Search Results (99)

This study presents a comprehensive performance evaluation of field-programmable gate array (FPGA), graphics processing unit (GPU), and central processing unit (CPU) platforms for implementing finite impulse response (FIR) filters in semiconductor-based digital signal processing (DSP) systems. Utilizing a standardized FIR filter designed with the Kaiser window method, we compare computational efficiency, latency, and energy consumption across the ZYNQ XC7Z020 FPGA, Tesla K80 GPU, and Arm-based CPU, achieving processing times of 0.004 s, 0.008 s, and 0.107 s, respectively, with FPGA power consumption of 1.431 W and comparable energy profiles for GPU and CPU. The FPGA is 27 times faster than the CPU and 2 times faster than the GPU, demonstrating its suitability for low-latency DSP tasks. A detailed analysis of resource utilization and scalability underscores the FPGA’s reconfigurability for optimized DSP implementations. This work provides novel insights into platform-specific optimizations, addressing the demand for energy-efficient solutions in edge computing and IoT applications, with implications for advancing sustainable DSP architectures. Full article

As the proportion of renewable energy continues to increase, the large-scale grid integration of photovoltaic (PV) generation presents new technical challenges for reactive power balance in power systems. This paper proposes a coordinated reactive power and voltage optimization method based on Filtered Multiband Decomposition (FMD). First, to address the stochastic fluctuations of PV power, an improved FMD-based prediction model is developed. The model employs an adaptive finite impulse response (FIR) filter to decompose signals and captures periodicity and uncertainty through kurtosis-based feature extraction. By utilizing adaptive function windows for multiband signal decomposition, combined with kernel principal component analysis (KPCA) for dimensionality reduction and a long short-term memory (LSTM) network for prediction, the model significantly enhances forecasting accuracy. Second, to tackle the challenges of integrating high-penetration distributed PV while maintaining reactive power balance, a multi-head attention-based velocity update strategy is introduced within a multi-objective particle swarm optimization (MOPSO) framework. This strategy quantifies the spatial distance and fitness differences of historical best solutions, constructing a dynamic weight allocation mechanism to adaptively adjust particle search direction and step size. Finally, the effectiveness of the proposed method is validated through an improved IEEE 33-bus test case. Full article

Precise analysis of electroencephalogram (EEG) signals is critical for advancing the understanding of neurological conditions and mapping brain activity. However, accurately visualizing brain regions and behavioral patterns from neural signals remains a significant challenge. The present study proposes a novel methodology for the automated calculation of the average power of EEG signals, with a particular focus on the beta frequency band which is known for its pronounced activity during cognitive tasks such as 2D content engagement. An optimization algorithm is employed to determine the most appropriate digital filter type and order for EEG signal processing, thereby enhancing both signal clarity and interpretability. To validate the proposed methodology, an experiment was conducted with 22 students, during which EEG data were recorded while participants engaged in cognitive tasks. The collected data were processed using MATLAB (version R2023a) and the EEGLAB toolbox (version 2022.1) to evaluate various filters, including finite impulse response (FIR) and infinite impulse response (IIR) Butterworth and IIR Chebyshev filters with a 0.5% passband ripple. Results indicate that the IIR Chebyshev filter, configured with a 0.5% passband ripple and a fourth-order design, outperformed the alternatives by effectively reducing average power while preserving signal fidelity. This optimized filtering approach significantly improves the accuracy of neural signal visualizations, thereby facilitating the creation of detailed brain activity maps. By refining the analysis of EEG signals, the proposed method enhances the detection of specific neural behaviors and deepens the understanding of functional brain regions. Moreover, it bolsters the reliability of real-time brain activity monitoring, potentially advancing neurological diagnostics and insights into cognitive processes. These findings suggest that the technique holds considerable promise for future applications in brain–computer interfaces and advanced neurological assessments, offering a valuable tool for both clinical practice and research exploration. Full article

As a model predictive control (MPC) technique, dynamic matrix control (DMC) has gained widespread industrial adoption due to its straightforward model construction and clear physical interpretation. However, its effectiveness relies on the accuracy of the predictive model, where measurement inaccuracies or excessive noise in step-response coefficients may significantly degrade control performance. This study enhances robustness of DMC by implementing finite impulse response (FIR) filters on measured step-response coefficients while providing theoretical proof of its stability. The improved algorithm is applied to cold helium temperature control of the modular High-Temperature Gas-Cooled Reactor (mHTGR). A cascade control structure is adopted, where the inner loop uses a PID controller to ensure system stability, while the outer loop uses DMC to adjust the setpoint of the hot helium temperature, thereby controlling the cold helium temperature. Numerical simulation results demonstrate significant improvements in temperature control performance and enhanced robustness of the modified DMC method. Full article

