Digital feedback control loops for scanning tunneling microscopes

Energy consumption in Digital Signal Processing (DSP) application is a vital parameter of consideration. Mostly DSP applications deplete power. Battery constraint is important in many mobiles, processors, and sensor-related devices. The major critical operations in DSP architecture are multiplication and addition. Multiplication process is performed using repetitive addition. Hence, adder is the basic component used in digital signal processor. DSP architecture is proposed with the energy-efficient goal. Traditional carry select adder contains ripple carry adder block is replaced by parallel prefix adder. By replacing that block and introducing folded tree, we can reduce power used by DSP processors. This architecture can be used in all power constrained DSP applications. In the existing system, folded tree is used in architecture to reduce the number of processing elements, and they used carry look-ahead adder to perform the internal processing element operations. In the proposed system, carry look-ahead adder is replaced by LFA and PA to harness the energy depleted in an application (e.g., DSP application). Power consumption is reduced by 12–15% as compared with the existing algorithms using Cyclone III (EP3C16F484C6).

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.

Finite Impulse Response (FIR) Filter occupied a significant position in most of Digital Signal Processing (DSP) Applications. The behaviour of FIR filters can be realized in software for DSP applications and executed on Digital Signal Processor architectures. These kinds of digital filters consider digital components, digital input and perform mathematical computations to produce digital output. In modern DSP applications demanding area efficient architecture for DSP algorithms while optimizing throughput. This can be achieved by design of hardware architectures for DSP algorithms and apply design optimization techniques to accelerate the throughput and minimize area. This article attempts to develop hardware architectures for FIR Filter and apply the design optimization techniques present in literature. In this article, High Level Synthesis concepts were adapted to develop hardware architecture for FIR filter and optimization techniques have been applied to optimize design attributes like area, throughput and power. Finally, design attributes are summarized after applying optimization techniques and published significant architectural attributes suitable for real life DSP applications.

Multiplication is an important fundamental function in arithmetic operations. Multiplication-based operations such as Multiply and Accumulate(MAC) and inner product are among some of the frequently used computation Intensive Arithmetic Functions(CIAF) currently implemented in many Digital Signal Processing (DSP) applications such as convolution, Fast Fourier Transform(FFT), filtering and in microprocessors m its arithmetic and logic unit. Since multiplication dominates the execution tune of most DSP algorithms, so there is a need of high speed multiplier. Currently, multiplication time is still the dominant factor in determining the instruction cycle time of a DSP chip. The demand for high speed processing has been increasing as a result of expanding computer and signal processing applications. Higher throughput arithmetic operations are important to achieve the desired performance in many real-time signal and image processing applications . One of the key arithmetic operations in such applications is multiplication and the development of fast multiplier circuit has been a subject of interest over decades. Multiplier based on Vedic Mathematics is one of the fast and low power multiplier. Employing this technique in the computation algorithms will reduce the complexity, execution time, power etc. This vedic based multiplier is compared with binary multiplier(partial products method).

The rapid increase in digital signal processing (DSP) applications has generated a strong demand for electrical engineers with DSP backgrounds; however, the gap between industry needs and university curricula still exists. To answer this challenge, a sequence of innovative DSP courses that emphasize hands-on experiments and practical applications were developed for continuing education in electrical and computer engineering. These courses are taught in the evening for night-school students having at least three years of work experience. These courses enable students to experiment with sophisticated DSP applications to augment the theoretical, conceptual, and analytical materials provided in traditional DSP courses. The inclusion of both software and hardware developments allows students to undertake a wide range of DSP projects for real-world applications. Assessment data concludes that the digital signal processor fundamentals course can increase learning interest and overcome the prerequisite problem of DSP laboratory experiments. This paper also briefly introduces representative examples of some challenging DSP applications.

