This work proposes an efficient circuit implementation of a mechanism for charge-sharing suppression in photon-counting pixel arrays based on current-mode circuits for the analog parts. The additional circuits needed for charge-sharing suppression in a four-pixel cluster, leads to an increase in power consumption of 36% and only a marginal increase in circuit area. The implemented pixel with window-discrimination, managing charge-sharing in a four-pixel cluster and with an event-counter of 13 bits, consists of 300 transistors and has a power consumption of 2.7 μW when idle. It is implemented in a 120nm CMOS process and the presented results are based on simulations.
This paper proposes an area- and power-efficient implementation of the read-out electronics for color X-ray pixel detectors for imaging. Introducing multiple levels of energy discrimination will increase the complexity of the read-out electronics in each pixel. The proposed architecture has full resolution for the intensity and reduced resolution for the energy spectrum (color), which leads to a good compromise of image quality and circuit complexity. We show that the increase in complexity, compared to single energy-range pixel, will lead to increase in circuit area of less than 20%.
Postural sway is a critical measure for evaluating postural control, and its analysis plays a vital role in preventing falls among the elderly. Typically, physiotherapists assess an individual's postural control using tests such as the Berg Balance Scale, Tinetti Test, and time up-and-go test. Sensor-based analysis is available based on devices such as force plates or inertial measurement units. Recently, machine learning methods have demonstrated promising results in the sensor-based analysis of postural control. However, these models are often complex, slow, and energy-intensive. To address these limitations, this study explores the design space of lightweight machine learning models deployable to microcontrollers to assess postural stability. We developed an artificial neural network (ANN) model and compare its performance to that of random forests, gaussian naive bayes, and extra tree classifiers. The models are trained using a sway dataset with varying input sizes and signal-to-noise ratios. The dataset comprises two feature vectors extracted from raw accelerometer data. The developed models are deployed to an ARM Cortex M4-based microcontroller, and their performance is evaluated and compared. We show that the ANN model has 99.03% accuracy, higher noise immunity, and the model performs better with a window size of one second with 590.96 us inference time.
Acoustic emission (AE) is a widely used non-destructive test method in structural health monitoring applications to identify the damage type in the material. Usually, the analysis of the AE signal is done by using traditional parameter-based methods. Recently, machine learning methods showed promising results for the analysis of AE signals. However, these machine learning models are complex, slow, and consume significant amounts of energy. To address these limitations and to explore the trade-off between model complexity and the classification accuracy, this paper presents a lightweight artificial neural network model to classify damage types in concrete material using raw acoustic emission signals. The model consists of one hidden layer with four neurons and is trained on a public acoustic emission signal dataset. The created model is deployed to several microcontrollers and the performance of the model is evaluated and compared with a state-of-the-art machine learning model. The model achieves 98.4% accuracy on the test data with only 4019 parameters. In terms of evaluation metrics, the proposed tiny machine learning model outperforms previously proposed models 10 to 1000 times. The proposed model thus enables machine learning in real-time structural health monitoring applications.
Visual Sensor Networks (VSNs) are networks which generate two dimensional data. The major difference between VSN and ordinary sensor network is the large amount of data. In VSN, a large number of camera nodes form a distributed system which can be deployed in many potential applications. In this paper we present a model of the physical parameters of a visual sensor network to track large birds, such as Golden Eagle, in the sky. The developed model is used to optimize the placement of the camera nodes in the VSN. A camera node is modeled as a function of its field of view, which is derived by the combination of the lens focal length and camera sensor. From the field of view and resolution of the sensor, a model for full coverage between two altitude limits has been developed. We show that the model can be used to minimize the number of sensor nodes for any given camera sensor, by exploring the focal lengths that both give full coverage and meet the minimum object size requirement. For the case of large bird surveillance we achieve 100% coverage for relevant altitudes using 20 camera nodes per km2 for the investigated camera sensors.
For digital circuits with ultra-low power consumption,floating-gate circuits have been considered to be a techniquepotentially better than standard static CMOS circuits.By having a DC offset on the floating gates, theeffective threshold voltage of the floating-gate transistoris adjusted and the speed and power performance can bealtered. In this paper the basic performance related propertiessuch as power, delay, power-delay product (PDP),and energy-delay product (EDP) for floating-gate circuitsoperating in subthreshold are investigated. Based on circuitsimulations in a 120nm process technology, it isshown that for the best case, the power can be reducedapproximately by one order of magnitude at the expenseof increased delay, while the PDP is more or less constantin comparison to static CMOS. The EDP can be reducedby two orders of magnitude at the expense of reducednoise margins.
