Hardware Modules Research Articles

High Performance Implementation of Next Generation Aeronautical Communication Systems

Current air traffic communication systems are mainly based on voice communication, with a newer digital communication technology called L-Band Digital Aeronautical Communication System (LDACS) being investigated in the Single European Sky Air Traffic Management Research (SESAR) project. An essential feature of this communication infrastructure is the encoding of data that guarantees reliable transport. While the encoding in the transmission path is straightforward, the decoding is computationally expensive due to the peculiarities of the convolutional codes used. As the target platform for the communication equipment is an embedded system, a proper system design is essential for the receiving path to ensure real-time processing. This paper therefore focuses on the hardware/software co-design of the functional system parts needed for decoding in the LDACS radio receiver. We describe the fundamental design considerations, followed by the actual implementation in form of software and FPGA-based hardware modules. Subsequently, the decoding solution was verified to prove a standard-compliant system. In an experimental validation, the actual system was fed with test data from a reference system. This allows conclusions to be drawn about system characteristics like data throughput, latency, and error correction. The resulting system demonstrates high-performance decoding that can exceed the desired requirements for quality and speed for use in the LDACS communication system.

Dynamic Partial Reconfiguration for Adaptive Sensor Integration in Highly Flexible Manufacturing Systems

As new production system concepts emerge to face an increasing demand for individualized production, sensor integration for Industry 4.0 functions is becoming a major challenge. Flexibility- and scalability-focused concepts rely on reconfigurable machines that automatically replace smart end effectors between production steps. To ensure adaptability to future demands, these smart components, comprising the main tool and sensors, are connected to the static part of the machine via a unified electro-mechanical interface. In robot-based concepts, these components are furthermore space- and weight-constrained and should be cost-optimized. With respect to sensor integration, this leads to the challenge of interfacing sensors using different communication protocols and electrical signal properties through a fixed, minimal set of signal lines. In this paper, an architecture for an adaptive sensor integration unit is presented, targeting highly flexible production systems. Leveraging dynamic partial FPGA reconfiguration to exchange communication logic and an extensible hardware module located in the static part of the machine, it efficiently supports alternating communication protocols over static signal lines. It thereby reduces the number of signal lines as well as the need for protocol conversion units in the exchangeable components. A prototype implementation using industrial bus protocols shows its suitability and is evaluated concerning relevant timing characteristics and resource usage.

A 12-nm Autonomous Driving Processor With 60.4 TOPS, 13.8 TOPS/W CNN Executed by Task-Separated ASIL D Control

This article proposes an automotive safety integrity level (ASIL) D, which is the highest automotive integrity level specified in ISO 26262, and a target application processor for driving automation systems with a high convolutional neural network (CNN) processing performance of 60.4 trillion operations per second (TOPS) with high power efficiency of 13.8 TOPS/W. To achieve both performance and power efficiency, a stream processing architecture specialized for CNN hardware module is adopted to implement many low-power arithmetic units. In addition, 6 MB of local memories are implemented to reduce data transfers between dynamic random access memory (DRAM) and the CNN hardware, thereby improving the execution efficiency of the arithmetic units and saving power consumption caused by the data transfer. In order to support high safety integrity level with power efficiency, we provide safety mechanisms with high failure detection rates only for the hardware used in applications that require ASIL D support. The configuration of two CNN modules can switch between lockstep and single operation depending on the use case, thus achieving a tradeoff between performance and ASIL while maintaining power efficiency. In addition, the freedom from interference (FFI) concept defined in ISO 26262 is achieved by providing mechanisms to prevent low safety level tasks from interfering with high safety level tasks running concurrently inside the processor.

