Machine Hardware Research Articles

Performance of Free Software and Freeware in Telecommunication System

This article discusses the Performance of Free Software and Freeware in Telecommunication System. Software or software is a special term for data that is digitally formatted and stored, including computer programs, their documentation, and various information that can be read and written by a computer. In other words, part of a computer system that is intangible. This term emphasizes the difference from computer hardware. Making the software itself requires a "programming language" written by the programmer which is then compiled with the compiler application so that it becomes code that can be recognized by the hardware machine.

Concepts and applications of ultrafast cardiac ultrasound imaging.

The concept of ultrafast echocardiographic imaging has been around for decades. However, only recent progress in ultrasound machine hardware and computer technology allowed to apply this concept to echocardiography. High frame rate echocardiography can visualize phenomena that have never been captured before. It enables a wide variety of potential new applications, including shear wave imaging, speckle tracking, ultrafast Doppler imaging, and myocardial perfusion imaging. The principles of these applications and their potential clinical use will be presented in this manuscript.

Analysis of Host Resources Utilization by OpenStack in Ubuntu Environment

Cloud computing is one of the frontier technologies, which over the last decade has gained a widespread commercial and educational user base. OpenStack is one of the popular open source cloud management platforms for establishing a private or public Infrastructure-as-a-Service (IAAS) cloud. Although OpenStack started with very few core modules, it now houses nearly 38 modules and is quite complex. Such a complex software bundle is bound to have an impact on the underlying hardware utilization of the host system. The objective is to monitor the usage of system resources by OpenStack on commodity hardware. This paper analyzes the effect of OpenStack on the host machine's hardware. An extensive empirical evaluation has been done on different types of hardware, at different virtualization levels and with different flavours of operating systems comparing the CPU utilization, memory consumption, disk I/O, network, and I/O requests. OpenStack was deployed using Devstack on a single node. The novel aspect of this work is monitoring the resource usage by OpenStack without creating virtual machines on commodity hardware. From the analysis of data, it is observed that standalone machine with Ubuntu server operating system is the least effected by OpenStack and thereby has more available resources for computation of user workloads. Doi: 10.28991/esj-2020-01246 Full Text: PDF

Model based diagnosis in complex industrial systems: a methodology

To fulfil market demand, the complexity of high-tech industrial systems is increasing every year. As a consequence, diagnosing failures leads to long down times, often because the issue at hand has to be escalated all the way to the development department to access the required knowledge. In this paper we propose a model based diagnostic methodology to support the field service engineer in finding the root cause of an unscheduled downtime in a timely way by bringing actionable design knowledge at the service engineer’s fingertips. We start by modeling the knowledge on the machine hardware decomposition, deployment and functional behavior by using a domain specific language. The language allows for an object oriented description of the system, with the functional behavior captured as probabilistic relationships between hardware components. This system description is then automatically transformed into a Bayesian belief network (BN): the diagnostic model. When a diagnosis is required, we infer the health state of the system’s components by instantiating the BN with discretized sensor and control data collected prior to the occurrence of a downtime. Finally, we compute the root cause hypotheses as the minimum sets of faulty components that are consistent with the evidence. We explain in a visual way the essential part of the diagnostic reasoning by pruning the causal graph underlying the BN, augmented with the data items supporting the hypothesis. We illustrate the methodology on a thermal conditioning subsystem. Our approach is domain independent and applicable to a wider range of cyber physical systems (CPS).

The Evolution of Artificial Intelligence and the Possibility of its Application in Cyber Games

Artificial intelligence, as a separate field of research, is currently experiencing a boom - new methods of machine learning and hardware are emerging and improving, and the results achieved change the life of society. Machine translation, handwriting recognition, speech recognition are changing our reality. The work of creating unmanned vehicles, voice assistants and other devices using these technologies is in an active process. The article examines the historical context of the artificial intelligence development, it evaluates the possibilities of its introduction into cyber games, as a safe and effective platform for testing new methods of machine learning. The promotion of such projects can increase the reputation of development companies, ensure increased user confidence in other products and, with a competent marketing strategy, cause a significant public resonance among video game fans, providing the developer with economic profit.