For digital circuits with ultra-low power consumption, floating-gate circuits (FGMOS) have been considered to be a potentially better technique than standard static CMOS circuits. One reason for this is because FGMOS only requires a few transistors per gate while it still can have a large fan-in. When power supply is reduced to subthreshold region it will influence the maximum fan-in that is possible to use in designs. In this paper we have investigated how the performance of FGMOS circuits will change in subthreshold region. Simulation in a 120 nm process technology shows that FGMOS will not be working for circuits that have a large fan-in and might not be useable for many designs. At 250 mV power supply it can have a maximum fan-in of 5 and for 150 mV the maximum is 3. FGMOS simulations of an improved full-adder structure with fan-in of 3 is also proposed and compared to a conventional structure with fan-in of 5. It is shown that the improved full-adder with fan-in 3 will have more than 36 times better energy-delay product (EDP)
For digital circuits with ultra-low power consumption, floating-gate circuits (FGMOS) have been considered to be a potentially better technique than standard static CMOS circuits. By having a DC offset on the floating gates, the effective threshold voltage of the floating-gate transistor is adjusted and the speed and power performance can be altered. In this paper we have investigated how the floating-gate capacitances can be selected to achieve the best performance in floating-gate circuits operating at subthreshold power supply. Based on circuit simulations in a 120nm process technology, it is shown that the EDP offers a reduction of more than one order of magnitude for FGMOS with capacitance selection in comparison to static CMOS circuits. This paper also deals with the possibilities available for trade-offs between lower power consumption and higher speed to achieve a better performance for FGMOS than for static CMOS. The main cost involved in achieving these performance improvements is reduced noise margins
For digital circuits with ultra-low power consumption, floating-gate circuits (FGMOS) have been considered to be a potentially better technique than standard static CMOS circuits. For each new generation of process technology the thickness of the transistor gate-oxide will be reduced. This will increase charge leakage in FGMOS circuits and it is therefore necessary to introduce techniques to keep the charge in the node. In this paper we investigate how the most commonly used refresh circuits (quasi-and pseudo-floating gate) affect the performance when they are connected to an FGMOS circuit working with subthreshold power supply. The simulations show that refresh circuits equal in size compared to FGMOS will not have much influence on performance while it is reduced up to an order in magnitude when the size increase 8 times. This strong impact from the refresh circuitry also indicates that it might not be an option for future technologies.
Condition monitoring devices in hydraulic systems that use batteries or require wired infrastructure have drawbacks that affect their installation, maintenance costs, and deployment flexibility. Energy harvesting technologies can serve as an alternative power supply for system loads, eliminating batteries and wiring requirements. Despite the interest in pressure fluctuation energy harvesters, few studies consider end-to-end implementations, especially for cases with lowamplitude pressure fluctuations. This generates a research gap regarding the practical amount of energy available to the load under these conditions, as well as interface circuit requirements and techniques for efficient energy conversion. In this paper, we present a self-powered sensor that integrates an energy harvester and a wireless sensing system. The energy harvester converts pressure fluctuations in hydraulic systems into electrical energy using an acoustic resonator, a piezoelectric stack, and an interface circuit. The prototype wireless sensor consists of an industrial pressure sensor and a low-power Bluetooth System-on-chip that samples and wirelessly transmits pressure data. We present a subsystem analysis and a full system implementation that considers hydraulic systems with pressure fluctuation amplitudes of less than 1 bar and frequencies of less than 300 Hz. The study examines the frequency response of the energy harvester, the performance of the interface circuit, and the advantages of using an active power improvement unit adapted for piezoelectric stacks. We show that the interface circuit used improves the performance of the energy harvester compared to previous similar studies, showing more power generation compared to the standard interface. Experimental measurements show that the self-powered sensor system can start up by harvesting energy from pressure fluctuations with amplitudes starting at 0.2 bar at 200 Hz. It can also sample and transmit sensor data at a rate of 100 Hz at 0.7 bar at 200 Hz. The system is implemented with off-the-shelf circuits.
