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In pedestrian detection, as sophisticated feature descriptors are used for improving detection accuracy, its processing speed becomes a critical issue. In this paper, we propose a novel speed-up scheme based on multiple-instance pruning (MIP), one of the soft cascade methods, to enhance the processing speed of support vector machine (SVM) classifiers. Our scheme mainly consists of three steps. First, we regularly split an SVM classifier into multiple parts and build a cascade structure using them. Next, we rearrange the cascade structure for enhancing the rejection rate, and then train the rejection threshold of each stage composing the cascade structure using the MIP. To verify the validity of our scheme, we apply it to a pedestrian classifier using co-occurrence histograms of oriented gradients trained by an SVM, and experimental results show that the processing time for classification of the proposed scheme is as low as one-hundredth of the original classifier without sacrificing detection accuracy. [ABSTRACT FROM AUTHOR]

Power integrity is a crucial design issue for nano-meter technologies because of decreased supply voltage and increased current. We focused on gate delay variation caused by power/ground noise, and developed a full-chip simulation current model with capacitance and a variable resistor to accurately model current dependency on voltage drop. Measurement results for 90-nm technology are well reproduced in simulation. The error of average supply voltage is 0.9% in average. Measurement results also demonstrate that gate delay depends on average voltage drop. [ABSTRACT FROM AUTHOR]

To construct an energy-efficient wireless home network based on IEEE 802.15.4, a novel architecture is proposed. In this architecture, all nodes are classified into stationary nodes and mobile nodes according to the functionality of each node. Mobile nodes are usually battery-powered, and therefore need low-power operation. In order to improve power consumption of mobile nodes, effective handover sequence based on MAC broadcast and transmission power control based on LQ (Link Quality) are employed. Experimental results demonstrate that by using the proposed architecture, communication time and power consumption of mobile nodes can be reduced by 1.2 seconds and 42.8%, respectively. [ABSTRACT FROM AUTHOR]

VLSI implementation of a sound movement system is described, which is dedicated to mobile applications. This system is based on low cost 3D sound localization method, where feature extraction of head-related transfer function is attempted in order to realize the sound movement at all positions in 3D space by means of efficient coefficient storing scheme. The proposed system can be implemented by only 30,000 gates, which is small enough to mobile applications. Moreover, a prototype system is constructed by using an FPGA board and subjective tests are performed to evaluate the practicability of the proposed 3D sound movement system. [ABSTRACT FROM AUTHOR]

Considers embedded implementation of acoustic field enhancement for stereo sound sources. Approaches devised for two-channel stereo sources including headphones; Acoustic field enhancement; Implementation of the algorithms by a fixed-point digital signal processing.

Compressive sensing (CS) is recognized for its adeptness at compressing signals, making it a pivotal technology in the context of sensor data acquisition. With the proliferation of image data in Internet of Things (IoT) systems, CS is expected to reduce the transmission cost of signals captured by various sensor devices. However, the quality of CS-reconstructed signals inevitably degrades as the sampling rate decreases, which poses a challenge in terms of the inference accuracy in downstream computer vision (CV) tasks. This limitation imposes an obstacle to the real-world application of existing CS techniques, especially for reducing transmission costs in sensor-rich environments. In response to this challenge, this paper contributes a CV-oriented adaptive CS framework based on saliency detection to the field of sensing technology that enables sensor systems to intelligently prioritize and transmit the most relevant data. Unlike existing CS techniques, the proposal prioritizes the accuracy of reconstructed images for CV purposes, not only for visual quality. The primary objective of this proposal is to enhance the preservation of information critical for CV tasks while optimizing the utilization of sensor data. This work conducts experiments on various realistic scenario datasets collected by real sensor devices. Experimental results demonstrate superior performance compared to existing CS sampling techniques across the STL10, Intel, and Imagenette datasets for classification and KITTI for object detection. Compared with the baseline uniform sampling technique, the average classification accuracy shows a maximum improvement of 26.23%, 11.69%, and 18.25%, respectively, at specific sampling rates. In addition, even at very low sampling rates, the proposal is demonstrated to be robust in terms of classification and detection as compared to state-of-the-art CS techniques. This ensures essential information for CV tasks is retained, improving the efficacy of sensor-based data acquisition systems. [ABSTRACT FROM AUTHOR]

An architecture of a matrix-vector multiplier (MVM) is devised, which is dedicated to MPEG-4 natural/synthetic video decoding. The MVM can perform the matrix-vector multiplication both in the inverse discrete cosine transform (IDCT) and in the geometrical transformation of three-dimensional computer graphics (3-D CG); or, specifically, it can achieve the multiplication of a 4×4 matrix by a four-tuple vector necessary in the one-dimensional IDCT for eight pixels and in the geometrical transformation for a point in a 3-D space. This paper describes a new architecture of this MVM and also shows the implementation result of a functional module composed of four MVMs with the use of 440-k transistors, which can operate at 20 MHz or less [ABSTRACT FROM PUBLISHER]