Digital Signal Processing (DSP) applications in Bioinformatics have received great attention in recent years, where new effective methods for genomic sequence analysis, such as the detection of coding regions, have been developed. The use of DSP principles to analyze genomic sequences requires defining an adequate representation of the nucleotide bases by numerical values, converting the nucleotide sequences into time series. Once this has been done, all the mathematical tools usually employed in DSP are used in solving tasks such as identification of protein coding DNA regions, identification of reading frames, and others. In this article we present an overview of the most relevant applications of DSP algorithms in the analysis of genomic sequences, showing the main results obtained by using these techniques, analyzing their relative advantages and drawbacks, and providing relevant examples. We finally analyze some perspectives of DSP in Bioinformatics, considering recent research results on algebraic structures of the genetic code, which suggest other new DSP applications in this field, as well as the new field of Genomic Signal Processing.

The aim of this contribution is first to show how the Szego theory of orthogonal polynomials on the unit circle is intimately related to the celebrated Levinson algorithm, which is commonly used in digital signal processing (DSP) applications to solve various least-squares problems. A computationally more efficient substitute for the Levinson algorithm, termed the split Levinson algorithm, has recently been proposed in the DSP literature. In the case of real data, this new algorithm can be interpreted naturally in the framework of a well-defined one-to-one correspondence between the families of real Szego polynomials and the families of polynomials orthogonal on the interval [-1, 1] with respect to a symmetric measure. More generally, the philosophy underlying the split Levinson algorithm opens the door to an interesting “tridiagonal approach” to the theory of complex Szego polynomials, nonnegative definite Hermitian Toeplitz matrices, and related algebraic and function theoretic questions. Some of the main topics of this new mathematical framework are briefly reviewed and are shown on specific examples to be of particular interest in DSP applications.

Wireless sensor network (WSN) applications have been studied extensively in recent years. Such applications involve resource-limited embedded sensor nodes that have small size and low power requirements. Based on the need for extended network lifetimes in WSNs in terms of energy use, the energy efficiency of computation and communication operations in the sensor nodes becomes critical. Digital Signal Processing (DSP) applications typically require intensive data processing operations and as a result are difficult to implement directly in resource-limited WSNs. In this article, we present a novel design methodology for modeling and implementing computationally intensive DSP applications applied to wireless sensor networks. This methodology explores efficient modeling techniques for DSP applications, including data sensing and processing; derives formulations of Energy-Driven Partitioning (EDP) for distributing such applications across wireless sensor networks; and develops efficient heuristic algorithms for finding partitioning results that maximize the network lifetime. To address such an energy-driven partitioning problem, this article provides a new way of aggregating data and reducing communication traffic among nodes based on application analysis. By considering low data token delivery points and the distribution of computation in the application, our approach finds energy-efficient trade-offs between data communication and computation.

The article highlights the latest developments in the design of low-power 4-bit Absolute Value Detector (AVD) circuits that are utilized in digital signal processing (DSP) applications. DSP applications like audio and video processing, biological signal processing, and digital communication systems rely heavily on the AVD circuit, which determines the absolute value of an input signal. The article introduces a low-energy 4-bit AVD circuit based on pass transistors that incorporates advanced optimization techniques like adiabatic logic, approximation techniques, and layout optimization. This optimized AVD circuit achieves remarkable results in terms of power consumption and energy efficiency. With a power consumption of just 0.6 nW and an energy efficiency of 0.6 pJ per cycle, the circuit maintains precision and rapid response time. These advancements in AVD circuit design can be highly beneficial for portable and battery-powered devices such as earplugs, implants, and cell phones, as well as electronic components like Static Random Access Memory (SRAM) and motherboards. Overall, the proposed low-energy 4-bit AVD circuit is a significant development in the DSP field, enabling more efficient and effective processing of digital signals.

This work presents an analysis of power efficiency in microprocessor architectures targeting wide-range digital signal processing (DSP) applications. We defined a circuit level power estimation technique based on the integration of traditional analytical power models so as to account for both block-internal and interconnects-dependent dissipation and we extended it to provide applicability to specific DSP-related structures. We applied the modeling approach to several architecture schemes and demonstrated that a relatively novel solution, namely the transfer triggered architecture, can be the most power-efficient scheme in DSP applications.