Pressure fluctuation energy harvesting devices are promising alternatives to power up wireless sensors in fluid power systems. In past studies, classical Helmholtz resonators have been used to enhance the energy harvesting capabilities of these harvesters. Nevertheless, for fluctuations with frequency components in the range of less than 1000 Hz, the design of compact resonators is difficult, mostly for their poor acoustic gain. This paper introduces a space-coiling resonator fabricated using 3D printing techniques. The proposed resonator can achieve a better acoustic gain bounded by a small bulk volume compared to a classic Helmholtz resonator, improving the energy harvesting capabilities of pressure fluctuation energy harvesters. The resonator is designed and evaluated using finite-element-method techniques and examined experimentally. Three space-coiling-resonators are designed, manufactured and compared to classic Helmholtz resonators for three frequencies: 280 Hz, 480 Hz and 920 Hz. This work displays the possibility of compact, high-performance pressure fluctuation energy harvesters and the advantages of the space-coiling printed resonators to enhance the harvesting performance.
Hydraulic pressure fluctuation energy harvesters are promising alternatives to power up wireless sensor nodes in hydraulic systems. The characterization of these harvesters under dynamic and band-limited pressure signals is imperative for the research and development of novel concepts. To generate and control these signals in a hydraulic medium, a versatile apparatus capable of reproducing pressure signals is proposed. In this paper, a comprehensive discussion of the design considerations for this apparatus and its performance is given. The suggested setup enables the investigation of devices tailored for the harvesting of energy in conventional hydraulic systems. To mimic these systems, static pressures can be tuned up to 300 bar, and the pressure amplitudes with a maximum of 28 Bar at 40 Hz and 0.5 bar at 1000 Hz can be generated. In addition, pressure signals found in commercial hydraulic systems can be reproduced with good accuracy. This apparatus proves to be an accessible, robust, and versatile experimental setup to create environments for the complete performance estimation of pressure fluctuation energy harvesters.
Wireless sensor nodes in state of the art fluid power systems used in monitoring and maintenance prediction demand long lasting power sources that do not rely on batteries. Energy harvesting is a promising technology that can provide the required energy to power wireless sensors. Pressure fluctuation energy harvesters can be employed in conventional hydraulic systems to convert the acoustic pressure fluctuation to electrical power. Present studies have explored the overall efficiency of these devices while experimentally describing losses in piezoelectric and circuit interfaces, nevertheless there is no study on the fluid to mechanical force transmission efficiency. In this paper we investigate the pressure to force transmission rate of two types of fluid to mechanical interfaces: a flat metal plate and a conventional hydraulic piston. The interfaces are investigated in conditions similar to those found in conventional hydraulic systems. The study shows that flat plate exhibit good force transmission for low pressure applications with a constant rate across frequencies, while exhibiting a decrease in force transmission at higher pressures. On the other hand the piston exhibit a more robust pressure design, with a constant force transmission rate at all pressures but with a dampening of force at higher frequencies. It is shown that small differences in force transmission ratios can have a considerable impact on the power generation.
The need for wireless sensor networks that can run for long times without the need of battery replacement has risen the need for energy harvesters. Industrial environments have plenty of energy sources that can be harvested; pressure fluctuations are a high energy density source that can be harvested using piezoelectric devices. Present devices have introduced flat metallic plates as the main force transmission elements for hydraulic fluctuations energy harvesters. In this paper, we analyze the force transmission efficiency of flat plates when used as the primary fluid coupling interface in hydraulic energy harvesters. Previous work has been focused on the optimization of circuit matching and pressure ripple amplification. In this work, we offer a look into the efficiencies of flat plates in different configurations and pressure loads. The analysis shows that despite the reasonable force transmission efficiency of flat plates in low-pressure environments, the overall efficiency of hydraulic energy harvesters can be improved if instead of flat plates, conventional hydraulic actuators, such as piston cylinders, could be used.
In this paper, a new, real-time reconfigurable perceptron circuit element is presented. A six-transistor version used as a threshold gate, having a fan-in of three, producing adequate outputs for threshold of T = 1, 2 and 3 is demonstrated by chip measurements. Subthreshold operation for supply voltages in the range of 100-350 mV is shown. The circuit performs competitively with a standard static complimentary metal-oxide-semiconductor (CMOS) implementation when maximum speed and energy delay product are taken into account, when used in a ring oscillator. Functionality per transistor is, to our knowledge, the highest reported for a variety of comparable circuits not based on floating gate techniques. Statistical simulations predict probabilities for making working circuits under mismatch and process variations. The simulations, in 120-nm CMOS, also support discussions regarding lower limits to supply voltage and redundancy. A brief discussion on bow the circuit may be exploited as a basic building block for future defect tolerant mixed signal circuits, as well as neural networks, exploiting redundancy, is included.
