Showing papers on "Digital signal processing published in 2018"

Graph Signal Processing: Overview, Challenges, and Applications

Abstract: Research in graph signal processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper, we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing, along with a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas. We then summarize recent advances in developing basic GSP tools, including methods for sampling, filtering, or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning.

Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features

Abstract: Recurrent neural network (RNN) and long short-term memory (LSTM) have achieved great success in processing sequential multimedia data and yielded the state-of-the-art results in speech recognition, digital signal processing, video processing, and text data analysis. In this paper, we propose a novel action recognition method by processing the video data using convolutional neural network (CNN) and deep bidirectional LSTM (DB-LSTM) network. First, deep features are extracted from every sixth frame of the videos, which helps reduce the redundancy and complexity. Next, the sequential information among frame features is learnt using DB-LSTM network, where multiple layers are stacked together in both forward pass and backward pass of DB-LSTM to increase its depth. The proposed method is capable of learning long term sequences and can process lengthy videos by analyzing features for a certain time interval. Experimental results show significant improvements in action recognition using the proposed method on three benchmark data sets including UCF-101, YouTube 11 Actions, and HMDB51 compared with the state-of-the-art action recognition methods.

A Survey on Hybrid Beamforming Techniques in 5G: Architecture and System Model Perspectives

Abstract: The increasing wireless data traffic demands have driven the need to explore suitable spectrum regions for meeting the projected requirements. In the light of this, millimeter wave (mmWave) communication has received considerable attention from the research community. Typically, in fifth generation (5G) wireless networks, mmWave massive multiple-input multiple-output (MIMO) communications is realized by the hybrid transceivers which combine high dimensional analog phase shifters and power amplifiers with lower-dimensional digital signal processing units. This hybrid beamforming design reduces the cost and power consumption which is aligned with an energy-efficient design vision of 5G. In this paper, we track the progress in hybrid beamforming for massive MIMO communications in the context of system models of the hybrid transceivers’ structures, the digital and analog beamforming matrices with the possible antenna configuration scenarios and the hybrid beamforming in heterogeneous wireless networks. We extend the scope of the discussion by including resource management issues in hybrid beamforming. We explore the suitability of hybrid beamforming methods, both, existing and proposed till first quarter of 2017, and identify the exciting future challenges in this domain.

Digital Signal Processing for Short-Reach Optical Communications: A Review of Current Technologies and Future Trends

Abstract: Driven primarily by cloud service and data-center applications, short-reach optical communication has become a key market segment and growing research area in recent years. Short-reach systems are characterized by direct detection-based receiver configurations and other low-cost and small form factor components that induce transmission impairments unforeseen in their coherent counterparts. Innovative signaling and digital signal processing (DSP) play a pivotal role in enabling these components to realize their ultimate potentials and meet data rate requirements in cost-effective manners. This paper presents an overview of recent DSP developments for short-reach communications systems and discusses future trends.

Crepe: A Convolutional Representation for Pitch Estimation

Abstract: The task of estimating the fundamental frequency of a monophonic sound recording, also known as pitch tracking, is fundamental to audio processing with multiple applications in speech processing and music information retrieval. To date, the best performing techniques, such as the pYIN algorithm, are based on a combination of DSP pipelines and heuristics. While such techniques perform very well on average, there remain many cases in which they fail to correctly estimate the pitch. In this paper, we propose a data-driven pitch tracking algorithm, CREPE, which is based on a deep convolutional neural network that operates directly on the time-domain waveform. We show that the proposed model produces state-of-the-art results, performing equally or better than pYIN. Furthermore, we evaluate the model's generalizability in terms of noise robustness. A pre-trained version of CREPE is made freely available as an open-source Python module for easy application.

Chaotic optical communications over 100-km fiber transmission at 30-Gb/s bit rate.