Fatigue of the automobile structural materials in corrosive environment

Operation of machines and equipment is accompanied by an impact produced by various types of loads leading to fatigue rupture of the structural materials. The most harmful are the vibration loads which in a corrosive environment increase the potential for a failure resulting in human casualties. Therefore, the task of ensuring the operating capability of machine elements and assemblies is one of the outstanding tasks for all industry sectors. In addition, the need for extending the service life and increasing the operational reliability is also determined by a relatively high cost of the machine structural materials and hardware items. Therefore, the execution of the experimental studies of the structural material fatigue features with a view to reduce the metal consumption, to establish new processing methods as well as to select a competitive material, are of the priority for the up-to-date machine building sector. The purpose of this article is to determine any fatigue behavior regularities in automotive materials which were pre-processed according to various technology types and modes and which were operating in a corrosive environment. The experimental data analysis has demonstrated that the longer the cyclic metal materials testing is run, the more sufficient is the decrease of the fatigue resistance of these.

Research on FFT Algorithm Use SMP System

Based on the theoretical research of a series of algorithms of adaptive beamforming and space-time adaptive processing, it is necessary to discuss the implementation of related algorithms on the machine hardware platform. Fast Fourier transform (FFT) is an essential process in this implementation. With the development of array antennas, the number of points to be calculated by the FFT has also increased significantly. Therefore, the ultra-large point FFT has become a key technology for current antenna signal processing. Most of the existing FFT algorithms are researched based on single-core processors, and there is very little literature on the research of super-large-point FFT algorithms suitable for Symmetric Multiprocessor (SMP). In this paper, through analyzing the characteristics of a symmetric multi-processor (SMP) parallel processing system, the very large FFT fast algorithm (VLFFT) is proposed. This algorithm significantly reduces the dependence on memory and improves the FFT's performance using the limited rules of the one-dimensional sequence split, changing the twiddle factor calculation method, and optimizing the data distribution and storage access. And the research's optimized very large FFT algorithms is also summarized in the paper. Experiment results show that the algorithm is suitable for the SMP platform and can effectively solve the problem of the very large FFT, which make single-core processors harder to realize.

Detection of Counterfeit Currency using Discrete Wavelet Transform in MATLAB

Counterfeit notes are one of the major issue in money transactions. In a growing country like India, it is becoming big problem for the economy. As the advances in printing and scanning technologies are developing, it is very easy for any person to print counterfeit notes with use of latest hardware machines. Identifying counterfeit notes in manual way takes a lot of time and manpower. Hence there is requirement of automation technique using which the counterfeit currency recognition process can be done effectively. Many methods have been proposed and implemented with MATLAB. By using Discrete cosine transform (DCT) algorithm blocks of the image are represented by coefficients of DCT. So, due to the presence of blocking articrafts in DCT, it is a drawback for this method. Hence, We have implemented a counterfeit note detection unit with a different algorithm which uses discrete wavelet transform (DWT) in MATLAB. This paper is another attempt on the same project to give a better solution for counterfeit currency problem.

Stochastic Hill Climbing- Computing Perspective for Load Balancing in Cloud Computing

A new concept for cloud computing is to keep resources in a virtual pool.an Internet environment in which virtual resources those are dynamically scalable are served over internet as service has become a main problem. Cloud computing serves as platform and as an application too. As needed, a cloud computing platform offers, instantiates, reshapes and deprivations data centers dynamically. Cloud servers can be hardware machines or network-wide VM..To implement complex tasks which needs the large processing facility it uses computing resources over the network .Choosing nodes to perform menial tasks in cloud computing must be termed and, in order to capture the resource efficiency, they must be selected properly according to the task's properties. In this article, a load balancing approach has been suggested. Randomize Hill climbing uses a local computation approach to resource allocation to the servers or VM. Using CloudSim, the performance of the algorithm is examined objectively. Cloud Analyst is a visual modeler based on CloudSim to examine environments and applications in cloud computing.