Indoor near-field target detection characteristics under radio and radar joint operation at 2.4 GHz ISM band

This paper presents an experimental research work on detecting an indoor near-field target at 2.4-GHz industrial, scientific, and medical (ISM) band. The research is based on hypotheses that if the detection target is in proximity to antennae, then the dominant electromagnetic (EM) wave couplings would happen between the target and antennas. Any other EM wave interaction between the antennas and indoor background objects would be secondary to the target-to-antennas EM-wave interaction. This unique coupling may allow us to distinguish and measure the EM wave energy scattered by the target to the receive antenna in the form of signal power using a spectrum analyzer. The detection power is a strong function of the distance between the target and receiving antenna, which could be characterized by radar equations. In such scenario, the hardware module configured to test the hypotheses plays the role of a radar sensor that detects indoor targets at near-field region under the coexistence and cooperation of WiFi radio link at the ISM band. In the experiment, at the presence of a flat round metal target with radius 5 cm, the measured detection signal power ranged from 10 nW to 1 mW (-30 dBm) when the sensing distance (r) decreased from the edge of a radiative near-field (r = λ = 12.5 cm) to the reactive near-field region (r = λ/2π = 2 cm). These were well-agreeable with theoretical estimations by radar equations, validating the hypotheses.

Integrated Mobile Robotic Platform Model

Introduction. The “Smart Agroˮ committee of Research and Education Center “Engineering of the Future” has identified a number of tasks relevant for improving the efficiency of precision, soil-protecting and conservation agriculture. One of these tasks is the development of a digital multi-agent system, which provides a number of services for agricultural enterprises, developers and manufacturers of agricultural machinery. The purpose of the present study is to model an autonomous mobile robotic platform, including the development of software and hardware for trajectory control. Materials and Methods. To solve the problem, there are used modern CAx systems and their applications, the methods of 3D and full-body modeling, and the method of numerical solution of problems in solid mechanics. To expand and improve the standard functionality of CAx-systems (SolidWorks) in the software implementation of trajectory control algorithms, the methods and technologies of programming using API SolidWorks, VisualStudio C++ (MFC, ATL, COM) are used, and to build physical full-scale models ‒ Arduino and fischertechnik platforms. Results. The result of the study is a software and hardware module of trajectory control for an integrated (physical and virtual) model of a mobile robotic platform, which can be provided to the consumer as a service for technology autonomation. For the developed integrated model, control algorithms for various types of trajectories were tested. Discussion and Conclusion. The developed integrated software and hardware model of trajectory control can be used by developers and manufacturers of agricultural machinery, and directly by agro-enterprises for implementing typical technological processes. A feature of the implementation is an open hardware and software interface that provides the integration of mobile robotic platforms based on a digital multi-agent system.

Research on Energy-Saving Optimization of Rural Houses in Specific Regions Based on Internet of Things

With the expansion of social energy use, the optimization of residential energy conservation has become urgent and important. The research on rural housing in areas with hot summers and cold winters started late and is relatively backward in terms of housing energy saving, which greatly hinders the sustainable development of rural areas. The application of the Internet of Things technology helps to provide a stable technical guarantee for energy-saving optimization. Therefore, this paper takes the energy-saving optimization design of rural houses in hot summer and cold winter areas based on the Internet of Things technology as the research theme. This article first takes H rural area as the research object and analyzes the climatic conditions of the place, that is, the typical characteristics of hot summer and cold winter. Then, the intelligent temperature acquisition system is designed, and the working process and main hardware modules of the system are introduced. This text combines GPRS and Internet of Things technology, designs the temperature control system, and carries on the test to the system operation effect. The test results show that during the heating period in January, before and after the temperature control system controls the room temperature, the maximum relative error between the set temperature value and the actual temperature value is 0.49 and 0.27, respectively. It can be seen that the intelligent temperature energy-saving control system can collect the indoor temperature in real time and can well control the heating equipment.