Abstract: For the first time, to the best of our knowledge, we experimentally demonstrate a successful 30-Gb/s signal transmission of a duobinary message hidden in a chaotic optical carrier over 100-km fiber. Thanks to the duobinary modulation format with high spectral efficiency, the 30-Gb/s signal can be encrypted by a 10-GHz-wide chaotic carrier. A digital signal processing technique can be used to convert duobinary data into binary data on the receiver side. This proposal lowers the requirement for wideband chaos generation and synchronization in high-speed long-distance chaotic optical communications, and fiber dispersion compensation can also be simplified, which has potential to be used in high-speed long-distance secure optical communications.

CREPE: A Convolutional Representation for Pitch Estimation

Abstract: The task of estimating the fundamental frequency of a monophonic sound recording, also known as pitch tracking, is fundamental to audio processing with multiple applications in speech processing and music information retrieval. To date, the best performing techniques, such as the pYIN algorithm, are based on a combination of DSP pipelines and heuristics. While such techniques perform very well on average, there remain many cases in which they fail to correctly estimate the pitch. In this paper, we propose a data-driven pitch tracking algorithm, CREPE, which is based on a deep convolutional neural network that operates directly on the time-domain waveform. We show that the proposed model produces state-of-the-art results, performing equally or better than pYIN. Furthermore, we evaluate the model's generalizability in terms of noise robustness. A pre-trained version of CREPE is made freely available as an open-source Python module for easy application.

1.72-Tb/s Virtual-Carrier-Assisted Direct-Detection Transmission Over 200 km

Abstract: Supporting the ever-increasing data-center-inter-connect traffic in a cost-effective manner is a great challenge, which requires innovative transmission and digital signal processing (DSP) techniques. Recently, single-side-band (SSB) direct-detection (DD) transmissions have been actively considered for data rates beyond 100 Gb/s per channel and distance of hundreds of kilometers due to its capability of electronic chromatic dispersion compensation. In addition, several effective DSP techniques to mitigate or suppress the signal-signal beating interference (SSBI) due to the squared-law detection of the photodiode have been intensively investigated, such as Kramers–Knonig (KK) and SSBI cancellation schemes, showing promising performance at data rates over 200 Gb/s and distance beyond 100 km. In this paper, we demonstrate that high-performance low-complexity SSB DD transmissions can be achieved by generating a digital carrier (virtual carrier) together with the complex information-bearing signal at the transmitter using only two digital-to-analog converters. Combining this transmission technique with either the KK field reconstruction or a two-stage SSBI cancellation scheme at the receiver, eight-channel WDM signals with a net data rate of 1.72 Tb/s have been transmitted successfully over a record span length of 200 km at 1550 nm.

Optimized digital speckle patterns for digital image correlation by consideration of both accuracy and efficiency.

Abstract: The technique of digital image correlation (DIC), which has been widely used for noncontact deformation measurements in both the scientific and engineering fields, is greatly affected by the quality of speckle patterns in terms of its performance. This study was concerned with the optimization of the digital speckle pattern (DSP) for DIC in consideration of both the accuracy and efficiency. The root-mean-square error of the inverse compositional Gauss-Newton algorithm and the average number of iterations were used as quality metrics. Moreover, the influence of subset sizes and the noise level of images, which are the basic parameters in the quality assessment formulations, were also considered. The simulated binary speckle patterns were first compared with the Gaussian speckle patterns and captured DSPs. Both the single-radius and multi-radius DSPs were optimized. Experimental tests and analyses were conducted to obtain the optimized and recommended DSP. The vector diagram of the optimized speckle pattern was also uploaded as reference.

Kramers-Kronig receiver operable without digital upsampling.

Abstract: The Kramers-Kronig (KK) receiver is capable of retrieving the phase information of optical single-sideband (SSB) signal from the optical intensity when the optical signal satisfies the minimum phase condition. Thus, it is possible to direct-detect the optical SSB signal without suffering from the signal-signal beat interference and linear transmission impairments. However, due to the spectral broadening induced by nonlinear operations in the conventional KK algorithm, it is necessary to employ the digital upsampling at the beginning of the digital signal processing (DSP). The increased number of samples at the DSP would hinder the real-time implementation of this attractive receiver. Hence, we propose a new DSP algorithm for KK receiver operable at 2 samples per symbol. We adopt a couple of mathematical approximations to avoid the use of nonlinear operations such as logarithm and exponential functions. By using the proposed algorithm, we demonstrate the transmission of 112-Gb/s SSB orthogonal frequency-division-multiplexed signal over an 80-km fiber link. The results show that the proposed algorithm operating at 2 samples per symbol exhibits similar performance to the conventional KK one operating at 6 samples per symbol. We also present the error analysis of the proposed algorithm for KK receiver in comparison with the conventional one.