Fault Detection and Diagnosis of Industrial Robot Based on Power Consumption Modeling

Fault detection via power consumption monitoring of industrial robots is a substantial problem considered in this article, in which the healthy measurements of power consumption and encoders data for a prespecified task are employed as a reference for comparison to diagnose the potential failures or excessive degradation in the robot joints. Since most electrical and mechanical faults directly affect the consumed energy, the proposed solution analyzes the comparison outcomes between the healthy reference data with that monitored in a real time for each individual task. To integrate the power measurements with a base station, a ZigBee-based wireless data acquisition circuit has been developed to process the joints data. This article suggests a measurement-based mathematical model called Bode equations vector fitting as a robust fitting method to estimate such power consumption patterns. The achieved estimates allow a clear distinction for the potential failures in the robot joints that affect the power rate patterns even when involving sharp fluctuations. A table-based neural network classifier is presented to indicate the faulty joint or encoder according to the time intervals that divided for the executed task. The experimental results demonstrate the performance verification and feasibility of the proposed approach in ABB-IRB-1200 robot manipulator. Note to Practitioners— Industrial machines are seeking to achieve energy optimization to verify the sustainability demand goal. Currently, many industrial robotic systems are not effectively monitored and modeled mathematically toward detecting the potential faults. In this context, a faults diagnosis method with an accurate mathematical model based on reference power patterns is proposed for monitoring the performance of that system. The proposed energy-based diagnosis technique can be readily integrated with the existent industrial robots supply and can be monitored remotely. Furthermore, no significant changes in the machine's hardware, but a reference pattern of a power consumption per each individual task per each robot, are required.

Enablers, Barriers, and Critical Success Factors for Effective Adoption of Color-Jet 3D Printing Technology

Color-jet 3D printing (CJP) technology-based machines are now being developed, and there is a need to identify the successful adoption of this technology for additive manufacturing (AM). Extensive literature review complemented with experts’ advice utilized systematically helped in the identification of 12 critical factors for the effective adoption of color-jet printing technology. A hierarchical structural model is developed using interpretive structural modeling (ISM) and analyzed using Matriced Impacts Croise’s Multiplication Appliquée a UN Classement (MICMAC). Identified software and the machine cost are primary drivers for this color-jet 3D printing machines (technology). The value of this technology is to provide the product at a lower cost and give useful applications in the area of medical, engineering and concept building. There is a need for developing better quality core material and post-processing material, further supported by enabling machine hardware and software. Accuracy and color texture of the part produced are essential to provide a technological advantage over existing technologies. Post-processing assists in a big way in imparting necessary properties to the part produced. Presently, the infiltrate/post-processing materials are limited, and for large-scale acceptability, one needs extensive multidisciplinary research and development. This research provides a structural model toward the adoption of color-jet 3D printing technology, which can also help users in evaluating this technology in broader AM framework.

AAPM task group 224: Comprehensive proton therapy machine quality assurance.

Task Group (TG) 224 was established by the American Association of Physicists in Medicine's Science Council under the Radiation Therapy Committee and Work Group on Particle Beams. The group was charged with developing comprehensive quality assurance (QA) guidelines and recommendations for the three commonly employed proton therapy techniques for beam delivery: scattering, uniform scanning, and pencil beam scanning. This report supplements established QA guidelines for therapy machine performance for other widely used modalities, such as photons and electrons (TG 142, TG 40, TG 24, TG 22, TG 179, and Medical Physics Practice Guideline 2a) and shares their aims of ensuring the safe, accurate, and consistent delivery of radiation therapy dose distributions to patients. To provide a basis from which machine-specific QA procedures can be developed, the report first describes the different delivery techniques and highlights the salient components of the related machine hardware. Depending on the particular machine hardware, certain procedures may be more or less important, and each institution should investigate its own situation. In lieu of such investigations, this report identifies common beam parameters that are typically checked, along with the typical frequencies of those checks (daily, weekly, monthly, or annually). The rationale for choosing these checks and their frequencies is briefly described. Short descriptions of suggested tools and procedures for completing some of the periodic QA checks are also presented. Recommended tolerance limits for each of the recommended QA checks are tabulated, and are based on the literature and on consensus data from the clinical proton experience of the task group members. We hope that this and other reports will serve as a reference for clinical physicists wishing either to establish a proton therapy QA program or to evaluate an existing one.

GeCo: Classification Restricted Boltzmann Machine Hardware for On-Chip Semisupervised Learning and Bayesian Inference.

