Computational Power Consumption Research Articles

A novel change-detection scheduler for a network of depth sensors

In many monitoring applications such as smart home and surveillance, deployment of multiple depth sensors increases monitoring area and offers better occlusion handling which is not sensitive to illumination condition in comparison with RGB sensors. However, multiple sensors also increase the volume of data associated with signal processing alongside the associated computational complexity and power consumption. In order to address these drawbacks, this paper proposes a novel change detection algorithm that can be used as a part of a sensor scheduler in a centralized (e.g. star) network configuration. Initially, each sensor in the network performs a unique single scan of the common environment in order to detect any incremental changes in the sensed depth signal. This initial change detection is then used as a basis for several follow-up tasks such as foreground segmentation, background detection, target detection, and tracking for monitoring tasks. Here, instead of processing a complete depth frame, we proposed to utilize a collection of 1D scans of the depth frames. A confidence function is defined that can be used to estimate the reliability of the detected changes in each sensor and to reduce any false positive events which can be triggered by the noise and outliers. Analysis of the proposed confidence function is carried out through performance analysis in the presence of sensor noise and other parameters which can affect the reliability of the sensed data of each sensor. Finally, a score function is defined based on the confidence of the detected parameters and sensor resolution in order to rank and match sensors with the associated objects to be tracked. It results in tracking target(s) by a sensor (or sensors) that offer a high tracking score. This approach offers many advantages such as decreasing the overall system power consumption by placing the sensors with a low confidence value on standby mode and reducing the overall computational overheads.

Approach to Positioning Links of the Manipulator Using Neural Networks

This paper considers development of positioning systems for manipulator links to solve the forward kinematics problem (FKP) and inverse kinematics problem (IKP). Here we study a robotic manipulator with four degrees of freedom. It should be noted, that one of the relevant research problems of modern modular robotic devices consists in the lack of the universal algorithms, that would ensure kinematics problem recalculations in the cases of reconfigurations of the whole system. Challenges, the researchers are facing with when solving this problem, have to do with geometrical and non-linear equations (trigonometric equations), finding of inverse matrix of the Denavit—Hartenberg presentation, as well with other problems, such as multiple solutions when using the analytical approach. Common mathematical solutions of the inverse kinematics problem, such as geometric, iterative and algebraic ones, may not always lead to physically appropriate solutions. It’s also noteworthy, that, trying to introduce physical solutions for the manipulator, we need to take into account, that the number of calculation formulas increases, what, in turn, causes further computing power consumption increase. If the manipulator acquires additional degrees of freedom, analytical modeling becomes virtually impossible. One of relevant inverse kinematics solution methods consists in implementation of neural networks to that end. To solve this problem various sources were analyzed, considering alternative ways of target point discovery. Considering the analyzed papers, we propose to use a perceptron. Before training the network, we compose an algorithm, calculating the Denavit—Hartman presentation matrix and check for correctness of target point reach by the terminal manipulator link. We did calculations for a thousand positions of manipulator and object in the environment, fed to the neural network. When solving FKP we obtain object coordinates as network output, whereas in the case of IKP — manipulator link angles. We present kinematic scheme testing results, as well a control scheme for a manipulator with four degrees of freedom.

A Real-Time Flexible Telecommunication Decoding Architecture Using FPGA Partial Reconfiguration

Modern telecommunication decoding schemes, leading to high computational complexity and power consumption, are weakening the feasibility of real-time applications. In this brief, a real-time reconfigurable decoding architecture is introduced to alleviate the contradiction mentioned above. The proposed architecture is partially reconfigurable on the fly so that different decoding schemes can be deployed onto merely one physical partition of reconfigurable hardware and be switched with another without significant penalty on timing and complexity. The decoding system is implemented on an FPGA evaluation board and the experiment result shows that our approach renders ple decoding schemes implemented in one system with feasibility, simplicity and power-saving ability. By adopting our architecture and the development flow, a three-LDPC-code decoder is implemented. Moreover, nearly 50% savings on hardware logic and milli-second-level real-time switching speed for decoders are both achieved.