Nowadays, air pollution is monitored with accurate, but large-sized measurement stations, leading to an overall limited number of monitored locations. Combining these stations, with a higher number of less accurate stations can provide additional information, such as with regards to pollutant distributions. In this paper we present the design, implementation and initial results of such stations based on Wireless Sensor Network technology. For the implementation of the network purely off-the-shelf equipment was chosen, which allows us to analyze the current status of commercially available Wireless Sensor Network technology. While the system was fully implemented and demonstrated operationally, the experiences found during the project showed a limited matureness with regards to the off-the-shelf equipment and uncovered flaws in typical assumptions underlying Wireless Sensor Network research. © 2011 IEEE.
Wireless visual sensor networks provide featurerich information about their surrounding and can thus be used as a universal measurement tool for a great number of applications. Existing solutions, however, have mainly been focused on high sample rate applications, such as video surveillance, object detection and tracking. In this paper, we present a wireless camera node architecture that targets low sample rate applications (e.g., manual inspections and meter reading). The major design considerations are a long system lifetime, a small size and a low production cost.We present the overall architecture with its individual design choices, and evaluate the architecture with respect to its application constraints. With a typical image acquisition cost of 1.5 J for medium quality images and a quiescent power demand of only 7 uW, the evaluation results demonstrate that long operation periods of the order of years can be achieved in low sample rate scenarios.
Wireless Sensor Networks have the ability to improve a multitude of existing application domains. These networks are built up from a number of sensor nodes with sensing, communication and processing capabilities and the performance of the networked system is defined by the performance of the node platform it is based on. In this paper, we present SENTIO-em, a hardware platform for research in the environmental monitoring application domain. Based on the application domain requirements, the architecture and implementation of SENTIO-em is optimized for environmental monitoring constraints, while it is sufficiently flexible to be reused for different applications within the domain. The architecture of the platform is presented and evaluated under both laboratory and different environmental conditions. The obtained results are compared to a number of existing node platforms, demonstrating that SENTIO-em provides high energy efficiency with increased processing performance, short state transition times, and low quiescent currents.
Indoor photovoltaic (PV) application gains in attraction for low-power electronic systems, which requires accurate methods for performance predictions in indoor environments. Despite this, the knowledge on the performance of commonly used photovoltaic device models and their parameter estimation techniques in these scenarios is very limited. Accurate models are an essential tool for conducting feasibility analyses and component dimensioning for indoor photovoltaic systems. In this paper, we therefore conduct a comparison of the one- and two-diode models with parameters estimated based on two well-known methods. We evaluate the models' performance on datasets of photovoltaic panels intended for indoor use, and illumination conditions to be expected in indoor environments lit by artificial light sources. The results demonstrate that the one-diode model outperforms the two-diode model with respect to the estimation of the overall I-V characteristics. The two-diode model results instead in lower maximum power point errors. Both models show a sensitivity to initial conditions, such as the selection of the diode ideality factor, as well as the curve form of the photovoltaic panel to be modeled, which has not been acknowledged in previous research.
Ambient light measurements and an understanding of light conditions are essential for the accurate estimation of available energy in indoor photovoltaic applications. Light conditions may vary with respect to illumination intensity, duration, and spectral composition. Although the importance of the light spectrum has been documented in laboratory studies, previous distributed measurement methods are limited to intensity as a measure for output power. In this paper, we propose and implement a system for distributed measurement of light conditions that includes spectral information with low overhead. Based on a prototype implementation, we demonstrate that the illumination intensity and spectrum varies considerably over time and space, which confirms the demand for the proposed solution. We, moreover, characterize the energy consumption of the prototype, demonstrating that long-term, unattended characterization of light conditions can be achieved.
Solar energy harvesting allows for wireless sensor networks to be operated over extended periods of time. In order to select an appropriate harvesting architecture and dimension for its components, an effective method for the comparison of system implementations is required. System simulations have the capability to accomplish this in an accurate and efficient manner. In this paper, we evaluate the existing work on solar energy harvesting architectures and common methods for their modeling. An analysis of the existing approaches demonstrates a mismatch between the requirement of the task to be both accurate and efficient and the proposed modeling methods, which are either accurate or efficient. As a result, we propose a data-driven modeling method based on artificial neural networks for further evaluation by the research community. Preliminary results of an initial investigation demonstrate the capability of this method to accurately capture the behavior of a solar energy harvesting architecture, while providing a time-efficient model generation procedure based on system-level data.