The probabilistic Bayesian inference of real-time input data is becoming more popular, and the importance of semisupervised learning is growing. We present a classification restricted Boltzmann machine (ClassRBM)-based hardware accelerator with on-chip semisupervised learning and Bayesian inference capability. ClassRBM is a specific type of Markov network that can perform classification tasks and reconstruct its input data. ClassRBM has several advantages in terms of hardware implementation compared to other backpropagation-based neural networks. However, its accuracy is relatively low compared to backpropagation-based learning. To improve the accuracy of ClassRBM, we propose the multi-neuron-per-class (multi-NPC) voting scheme. We also reveal that the contrastive divergence (CD) algorithm, which is commonly used to train RBM, shows poor performance in this multi-NPC ClassRBM. As an alternative, we propose an asymmetric contrastive divergence (ACD) training algorithm that improves the accuracy of multi-NPC ClassRBM. With the ACD learning algorithm, ClassRBM operates in the form of a combination of Markov Chain training and Bayesian inference. The experimental results on a field-programmable gate array (FPGA) board for a Modified National Institute of Standards and Technology data set confirm that the inference accuracy of the proposed ACD algorithm is 5.82% higher for a supervised learning case and 12.78% higher for a 1% labeled semisupervised learning case than the conventional CD algorithm. Also, the GeCo ver.2 hardware implemented on a Xilinx ZCU102 FPGA board was 349.04 times faster than the C simulation on CPU.

PAM-4 Transmission at 1550 nm Using Photonic Reservoir Computing Post-Processing

The efficacy of data decoding in contemporary ultrafast fiber transmission systems is greatly determined by the capabilities of the signal processing tools that are used. The received signal must not exceed a certain level of complexity, beyond which the applied signal processing solutions become insufficient or slow. Moreover, the required signal-to-noise ratio of the received signal can be challenging, especially when adopting modulation formats with multi-level encoding. Lately, photonic reservoir computing (RC) - a hardware machine learning technique with recurrent connectivity - has been proposed as a post-processing tool that deals with deterministic distortions from fiber transmission. Here we show that RC post-processing is remarkably efficient for multilevel encoding and for the use of very high launched optical peak power for fiber transmission up to 14dBm. Higher power levels provide the desired high signal-to-noise ratio (SNR) values at the receiver end, at the expense of a complex nonlinear transformation of the transmission signal. Our demonstration evaluates a direct fiber communication link with 4-level pulse amplitude modulation (PAM-4) encoding and direct detection, without including optical amplification, dispersion compensation, pulse shaping or other digital signal processing (DSP) techniques. By applying RC post-processing on the distorted signal, we numerically estimate fiber transmission distances of 27km at 56Gb/s and of 5.5 km at 112Gb/s data encoding rates, while fulfilling the hard-decision forward error correction (HD-FEC) bit-error-rate (BER) limit for data recovery. In an experimental equivalent demonstration of our photonic reservoir, the achieved distances are 21km and 4.6km respectively.

Failure Analysis and Maintenance of PHILPS Color Doppler Machine Hardware and Software

Firstly, the structure and work flow of PHILPS ultrasound machine were analyzed, and the fault of electronic box of PHILPS M2540A and PHILPS HD15T color Doppler ultrasound of the failure of hardware and software are used to analysis the application of circuit principle in the process of maintenance and installation of software operation method, provides the reference for the daily fast repair of B color ultrasonic diagnostic equipment.

8-Bit Quantizer for Chaotic Generator With Reduced Hardware Complexity

This article describes how nowadays, data is widely transmitted over the internet in the real time. Wherever the transmission or storage is required, security is needed. High speed processing hardware machine with reduced complexity are used for the security of the data, that are transmitted in real time. The information which is to be secure are encoded by pseudorandom key. Chaotic numbers are used in place of a pseudorandom key. The generated chaotic values are analogous in nature, these analog values are digitized to generate encryption key like 8-bit, 16-bit, 32-bit. To generate an 8-bit key, an 8-bit quantizer is required. The design of 8-bit quantizer requires 256 levels which needs lot of complex hardware to implement. In this article, an 8-bit quantizer is designed with reduced complexity, where hardware requirement is reduced by more than 12 times. Without compromising the randomness of the sequence generated. To increase the randomness and confusion timed hop random selection is used. The randomness of the sequence generated by the chaotic generators is analyzed by NIST test suite, to test for its randomness.

