Digital signal processing in bio-implantable systems: Design challenges and emerging solutions

Magnetic-bearings, which support shafts with magnetic levitation rather than mechanical contact, have been in industrial use for decades. Recent technological developments, especially in digital processing and control, have made magnetic bearings a more-robust and cost-effective design solution than ever. The dynamic characteristic of electromagnetic bearing depends upon adopted controller; the active control can makes the electromagnetic bearings to realize complex control and special control. With the development of signal processing technology and modern control theory, the main parts of the control system are the digital signal-processing (DSP) electronics, a power supply, and amplifiers. An Active Magnetic Bearing (AMB) controller is mainly discussed in the paper, which is to be solved to realize this flexible control by hardware design based on DSP using TMS320C32 processor. It is proved by experiment that this kind of controller can optimize for this system, improve its stability and also have a very important referential value on the further study of AMB system.

With the increasing use of digital array radars, radar signal processing systems have higher performance requirements. This article introduces a signal processing hardware design for the two-dimensional phased array digital multi-beam system. Because of its digital multi-beam characteristics, it is very demanding on the radar signal processing system's computing power, processing speed, and data throughput. This paper proposes a design of signal processing hardware based on Xilinx FPGA Virtex-7 and two multi-core digital signal processors (DSP) to meet the requirements of two-dimensional phased array digital multi-beam system. Subsequent experiments and engineering practices show that this design scheme can fully meet the requirements of the system.

The development of modern technologies has fundamentally transformed the field of radar signal and data processing. With the use of advanced algorithms and computational power, radars are now capable of extracting crucial information from received signals, facilitating improved target identification and tracking. This article presents some of the advanced technologies employed in radar signal and data processing and their impact on adaptability of radar systems. It traces the evolution of radar technology from old systems to the present, emphasizing the benefits of adaptive radar signal processing, which includes algorithms such as adaptive beamforming, Space-Time Adaptive Processing, and the integration of Machine Learning and Artificial Intelligence. In conclusion, challenges, and future prospects in the field of radar systems are discussed, with a focus on the potential integration of Artificial Intelligence methods, Cognitive radars, and Multiple Input Multiple Output technologies. Despite technical obstacles, opportunities emerge to enhance the performance of radar systems and achieve new levels of efficiency.

Wireless communications is evolving in different directions from several starting points. In virtually all cases, evolution has proceeded from analogue systems to digital systems having more capability to cope with the radio environment and at a lower cost. The synergy between digital radio communications and VLSI is revolutionising the design of wireless terminals. More and more these terminals look like dedicated computers capable of executing digital signal processing (DSP) algorithms of extremely high complexity. On the one side, these algorithms are interfaced with the radio channel via the RF analogue front end and antenna of the terminal. On the other side with the user through a microphone, speaker and keyboard. In nearly all cases, the RF subsystem also contains a frequency synthesiser which is again a dedicated digital hardware. The evolution of these terminals is characterised by the disappearance of analogue signal processing components and subsystems and their replacement in the software domain by DSP code which runs on highly optimised hardware for maths. At the same time the DSP number crunching heart of the terminals is going faster, stronger, smaller and cheaper. One should expect that this type of evolution may continue until a point where the analogue hardware will be minimum dealing with signal amplification at RF frequencies, conversion to, and from, near baseband and interfacing via a discrete time analogue circuitry to the DSP domain.

The KASSPER project is a Defense Advanced Research Projects Agency (DARPA) program which has the goal of improving the performance of ground moving target indicator (GMTI) radar systems by incorporating external sources of knowledge into the signal processing chain. The KASSPER real-time signal processing architecture is a radar system scheduling and signal processing framework that is being developed at Massachusetts Institute of Technology Lincoln Laboratory (MIT LL). This paper discusses the design of the architecture, knowledge handling issues. resource scheduling issues, the current state of the prototype implementation of the framework, and the current state of the project's real-time processor testbed.

Electronic digital signal processing is currently introduced in various subsystems of modern optical high-speed transceivers. Traditionally, electronic processing was basically limited to tasks like MUX/DEMUX, clock and data recovery and various synchronization loops as well as analog circuitry like modulator drivers or limiting amplifiers. In order to deal with advanced systems and with the need for increased performance at long reach and higher speed, additional signal processing requirements have appeared. Examples are FEC and electronic equalizers (EDC), electronic pre-compensation, differential encoders, clock and carrier recovery, and polarization control. Recent challenges employ pure digital processing, where ADCs and DACs are required, like OFDM (orthogonal frequency division multiplexing) modulation or MLSE (maximum likelihood sequence estimation) equalizers. In this paper some of these signal processing algorithms are considered in detail and their impact on performance and cost in terms of implementation effort is reviewed.