This study investigates the use of surface electromyography (sEMG) sensors in measuring muscle activity and mapping it onto wearable textile stretch sensors using a basic deep learning model, the Multi-Layer Perceptron (MLP). Wearable sensors are gaining attention for their ability to monitor physiological data while maintaining user comfort. A three-stage experimental approach was employed to evaluate the mapping process. In the first stage, the impact of applying a low-pass finite impulse response (FIR) filter was assessed by comparing filtered and unfiltered sEMG data. The results showed minimal impact on accuracy (R-squared ~ 0.77), as RMS preprocessing effectively reduced noise. In the second stage, adding tensile velocity data improved the model’s predictive performance (R-squared ~ 0.80), emphasizing the importance of integrating dynamic variables. In the third stage, data from multiple muscle groups, including the biceps brachii, forearm muscles, and triceps brachii, were incorporated, achieving the highest R-squared value of ~0.94. These findings establish wearable textile stretch sensors as reliable tools for monitoring muscle activity during exercise. By demonstrating improved accuracy with a basic MLP model, this study provides a foundation for advancing wearable health monitoring systems and exploring additional physiological parameters and activities. Full article

Trajectory tracking is a critical component of autonomous driving and robotic motion control. This paper proposes a novel robust finite impulse response (FIR) filter for linear time-invariant systems, aimed at enhancing the accuracy and robustness of trajectory tracking. To address the limitations of infinite impulse response (IIR) filters in complex environments, we integrate a cumulative risk–sensitive criterion with an FIR structure. The proposed filter effectively mitigates model mismatches and temporary modeling uncertainties, making it highly suitable for trajectory tracking in dynamic and uncertain environments. To validate its performance, a comprehensive vehicle trajectory tracking experiment is conducted. The experimental results demonstrate that, compared to the Kalman filter (KF), risk–sensitive filter (RSF), and unbiased FIR (UFIR) filter, the proposed algorithm significantly reduces the average tracking error and exhibits superior robustness in complex scenarios. This work provides a new and effective solution for trajectory tracking applications, with broad potential for practical implementation. Full article

This paper proposes an accuracy enhancement method for fiber-longitudinal power profile estimation (PPE) based on convolutional neural networks (CNN). Two types of CNNs are designed. The first network treats different polarization streams identically and is denoted as CNN. The second network considers the difference between the contributions of different polarization streams to the nonlinear phase shift and is denoted as enhanced CNN (ECNN). The numerical simulation results confirm the effectiveness of the method for a 64 Gbaud/s quadrature phase-shift keying (QPSK) polarization-division-multiplexed (PDM) coherent optical communication system with a fiber length of 320 km. The effects of finite impulse response (FIR) filter length, power into the fiber, and polarization mode dispersion on the PPE accuracy are examined. Finally, the results of the proposed method are monitored in the presence of several simultaneous power attenuation anomalies in the fiber optic link. It is found that the accuracy of the PPE substantially improves after using the proposed method, achieving a relative gain of up to 71%. When the modulation format is changed from QPSK to 16-ary quadrature amplitude modulation (16-QAM), and the fiber length is increased from 360 km to 480 km, the proposed method is still effective. This work provides a feasible solution for implementing fiber-longitudinal PPE, enabling significantly improved estimation accuracy in practical applications. Full article

To obtain more accurate information on using an inertial navigation system (INS)-based integrated localization system, an integrated filter with maximum correntropy criterion Kalman filter (mccKF) and finite impulse response (FIR) is proposed for the fusion of INS-based multisource sensor data in this work. In the realm of medical applications, precise localization is crucial for various aspects, such as tracking the movement of a medical instrument within the human body or monitoring its position in the human body during procedures. This study uses ultra-wideband (UWB) technology to rectify the position errors of the INS. In this method, the difference between the positions of the INS and UWB is used as the measurement of the filter. The main data fusion filter in this study is the mccKF, which utilizes the maximum correntropy criterion (mcc) method to enhance the robustness of the Kalman filter (KF). This filter is used for fusing data from multiple sources, including the INS. Moreover, we use the Mahalanobis distance to verify the performance of the mccKF. If the performance of the mccKF is lower than the preset threshold, the Allan Variance-assisted FIR filter is used to replace the mccKF, which is designed in this work. This adaptive approach ensures the resilience of the system in demanding medical environments. Two practical experiments were performed to evaluate the effectiveness of the proposed approach. The findings indicate that the mccKF/FIR integrated method reduces the localization error by approximately 32.43% and 37.5% compared with the KF and mccKF, respectively. These results highlight the effectiveness of the proposed approach. Full article