On improved DFT-based low-complexity channel estimation algorithms for LTE-based uplink NB-IoT systems

Channel estimation is crucial to achieving wide-area coverage for ultra-low-cost and low-power narrowband Internet of Things (NB-IoT) devices that are in coverage extremities. Radio coverage can be extended by repeatedly transmitting the same signal over a protracted period. In repetition dominated NB-IoT systems, existing channel estimators extensively used in the orthogonal frequency-division multiplexing (OFDM) system may be no longer applicable due to their considerable computational complexity and power consumption. In this paper, we propose narrowband demodulation reference signal (NDMRS)-assisted transform-domain low-complexity channel estimation algorithms named random sorting least squares (RS-LS), and de-noising LS (D-LS). Another sub-optimal estimator, stemming from the filtered channel estimates called linear minimum mean square error-approximation (LMMSE-A) is also studied. We first estimate initial channel response at pilot frequencies using the conventional LS method; and then, apply several additional operations in time-domain to suppress LS estimation error without exploiting extra frequency-band resources, and increasing significant computational complexity. Finally, channel estimates for the remaining OFDM symbols within an NB-IoT subframe are obtained by employing the time dimensional linear interpolation. Through several simulation examples, the viability of the proposed estimators is verified in comparison with the conventional LS, denoise, and optimal LMMSE estimators in terms of channel mean square error (MSE), block error rate (BLER), and throughput against signal-to-noise ratio (SNR) for Long Term Evolution (LTE)-based uplink NB-IoT systems.

Migration Modeling and Learning Algorithms for Containers in Fog Computing

Fog Computing (FC) is a flexible architecture to support distributed domain-specific applications with cloud-like quality of service. However, current FC still lacks the mobility support mechanism when facing many mobile users with diversified application quality requirements. Such mobility support mechanism can be critical such as in the industrial internet where human, products, and devices are moveable. To fill in such gaps, in this paper we propose novel container migration algorithms and architecture to support mobility tasks with various application requirements. Our algorithms are realized from three aspects: 1) We consider mobile application tasks can be hosted in a container of a corresponding fog node that can be migrated, taking the communication delay and computational power consumption into consideration; 2) We further model such container migration strategy as multiple dimensional Markov Decision Process (MDP) spaces. To effectively reduce the large MDP spaces, efficient deep reinforcement learning algorithms are devised to achieve fast decision-making and 3) We implement the model and algorithms as a container migration prototype system and test its feasibility and performance. Extensive experiments show that our strategy outperforms the existing baseline approaches 2.9, 48.5 and 58.4 percent on average in terms of delay, power consumption, and migration cost, respectively.

Improving Coefficient of Thermal Performance and Electricity Saving in the Water Chiller System

A water chiller air-conditioning system has been completed. Energy savings was estimated using different variables: calculation power consumption, electricity consumption, the cooling load of the testing room. The system tested during the environment of Al- Muthanna province (Al-Rummaitha city), Iraq. The measurements were taken during the summer months (May - June – July and August). The results indicated a difference in energy saving during the daily operating hours, the highest reduction rate was obtained of cooling loads, which were based on the change in evaporator temperatures and also the highest percentage was achieved during August by 23% and the lowest percentage during May by 5%. Thus, the study concluded that the percentage of electricity consumption during the months of May and August was 56% and 41%, respectively. This paper concludes the power electricity was 14.7 amperes in conventional air conditioners whereas the chiller air-conditioning was up to 1.07 amperes. Also, the electricity saving was effected with lower storage tank water

FPGA-Based Implementation of a Real-Time Object Recognition System Using Convolutional Neural Network

High computational complexity and power consumption makes convolutional neural networks (CNNs) ineligible for real-time embedded applications. In this brief, we introduce a low power and flexible platform as a hardware accelerator for CNNs. The proposed architecture is fully configurable by a software library so that it can perform different CNN models with a reconfigurable hardware. The hardware accelerator is evaluated on a ZC706 evaluation board. We make use of the AlexNet architecture in a real-time object recognition application to demonstrate the effectiveness of the proposed CNN accelerator. The results show that the performance rates of 198.1 GOP/s using 512 DSP blocks and 23.14 GOP/s using 64 DSP blocks are achievable for the convolution and fully connected layers, respectively. Moreover, images are processed at 82 frames/s, which is significantly higher than existing implementations.