Conventional software radios take advantage of vastly improved analog to digital converters (ADCs) and digital signal processing (DSP) hardware. Our approach, which we refer to as virtual radios, also depends upon high performance ADCs. However, rather than use DSPs, we have chosen to ride the curve of rapidly improving workstation hardware. We use wideband digitization and then perform all of the digital signal processing in user space on a general purpose workstation. This approach allows us to experiment with new approaches to signal processing that exploit the hardware and software resources of the workstation. Furthermore, it allows us to experiment with different ways of structuring systems in which the radio component of communication devices is integrated with higher-level applications. This paper describes the design and performance of an environment we have constructed that facilitates building virtual radios and of two applications built using that environment. The environment consists of an input/output (I/O) subsystem that provides high bandwidth low latency user-level access to digitized signals and a programming environment that provides an infrastructure for building applications. The applications, which exemplify some of the benefits of virtual radios, are a software cellular receiver and a novel wireless network interface.

This paper presents the use of the viciLogic viciLab tool suite for prototyping of audio digital signal processing (DSP) hardware on remote or locally connected PYNQ Systems on Chip (SoCs), along with flexible computer control software interfaces. The tool suite enables rapid prototyping of DSP hardware components, including demonstration, control and visualisation of components on local or remote hardware. Hardware components can be developed directly in existing hardware description languages (HDLs) or using higher-level design interfaces such as MathWorks HDL coder. Two configurable software control interfaces are presented: a low-level console builder facilitating fast graphical user interface (GUI) creation for hardware interaction, along with script-based automation; and a high-level Python-based GUI console creator integrates audio sampling, automated DSP hardware effect selection and SoC control, audio streaming and playback, and real-time waveform graphing. The two interfaces presented in this paper are illustrated through a series of DSP examples of increasing complexity: 8- and 16-tap FIR filters (with user-created VHDL model source); distortion and tremolo DSP functions (developed MathWorks HDL coder VHDL model source); and combinations of these components.

10 - Hardware and software development tools

Today, DAW (Digital Audio Workstation) software lies at the heart of music production and has become an indispensable part of the industry. With the advancement of technology, music production is no longer confined to large studios but has also become accessible to individual users. Now, recording, mixing, and mastering processes can be carried out in a home environment using computer-based systems, without the need for expensive equipment. This fully digital workflow is referred to as "in-the-box" production. Technological developments have enabled individuals to produce music at a professional level in home studios, leading to the emergence of digital alternatives to analog devices. Digital processors have begun to replace analog equipment due to their cost-effectiveness and practicality. This study examines the differences between digital signal processors and analog devices in terms of usage, application areas, and sound processing methods. The research aims to compare digital and analog signal processors, particularly focusing on their processing methods, speed, flexibility, quality, and impact on sound. Based on the findings, it is concluded that digital signal processors, which are increasingly replacing analog processors in the music industry, do not possess the same characteristics as their analog counterparts.

A lot of modern ultrasonic measurement (UM) applications use digital signal processing (DSP). In many cases DSP comprises a significant part of system hardware and cost, also it sets certain restrictions on measurement speed and signal-to-noise ratio. This paper deals with the influence of the DSP into signal-to-noise ratio and its relevant characteristicsIt presents a concept of DSP exploration in applications. According to this concept the integrated model UM application - DSP architecture is used to select the rational DSP following the application requirements. The exploration is illustrated with the correlational time delay estimation task. The exploration method was suggested and applied in two scientific projects that have done by Ultrasound Research Center and DSP Laboratory at Kaunas University of Technology. During these projects the ultrasonic vision system for mobile robot (project INCO-COPERNICUS No. ERB IC15-CT96-0726) and high precision ultrasonic gas flowmeter have been designed. In both cases signal processing was necessary and the DSP design presented quite serious problem. The model is implemented in software and provides high flexibility. It can be modified for different DSP methods and signals, therefore, it is applicable for the design of various systems.

