Showing papers on "Digital signal published in 2019"

Deep Neural Network Compression Technique Towards Efficient Digital Signal Modulation Recognition in Edge Device

Abstract: Digital signal modulation recognition is meaningful for military application and civilian application. In the non-cooperation communication scenario, digital signal modulation recognition will help people identify communication target and have better management over them. In order to the classification accuracy, deep learning is widely used to complete this task. However, current papers have not considered the deployment of deep learning in compute capability and storage limited edge equipment. In this paper, we utilize neural network pruning techniques to reduce the convolution parameters and floating point operations per second (FLOPs), which will pave a wide way to deploy signal classification convolution neural network (CNN) in edge equipment. We set the Average Percentage of Zeros (APoZ) criterion for convolution layers. Compared to original CNN, the experiment result shows that light CNN convolution layer could use only 1.5%~5% parameter and 33%~35% time without losing significant accuracy.

Performance, Power, and Area Design Trade-Offs in Millimeter-Wave Transmitter Beamforming Architectures

Abstract: Millimeter wave (mmW) communications is viewed as the key enabler of 5G cellular networks due to vast spectrum availability that could boost peak rate and capacity. Due to increased propagation loss in mmW band, transceivers with massive antenna array are required to meet a link budget, but their power consumption and cost become limiting factors for commercial systems. Radio designs based on hybrid digital and analog array architectures and the usage of radio frequency (RF) signal processing via phase shifters have emerged as potential solutions to improve radio energy efficiency and deliver performances close to the conventional digital antenna arrays. In this paper, we provide an overview of the state-of-the-art mmW massive antenna array designs and comparison among three array architectures, namely digital array, partially-connected hybrid array (sub-array), and fully-connected hybrid array. The comparison of performance, power, and area for these three architectures is performed for three representative 5G downlink use cases, which cover a range of pre-beamforming signal-to-noise-ratios (SNR) and multiplexing regimes. This is the first study to comprehensively model and quantitatively analyze all design aspects and criteria including: 1) optimal linear precoder, 2) impact of quantization error in digital-to-analog converter (DAC) and phase shifters, 3) RF signal distribution network, 4) power and area estimation based on state-of-the-art mmW circuits including baseband digital precoding, digital signal distribution network, high-speed DACs, oscillators, mixers, phase shifters, RF signal distribution network, and power amplifiers. Our simulation results show that the fully-digital array architecture is the most power and area efficient compared against optimized designs for sub-array and hybrid array architectures. Our analysis shows that digital array architecture benefits greatly from multi-user multiplexing. The analysis also reveals that sub-array architecture performance is limited by reduced beamforming gain due to array partitioning, while the system bottleneck of the fully-connected hybrid architecture is the excessively complicated and power hungry RF signal distribution network.

Event-triggered natural hazard monitoring with convolutional neural networks on the edge

Abstract: In natural hazard warning systems fast decision making is vital to avoid catastrophes. Decision making at the edge of a wireless sensor network promises fast response times but is limited by the availability of energy, data transfer speed, processing and memory constraints. In this work we present a realization of a wireless sensor network for hazard monitoring based on an array of event-triggered single-channel micro-seismic sensors with advanced signal processing and characterization capabilities based on a novel co-detection technique. On the one hand we leverage an ultra-low power, threshold-triggering circuit paired with on-demand digital signal acquisition capable of extracting relevant information exactly and efficiently at times when it matters most and consequentially not wasting precious resources when nothing can be observed. On the other hand we utilize machine-learning-based classification implemented on low-power, off-the-shelf microcontrollers to avoid false positive warnings and to actively identify humans in hazard zones. The sensors' response time and memory requirement is substantially improved by quantizing and pipelining the inference of a convolutional neural network. In this way, convolutional neural networks that would not run unmodified on a memory constrained device can be executed in real-time and at scale on low-power embedded devices. A field study with our system is running on the rockfall scarp of the Matterhorn Hornligrat at 3500 m a.s.l. since 08/2018.