Models of photovoltaic devices are used to compare the properties of photovoltaic cells and panels, and to predict their I-V characteristics. To a large extent, modeling methods are based on the one-diode equivalent circuit. Although much research exists on the implementation and evaluation of these methods for typical outdoor conditions, their performance at indoor illumination levels is largely unknown. Consequently, this work performs a systematic study of methods for the parameter extraction of one-diode models under indoor conditions. We selected, reviewed and implemented commonly used methods, and compared their performance at different illumination levels. We have shown that most methods can achieve good accuracies with extracted parameters regardless of the illumination condition, but their accuracies vary significantly when the parameters are scaled to other conditions. We conclude that the physical interpretation of extracted parameters at low illumination is to a large extent questionable, which explains errors based on standard scaling approaches.
Existing solar energy harvesting systems are typically evaluated with a single configuration. However, results on different harvester configurations are often desired in order to select the appropriate match to specific ambient conditions and application requirements. In this paper, we therefore present a concept for remotely reconfigurable solar energy harvesting testbeds, which allows for multiple harvester configurations to be evaluated with a single system deployment. We demonstrate that such a testbed can be implemented in an efficient manner by utilizing the benefits of wireless sensor networks, resulting in a scalable and flexible system with low power consumption.
In wireless sensor networks, as energy limited systems, communication is a costly activity. For this reason duty cycling approaches are commonly used, because they can limit the overall power consumption of a sensor node tremendously by shutting down communication sub-circuits whenever they are not used. However, for efficient power reduction nodes have to know the exact times when they are supposed to communicate in the network. Synchronization can be used to accomplish this and comes with additional features such as the possibility of cooperative sampling at a given time. In this paper we propose a synchronization protocol that introduces low overhead due to broadcast master-node synchronization, while still accomplishing synchronization accuracies in the order of 100 μs. The protocol is intended for periodic data collection applications that are common tasks in environmental monitoring systems. Since changes in environmental conditions can have a large effect on the synchronization behavior, we further present a temperature compensation algorithm for the proposed synchronization protocol that allows stable usage of synchronization in a wide range of temperatures. Measurement results are taken from implementing the protocol on sensor node platforms and show the real world performance of the presented methods.
There is a significant potential for Wireless sensor networks to be used as a general distributed measurement and monitoring system. The integration of computation, communication and sensing enables smart sensors to be built that can be adapted to a plethora of application requirements and allow for automated data collection throughout the network. However, the potential end users of this systems are domain experts, who usually do not possess the technical expertise to program, and thus operate, wireless sensor nodes, which prohibits the technology from becoming off-the-shelf equipment. In this paper, we present a method which enables the complexity of programming sensor nodes to be concealed in order to allow domain experts to use wireless sensor networks in basic applications without the requirement of technical assistance. We propose to use a computer-based specification entry, which generates a configuration parameter set to adjust the sensor node's application behavior. The method has been implemented in a proof-of-concept system and evaluated with test subjects who possess limited programming skills. The results show that users without any prior programming knowledge, or experience with embedded systems, are capable of configuring a sensor node according to a given application scenario within minutes.