SCIFI: 3D face reconstruction via smartphone screen lighting

3D face reconstruction based on smartphones has a wide range of applications, such as face recognition, liveness detection, face animation, etc. To realize this function, these smartphones have been deployed with specialized hardware modules (infrared dot projector or depth sensor). However, it will inevitably increase the production cost of smartphones and the operation difficulty of users. In this article, we propose a smartphone screen illumination-based face reconstructIon (SCIFI) framework, which only relies on the front camera and screen lighting. Specifically, we have investigated the calibrated planar lighting to achieve the fine-grained textures in Lambertian-based reconstruction. Further, we introduce the face landmark to align multiple photographs, which aims to adjust the position mismatch caused by hand jitter. Moreover, we propose two different methods to eliminate outlier normals based on the characteristics of human face. Extensive experiments based on different environments (dark and bright), different lighting pattern (4-zones and 9-zones), and different testing subjects have validated the effectiveness and robustness of SCIFI in reconstructing the 3D face surface with a favorable surface shape as well as micro facial texture.

Hardware Update through Digital TV Signals

This paper presents a new hardware reconfiguration approach named hardware reconfiguration through digital television (HARD), which can update FPGA hardware modules based on digital TV (DTV) signals. Such a scheme allows several synthesized hardware cores (bitstreams) signaled and broadcast through open DTV signals via data streaming to be identified, acquired, decoded, and then used for system updates. Reconfiguration data are partitioned, encapsulated into private sections, and then sent in a carrousel fashion in order to be recovered by modified receivers. Service information content, specially designed for identifying and describing the characteristics of multiplexed hardware bitstreams, was added to the transmitted signal and provided all necessary information in the traditional DTV style. The receiver framework, in turn, checked whether those characteristics corresponded to its embedded reconfigurable devices and, if a match was found, it reassembled the related bitstreams and reconfigured the respective internal circuits. Experiments performed with an implementation of the proposed methodology confirmed its feasibility and showed that remounting and reconfiguration times were satisfactory and presented no blocking aspect. Finally, HARD can be used in several designs regarding intelligent reconfigurable devices, minimize device costs in the long term, and provide better hardware reuse.

Production Tests of a Grain-Cleaning Machine with Software and Hardware Control as Part of a Technological Line

The quality of seeds is one of the most important factors of the good harvest. According to the State Seed Inspection of the Russian Federation, often up to half of the seed material does not meet the requirements of the standard; a significant part is due to the increased content of difficult-to-separate impurities. (Research purpose) The research purpose is to determine the main agrotechnical performance indicators of the designed grain cleaning machine VIM-4 for cleaning flax seeds as part of a typical production technological line for cleaning agricultural crops. (Materials and methods) There were conducted experimental studies based on the “SPRING FIELD” company of the Tula region using a typical grain cleaning line for cleaning flax. The line includes: a blockage pit, a belt conveyor, band elevator, Petkus K-218, MS-10/5, a gate device, a new grain cleaning machine VIM-4, a hardware and software module. The experiment was planned using the method of active experiment planning. Experiments were carried out using a compiled planning matrix, including the main parameters: flow, air flow velocity, diameter of the outlet pipe. The results were processed by methods of mathematical statistics and by means of Excel computer programs. (Results and discussion) The main performance indicators of the new VIM-4 grain- cleaning machine were evaluated in comparison with the basic version of the VIM-3 for cleaning flax seeds. (Conclusions) The article presents the comparative technical and economic results of the VIM-4 grain- cleaning machine and the VIM-3 model variant. The developed grain cleaning machine VIM-4 as part of the grain cleaning line for final seed cleaning has high technological reliability and resource-energy efficiency. The VIM-4 grain cleaning machine is universal. In the conditions of the technological line VIM-4 can functionally replace a line of two machines – the main and final cleaning.

Probabilistic Neural Network based Managing Algorithm for Building Automation System

A building automation system is a centralized intelligent system, which controls the operation of energy, security, water, and safety by the help of hardware and software modules. The general software modules employed for automation process have an algorithm with pre-determined decisions. However, such pre-determined decision algorithms won’t work in a proper manner at all situations like a human brain. Therefore a human biological inspired algorithms are developed in recent days and termed as neural network algorithms. The Probabilistic Neural Network (PNN) is a kind of artificial neural network algorithm which has the ability to take decisions same as like of human brains in an efficient way. Hence a building automation system is proposed in the work based on PNN for verifying the effectiveness of neural network algorithms over the traditional pre-determined decision making algorithms. The experimental work is further extended to verify the performances of the basic neural network algorithm called Convolution Neural Network (CNN).