CNN and RNN-based Deep Learning Methods for Digital Signal Demodulation

Abstract: In this paper we presented a neural network-based method to demodulate digital signals. After training with different modulation schemes, the learning-based receiver can perform demodulation without changing receiver hardware by loading certain parameters based on the modulation scheme. Combining CNN's ability to extract local features and RNN's time series modeling ability, we designed a mixed neural network model with parallel architecture and simulate FSK, PSK, QAM demodulation over AWGN and Raleigh-faded channels. The results show that the mixed neural network model can equal or even exceed the performance of the conventional demodulation method (matched filter or correlation-based demodulation). With this kind of receiver, we can intelligently process multiple types of digital modulated signals with flexibility.

Secure Communication of Fractional Complex Chaotic Systems Based on Fractional Difference Function Synchronization

Abstract: Fractional complex chaotic systems have attracted great interest recently. However, most of scholars adopted integer real chaotic system and fractional real and integer complex chaotic systems to improve the security of communication. In this paper, the advantages of fractional complex chaotic synchronization (FCCS) in secure communication are firstly demonstrated. To begin with, we propose the definition of fractional difference function synchronization (FDFS) according to difference function synchronization (DFS) of integer complex chaotic systems. FDFS makes communication secure based on FCCS possible. Then we design corresponding controller and present a general communication scheme based on FDFS. Finally, we respectively accomplish simulations which transmit analog signal, digital signal, voice signal, and image signal. Especially for image signal, we give a novel image cryptosystem based on FDFS. The results demonstrate the superiority and good performances of FDFS in secure communication.

Highly Integrated MEMS Magnetic Sensor Based on GMI Effect of Amorphous Wire

Abstract: In this paper, a highly integrated amorphous wire Giant magneto-impedance (GMI) magnetic sensor using micro electron mechanical system (MEMS) technology is designed, which is equipped with a signal conditioning circuit and uses a data acquisition card to convert the output signal of the circuit into a digital signal. The structure and package of the sensor are introduced. The sensor sensing principle and signal conditioning circuit are analyzed. The output of the sensor is tested, calibrated, and the relationship between the GMI effect of the amorphous wire and the excitation current frequency is explored. The sensor supplies voltage is ±5 V, and the excitation signal is a square wave signal with a frequency of 60 MHz and an amplitude of 1.2 V generated by the quartz crystal. The sensor has the largest GMI effect at 60 MHz with a sensitivity of 4.8 V/Oe and a resolution of 40 nT.

Research on Digital Signal Recognition Based on Higher Order Cumulants

Abstract: This paper proposes a high-order cumulant based digital signal modulation recognition algorithm, which uses the fourth-order cumulant of the signal as the feature recognition parameter to calculate and obtain the fourth-order cumulant of the four signals 8PSK, 16QAM, 4PAM and BPSK. And the classification and identification under the Gaussian white noise channel, the simulation results show that the recognition rate of the algorithm can reach more than 90% when the number of symbols is above 250 and the signal-to-noise ratio is greater than 10dB.

A High-Performance Common-Mode Noise Absorption Circuit Based on Phase Modification Technique

Abstract: In this article, a high-performance common-mode noise absorption circuit (CMNAC) is proposed for solving electromagnetic interference (EMI) or radio-frequency interference (RFI) problems in high-speed differential digital systems. Instead of reflecting common-mode noises by conventional common-mode filters (CMFs), the common-mode noises can be absorbed in the proposed circuit. In addition, the phase modification technique is utilized to enhance the common-mode noise absorption rate and the differential-mode eye diagram in this CMNAC. In the common-mode half circuit (CMHC), the phase inversion of the subnetwork adds a destructive combination of the stopband energy, resulting in higher stopband attenuation. In the differential-mode half circuit (DMHC), the phase linearization leads to flat group delay, reducing the distortion in high-speed digital signal transmission in the lumped circuit. Using a standard integrated passive device (IPD) process, this CMNAC is implemented for the demonstration. The circuit occupies an area of only 1.138 mm $\times0.822$ mm. From the measured results, the proposed circuit exhibits the common-mode suppression level larger than 15 dB from 4 to 20 GHz and the absorption rate over 90% from 3.8 to 17.4 GHz with a fractional bandwidth 128%. In addition, the measured eye diagram shows that the proposed circuit can support high-speed transmission up to 10 Gb/s.