Frequency Comb-Based WDM Transmission Systems Enabling Joint Signal Processing

Abstract: We review the use of optical frequency combs in wavelength-division multiplexed (WDM) fiber optic communication systems. In particular, we focus on the unique possibilities that are opened up by the stability of the comb-line spacing and the phase coherence between the lines. We give an overview of different techniques for the generation of optical frequency combs and review their use in WDM systems. We discuss the benefits of the stable line spacing of frequency combs for creating densely-packed optical superchannels with high spectral efficiency. Additionally, we discuss practical considerations when implementing frequency-comb-based transmitters. Furthermore, we describe several techniques for comb-based superchannel receivers that enables the phase coherence between the lines to be used to simplify or increase the performance of the digital carrier recovery. The first set of receiver techniques is based on comb-regeneration from optical pilot tones, enabling low-overhead self-homodyne detection. The second set of techniques takes advantage of the phase coherence by sharing phase information between the channels through joint digital signal processing (DSP) schemes. This enables a lower DSP complexity or a higher phase-noise tolerance.

Performance comparison of DML, EML and MZM in dispersion-unmanaged short reach transmissions with digital signal processing.

Abstract: In this paper, transmission performances of directly modulated laser (DML), electro-absorption modulated laser (EML) and Mach-Zehnder modulator (MZM) are experimentally compared in dispersion-unmanaged high-speed transmission systems with digital signal processing (DSP). We show that, although the DML based transmitter is often believed to be less favorable in C-band high-speed transmissions, it exhibits superior performance over the other two transmitters when either linear or nonlinear digital signal processing is adopted. By theoretical and experimental analysis, we reveal that the superiority of DML can be attributed to the compensation of fiber power fading by its inherent adiabatic chirp as well as the mitigation of chirp induced distortions by the linear or nonlinear equalization. Experimental results of 56Gb/s 4-level pulse amplitude modulation (PAM4) signals under various equalization schemes including linear feedforward equalization, simplified nonlinear Volterra equalization and partial response signaling are presented. Particularly, we show that for DML a 40km transmission distance can be achieved to satisfy the extended range-4 (ER4) Ethernet interconnect using a simplified Volterra equalizer, and a 20km transmission distance can be supported using a linear equalizer. In contrast, for MZM and EML, the achievable transmission distances are respectively 20km and 15km using the Volterra equalizer, respectively, and 15km and 10km using linear equalizer, respectively. Moreover, we show that even using the combination of the Volterra equalizer and partial response signaling, the transmission distances of MZM and EML based systems are limited to 30km and 20km.

Phased Arrays for Radio Astronomy, Remote Sensing, and Satellite Communications

Abstract: Discover a modern approach to the analysis, modeling and design of high sensitivity phased arrays. Network theory, numerical methods and computational electromagnetic simulation techniques are uniquely combined to enable full system analysis and design optimization. Beamforming and array signal processing theory are integrated into the treatment from the start. Digital signal processing methods such as polyphase filtering and RFI mitigation are described, along with technologies for real-time hardware implementation. Key concepts from interferometric imaging used in radio telescopes are also considered. A basic development of theory and modeling techniques is accompanied by problem sets that guide readers in developing modeling codes that retain the simplicity of the classical array factor method while incorporating mutual coupling effects and interactions between elements. Combining current research trends with pedagogical material suitable for a first-year graduate course, this is an invaluable resource for students, teachers, researchers, and practicing RF/microwave and antenna design engineers.