The computational complexity of existing multi-symbol detection (MSD) algorithms grows exponentially as the observation intervals increase, resulting in difficulties in algorithm implementation for detecting pulse code modulation/frequency modulation (PCM/FM) signals, especially for multi-channel signals. To address the challenges, we proposed a low-complexity MSD algorithm based on the averaged matched filtering. The proposed algorithm groups the local reference signals based on the different importance levels of the middle and edge bits in the correlation operations and averages the edge bits, leading to a considerable decrease in matching branches. Furthermore, it leverages the phase symmetry, and the proposed algorithm retains half of the averaged local reference signals for the matching operation, thus further reducing the matching branches. The proposed algorithm reduces the storage of the local signals and correlation operations to one-eighth compared to the traditional MSD algorithm under different observation lengths. Additionally, based on the structure of multiplexed FIR filters, the proposed algorithm optimizes single-channel single-coefficient FIR filters into four-channel double-coefficient FIR filters, further reducing the hardware resource consumption by approximately 25%. The simulation results showed that the proposed algorithm achieved demodulation performance comparable to the traditional MSD algorithms while reducing the computational complexity by 87.5%. Compared to the decision-feedback MSD algorithm, it achieves higher demodulation gain with a 75% complexity reduction. The Field Programmable Gate Array (FPGA) platform implementation results showed that the proposed algorithm reduces hardware resource consumption by nearly 90% compared with the traditional algorithm, and the hardware demodulation performance loss is less than 1 dB compared with the simulation results. Full article

In recent years, the macro–micro structure (servo platform for macro motion and galvanometer for micro motion) composed of a galvanometer and servo platform has been gradually applied to laser processing in order to address the increasing demand for high-speed, high-precision, and large-format precision machining. The research in this field has evolved from step-and-scan methods to linkage processing methods. Nevertheless, the existing linkage processing methods cannot make full use of the field-of-view (FOV) of the galvanometer. In terms of motion distribution, the existing methods are not suitable for continuous micro segments and generate the problem that the distribution parameter can only be obtained through experience or multiple experiments. In this research, a new laser linkage processing method for global trajectory smoothing of densely discretized paths is proposed. The proposed method can generate a smooth trajectory of the servo platform with bounded acceleration by the finite impulse response (FIR) filter under the global blending error constrained by the galvanometer FOV. Moreover, the trajectory of the galvanometer is generated by vector subtraction, and the motion distribution of macro–micro structure is accurately realized. Experimental verification is carried out on an experimental platform composed of a three-axis servo platform, a galvanometer, and a laser. Simulation experiment results indicate that the processing efficiency of the proposed method is improved by 79% compared with the servo platform processing only and 55% compared with the previous linkage processing method. Furthermore, the method can be successfully utilized on experimental platforms with good tracking performance. In summary, the proposed method adeptly balances efficiency and quality, rendering it particularly suitable for laser precision machining applications. Full article

Passive pulse shaping at frequencies above 1 GHz is mainly achieved through frequency-domain processing using filters. Unfortunately, the conventional frequency domain approach does not allow precise control of the impulse response of the filter, therefore, setting limitations to the pulse shaping accuracy. Sub-nanosecond pulse expansion that preserves steep pulse transitions is one of the ultra-wideband (UWB) applications where frequency domain approaches do not provide satisfactory results. This paper proposes a highly innovative approach based on time-domain signal processing using a set of parallel microstrip delay lines connected in a network accompanied by a splitter at the input and a combiner at the output. The proposed design, analogous to finite impulse response (FIR) filters in digital signal processing (DSP), provides fine-grained control over time-domain characteristics and supports the implementation of complex functions, including pulse expansion. This paper presents a detailed analysis of previous work and theoretical considerations regarding the advantages and limitations of UWB pulse time-domain processing. Moreover, detailed HFSS simulations of components, such as a microstrip pulse splitter, delay lines, a combiner, and their combinations, are presented. Finally, the results of the experimental validation of the device, fabricated on an FR-4 substrate, are presented. Technology for effective implementation of a pulse splitter, delay lines, and a pulse combiner, as well as their matching, can be considered as key findings of the given research. Limitations associated with matching and delay estimation for pulsed UWB signals are highlighted. Full article