Digital Signal Modulation Recognition Algorithm Based on VGGNet Model

Abstract: Aiming at the problem that feature extraction in traditional modulation recognition relies on manual experience and the poor performance of traditional methods in low signal-to-noise ratio (SNR), a deep learning intelligent modulation recognition algorithm based on VGG convolution neural network model is proposed in this paper, which replaces manual design features and improves the recognition performance of digital signals in low SNR. The algorithm first converts the sampled data of communication signals into gray images, then trains the VGGNet model built under PyTorch to extract and select the features of six kinds of digital modulation signals automatically, thus realizing the automatic recognition of digital modulation signals. The simulation results show that the recognition rate can reach over 98% when SNR is -2dB, which is better than the recognition results using support vector machine and other algorithms, thus verifying the validity of this method for digital modulation signal recognition at low SNR.

Underwater Acoustic Time Delay Estimation Based on Envelope Differences of Correlation Functions

Abstract: This paper proposes underwater acoustic time delay estimation based on the envelope differences of correlation functions (EDCF), which mitigates the delay estimation errors introduced by the amplitude fluctuations of the correlation function envelopes in the traditional correlation methods (CM). The performance of the proposed delay estimation method under different time values was analyzed, and the optimal difference time values are given. To overcome the influences of digital signal sampling intervals on time delay estimation, a digital time delay estimation approach with low complexity and high accuracy is proposed. The performance of the proposed time delay estimation was analyzed in underwater multipath channels. Finally, the accuracy of the delay estimation using this proposed method was demonstrated by experiments.

Recognition of electromagnetic sources with the use of deep neural networks

Abstract: The article presents a method of recognizing sources of electromagnetic signal emission on the basis of signals generated by using deep neural networks. These signals are measured in electronic recognition receivers, processed into a digital signal and then undergo recognition. The main purpose of the article is to present software which, based on the detected signal, is to recognize it. The software can also be used as a subsystem in Electronic Intelligence (ELINT) devices, including detection of radiolocation systems, jammers, recognition of aircrafts, ships, vehicles based on the signal shape of radar cross section (RCS) and subsequently comparison them to the emitter database (EDB). The implementation of this system is presented in a simulation environment and with the help of a signal generator that has the ability to make changes in signal signatures earlier recognized, calculated and written in database. The proposed software allows to examine a significant number of different signals. The article contains a description of components of software, such as signal base, learning subsystem and signal generator. The results of the system operation are presented in the form of screenshots from individual software components. The speed of software operation, the effectiveness of recognition systems using artificial neural networks is presented by means of tables and appropriate illustrations. Also presented is the problem of learning the neural networks at the GPUs (graphics processing units) and the way of choice the learning coefficients.

System and method for automated fault diagnosis and prognosis for rotating equipment

Abstract: Techniques, including systems and methods for monitoring a rotating equipment, are provided. A sensor that is in proximity of the rotating equipment senses vibrations of the rotating equipment. The sensor generates a digital signal corresponding to the vibrations of the rotating equipment and transmits the digital signal over a communication network. A server receives the digital signal and pre-processes the digital signal using ensemble empirical mean decomposition (EEMD) technique. The server processes the digital signal using wavelet neural network (WNN) to detect faults in the rotating equipment. Further, the server processes the digital signal using the wavelet neural network to predict remaining useful life (RUL) of the rotating equipment.