Typical wireless sensor network applications inthe domain of environmental monitoring require or profitfrom extended system lifetime. However, restrictions in sensornode resources, especially due to the usage of capacity limitedbatteries, forbid these desired lifetimes to be reached. Asopposed to batteries, energy harvesting from ambient energysources enables for near-perpetual supply of sensor nodes, asthe utilized energy source is inexhaustible. Nevertheless, thesupply from ambient energy sources is rate-limited, whereinthis supply-rate is mainly defined by the system deploymentlocation. On the other hand, the attached sensor node hasa consumption-rate, which has to be supplied to guaranteecontinuous node operation. In this paper, we address thematching of supply-rate and consumption-rate in solar energyharvesting systems at locations with limited insolation. Thefocus lies on the reduction of harvester energy overhead, whichin low-duty cycled system easily reaches similar or higherconsumption levels than the load it supplies. We suggest andpresent two harvester architectures [1], that have their maindesign consideration on simplicity. The individual modulesof the architectures are tested and verified in laboratorymeasurements and we evaluate the fully implemented systemsin an outdoor deployment. Based on the laboratory results,implementation choices for the architecture modules have beenmade. Whereas both harvesting architectures continuouslysupplied the attached load during the deployment period, wewere able to compare their behavior with each other andpresent individual advantages and drawbacks
Environmental monitoring applications demand wireless sensor networks to operate over a long period of time. Although energy consumption of these systems has been tremendously reduced, lifetime of sensor nodes is still limited by the capacity and lifetime of batteries used as energy sources. Energy harvesting, and in outdoor deployments particular, solar energy harvesting becomes a suitable way of powering wireless sensor nodes as their power consumption decreases. In this paper we address the feasibility of battery-less operation of wireless sensor nodes using solar energy harvesting at locations where the amount of solar radiation is severely limited and seasonal variations are large. We present two circuit architectures optimized for low energy leakage and evaluate their performance based on data gathered in a deployment during winter in Sundsvall, Sweden. We show that both architectures allow operation of sensor nodes even in the darkest period of the year. Furthermore comparisons between the two architecture designs are provided. © 2010 IEEE.
With the advent of low-cost and low-power computation, communication and sensor devices, novel instrumentation and measurement applications have been enabled, such as real-time industrial condition monitoring and fine-grained environmental monitoring. In these application scenarios, a lack of available infrastructures for communication and power supply is a common problem. In industrial applications, for example, the machine to be monitored and the monitoring system itself have significantly different technology lifespans, which requires that the monitoring system be retrofitted to machines that are already in use. In environmental monitoring, measurement systems are deployed as standalone devices in potentially remote areas. Consequently, the more autonomous the sensor system can be in terms of required infrastructure, the better it can match application and business needs.
Solar energy harvesting has become a common energy source for outdoor wireless sensor networks. To avoid the lifetime limitation of traditional secondary battery technologies in these systems, energy harvesting architectures with short-term energy storage can be chosen. These technologies offer long shelf-life and many recharge cycles, but can buffer for only short periods of time due to their small storage capacity. In this paper we present the analysis of two of these short-term energy storage devices, namely double layer capacitors and thin-film batteries. We present different harvesting architectures using these buffer elements and compare their advantages and disadvantages in relation to being used in low-power wireless sensor network applications. Experimental results show that both storage types are viable options for the intended application, each bringing their own strengths and weaknesses.
In applications where a priori determination of location is infeasible, node localization schemes are desirable, which allow the node to estimate its location during network operation. The majority of these schemes are based on ranging between node pairs, which should ideally be performed without adding cost or size to the sensor node. Two-way time-of-flight schemes can fulfill this desire, by utilizing the measurement of the time-of-flight of electromagnetic waves to determine the distance between two sensor nodes. In this paper, we present the implementation and analysis of such a ranging scheme. Because a small error in time measurement can result in a large distance estimation error, the focus of this work lies on the determination and analysis of influencing factors, which limit the accuracy of round-trip-time measurements. We analyze two main contributing factors to the accuracy of the ranging scheme, namely the radio transceiver clock quantization and the link quality during round-trip-time measurement. These effects and their impact on the overall ranging error have been investigated by means of simulation and experimentation. Initial ranging errors as large as 24 m RMS were observed, which could be reduced to errors between 5 and 8 m RMS by utilizing compensation techniques.
Solar energy harvesting gains more and more attention in the field of wireless sensor networks. In situations, where these sensor systems are deployed outdoors, powering sensor nodes by solar energy becomes a suitable alternative to the traditional way of battery power supplies. Since solar energy, opposed to batteries, can be considered as an inexhaustible energy source, scavenging this source allows longer system lifetimes and brings wireless sensor networks closer to be an autonomous system with perpetual lifetime. Despite the possibility of designing and constructing these harvesting system, dimensioning becomes a crucial task to fit implemented components to application and load system demands. In this paper we present a way of dimensioning solar harvesting systems based on simulation. Method and implementation of component and system models are described on the basis of an example architecture that has been used in prior work. Furthermore we evaluate the model in comparison to deployment of the same architecture and show the suitability of using the simulation as a support to optimize choices for system parameters
Finite State Machine (FSM) partitioning together with a Dynamic Power Management (DPM) scheme is an efficient method for low-power FSM design. Taking both power and area into account at an early stage of FSM partitioning is important for choosing an efficient partitioning in terms of both power and area. There are certain FSMs that a partitioning solution with the lowest power has a big area overhead. For them, exploring the area-power trade-off is especially helpful for finding an alternative partitioning with slightly higher power consumption but much lower area. In this paper, we explore the area-power trade-off in FSM partitioning and propose the area cost functions that are verified by correlation coefficient. A relative comparison of the estimated area cost among the partitioning solutions gives the user more freedom to trade power for area. Since the gate-level implementation is unknown, the area constraint should be given in relative terms, not as the specific percentage of area increase allowed
Finite state machine (FSM) partitioning proves effective for power optimization. In this paper we propose a design model based on mixed synchronous/asynchronous state memory that results in implementations with low power dissipation and low area overhead for partitioned FSM.s. The state memory here is composed of the synchronous local state memory and asynchronous global state memory, where the former is used to distinguish the states inside a sub-FSM, and the latter is responsible for controlling sub-FSM communication. The input and output behaviour of the decomposed FSM is cycle by cycle equivalent to the undecomposed synchronous FSM. Together with clock gating technique, substantial power reduction can be demonstrated.