Efficient DSP and Circuit Architectures for Massive MIMO: State of the Art and Future Directions

Abstract: Massive MIMO is a compelling wireless access concept that relies on the use of an excess number of base-station antennas, relative to the number of active terminals. This technology is a main component of 5G New Radio and addresses all important requirements of future wireless standards: a great capacity increase, the support of many simultaneous users, and improvement in energy efficiency. Massive MIMO requires the simultaneous processing of signals from many antenna chains, and computational operations on large matrices. The complexity of the digital processing has been viewed as a fundamental obstacle to the feasibility of Massive MIMO in the past. Recent advances on system-algorithm-hardware co-design have led to extremely energy-efficient implementations. These exploit opportunities in deeply-scaled silicon technologies and perform partly distributed processing to cope with the bottlenecks encountered in the interconnection of many signals. For example, prototype ASIC implementations have demonstrated zero-forcing precoding in real time at a 55 mW power consumption (20 MHz bandwidth, 128 antennas, and multiplexing of 8 terminals). Coarse and even error-prone digital processing in the antenna paths permits a reduction of consumption with a factor of 2 to 5. This article summarizes the fundamental technical contributions to efficient digital signal processing for Massive MIMO. The opportunities and constraints on operating on low-complexity RF and analog hardware chains are clarified. It illustrates how terminals can benefit from improved energy efficiency. The status of technology and real-life prototypes discussed. Open challenges and directions for future research are suggested.

Intelligent fault diagnosis of rolling bearing using one-dimensional multi-scale deep convolutional neural network based health state classification

Abstract: Fault diagnosis of rolling element bearings based on vibration signal is the most popular way to avoid underlying damage for any unexpected fault. In recent years, intelligent fault diagnosis algorithms using machine learning technique have achieved much success, and many deep learning techniques have also found their way into fault diagnosis of rotating machines. Considering that convolution is the most important method to analyze signals in digital signal processing, a novel deep convolutional neural networks is developed to operate directly on the raw vibration signal. The proposed MS-DCNN model could broaden and deepen the neural networks to learn better and more robust feature representations owing to multi-scale convolution layer, meanwhile, reduce the network parameters and the training time. Fault classification experiments of rolling element bearings have been undertaken to indicate the effectiveness of the MS-DCNN model. Compared with 1d-DCNN and 2d-DCNN, MS-DCNN can not only achieve higher accuracy rate in the testing set, but also run more smoothly in the training process.

Non-Adaptive Methods for Fetal ECG Signal Processing: A Review and Appraisal.

Abstract: Fetal electrocardiography is among the most promising methods of modern electronic fetal monitoring. However, before they can be fully deployed in the clinical practice as a gold standard, the challenges associated with the signal quality must be solved. During the last two decades, a great amount of articles dealing with improving the quality of the fetal electrocardiogram signal acquired from the abdominal recordings have been introduced. This article aims to present an extensive literature survey of different non-adaptive signal processing methods applied for fetal electrocardiogram extraction and enhancement. It is limiting that a different non-adaptive method works well for each type of signal, but independent component analysis, principal component analysis and wavelet transforms are the most commonly published methods of signal processing and have good accuracy and speed of algorithms.

A 112Gb/S 2.6pJ/b 8-Tap FFE PAM-4 SST TX in 14nm CMOS

Abstract: The ongoing demand for higher data rates in wireline and optical communications has led to emerging standards in the 100Gb/s+ regime [1]. Although these standards are still in the definition phase they will rely on multi-level signaling such as PAM-4 along with an increasing amount of digital signal processing. In the foreseeable future, a high-performance TX will consist of a CMOS DSP frontend followed by a high sampling rate data converter [2,3], whose design remains a significant challenge. This paper presents a 112Gb/s PAM-4 SST Tx that is based on a quarter-rate 56GS/s 8b SST DAC along with a digital 8-tap FIR filter for channel equalization.

Digital Signal Processing with Kernel Methods

Abstract: Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research.