Distortion Estimation-Based Adaptive Power Allocation for Hybrid Digital–Analog Video Transmission

Abstract: Hybrid digital–analog (HDA) video transmission schemes have shown advantages in avoiding the cliff effect . However, most current HDA schemes assume perfect transmission of the digital signal, which is hardly the case in practice. In this paper, we propose an adaptive recursive distortion estimate for the HDA system (ARDE-HDA), which recursively estimates the decoder-side distortion from the encoder and adaptively allocates the transmission power between the digital and analog signals in HDA. First, we derive the closed-form expression of a recursive distortion estimation (RDE) method, which does not require the digital part of the HDA output to be decoded perfectly, and both transmission error and superposition process of digital and analog parts are taken into consideration. Then, based on the deduced RDE model, an adaptive power allocation is proposed for the digital and analog parts to minimize the decoder-side distortion. Finally, simulation results are presented, which show the accuracy of the proposed RDE model and the ARDE-HDA method. Our method can achieve an average of 4.10 dB gain over existing HDA methods and 15.14-dB gain over the Softcast method in terms of the peak signal-to-noise ratio.

Metrological traceability for digital sensors in smart manufacturing: calibration of MEMS accelerometers and microphones at INRiM

Abstract: The use of digital sensors in engineering applications and monitoring systems is widely increased in the last years, in particular in the context of smart manufacturing, Industry 4.0, Internet of Things (IoT) sensors networking, as well as in environmental and infrastructural monitoring. Nevertheless, digital MEMS accelerometers and microphones are currently not reliable to quantify with adequate accuracy the vibratory phenomena and the sound pressure fluctuations, in terms of amplitude and frequency, due to the lack of metrological traceability and suitable sensitivity parameters for digital sensors. This paper presents two calibration procedures, recently realized at INRiM, suitable to characterize digital MEMS accelerometers and microphones. In particular, the possibility to provide a proper sensitivity for digital signal outputs, in order to ensure traceability to primary standard, is addressed.

Digital radio signal cancellation attacks: an experimental evaluation

Abstract: Attacker models are the cornerstone of any security assessment. As attacker's capabilities evolve over time, it is key to re-evaluate periodically if attacker models that were deemed unrealistic in the past might not pose a possible threat today. In this work, we evaluate the threat of wireless radio signal cancellation attacks in the face of recent advancements in software-defined radio attacker capabilities. Unlike classical radio interference or jamming attacker models which add noise to the legitimate communication, signal cancellation attacks aim at interfering destructively with the legitimate signal in order to remove those signals from the spectrum. While signal cancellation attacks were deemed unrealistic in the analogue domain, we analyse the system requirements to perform such attacks digitally using SDRs and evaluate the feasibility to launch such attacks against wireless communication systems such as GPS. Our evaluation reveals that signal cancellation attacks that manage to attenuate up to 40 dB of the signal at the receiver are feasible over the air. We further show that even complex CDMA signals such as GPS can be attenuated by 30 dB, even below a receiver's noise floor. These results indicate that digital signal cancellation attacks - especially against systems like GPS - should not be considered impossible per se, but deserve consideration when assessing the threat of attacks on wireless communication systems.

The Use of Power Line Communication Technology for Electronic Relays of Car Alarms

Abstract: The growing number of hijackings with the use of special technical equipment makes actual the development of new types of electronic relays that disconnect an automobile circuits and prevent hijacking. It is promising to create relays that work using the Power Line Communication technology and receive control signals from the main module of car alarms by car power wiring. Such relays are difficult to detect and deactivate. Famous publications on the PLC technology are devoted to the creation of devices controlled from the main electronic unit of the car. They involve the use of blocking filters built in the power wiring of the car, which is unacceptable when installing anti-theft systems, which are optional equipment. In the presented article a method of digital signal transmission is proposed, an electrical model of wiring is developed and the results of its verification are presented. To increase the probability of receiving a code signal on the background of impulse noise, it is proposed to install floating reference levels of the internal comparator of the microcontroller.

Research on Subdivision System of Sin-Cos Encoder Based on Zero Phase Bandpass Filter.