Research and Application of Eye Movement Interaction based on Eye Movement Recognition

Generally, human-computer interaction is an interaction and operation between users and machine hardware. The user submits instructions to the machine, and the machine outputs the processed data and results to the user after receiving the instructions from the user. Mouse, keyboard, etc. are common input channels. With the maturity of eye tracking technology and the development of equipment miniaturization, turning eye movements into human-computer interaction input channels has become a hot spot in the field of human-computer interaction. Therefore, this paper analysed the physiological characteristics of eye movement, proposed the design principles and framework of eye movement interaction, and designed three kinds of eye movement recognition algorithms of fixation, saccade and blink. On this basis, using Unity 3D cross-platform development engine as a development tool, a children’s attention training game application based on eye movement interaction is designed. The game is designed to combine eye movement interaction technology with attention training mode, simplify the control mode of the game, get attention feedback at the first time, achieve better training effect and improve the efficiency of human-computer interaction.

Parallel Community Detection Based on Distance Dynamics for Large-Scale Network

Data mining task is a challenge on finding a high-quality community structure from large-scale networks. The distance dynamics model was proved to be active on regular-size network community, but it is difficult to discover the community structure effectively from the large-scale network (0.1–1 billion edges), due to the limit of machine hardware and high time complexity. In this paper, we proposed a parallel community detection algorithm based on the distance dynamics model called P-Attractor, which is capable of handling the detection problem of large networks community. Our algorithm first developed a graph partitioning method to divide large network into lots of sub-networks, yet maintaining the complete neighbor structure of the original network. Then, the traditional distance dynamics model was improved by the dynamic interaction process to simulate the distance evolution of each sub-network. Finally, we discovered the real community structure by removing all external edges after evolution process. In our extensive experiments on multiple synthetic networks and real-world networks, the results showed the effectiveness and efficiency of P-Attractor, and the execution time on 4 threads and 32 threads are around 10 and 2 h, respectively. Our proposed algorithm is potential to discover community from a billion-scale network, such as Uk-2007.

Energy Efficiency Aware Task Assignment with DVFS in Heterogeneous Hadoop Clusters

While Hadoop ecosystems become increasingly important for practitioners of large-scale data analysis, they also incur tremendous energy cost. This trend is driving up the need for designing energy-efficient Hadoop clusters in order to reduce the operational costs and the carbon emission associated with its energy consumption. However, despite extensive studies of the problem, existing approaches for energy efficiency have not fully considered the heterogeneity of both workload and machine hardware found in production environments. In this paper, we find that heterogeneity-oblivious task assignment approaches are detrimental to both performance and energy efficiency of Hadoop clusters. Our observation shows that even heterogeneity-aware techniques that aim to reduce the job completion time do not guarantee a reduction in energy consumption of heterogeneous machines. We propose a heterogeneity-aware task assignment approach, E-Ant, that aims to improve the overall energy consumption in a heterogeneous Hadoop cluster without sacrificing job performance. It adaptively schedules heterogeneous workloads on energy-efficient machines, without a priori knowledge of the workload properties. E-Ant employs an ant colony optimization approach that generates task assignment solutions based on the feedback of each task’s energy consumption reported by Hadoop TaskTrackers in an agile way. Furthermore, we integrate DVFS technique with E-Ant to further improve the energy efficiency of heterogeneous Hadoop clusters. It relies on a DVFS controller to dynamically scale the CPU frequency of each slave machine in response to time-varying resource demands. Experimental results on a heterogeneous cluster with varying hardware capabilities show that E-Ant with DVFS improves the overall energy savings for a synthetic workload from Microsoft by 23 and 17 percent compared to Fair Scheduler and Tarazu, respectively.

Programming gate-based hardware quantum computers for music

There have been significant attempts previously to use the equations of quantum mechanics for generating sound, and to sonify simulated quantum processes. For new forms of computation to be utilized in computer music, eventually hardware must be utilized. This has rarely happened with quantum computer music. One reason for this is that it is currently not easy to get access to such hardware. A second is that the hardware available requires some understanding of quantum computing theory. This paper moves forward the process by utilizing two hardware quantum computation systems: IBMQASM v1.1 and a D-Wave 2X. It also introduces the ideas behind the gate-based IBM system, in a way hopefully more accessible to computerliterate readers. This is a presentation of the first hybrid quantum computer algorithm, involving two hardware machines. Although neither of these algorithms explicitly utilize the promised quantum speed-ups, they are a vital first step in introducing QC to the musical field. The article also introduces some key quantum computer algorithms and discusses their possible future contribution to computer music.