Threshold-Free Interference Cancellation Method for Automotive FMCW Radar Systems

Abstract: Radar systems are key components for today's advanced driver assistance systems such as adaptive cruise control or emergency brake assistants. Along with the rising utilization of radars in modern cars, the issue of interference amongst themselves arises. It has been shown in previous work that interference between different frequency modulated continuous wave (FMCW) radar systems leads to an increased noise floor. This may severely impact the detectability of objects, especially those with a small radar cross section like pedestrians. In this work we propose a novel concept to mitigate interference in FMCW radar transceivers using digital signal processing. The actual interference cancellation is carried out in frequency domain taking into account a sequence of FMCW chirps. Therewith noise suppression is performed, the interference is cancelled, and the object information is retained in the radar image. In contrast to existing interference cancellation concepts, no threshold needs to be chosen in advance. We prove our method with simulation results, and compare it to existing work.

Digital Code-Division Multiplexing Channel Aggregation for Mobile Fronthaul Architecture With Low Complexity

Abstract: A low-complexity mobile fronthaul architecture via digital code-division multiplexing (CDM) is proposed to enable channel aggregation of 4G-LTE signals. In comparison with traditional frequency division multiplexing based aggregation scheme, the fast Fourier transformation/inverse fast Fourier transformation operations are replaced by simple sign selection and addition, leading to the significant reduction of computational complexity. Moreover, synchronous transmission of both the I/Q waveforms of wireless signals and the control words (CWs) used for the purpose of control and management using the CDM approach is also presented to be compliant with the common public radio interface (CPRI). In a proof-of-concept experiment, we demonstrate the transmission of 48 × 20 MHz LTE signals with CPRI equivalent data rate of 59 Gb/s, achieving an average error vector magnitude (EVM) of ∼3.6% and ∼4.3% after 5 and 20 km transmission over standard single-mode fiber (SSMF), respectively. Furthermore, we successfully demonstrate the transmission of 32 × 20 MHz LTE signals together with CPRI-compliant CWs, corresponding to CPRI-equivalent data rate of 39.32 Gb/s, only using single optical wavelength channel with analog bandwidth of ∼1.96 GHz. After transmission over 5 km SSMF, CWs can be error-free recovered while the LTE signals are recovered with an EVM of ∼3.6%.

Realization of true all-optical AND logic gate based on nonlinear coupled air-hole type photonic crystal waveguides.

Abstract: In this manuscript we propose an easily scalable true all-optical AND logic gate for pulsed signal operation based on band-gap transmission within nonlinear realistic air-hole type coupled photonic crystal waveguides (C-PCW) We call it “true” all-optical AND logic gate, because all AND gate topologies operate with temporal solitons that maintain a stable pulse envelope during the optical signal processing along the different C-PCW modules yielding ultrafast full-optical digital signal processing We directly use the registered (output) signal pulse as new input signal between multiple concatenated nonlinear C-PCW modules (ie AND gates) to setup a multiple-input true all-optical AND logic gate Extensive full-wave computational electromagnetic analysis proves the correctness of our theoretical studies and the proposed operation principle of the multiple-input AND logic gate is vividly demonstrated for realistic C-PCWs

Majority-Based Spin-CMOS Primitives for Approximate Computing

Abstract: Promising for digital signal processing applications, approximate computing has been extensively considered to tradeoff limited accuracy for improvements in other circuit metrics such as area, power, and performance. In this paper, approximate arithmetic circuits are proposed by using emerging nanoscale spintronic devices. Leveraging the intrinsic current-mode thresholding operation of spintronic devices, we initially present a hybrid spin-CMOS majority gate design based on a composite spintronic device structure consisting of a magnetic domain wall motion stripe and a magnetic tunnel junction. We further propose a compact and energy-efficient accuracy-configurable adder design based on the majority gate. Unlike most previous approximate circuit designs that hardwire a constant degree of approximation, this design is adaptive to the inherent resilience in various applications to different degrees of accuracy. Subsequently, we propose two new approximate compressors for utilization in fast multiplier designs. The device-circuit SPICE simulation shows 34.58% and 66% improvement in power consumption, respectively, for the accurate and approximate modes of the accuracy-configurable adder, compared to the recently reported domain wall motion-based full adder design. In addition, the proposed accuracy-configurable adder and approximate compressors can be efficiently utilized in the discrete cosine transform (DCT) as a widely-used digital image processing algorithm. The results indicate that the DCT and inverse DCT (IDCT) using the approximate multiplier achieve $\sim$ 2x energy saving and 3x speed-up compared to an exactly-designed circuit, while achieving comparable quality in its output result.