Abstract: A novel high-precision subdivision system for high-speed encoders is designed in this work. The system is designed with an arc second of Sin-Cos Encoder (SCE) based on zero phase bandpass filter. The system collects the analog output signals of an encoder with a high-speed data acquisition system (DAS); the noise of a digital signal can be effectively eliminated by zero phase bandpass filter with appropriate prior parameters. Finally, the actual rotation angle of the encoder is calculated by the software subdivision technique in the system. The software subdivision technique includes two methods, which are the Analog Pulse Counter (APC) and the Arc Tangent Subdivision (ATS). The APC method calculates the encoder angle by counting the analog pulses acquired by the arc tangent signal. The ATS method calculates the encoder angle by computing the arc tangent results of each point. The accuracy and stability of the system are first verified with a simulated signal; second, the real signals of an SCE are acquired by a high speed DAS on a test bench of a precision reducer, which is employed in industrial robots. The results of the proposed system are compared. The experimental results show that the system can significantly improve the accuracy of the encoder angle calculation, with controllable costs.

Adaptive parameter-tuning stochastic resonance based on SVD and its application in weak IF digital signal enhancement

Abstract: Parameter-tuning stochastic resonance can effectively use noise to enhance signal energy, whereas its system parameters are hard to select, and how to combine it with more practical signals needs to be researched. In this study, the IF (intermediate frequency) digital signal with low SNR (signal-noise ratio) is selected as the research object, and the measuring function based on SVD (singular value decomposition) that is not dependent on prior knowledge is proposed as the evaluation function to optimize the parameters of stochastic resonance system. The nature of the stochastic resonance is first described from the eigenspace of the signal. After the analysis of the effects of different system parameters, amplitude normalization is employed to optimize only one parameter, simplifying the algorithm. Finally, an adaptive parameter-tuning stochastic resonance method based on AFSA (artificial fish swarm algorithm) is developed for three types of modulated signals, achieving an optimum matching of noisy signals and non-linear systems at fast convergence speed. According to the simulation, the proposed algorithm is proven effective, efficient, and robust, laying a solid foundation for the subsequent signal processing work.

Multi radio access technology power management circuit

Abstract: A multi radio access technology (RAT) circuit is provided. The multi RAT power management circuit can concurrently support multiple different RATs using a single power management integrated circuit and a single power amplifier. The multi RAT power management circuit receives a first digital signal modulated based on a first RAT and a second digital signal modulated based on a second RAT. Control circuitry generates a composite output signal, which includes the first digital signal and the second digital signal and corresponds to a time-variant composite signal envelope derived from a respective peak envelope of the first and the second digital signals. The control circuitry generates a voltage control signal having a time-variant target voltage envelope tracking the time-variant composite signal envelope of the composite output signal. As such, the multi RAT power management circuit can concurrently support the multiple different RATs without increasing size, costs, complexity, and/or power consumption.

Light and small spatial infrared quantitative measurement device with wide dynamic range

Abstract: Disclosed is a light and small spatial infrared quantitative measurement device with a wide dynamic range. The device comprises an optical system, an infrared detector, a non-uniform correction mechanism, an image preprocessing circuit, and an information processor. The optical system collects infrared radiation from a target and a background, and converges to the infrared detector. The infrared detector converts an optical signal into an analog electrical signal, and outputs the analog electrical signal to the image preprocessing circuit. The image preprocessing circuit converts the analog electrical signal into a digital signal, controls the non-uniform correction mechanism to move to complete a non-uniform correction of the system, so as to obtain a corrected digital image and output the digital image to the information processor. The non-uniform correction mechanism provides a uniform scene for the non-uniform correction of the system. The information processor performs infrared radiation quantitative calculation and correction on the obtained digital image, and outputs a result to the outside. The device realizes the infrared radiation quantitative measurement for a spatial target, and has the characteristics of being wide in measurement dynamic range and high in temperature measurement precision.

An audio fingerprinting extraction algorithm based on lifting wavelet packet and improved optimal-basis selection

Abstract: Audio fingerprinting technology is widely applied to the analysis and processing of digital signal, especially in the application of speech recognition which is one of the most popular fields of the intelligent multimedia and artificial intelligence. Traditional audio fingerprinting extraction algorithm is based on the decomposition and reconstruction of the wavelet packet. But the requirement of computational capacity and memory is so large. So this paper proposed an algorithm which is based on the lifting wavelet packet and the improved optimal-basis selection to find the coefficient of optimal wavelet packet. Then the average of the logarithmic energy entropy is adopted as the characteristic parameter. And the capacity of computing and memory is better than the traditional algorithm because of the lifting wavelet packet which is more suitable for processing of speech online and the design of intelligent multimedia. And the experiment results indicate that this algorithm is not only robust for the audio which is handled by some kinds of methods and can reflect the overall characteristics of the audio very well, but also has good distinguishability between different audio.