This paper presents a highly efficient, low-power, compact mixed-signal sinusoidal current generator (CG) integrated circuit (IC) designed for bioelectrical impedance spectroscopy (BIS) with low total harmonic distortion (THD). The proposed system employs a 9-bit sine wave lookup table (LUT) which is simplified to a 4-bit data stream through a third-order digital delta–sigma modulator (ΔΣM). Unlike conventional analog low-pass filters (LPF), which statically limit bandwidth, the finite impulse response (FIR) filter attenuates high-frequency noise according to the operating frequency, allowing the frequency range of the sinusoidal signal to vary. Additionally, the output of the FIR filter is applied to a 6-bit capacitive digital-to-analog converter (CDAC) with data-weighted averaging (DWA), enabling dynamic capacitor matching and seamless interfacing. The sinusoidal CG IC, fabricated using a 65 nm CMOS process, produces a 5 μA amplitude and operates over a wide frequency range of 0.6 to 20 kHz. This highly synthesizable CG achieves a THD of 0.04%, consumes 19.2 μW of power, and occupies an area of 0.0798 mm². These attributes make the CG IC highly suitable for compact, low-power bio-impedance applications. Full article

This paper presents a programmable digital filtering unit dedicated to operating with signals from infrared (IR) detection modules. The designed device is quite useful for increasing the signal-to-noise ratio due to the reduction in noise and interference from detector–amplifier circuits or external radiation sources. Moreover, the developed device is flexible due to the possibility of programming the desired filter types and their responses. In the circuit, an advanced field-programmable gate array FPGA chip was used to ensure an adequate number of resources that are necessary to implement an effective filtration process. The proposed circuity was assisted by a 32-bit microcontroller to perform controlling functions and could operate at frequency sampling of up to 40 MSa/s with 16-bit resolution. In addition, in our application, the sampling frequency decimation enabled obtaining relatively narrow passband characteristics also in the low frequency range. The filtered signal was available in real time at the digital-to-analog converter output. In the paper, we showed results of simulations and real measurements of filters implementation in the FPGA device. Moreover, we also presented a practical application of the proposed circuit in cooperation with an InAsSb mid-IR detector module, where its self-noise was effectively reduced. The presented device can be regarded as an attractive alternative to the lock-in technique, artificial intelligence algorithms, or wavelet transform in applications where their use is impossible or problematic. Comparing the presented device with the previous proposal, a higher signal-to-noise ratio improvement and wider bandwidth of operation were obtained. Full article

Recently, solar and wind power generation have gained attention as pathways to achieving carbon neutrality, and Renewable Energy Resource Management System (RERMS) technology has been developed to monitor and control small-scale, distributed renewable energy resources. In this work, we present an Event-Triggered Transmission (ETT) algorithm for RERMS, which transmits sensor measurements to the base station only when necessary. The ETT algorithm helps prevent congestion in the communication channel between RERMS and the base station, avoiding time delays or packet loss caused by the excessive transmission of sensor measurements. We design a hybrid state estimation algorithm that combines Kalman and Finite Impulse Response (FIR) filters to enhance the estimation performance, and we propose a new ETT algorithm based on this design. We evaluate the performance of the proposed algorithm through experiments that transmit actual sensor measurements from a photovoltaic power generation system to the base station, demonstrating that it outperforms existing algorithms. Full article

This paper describes a revolutionary design paradigm for monitoring aquatic life. This unique methodology addresses issues such as limited memory, insufficient bandwidth, and excessive noise levels by combining two approaches to create a comprehensive predictive filtration system, as well as multiple-transfer route analysis. This work focuses on proposing a novel filtration learning approach for underwater sensor nodes. This model was created by merging two adaptive filters, the finite impulse response (FIR) and the adaptive line enhancer (ALE). The FIR integrated filter eliminates unwanted noise from the signal by obtaining a linear response phase and passes the signal without distortion. The goal of the ALE filter is to properly separate the noise signal from the measured signal, resulting in the signal of interest. The cluster head level filters are the adaptive cuckoo filter (ACF) and the Kalman filter. The ACF assesses whether an emitter node is part of a set or not. The Kalman filter improves the estimation of state values for a dynamic underwater sensor networking system. It uses distributed learning long short-term memory (LSTM-CNN) technology to ensure that the anticipated value of the square of the gap between the prediction and the correct state is the smallest possible. Compared to prior methods, our suggested deep filtering–learning model achieved 98.5% of the sensory filtration method in the majority of the obtained data and close to 99.1% of an adaptive prediction method, while also consuming little energy during lengthy monitoring. Full article