Signal processing has always been a critical aspect of interferometric spectroscopy. The main value of Fourier Transform Spectroscopy (FTS) is to provide the maximum information per unit time. This means that careful signal processing is required to maximize the signal to noise ratio of the result. Digital signal processing (DSP) is not new in FTS. DSP for apodization, Fourier transform, phase correction, and some digital filtering have been used for many years. The first difficulty encountered in using DSP is the required transformation of the continuous time signal to a sequence of finite precision numbers, that is a discrete time, discrete magnitude representation of the continuous signal. Today, sampling and digitization with conventional techniques are reasonably straightforward, with digitization noise of the order of 2−16. Advances in several aspects of FTS technology, including step scanning, have created the need to lower the digitization noise. The processing of signals from multiple modulation FTS experiments, specially step-scan signals, has presented serious limitations until recently, due primarily to the performance restrictions of the available hardware, and the fundamental limitations of the analog systems used. The application of DSP techniques to reduce the sampling and digitization errors are reviewed, and practical solutions that can provide digitization noise lower than 2−24 with low cost systems are described. Some of the causes and effects of sampling error are reviewed, to clarify common misunderstandings. DSP applications in the detection of multiple modulation step-scan experiments and photoacoustic spectroscopy are described in detail. These applications allow the measurement of the signal during the entire step time, eliminate interference of secondary effects of phase modulation, and integrate the entire experiment in the data system, eliminating all the analog adjustments (knobs) needed when lock-in amplifiers are used.

Increasing device parameter variations in nanometer CMOS technologies cause large spread in circuit parameters such as delay and power, leading to parametric yield loss. For digital signal processing (DSP) hardware, variations in circuit parameters can significantly affect the quality of service (QoS). Existing post-silicon calibration and repair approaches rely on adaptation of circuit operating parameters such as voltage, frequency, or body bias and typically incur large delay or power overhead. This paper presents a novel low-overhead approach of healing DSP chips by commensurately truncating the operand width based on their process shifts. The proposed approach exploits the fact that critical timing paths in typical DSP datapaths originate from the least significant bits. Hence, truncation of these bits, by setting them at constant values, can effectively reduce the delay of a unit, thereby avoiding delay failures. The proposed technique is applied to two common DSP blocks, namely discrete cosine transform (DCT) and finite impulse response (FIR) filter. Simulation results show significant reduction in critical path delay along with a graceful degradation in the QoS. They also show large improvement in manufacturing yield (41.6%) with up to 5X savings in power compared to existing approaches such as voltage scaling and body biasing.

On-line near infrared (NIR) analysers are used widely for quantitative composition measurements in real-time process control systems. The accuracy and repeatability of the measurements are amongst the most important factors when evaluating the total performance of these analysers, but the lower detection limit is often limited by noise in the measurement signal. There are two major alternatives for reducing noise in an optical analyser: prevention of noise contamination and post-processing of the signal by filtering. In the second alternative, the measurement signal can be post-processed by digital filtering techniques, for example, to enhance the desired signal component. Although digital signal processing (DSP) technology offers many advantages for on-line process measurements, the behaviour of the signal must be understood thoroughly before a successful application of this technology can be developed. A digital filtering technique called matched filter was used in an experimental set-up. The performance of this filter was compared to an analog filtering of a pulse shaped signal. Experimental data were collected and filtered with a novel digital spectrometer which consists of a modulated light source, a spectrograph, a linear array detector and the analog and digital signal processing electronics needed to control and filter the signal. In this case the matched filter gave a clear improvement of 2.2–4.6 dB in the signal-to-noise ratio (SNR) relative to an analog lock-in amplifier. Among the other advantages afforded by digital filters are that they are programmable, easy to design, test and implement on a PC and do not suffer from drift. Also digital filters are extremely stable with respect to both time and temperature and versatile in their ability to process signals in a variety of ways.

In optical communication systems, fibre nonlinearity is the major obstacle in increasing the transmission capacity. Typically, digital signal processing techniques and hardware are used to deal with optical communication signals, but increasing speed and computational complexity create challenges for such approaches. Highly parallel, ultrafast neural networks using photonic devices have the potential to ease the requirements placed on the digital signal processing circuits by processing the optical signals in the analogue domain. Here we report a silicon photonice-lectronic neural network for solving fibre nonlinearity compensation of submarine optical fibre transmission systems. Our approach uses a photonic neural network based on wavelength-division multiplexing built on a CMOS-compatible silicon photonic platform. We show that the platform can be used to compensate optical fibre nonlinearities and improve the signal quality (Q)-factor in a 10,080 km submarine fibre communication system. The Q-factor improvement is comparable to that of a software-based neural network implemented on a 32-bit graphic processing unit-assisted workstation. Our reconfigurable photonic-electronic integrated neural network promises to address pressing challenges in high-speed intelligent signal processing.