Digital image correlation with reduced bias error based on digital signal upsampling theory

Abstract: Based on digital signal upsampling theory, a new computing strategy has been proposed to reduce the bias error in digital image correlation (DIC) caused by intensity interpolation. For each subset, before subpixel image matching, the subimage around the target subset was processed by increasing the sampling rate with an integer factor. The increase of the sampling rate is realized by resampling in the digital domain. The combination of digital signal upsampling processing with DIC can greatly reduce the interpolation bias error. The measurement accuracy of the proposed computing strategy was investigated in this study. Both numerical experiments and real-world experiments have been conducted in order to verify the effectiveness of the proposed computing strategy. The results indicate that the bias error can be significantly reduced without sacrificing the standard deviation error. With the proposed computing strategy, high-accuracy DIC measurement with near-negligible bias error is expected.

VCO-based Feature Extraction Architecture for Low Power Speech Recognition Applications

Abstract: This work proposes a new approach for speech recognition and voice activity detection tasks. The main limitation of the state-of-the-art solutions for this type of applications is the high power consumption and the silicon area required, which strongly limit their implementation on portable devices. In this manuscript, we propose a new solution based on making use of voltage-controlled-oscillators based analog-to-digital converters (VCO-based ADCs). VCO-based ADCs have enabled efficient audio-oriented implementations in terms of power and area. Here, a VCO-based band-pass filter is proposed to extract the features of analog input sound signals and generate a digital output stream encoding the mel-frequency cepstrum coefficients (MFCCs). This digital signal will be processed afterwards by a decision circuit to determine whether the sound signal corresponds to a pattern. Additionally, the same VCO can be reused to implement both the band-pass filters in the decision mode and the standard ADC output once a keyword or voice has been detected, saving significant area. A behavioral model of the filter was built to validate the performance by simulation. Then, the architecture was designed using a 130-nm CMOS process to get a realistic outlook of the power consumption and the expected occupied area on silicon. Finally, the proposed solution was compared to other equivalent solutions.

Pixel unit, image sensor, image processing method and storage medium

Abstract: The invention discloses a pixel unit, an image sensor, an image processing method and a storage medium. The pixel unit comprises an optical filter, a photodiode PD array and a playback circuit, the optical filter is positioned in a first layer area of the pixel unit, and the PD array and playback circuit are positioned in a second layer area of the pixel unit; the optical filter carries out colorfiltering on incident light to obtain a monochromatic light signal in the preset wavelength; the PD array is arranged opposite to the optical filter, and used to absorb the monochromatic light signalin the preset wavelength, and converts the absorbed monochromatic light signal into an electric signal; the PD array comprises multiple PD columns in the same diameter; and the playback circuit is connected with the PD array, and used to read the electric signal and convert the electric signal into a digital signal for transmission.

Implement and traceability of adjustable EVM on ZigBee signal from vector signal generator

Abstract: Error Vector Magnitude (EVM) is the most important parameter of modulation quality of wireless digital signal. At present, there are some shortcomings in the calibration process as follows: 1) Lack of vector signal generator that can accurately set the required EVM value of the standard signal, so when calibrating the vector signal analyzer, only the measurement points near the zero modulation error are calibrated. 2) Closed-loop inspection between instruments, using vector signal analyzer and vector signal generator to calibrate the EVM in a closed-loop way. According to the calculation formula of error vector magnitude on digital modulation signal from the vector signal generator, the setting value of I/Q amplitude imbalance and signal-to-noise ratio at a given EVM value of the ZigBee signal are calculated, in the meantime the vector signal analysis software built in the N9030A is used to measure the EVM of the ZigBee signal. The result shows good consistency. At the end of the article, the traceability of adjustments on I/Q amplitude imbalance and signal-to-noise ratio is introduced.