High Speed Precompensated Nonlinear Frequency-Division Multiplexed Transmissions

Abstract: Nonlinear frequency division multiplexing (NFDM) with the modulation of the nonlinear Fourier spectrum (both discrete and/or continuous parts) have been recently considered as a potential transmission method to combat the fiber nonlinearity impairments. However, due to many challenges in design, digital signal processing (DSP), numerical algorithms, and hardware implementation, reported data rates of NFDM systems have been so far limited to 50 Gb/s. In this paper, we experimentally demonstrate a practical approach for increasing the data rate of NFDM transmission systems by increasing the number of modulated nonlinear subcarriers together with the application of a precompensation technique for the channel-induced phase-shift in the nonlinear Fourier domain. As a result, a record-high data rate of 125 Gb/s and spectral efficiency over 2 bits/s/Hz in burst-mode, single-polarization NFDM transmissions were achieved over 976 km of standard single mode fiber with EDFA-only amplification by transmitting and processing 222 32 QAM-modulated nonlinear subcarriers simultaneously.

Modulation format identification enabled by the digital frequency-offset loading technique for hitless coherent transceiver.

Abstract: We propose a blind and fast modulation format identification (MFI) enabled by the digital frequency-offset (FO) loading technique for hitless coherent transceiver. Since modulation format information is encoded to the FO distribution during digital signal processing (DSP) at the transmitter side (Tx), we can use the fast Fourier transformation based FO estimation (FFT-FOE) method to obtain the FO distribution of individual data block after constant modulus algorithm (CMA) pre-equalization at the receiver side, in order to realize non-data-aided (NDA) and fast MFI. The obtained FO can be also used for subsequent FO compensation (FOC), without additional complexity. We numerically investigate and experimentally verify the proposed MFI with high accuracy and fast format switching among 28 Gbaud dual-polarization (DP)-4/8/16/64QAM, time domain hybrid-4/16QAM, and set partitioning (SP)-128QAM. In particular, the proposed MFI brings no performance degradation, in term of tolerance of amplified spontaneous emission (ASE) noise, laser linewidth, and fiber nonlinearity. Finally, a hitless coherent transceiver enabled by the proposed MFI with switching-block of only 2048 symbols is demonstrated over 1500 km standard single mode fiber (SSMF) transmission.

Signal compression and enhancement using a new orthogonal-polynomial-based discrete transform

Abstract: Discrete orthogonal functions are important tools in digital signal processing. These functions received considerable attention in the last few decades. This study proposes a new set of orthogonal functions called discrete Krawtchouk–Tchebichef transform (DKTT). Two traditional orthogonal polynomials, namely, Krawtchouk and Tchebichef, are combined to form DKTT. The theoretical and mathematical frameworks of the proposed transform are provided. DKTT was tested using speech and image signals from a well-known database under clean and noisy environments. DKTT was applied in a speech enhancement algorithm to evaluate the efficient removal of noise from speech signal. The performance of DKTT was compared with that of standard transforms. Different types of distance (similarity index) and objective measures in terms of image quality, speech quality, and speech intelligibility assessments were used for comparison. Experimental tests show that DKTT exhibited remarkable achievements and excellent results in signal compression and speech enhancement. Therefore, DKTT can be considered as a new set of orthogonal functions for futuristic applications of signal processing.

Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM

Abstract: Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.