A Full-Solid-State Humidity Pump for Localized Humidity Control

Summary Humidity management is essential and widely needed in various fields. Traditional moisture removal technologies are mostly based on cooling condensation or desiccant dehumidification. These methods come with inherent defects of energy efficiency and are bulky and complex for small-space humidity-control applications. Here, we demonstrate a full-solid-state humidity pump using the advantages of commercial thermoelectric coolers and silica gel materials. Silica gel is coated on the surface of heat sinks to minimize contact thermal resistance between the material and heat source while maintaining enough heat and mass transfer area between the desiccant and the air. The humidity pump device exhibits 0.61 g W−1 h−1 humidity transfer efficiency, corresponding to a humidity transfer rate of 28.38 g h−1. The proposed device neither uses refrigerant nor introduces liquid water. It opens up the possibility of use in localized humidity control applications with higher efficiency and a wider ambient temperature range than traditional technologies.

Experimental setup of stretchable arid dry pad sensors for the signal acquisition fir filter design using Vedic approach

At present in Medical Image processing a hard point to settle any issue with the assistance of Computer collaborator using Arid-dry pad sensor plays a prominent role. Specialist is to investigate the medical diagnosis with the assistance of imaging system such as The CT scan, MRI scan, EEG, Ultrasound technique or any kind of medical images analyzing the system of the patient with the help of images. Design of low power and low delay digital finite-impulse response (FIR) which plays a vital role in medical image applications along with Arid-dry pad sensors. These days, there are numerous versatile applications requiring low power and high throughput than any time in recent memory. In this research the FIR channel framework configuration has turned into a huge execution objective. The Finite Impulse Response (FIR) Filter is the vital segment for planning a medical image processing. The adders and multipliers play an important role in FIR filter while considering these, not only the fast response but also the number of component, area and path delay. The proposed FIR filter is designed by using adder and Wave pipelined Vedic multiplier from the analysis of various adders and multipliers. The Wave pipelined Vedic multiplier performs multiplication process in a hierarchical manner. The work has demonstrated the effectiveness of Urdhva Triyagbhyam– Vedic technique for multiplication which strikes a distinction in the real procedure of increase itself. It empowers parallel age of moderate items, wipes out undesirable increase ventures with zeros and scaled to higher bit levels. Thus, the proposed method can minimize the setup time violation and path delay of the FIR filter with pad sensors. The proposed FIR Filter is simulated, synthesized, calculating power consumption using ISE simulator, Xilinx and Altera Quartus II.

Power Consumption Analysis of Conveying Device for Electric Sugarcane Leaves Returning Field Machine

Aiming at special structure of the conveying device for electric sugarcane leaves returning field machine, this paper presents a modeling method which can calculate power consumption according to different parameters. This method uses the thought of elementary work to build mathematical model models from two aspects of sugarcane leaf and brush cluster. The theoretical formula for calculating the power consumption of the drum type brush throwing device can be obtained by this method. Using the power consumption model and combined with the existing structural parameters and motion parameters, the working power of the conveying device for the returning field machine can be calculated. The research results in this paper will play a significant role in decreasing the power consumption and provide the feasible theoretical reference for further designing the conveying device for electric sugarcane leaves returning field machine.

Group collision attack

Key enumeration schemes are used to post-process the scores given by side channel distinguishers and enumerate the key candidates from the most possible one to the least possible one, which can be regarded as optimal tools of key search. However, the application of them is limited by very large key candidate space and computing power consumption. For example, the attacker may spend several weeks or months enumerating the whole 245 key candidates. Unlike the former literature that try to propose a more efficient algorithm to process the distinguishers, scores of key candidates directly, we focus on pre-processing and reducing the key candidate space. To achieve this goal, a new divide and conquer strategy named group collision attack (GCA) is proposed in this paper. The GCA works as follows in brief. The key candidates are first divided into groups on which intra-group collision attack is used to remove the impossible key combinations in each group. Then, the inter-group collision attack is performed to further remove the impossible key combinations between groups. Thus, the complexity of key enumeration is reduced significantly. A series of practical experiments are carried out by using our GCA and the experimental results verify its efficiency.