Detection of High impedance Fault in Distribution Network Using Fuzzy Logic Control

Abstract: High impedance faults HIFs are that faults less than the fault current to operate conventional protective devices: overcurrent, reclosers, relays, and fuses. This fault often occurs when one of the overhead conductors breaks and contact the ground this will result in an energized high-voltage conductor coming into the reach of personnel. With the techniques of Digital Signal Process, software and hardware can be also designed for improving the detection reliability in these kinds of current faults in the power system. In this paper, a new technique to evaluate the 3rd and 5th harmonics to detect HIF is proposed. The fuzzy logic control was trained by practical fault data from the practical test of a laboratory prototype under downed one conductor to Wet Sand condition. The performance studies of practical results show that the proposed technique was effective in detecting high impedance fault. With this integrated approach, the accuracy in detection fault was significantly improved over other types of detection techniques based on a conventional algorithm.

Ranking power spectra: a proof of concept

Abstract: Objective: To characterize the irregularity of the spectrum of a signal, spectral entropy is a widely adopted measure. However, such a metric is invariant under any permutation of the estimations of the powers of individual frequency components on a predefined grid. This erases the order structure inherent in the spectrum which is also an important aspect of irregularity of the signal. To disentangle the order structure and extract meaningful information from raw digital signal, novel analysis method is necessary. Approach: A novel method to depict the order structure by simply ranking power estimations on frequency grid of a evenly spaced signal is proposed. Two descriptors mapping real- and vector-valued power spectrum estimation of a signal into scalar value are defined in a heuristic manner. By definition, the proposed descriptor is capable of distinguishing signals with identical spectrum entropies. Main Results: The proposed descriptor showed its potential in diverse problems. Significant (p<0.001) differences were observed from brain signals and surface electromyography of different pathological/physiological states. Drastic change accompanied by the alteration of the underlying process of signals enables it as candidate feature for seizure detection and endpoint detection in speech signal. Significance: This letter explores the previously ignored order structure in the spectrum of physiological signal. We take one step forward along this direction by proposing two computationally efficient descriptors with guaranteed information gain. As far as the authors are concerned, this is the first work revealing the effectiveness of the order structure in the spectrum in physiological signal processing.

Amplitude and phase consistency correction system of radar receiver

Abstract: The invention relates to an amplitude and phase consistency correction system of a radar receiver and aims at providing a correction system that does not require a far-field radiation source and has the high amplitude phase correction reliability and small errors. The system is implemented as follow: when a radar is in a calibration mode, a signal routing subsystem sends a generated correction signal to an analog receiving channel subsystem and down-conversion is carried out on the correction signal to convert an intermediate-frequency analog signal into an intermediate-frequency digital signal; a digital signal processing subsystem receives an intermediate-frequency digital signal for each path of radar receiver receiving channel and generates one path of numerically controlled oscillator(NCO), a sine signal and a cosine signal outputted by the NCO are multiplied by the intermediate-frequency digital signal respectively to obtain an in-phase component and an orthogonal component, a next group of frequency values and phase values of the NCO are set, and then the above-mention process is repeated until the phase of the NCO changes one cycle, so that amplitude and phase consistencycorrection of the receiving channel is completed.

Optimal communication frequency for switching cabled ocean networks with commands carried over the power line

Abstract: Cabled ocean networks with tree or ring topologies play an important role in real-time ocean exploration. Due to the time-consuming need for field maintenance, cable switching technology that can actively switch the power on/off on certain branches of the network becomes essential for enhancing the reliability and availability of the network. In this paper, a novel switching-control method is proposed, in which we invert the power transmission polarity and use the current on the power line as the digital signal at low frequency to broadcast information with the address and commands to the network, and the corresponding branching unit (BU) can decode and execute the switching commands. The cable’s parasitic parameters, the network scale, and the number of BUs, as the influencing factors of the communication frequency on the power line, are theoretically studied and simulated. An optimized frequency that balances the executing accuracy and rate is calculated and proved on a simulated prototype. The results showed that the cable switching technology with optimized frequency can enhance the switching accuracy and configuring rate.