The availability of fast, general‐purpose laboratory computers has dramatically changed the way research is done in many areas of psychoacoustics. In studies of sound localization, for example, standard digital signal‐processing techniques are used to synthesize stimuli for headphone presentation that contain all of the localization cues (e.g., interaural differences, pinna effects) that are available from sounds presented in free field. This brings to localization research a much needed degree of stimulus control and specificity, and allows us to address certain basic issues that were heretofore inaccessible. This presentation will survey the general principles involved in the application of digital signal processing to localization research, and will discuss the practical limitations of the specific techniques that are used (e.g., FFTs, FIR filters, inverse filtering). Empirical data will be shown in order to address questions relating to the classic tradeoffs among signal/noise ratio, bandwidth, word‐length, and sampling frequency. The feasibility of real‐time digital processing in currently available PC‐based systems will also be discussed. [Work supported by NIH, NSF, and NASA.]

In order to transform the training method of new engineering talents and combine engineering practice, this paper designs a comprehensive experiment of digital signal processing spectrum refinement. Use Matlab and millimeter wave radar hardware platform to realize complex frequency shift, low-pass filtering, resampling, fast Fourier transform and other functions. This experiment not only enables students to master the basic knowledge of digital signal processing, but also understand the application of digital signal processing in engineering. It can also stimulate students' ability to innovate and combine theory with practice, which can effectively improve classroom effects.

A senior project course on applications in digital signal processing (DSP) with the TMS320 digital signal processor is described. All experiments and projects are performed using Texas Instruments' TMS320 digital signal processor. The laboratory emphasis in the senior project course reinforces the concepts of DSP covered in the lecture course. Applications in DSP are in areas such as communications, controls, speech, graphics, etc. The DSP projects presented and demonstrated by the students in the spring of 1987 are discussed. These projects can provide a guide to individuals who may incorporate a hands-on approach to DSP in courses such as senior project, senior lab, or DSP lab using the versatile TMS 320 digital signal processor.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The advent of single-chip programmable digital signal processors (OSP) has expanded digital signal processing into automotive applications. Digital signal processors, compatible in cost to general-purpose microcomputers, offer much higher throughput in performing computationally intensive tasks. Because of this advantage, closed loop control, adaptive control, and digital audio processing can now be implemented in a cost effective manner. Some of the DSP automotive applications include combustion feedback engine control, active suspension systems, anti -skid brakes incorporating traction control and digital audio-based entertainment systems. The availability of digitaI signal processors i s changing many aspects of automotive designs. Early adopters of this technology for innovative automotive products wiII enjoy leadership and financial benefits over their competition. The impact of DSP to the automobile has just begun and will continue beyond the year 2000 ( I ) . This new technology has also presented tremendous challenges to both automotive and semiconductor industries. This paper first discusses digital signal processing characteristics. After reviewing historical digital signal processing solutions, the paper focuses on single-chip programmable digital signal processors and compares their architectural designs to general- purpose microprocessors and microcontrollers. Deficiencies of early digital signal processors are discussed and trends of DSP are explored. Performance and cost benefits of using DSP in digital control systems are explained. Automotive applications of digital signal processors are discussed in areas of powertrain, body and chassis control, and entertainment systems. The last part of the paper discusses the challenges presented to both automotive industry and semiconductor vendors.

The role of software is becoming increasingly important in the implementation of digital signal processing (DSP) applications. As this trend intensifies, and the complexity of applications escalates, we are seeing an increased need for automated tools to aid in the development of DSP software. This paper reviews the state of the art in programming language and compiler technology for DSP software implementation. In particular, we review techniques for high-level block-diagram-based modeling of DSP applications; the translation of block-diagram specifications into efficient C programs using global target-independent optimization techniques; and the compilation of C programs into streamlined machine code for programmable DSP processors using architecture-specific and retargetable back-end optimizations. We also point out important directions for further investigation.