Computational Complexity Reduction of MMSE-IC MIMO Turbo Detection

High data rates and error-rate performance approaching close to theoretical limits are key trends for evolving digital wireless communication applications. To address the first requirement, multiple-input multiple-output (MIMO) techniques are adopted in emergent wireless communication standards and applications. On the other hand, turbo concept is used to alleviate the destructive effects of the channel and ensure error-rate performance close to theoretical limits. At the receiver side, the incorporation of MIMO techniques and turbo processing leads to increased complexity that has a severe impact on computation speed, power consumption and implementation area. Because of its increased complexity, the detector is considered critical among all receiver components. Low-complexity algorithms are developed at the cost of decreased performance. Minimum mean-squared error (MMSE) solution with iterative detection and decoding shows an acceptable tradeoff. In this paper, the complexity of the MMSE algorithm in turbo detection context is investigated thoroughly. Algorithmic computations are surveyed to extract the characteristics of all involved parameters. Consequently, several decompositions are applied leading to enhanced performance and to a significant reduction of utilized computations. The complexity of the algorithm is evaluated in terms of real-valued operations. The proposed decompositions save an average of [Formula: see text] and [Formula: see text] of required operations for 2 [Formula: see text] 2 and 4 [Formula: see text] 4 MIMO systems, respectively. In addition, the hardware implementation designed applying the devised simplifications and decompositions outperforms available state-of-the-art implementations in terms of maximum operating frequency, execution time, and performance.

Efficient Prediction of Network Traffic for Real-Time Applications

Accurate real-time traffic prediction is required in many networking applications like dynamic resource allocation and power management. This paper explores a number of predictors and searches for a predictor which has high accuracy and low computation complexity and power consumption. Many predictors from three different classes, including classic time series, artificial neural networks, and wavelet transform-based predictors, are compared. These predictors are evaluated using real network traces. Comparison of accuracy and cost, both in terms of computation complexity and power consumption, is presented. It is observed that a double exponential smoothing predictor provides a reasonable tradeoff between performance and cost overhead.

A machine learning based intrusion detection scheme for data fusion in mobile clouds involving heterogeneous client networks

The combination of traditional cloud computing and mobile computing leads to the novel paradigm of mobile cloud computing. Due to the mobility of network nodes in mobile cloud computing, security has been a challenging problem of paramount importance. When a mobile cloud involves heterogeneous client networks, such as Wireless Sensor Networks and Vehicular Networks, the security problem becomes more challenging because the client networks often have different security requirements in terms of computational complexity, power consumption, and security levels. To securely collect and fuse the data from heterogeneous client networks in complex systems of this kind, novel security schemes need to be devised. Intrusion detection is one of the key security functions in mobile clouds involving heterogeneous client networks. A variety of different rule-based intrusion detection methods could be employed in this type of systems. However, the existing intrusion detection schemes lead to high computation complexity or require frequent rule updates, which seriously harms their effectiveness. In this paper, we propose a machine learning based intrusion detection scheme for mobile clouds involving heterogeneous client networks. The proposed scheme does not require rule updates and its complexity can be customized to suit the requirements of the client networks. Technically, the proposed scheme includes two steps: multi-layer traffic screening and decision-based Virtual Machine (VM) selection. Our experimental results indicate that the proposed scheme is highly effective in terms of intrusion detection.

Performance Investigation of a Cost- and Power-Effective High Nonlinearity Tolerance OFDMA-PON Scheme Based on Sub-Nyquist Sampling Rate and DFT-Spread

We investigate the performance of a cost- and power-effective high nonlinearity tolerance orthogonal frequency-division multiple access passive optical network scheme based on sub-Nyquist sampling and discrete Fourier transform spread. The analog-to-digital converter with a sampling rate below the Nyquist sampling rate is used for signal detection for each optical network unit. The complicated pre-process procedure is located in the optical line terminal. Only Low-point FFT and IDFT procedures are required in the ONU, reducing the computational complexity and power consumption in the ONU, and the sampling rate of ADC, and improve the cost- and power-efficiency effectively. Moreover, the peak-to-average power ratio is reduced effectively with the scheme. The simulation results show that the measured bit error ratio of each ONU signal well less than the forward-error correction limit of 3.8 × 10 -3 is obtained after 40-km standard single mode fiber transmission.

Efficient Isolated Speech to Sign Conversion Based on the Adaptive Rate Processing

This paper proposes a novel approach, based on the adaptive rate processing and analysis, of achieving a visual transformation of the isolated speech. The idea is to smartly combine the adaptive rate signal processing and classification for realizing an effective speech recognition and its transformation in sign. The incoming speech signal is digitized with an event-driven A/D converter (EDADC). The output of EDADC is windowed with an activity selection process. These windows are later on resampled uniformly with an adaptive rate interpolator. The resampled windows are de-noised with an adaptive rate filter and their spectrum are computed with an adaptive resolution short time Fourier transform (ARSTFT). Later on, the magnitude, Delta and Delta-Delta spectral coefficients are extracted. The K-Nearest Neighbor (KNN) technique is employed to compare these extracted features with the reference templates. The comparison outcomes are the classification decisions. The classification decision is transformed into a systematic sign. The system functionality is tested for a case study and results are presented. A 7.9times reduction in acquired number of samples is achieved by the devised approach as compared to the classical one. It aptitudes a significant computational gain and power consumption reduction of the proposed system over the counter classical ones. An average subject dependent isolated speech recognition accuracy of 96.6% is achieved. It shows that the proposed approach can be employed in potential applications like industrial and noisy environments, integration of people with impaired hearing, etc.