Benchmarking is a common way to evaluate the effectiveness of finite state machine (FSM) low-power methodologies. The serious problem in the existing standard benchmarks is that power-related characteristics are not provided, and therefore these benchmarks are not complete for reliable evaluation and comparison of low-power methods and tools. To address this problem, this paper introduces the coefficient of variation, which is very useful for quantitative analysis of power-related features of an FSM, and for indicating the power optimization opportunity of the corresponding circuit. Based on the coefficient of variation, input-sensitivity analysis of the whole standard benchmark set is conducted. It reveals that the benchmark set is input-data dependant, and the set is insufficient for low-power FSM researches due to the limited coverage of power-related characteristics.
An efficient way to obtain Finite-State Machines (FSMs) with low power consumption is to,partition the machine into two or more sub-FSMs and use dynamic power management, where all sub-FSMs not active are shut down, to reduce dynamic power dissipation. In this paper we focus on FSM partitioning algorithms and RT-level power estimation functions that are the key issues in the design of a CAD tool for synthesis of low-power partitioned FSMS. We target an implementation architecture that is based on both synchronous and asynchronous state memory elements that enables larger power reductions than fully synchronous architectures do. Power reductions of up to 77% have been achieved at a cost of an increase in area of 18%.
An efficient way to obtain finite-state machines (FSMs) with low-power consumption is to partition the machine into two or more sub-FSMs and then use dynamic power management where all sub-FSMs not active are shut down, with the effect of reducing dynamic power dissipation. Thus, FSM partitioning algorithms and register-transfer-level power estimation functions are the main focus of the paper as these are key issues in the design of a computer-aided design tool for synthesis of low-power partitioned FSMs. An implementation architecture is targeted, which is based on both synchronous and asynchronous state memory elements that enable larger power reductions than fully synchronous architectures do. Power reductions of up to 77 have been achieved at a cost of an 18 increase in area.
This paper presents the analysis for joint angle measurement on rigid body that is based on distributed biaxial MEMS accelerometers. It focuses on two methods, one called CMR and another DCMR, and utilizes the property of rigid body kinematics to explain their advantages and weaknesses. Unlike CMR method, DCMR method has no requirement on placing the sensors close to the joint center. This provides greater flexibility for the sensor installation. On the basis of the error model of CMR method, we give an analysis outlining the advantage of theoretically error-free DCMR method. The sensor calibration and alignment is described and both methods are characterized on a rigid body robot arm model. The experiment shows the angular error up to 0.4rad from CMR method whereas just 0.03rad from DCMR method. The noise level from both methods is also compared and analyzed. The result reveals a larger but tunable noise for DCMR method.
The present invention relates to a torque sensor (1) comprising a body (2), at least one airtight chamber (3) provided in the body, a pressure sensor (4) measuring 1 the pressure in said at least one airtight chamber, and a pressure to torque converter (5) connected with the pressure sensor. Each airtight chamber is arranged to change its volume when the body is subjected to a torque, wherein the volume change causes a change of pressure of the enclosed air in the airtight chamber. The change of pressure is detected and converted to the corresponding torque.
