bandwidth.

Liner multimode fibers with high distributed optical amplification system based high efficient quadrature modulation system for reliable high capacity local area network

Abstract This paper clarified the liner multimode fibers with high optical amplification system based high efficient quadrature modulation system for high capacity local area optical networks. The clarified the lighted QPSK transmitter system based linear multimode fiber profile is clarified under modal bandwidth effects. The optimum power variations are studied with spectral base band wavelength variations for various modal bandwidths based linear multimode fiber. Optimum power is clarified with time variations for various modal bandwidths based linear multimode fiber. Total power for 1500 and 1700 MHz km modal bandwidth is demonstrated based linear multimode fiber. The Max-Min signal base band amplitude based QAM pulse generator is clarified after photo-detector by using electrical constellation visualizer. The optimum Q Factor form with Min BER is demonstrated after photo-detector for 1300, 1500, and 1700 MHz km modal bandwidth based linear multimode fiber. The base band lighted SNR is clarified and simulated with the modal bandwidth variations based linear multimode fiber. Optimum Q form factor through photo-detector is demonstrated with the modal bandwidth variations based linear multimode fiber. The electrical SNR through photo-detector is studied and simulated with the modal bandwidth variations based linear multimode fiber.

PArtNNer: Platform-Agnostic Adaptive Edge-Cloud DNN Partitioning for Minimizing End-to-End Latency

The last decade has seen the emergence of Deep Neural Networks (DNNs) as the de facto algorithm for various computer vision applications. In intelligent edge devices, sensor data streams acquired by the device are processed by a DNN application running on either the edge device itself or in the cloud. However, “edge-only” and “cloud-only” execution of State-of-the-Art DNNs may not meet an application’s latency requirements due to the limited compute, memory, and energy resources in edge devices, dynamically varying bandwidth of edge-cloud connectivity networks, and temporal variations in the computational load of cloud servers. This work investigates distributed (partitioned) inference across edge devices (mobile/end device) and cloud servers to minimize end-to-end DNN inference latency. We study the impact of temporally varying operating conditions and the underlying compute and communication architecture on the decision of whether to run the inference solely on the edge, entirely in the cloud, or by partitioning the DNN model execution among the two. Leveraging the insights gained from this study and the wide variation in the capabilities of various edge platforms that run DNN inference, we propose PArtNNer , a platform-agnostic adaptive DNN partitioning algorithm that finds the optimal partitioning point in DNNs to minimize inference latency. PArtNNer can adapt to dynamic variations in communication bandwidth and cloud server load without requiring pre-characterization of underlying platforms. Experimental results for six image classification and object detection DNNs on a set of five commercial off-the-shelf compute platforms and three communication standards indicate that PArtNNer results in 10.2× and 3.2× (on average) and up to 21.1× and 6.7× improvements in end-to-end inference latency compared to execution of the DNN entirely on the edge device or entirely on a cloud server, respectively. Compared to pre-characterization-based partitioning approaches, PArtNNer converges to the optimal partitioning point 17.6× faster.

On-Chip Circularly Polarized Circular Loop Antennas Utilizing 4H-SiC and GaAs Substrates in the Q/V Band.

This paper presents a comprehensive assessment of the performance of on-chip circularly polarized (CP) circular loop antennas that have been designed and fabricated to operate in the Q/V frequency band. The proposed antenna design incorporates two concentric loops, with the outer loop as the active element and the inner loop enhancing the CP bandwidth. The study utilizes gallium arsenide (GaAs) and silicon carbide (4H-SiC) semiconductor wafer substrates. The measured results highlight the successful achievement of impedance matching at 40 GHz and 44 GHz for the 4H-SiC and GaAs substrates, respectively. Furthermore, both cases yield an axial ratio (AR) of less than 3 dB, with variations in bandwidths and frequency bands contingent upon the dielectric constant of the respective substrate material. Moreover, the outcomes confirm that utilizing 4H-SiC substrates results in a significantly higher radiation efficiency of 95%, owing to lower substrate losses. In pursuit of these findings, a 4-element circularly polarized loop array antenna has been fabricated for operation at 40 GHz, employing a 4H-SiC wafer as a low-loss substrate. The results underscore the antenna's remarkable performance, exemplified by a broadside gain of approximately 9.7 dBic and a total efficiency of circa 92%. A close agreement has been achieved between simulated and measured results.