Embedded Low-Power Processor for Personalized Stress Detection

Abstract: Personal monitoring systems require sampling and processing on multiple streams of physiological signals to extract meaningful information. These systems require a large number of digital signal processing and machine learning kernels, which typically require significant amounts of power. However, to be used in a wearable environment, the processing system needs to be low-power, real-time, and light-weight. In this brief, we present a personalized stress monitoring processor that can meet these requirements. First, various physiological features are explored to maximize stress detection accuracy using two machine learning classifiers including support vector machine (SVM) and ${K}$ -nearest neighbors (KNN). Among different extracted features from four physiological sensors, heart rate and accelerometer features have 96.7% (SVM) and 95.8% (KNN) detection accuracy. In the second part, two fully flexible and multi-modal processing hardware designs are presented that consist of feature extraction and classification algorithms. We first demonstrate the ASIC post-layout implementation of both designs in 65-nm CMOS technology as well as the implementation on Artix-7 field-programmable gate array (FPGA). The proposed SVM and KNN processors on the ASIC platform occupy an area of 0.17 mm2 and 0.3 mm2 and dissipate 39.4 mW and 76.69 mW power, respectively. The ASIC implementation improves the energy efficiency by $42{\times }$ (SVM) and $12{\times }$ (KNN) over FPGA implementations. The entire stress monitoring system is further evaluated against a number of other platforms including Raspberry Pi 3B, NVIDIA TX1 GPU, and NVIDIA TX2 GPU. The experimental results indicate that ASIC and FPGA platforms have the highest throughput (decision/sec) as well as lowest power consumption over all other platforms. The ASIC/FPGA implementations improve the energy efficiency (throughput/power) by 6/5 and 5/4 order of magnitude compared to TX1 GPU and Raspberry pie ARM platforms, respectively.

Multi-Bit Digital Receiver Design For Radar Signature Estimation

Abstract: This paper brings out a unique FPGA based pulse detection and characterization approach for digital wideband ESM receiver targeted for EW applications. The proposed approach uses a high speed ADC and FPGA based architecture for sampling and digital signal processing of the received RADAR signals to extract the key parameters such as Frequency (F), Time of Arrival (TOA), Pulse Width (PW) and Pulse Repetition Interval (PRI). The proposed novel FFT based digital EW receiver is designed and verified by MATLAB and SIMULINK tools and VHDL code for same algorithm is generated using HDL Coder for FPGA implementation. The current paper uses 512-point FFT based digital EW receiver, requires minimum pulse width of 750 ns and frequency separation between two simultaneous pulses is 2.63 MHz to extract the key parameters accurately. The simulated and measured results for Pulse detection algorithm are presented.

Music Genre Classification and Recommendation by Using Machine Learning Techniques

Abstract: Music genre prediction is one of the topics that digital music processing is interested in. In this study, acoustic features of music have been extracted by using digital signal processing techniques and then music genre classification and music recommendations have been made by using machine learning methods. In addition, convolutional neural networks, which are deep learning methods, were used for genre classification and music recommendation and performance comparison of the obtained results has been. In the study, GTZAN database has been used and the highest success was obtained with the SVM algorithm.

Burst-mode digital signal processing that pre-calculates FIR filter coefficients for digital coherent pon upstream

Abstract: This paper proposes a burst-mode digital signal processing architecture for digital coherent time-divisionmultiplexed passive optical network (PON) upstream transmission, wherein two key advances are introduced to shorten the preamble length of burst signals required to complete adaptive equalization, as performed by a constant modulus algorithm: tap coefficients of a finite impulse response (FIR) filter are pre-calculated in the optical network unit (ONU) discovery process, and the feed-forward state of polarization compensation is employed before adaptive equalization. The bit error rate performance attained by the proposal is experimentally evaluated; successful burstmode coherent detection with a high sensitivity of -44.7 dBm is demonstrated for 20 Gbit/s single polarizationquadrature phase shift keying burst signals transmitted over a 40 km single mode fiber, even with the use of a short preamble of 1.3 μs, which meets the optical power specifications required for a 512-way split system. The proposal utilizes register request signals whose preamble is longer than those used in current PON systems to optimize the tap coefficients of the FIR filter, which may disrupt smooth ONU registration in high-splitting-ratio systems. To evaluate this impact, numerical calculations based on the Monte Carlo method are conducted, and the results show that the time required to complete the registration of 512 ONUs is just 40 s or so.