This paper proposes a torque sensor based on the differential air pressure measurement method using the volumetric strain of a mechanical sensing structure. A model of the measurement system based on the differential air pressure from the volumetric strain of the mechanical sensing structure is proposed and theoretically discussed. The error sources are identified and an error propagation model is presented for the proposed torque measurement method. Considering these error sources, a prototype torque sensor is presented as a case study for the method verification. Both the mechanical and readout electronics designs are discussed and analyzed. The mechanical sensitivity, resolution, and maximum stresses are analyzed using finite-element modeling. Based on the results from the simulation, a prototype torque sensor is manufactured and experimentally verified using a readout electronics design. For verification, the sensor prototype is measured under static torque to have a sensitivity of 0.04272V/N. m and a range of +/- 117N . m. Compared with the nominal mechanical sensitivity result from the FEM simulation, this measured sensitivity has a difference less than 6%. The noise analysis of the designed readout electronics shows that the resolution of 0.006% can be achieved with this design. Furthermore, hysteresis analysis shows an error of 0.012% of full scale. From these results, it is also shown that the actual performance of the sensor is mainly limited by the differential pressure sensor and the readout electronics design and is not by the mechanical design of the sensor.
This paper presents an analysis of rigid-body joint-angle measurement based on microelectromechanical-system (MEMS) biaxial accelerometers and uniaxial gyroscopes. In comparison to conventional magnetic and optical joint angular sensors, this new inertial sensing principle has the advantages of flexible installation and true contactless sensing. This paper focuses on the comparison of four different inertial-sensor combination methods that are reported in reference papers and utilizes the theory of rigid-body kinematics to explain and analyze their advantages and weaknesses. Experiments have also been conducted to further verify and strengthen the arguments put forward in the analysis. All experiments in this paper took place on a custom-built rigid-body robot arm model that can be manipulated by hand. Sensor calibration and accelerometer alignment issues are also described, and their details are discussed. The experiment results presented in this paper show significant differences with reference to the achieved angular accuracy for various situations when using the four different sensor combination methods. In some cases, the angular error based on one method is more than 0.04 rad, while that from another method is within +/-0.005 rad. The noise levels of angular readings from different methods are also experimentally compared and analyzed. The conclusion drawn serves to guide readers toward a suitable method for their particular application.
This paper presents a feasibility study that deals with a local positioning system for a loader crane based on battery-powered wireless sensors and consists of two joint angular sensors and one telescopic boom length ranger. The practical challenges associated with using conventional sensors are described in order to provide the motivation behind the choice to use the sensing methods proposed in this paper. A novel method is tested using microelectromechanical system inertial sensors mounted around the crane joints to indirectly measure the joint angles, as well as an ultrasound time-of-flight ranging method to measure the telescopic boom length. The local positioning system's wireless sensor prototype designs are described in detail. Data from the angular sensor experiments conducted on a loader crane and the ultrasound ranging experiments conducted both in the laboratory and on the loader crane are presented and analyzed. The preliminary results from these experiments show that the performance of the new sensors is promising. The conclusion is drawn from the experimental results, and future work for this local positioning system is also described.
This paper presents an experimental study using laser mouse sensors for the contactless revolutions per minute (RPM) measurement of a rotating shaft. The sensor performance characterization experiment is firstly conducted under different parameter setups. After the optimal parameter value has been found, the rotor RPM experiment is then conducted with a speed sweep from 500 to 3800 rpm, and data are gathered at 30 different speeds and processed using two different methods to convert the sensor readings into the RPM of the rotating shaft; the results are then displayed. The performance differences between the two methods are compared, and the observation is that both the linearity and the signal-to-noise ratio of the frequency correlation method are several times better than those for the amplitude correlation method. The conclusion summarizes the experimental results and the advantage associated with this new RPM sensing method and provides the motivation for its potential applications and its future works.
This paper presents the design and implementation of a prototype of a stator-free revolutions-per-minute (RPM) sensor based on two microelectromechanical-system uniaxial accelerometers. This paper first introduces the operating principle of the stator-free RPM sensor. It then discusses the associated architecture and design issues of this new sensing method. It then describes the detail of the prototype sensor hardware and software design of the common-mode rejection method and its signal processing. Experiments using the prototype sensor have been also conducted to further verify and strengthen the arguments put forward in the previous discussion. All experiments in this paper took place on a lathe machine in a laboratory. Sensor calibration under a MATLAB environment is also described. Experimental results confirm the interesting property of this sensor, namely, that it provides higher precision at higher RPM. The conclusion summarizes the design considerations, the experimental results, and the motivation in relation to future works for this stator-free RPM sensing method.