An inertial neural network approach for robust time-of-arrival localization considering clock asynchronization

This paper presents an inertial neural network to solve the source localization optimization problem with l1-norm objective function based on the time of arrival (TOA) localization technique. The convergence and stability of the inertial neural network are analyzed by the Lyapunov function method. An inertial neural network iterative approach is further used to find a better solution among the solutions with different inertial parameters. Furthermore, the clock asynchronization is considered in the TOA l1-norm model for more general real applications, and the corresponding inertial neural network iterative approach is addressed. The numerical simulations and real data are both considered in the experiments. In the simulation experiments, the noise contains uncorrelated zero-mean Gaussian noise and uniform distributed outliers. In the real experiments, the data is obtained by using the ultra wide band (UWB) technology hardware modules. Whether or not there is clock asynchronization, the results show that the proposed approach always can find a more accurate source position compared with some of the existing algorithms, which implies that the proposed approach is more effective than the compared ones.

Light-weight electrophysiology hardware and software platform for cloud-based neural recording experiments

Objective. Neural activity represents a functional readout of neurons that is increasingly important to monitor in a wide range of experiments. Extracellular recordings have emerged as a powerful technique for measuring neural activity because these methods do not lead to the destruction or degradation of the cells being measured. Current approaches to electrophysiology have a low throughput of experiments due to manual supervision and expensive equipment. This bottleneck limits broader inferences that can be achieved with numerous long-term recorded samples. Approach. We developed Piphys, an inexpensive open source neurophysiological recording platform that consists of both hardware and software. It is easily accessed and controlled via a standard web interface through Internet of Things (IoT) protocols. Main results. We used a Raspberry Pi as the primary processing device along with an Intan bioamplifier. We designed a hardware expansion circuit board and software to enable voltage sampling and user interaction. This standalone system was validated with primary human neurons, showing reliability in collecting neural activity in near real-time. Significance. The hardware modules and cloud software allow for remote control of neural recording experiments as well as horizontal scalability, enabling long-term observations of development, organization, and neural activity at scale.

Cost reduction for advanced driver assistance systems through hardware downscaling and deep learning

AbstractWith the ever‐growing population density, transportation sector has become haphazard causing accidents and fatalities. It can be reduced by the effective utilization of intelligent transportation systems (ITS). The applications of advanced driver‐assistance systems (ADAS) and deep learning are trending globally with immense research potential and combined with ITS results into an intelligent system with decision‐taking capabilities. High‐priced driving modules are getting introduced day‐by‐day offering humongous processing power and computational capabilities. This research aims at utilizing the downscaled hardware for real‐time vehicular and pedestrian detection using a deep learning algorithm. YOLO v3 deep learning network is incorporated with NVIDIA series of GTX 1060 for real‐time object detection for assisting the ADAS systems. The system offers high precision (0.9618) of object detection in real time with high frame rate (74.36 fps). The comparative analysis between different GPU‐based hardware modules and the proposed module has been carried out keeping in mind the Indian context of automobile usages. The work lays a solid foundation for carrying out research for transportation analysis based on ADAS and deep learning for ITS such as real‐time congestion estimation and accident detection.