Fast and Efficient Non-Contact Ball Detector for Picking Robots

Object detectors based on deep learning requires high-performance computing and large run-time memory footprint to maintain good detection performance. They bring high computation overhead and power consumption to on-board embedded devices of non-contact ball picking robot. Furthermore, it is difficult to deploy on the machine because the model size is so big. The accuracy of the existing simplified detectors deployed on embedded devices cannot meet the requirements of practical applications. Therefore, how to reduce floating point operations (FLOPs) and the size of model without notably sacrificing detection precision becomes an urgent problem to be solved. To solve this problem, a shuttle residual block which is more efficient network unit based on depthwise separable convolution was proposed. And we designed a non-contact ball object detector for picking robots, which is shallower than YOLOv3 and has narrower structure. We evaluate the proposed method on non-contact Ball dataset and compelling results are achieved by the proposed method. Compared with YOLOv3, the proposed method reduces FLOPs by 86.2%, declines parameter size by 89.5%. Overall, the proposed method achieves comparable detection accuracy than YOLOv3, and its speed is 2.2 times faster than YOLOv3.

Proof-of-Reputation Based-Consortium Blockchain for Trust Resource Sharing in Internet of Vehicles

Resource sharing among vehicles can highly improve the capability and efficiency of Internet of Vehicles (IoV). However, it is challenging to establish trust and preserve privacy during the resource sharing process because of the high mobility and topological variability in IoV. Emerging blockchain technology expresses the excellent performance in handling distributed trust due to its verifiable and immutable ledger. In this paper, we first propose a consortium blockchain-based resource sharing paradigm in IoV, in which the resource sharing interactions are encapsulated as transactions and recorded by Road Side Units (RSUs). Moreover, a lightweight consensus mechanism named as Proof-of-Reputation is proposed to reduce computational power consumption and motivate vehicles involved in resource sharing. Finally a differentiated resource pricing scheme is proposed based on the dynamic match game of resource demand and supply. The reputation value is designed to indicate the trustworthy degree of vehicles, and the trust is established via the consensus procedure. We couple the resource sharing process and consensus together by utilizing the reputation value of each vehicle. The security and privacy analysis as well as simulation experiments on communication performance can verify the efficiency of the proposed blockchain system.

Human-Like Interpretation of Lines Using Embedded GPU

In the applications where human like interpretation of shapes are required, the Hough Transform is popular because of its noise immunity and simple algorithm.Due to the computational capabilities and power consumption, the real time implementation of this algorithm on a standard CPU is not a good practice. In this paper, we have done line detection using Hough transform which is implemented on NVIDIA Jetson TK1 GPU in order to achieve less execution time with low power consumption. We have used the property of GPU to accelerate the process of line detection and compared the performance of CPU and GPU. We have shown different application of line detection which is implemented on Embedded GPU.

Band-Pass Sampling in High-Order BOC Signal Acquisition

The binary offset carrier (BOC) modulation, which has been adopted in modern global navigation satellite systems (GNSS), provides a higher spectral compatibility with BPSK signals, and better tracking performance. However, the autocorrelation function (ACF) of BOC signals has multiple peaks. This feature complicates the acquisition process, since a smaller time searching step is required, which results in longer searching time or greater amounts of hardware resources. Another problem is the high Nyquist frequency, which leads to high computational complexity and power consumption. In this paper, to overcome these drawbacks, the band-pass sampling technique for multiple signals is introduced to BOC signals. The sampling frequency can be reduced significantly. Furthermore, the ACF of the sampled signal has only two secondary peaks, so that the code phase can be searched with a larger searching step. An acquisition structure base on dual-loop is proposed, to completely eliminate the ambiguity and compensate the subcarrier Doppler. The acquisition performance and the computational complexity are also analysed.