Design of CAN Bus Communication Interfaces for Forestry Machines

This paper presents the initial developments of new hardware devices targeted for CAN (Controller Area Network) bus communications in forest machines. CAN bus is a widely used protocol for communications in the automobile area. It is also applied in industrial vehicles and machines due to its robustness, simplicity, and operating flexibility. It is ideal for forestry machinery producers who need to couple their equipment to a machine that allows the transportation industry to recognize the importance of standardizing communications between tools and machines. One of the problems that producers sometimes face is a lack of flexibility in commercialized hardware modules; for example, in interfaces for sensors and actuators that guarantee scalability depending on the new functionalities required. The hardware device presented in this work is designed to overcome these limitations and provide the flexibility to standardize communications while allowing scalability in the development of new products and features. The work is being developed within the scope of the research project “SMARTCUT—Remote Diagnosis, Maintenance and Simulators for Operation Training and Maintenance of Forest Machines”, to incorporate innovative technologies in forest machines produced by the CUTPLANT S.A. It consists of an experimental system based on the PIC18F26K83 microcontroller to form a CAN node to transmit and receive digital and analog messages via CAN bus, tested and validated by the communication between different nodes. The main contribution of the paper focuses on the presentation of the development of new CAN bus electronic control units designed to enable remote communication between sensors and actuators, and the main controller of forest machines.

Dynamic battery equalization scheme of multi-cell lithium-ion battery pack based on PSO and VUFLC

Aiming at three problems of over equalization, energy loss and time consumption, a dynamic equalization scheme is designed to control the equalization process of multi-cell Lithium-ion battery pack. First, a modified Buck-Boost circuit using inductor to transfer energy is proposed, which improves the equalization speed and is easy to realize in hardware modules. Then, for the problem of over equalization, state of charge is estimated by extreme learning machine based on genetic algorithm and used as the equalization variable. In order to improve energy utilization, particle swarm optimization is used to dynamically optimize the equalization process to obtain the optimal equalization energy path. On the basis of the optimal equalization energy path, variable universe fuzzy logic control based on fuzzy inference is used to achieve dynamic adjustment of the equalization current and reduce the time consumption. Finally, the simulation results show that the proposed equalization scheme can reduce the energy loss by 2.39%, improve the equalization speed by 36%, and effectively reduce the inconsistency of multi-cell Lithium-ion battery pack.

An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing

Joint communication and radar sensing (JCR) represents an emerging research field aiming to integrate the above two functionalities into a single system, sharing a majority of hardware and signal processing modules and, in a typical case, sharing a single transmitted signal. It is recognised as a key approach in significantly improving spectrum efficiency, reducing device size, cost and power consumption, and improving performance thanks to potential close cooperation of the two functions. Advanced signal processing techniques are critical for making the integration efficient, from transmission signal design to receiver processing. This paper provides a comprehensive overview of JCR systems from the signal processing perspective, with a focus on state-of-the-art. A balanced coverage on both transmitter and receiver is provided for three types of JCR systems, communication-centric, radar-centric, and joint design and optimization.

Dynamic modal control of unmanned vehicle movement in open pit mining

The purpose of this study is to present a number of aspects in the modern concept of computer-aided dynamic modal control of unmanned quarry vehicles in open pit mining. In particular, the software and hardware module that is a part of the “Smart Quarry” global structure deals with the conditions of matching a form of specific current trajectories (their deviation to the left or right of the nominal axial trajectory) to information “trajectory” chirp signals. The study employs the methods of wavelet transforms to convert one-dimensional signals that generate unmanned vehicle current trajectories into the time-frequency distributions of Cohen’s class. The formation of unmanned vehicle current trajectories under their deviation to the left / right from the nominal axial trajectory on straight and curved routes is considered schematically. It is noted that the tracking of unmanned current trajectories on quarry routes is carried out taking into account the nature of trajectory signals. The difference between the introduced dynamic modal control of the unmanned vehicle and the static one is formulated. Some fragments displaying 1D-signals in a wavelet medium are introduced into the autonomous and external control subsystems. The computer-aided control system uses such elements of the wavelet transforms technique as Gabor wavelet functions, the wavelet matching pursuit algorithm, and Cohen’s class time-frequency distributions. The research results in formulating the criteria for forming the unmanned vehicle current trajectories by the control system in the form of its reactions to sporadic disturbances caused by the occurrence of static or dynamic obstacles on a route. The algorithm of dynamic modal control of current trajectories has been developed. The concept of forward and reverse transient processes of signals of unmanned vehicle trajectory deviation has been introduced. The estimation procedure of modal controller parameters has been described. The algorithm has been developed for modal controller matrix recalculation, which has the form of the chain of sequentially implemented matrix procedures. It should be noted in conclusion that a computer-aided system for modal control of current trajectory deviation has been developed on the basis of the performed research. It enables to implement the functions of controlling the dynamics of technological and safe movement of unmanned vehicles along the quarry routes in a conflict environment of open pit mining.

Research on English Education Auxiliary Teaching System Based on MOOC

In the traditional English education auxiliary teaching system, the response time is long and the student satisfaction rate is low. Keeping these issues in mind, we propose a MOOC-based English education auxiliary teaching system that consists of hardware and functional modules of the teaching system and the database. The hardware of the English education auxiliary teaching system consists of the database server, memory, and remote controller. The functional modules include the basic teaching module based on MOOC, the assessment module, and the system management module. We conducted a number of experiments, and the simulations showed that the proposed system has a considerably shorter response time than its competitors. We conducted a survey of the proposed system and the existing systems in ten different colleges, and the results showed that the proposed system has a higher students’ satisfaction rate.

A neuromonitoring framework supporting the intraoperative placement of an epidural spinal cord electrode array for functional restoration in people with paraplegia

Abstract Targeted stimulation of dorsal nerve roots of the lumbosacral spinal cord (SC) represents a promising way to improve lower extremity functions for people with paraplegia due to SC injury. Stimulation pulses are selectively applied at different segments of the SC by means of an implanted electrode array. For an optimal clinical outcome, correct positioning of the array in rostrocaudal and mediolateral direction of the SC is essential. We developed a neuromonitoring framework to support the neurosurgeon during the implantation procedure by automated stimulation and real-time analysis of evoked muscular responses. For the implementation we developed and combined a new software with several hardware and software modules for intraoperative neuromonitoring. The new software coordinates different interfaces and components including a neurostimulator, a multiplexer module and a biosignal amplifier. In addition, a graphical user interface (GUI) was implemented, allowing control of the system and display of analyzed data. With the framework, stimulation sequences in respect to stimulation amplitudes and selection of contacts of the electrode array can be predefined and executed in an automated manner. Synchronously the evoked electromyographic responses of the target muscles can be recorded, analyzed and displayed together with the stimulation setup for visual evaluation. The framework serves as a fundamental basis for the integration of a clinical knowledge-based algorithm and the realization of a future neuromonitoring system for this purpose.

Design and Implementation of Environmental Monitoring System Using Photoelectric Wireless Sensor Network

Photoelectrochemical (PEC) sensing is a light-induced electron transfer sensing method based on photoelectric signal conversion to represent the electrode/solution interface. The analysis method is easily disturbed by environmental factors such as light intensity, which reduces the detection accuracy. PEC biosensor with sunlight as the light source is designed. CC2530 RF hardware module and Zstack software protocol stack produced by TI company are adopted to establish PEC wireless sensor network (WSN) for farmland environment detection. 5G-DTU module is selected to realize the remote data transmission between the PEC WSN and remote server. The data exchange is carried out through a 5G mobile communication network. The farmland environment monitoring adopts B/S architecture. The types of all PEC sensors need to be selected and the hardware circuit module is designed. The corresponding interface adopts a unified standard 485 bus communication interface to facilitate the data reading of the sensor. In the experiment, the designed PEC sensor can distinguish between carbon nitride nanosheets (CNNS) and nitrogen fossil graphene loaded silver/TiO2 (Ag/TiO2/D3NGH) by changing the bias potential, and the sensor can work in unstable sunlight. The photocurrent of anode and cathode of the PEC sensor increases with the increase of sunlight intensity, and the photocurrent ratio remains constant with the further increase of the concentration of double electrodes (CNNS, Ag/TiO2/D3NGH). In the field mold test, the designed PEC sensor shows good selectivity for aflatoxin B1 (AFB1), and the AFB1 is recovered from bean products in the field, with a recovery rate of more than 95%, which proves that the photoelectric monitoring system can accurately measure and analyze the field mold.