Monitoring of respiration and body movements during sleep is a part of screening sleep disorders related to health status. Nowadays, thermal-based methods are presented to monitor the sleeping person without any sensors attached to the body to protect privacy. A non-contact respiration monitoring based on thermal videos requires visible facial landmarks like nostril and mouth. The limitation of these techniques is the failure of face detection while sleeping with a fixed camera position. This study presents the non-contact respiration monitoring approach that does not require facial landmark visibility under the natural sleep environment, which implies an uncontrolled sleep posture, darkness, and subjects covered with a blanket. The automatic region of interest (ROI) extraction by temperature detection and breathing motion detection is based on image processing integrated to obtain the respiration signals. A signal processing technique was used to estimate respiration and body movements information from a sequence of thermal video. The proposed approach has been tested on 16 volunteers, for which video recordings were carried out by themselves. The participants were also asked to wear the Go Direct respiratory belt for capturing reference data. The result revealed that our proposed measuring respiratory rate obtains root mean square error (RMSE) of 1.82 ± 0.75 bpm. The advantage of this approach lies in its simplicity and accessibility to serve users who require monitoring the respiration during sleep without direct contact by themselves. [ABSTRACT FROM AUTHOR]

Non-Intrusive Load Monitoring (NILM), which provides sufficient load for the energy consumption of an entire building, has become crucial in improving the operation of energy systems. Although NILM can decompose overall energy consumption into individual electrical sub-loads, it struggles to estimate thermal-driven sub-loads such as occupants. Previous studies proposed Non-Intrusive Thermal Load Monitoring (NITLM), which disaggregates the overall thermal load into sub-loads; however, these studies evaluated only a single building. The results change for other buildings due to individual building factors, such as floor area, location, and occupancy patterns; thus, it is necessary to analyze how these factors affect the accuracy of disaggregation for accurate monitoring. In this paper, we conduct a fundamental evaluation of NITLM in various realistic office buildings to accurately disaggregate the overall thermal load into sub-loads, focusing on occupant thermal load. Through experiments, we introduce NITLM with deep learning models and evaluate these models using thermal load datasets. These thermal load datasets are generated by a building energy simulation, and its inputs for the simulation were derived from realistic data like HVAC on/off data. Such fundamental evaluation has not been done before, but insights obtained from the comparison of learning models are necessary and useful for improving learning models. Our experimental results shed light on the deep learning-based NITLM models for building-level efficient energy management systems. [ABSTRACT FROM AUTHOR]

The main target of Single image super-resolution is to recover high-quality or high-resolution image from degraded version of low-quality or low-resolution image. Recently, deep learning-based approaches have achieved significant performance in image super-resolution tasks. However, existing approaches related with image super-resolution fail to use the features information of low-resolution images as well as do not recover the hierarchical features for the final reconstruction purpose. In this research work, we have proposed a new architecture inspired by ResNet and Xception networks, which enable a significant drop in the number of network parameters and improve the processing speed to obtain the SR results. We are compared our proposed algorithm with existing state-of-the-art algorithms and confirmed the great ability to construct HR images with fine, rich, and sharp texture details as well as edges. The experimental results validate that our proposed approach has robust performance compared to other popular techniques related to accuracy, speed, and visual quality. [ABSTRACT FROM AUTHOR]

Non-Intrusive Thermal Load Monitoring (NITLM) tracks the sub-loads generated by each heat source (e.g. occupants, equipment, solar radiation etc.) from the total thermal load and indirectly provides a room's thermal properties without additional sensors. Since sub-loads can improve the efficiency of HVAC systems, NITLM is a very attractive technology for building energy management. NITLM has traditionally focused on analyzing past and present sub-loads. However, by forecasting future sub-loads, HVAC systems will be able to schedule operations that take into account the thermal properties of future rooms. This work focuses on a new NITLM framework that forecasts future sub-loads based on the current and past total thermal loads. In experiments, we selected occupant loads that are closely connected to HVAC systems and performed sub-load forecasting using two types of approaches. One is a two-step approach that separately performs them in turn. This approach use separately trained model for disaggregation and forecasting, this allow us to fine-tuning the hyper-parameter for dedicate model. Moreover, the two-step approach can take into account the different properties and difficulties of each inference, resulting in smaller errors in sub-load forecasting. The other is an integrated approach. This approach combines load disaggregation and forecasting into a single estimation process, eliminating error propagation and reducing overall error in sub-load forecasting. Moreover, this approach utilizes the Adaptive Moment Estimation (Adam) algorithm for effective parameter optimization, enabling complex training and improving the accuracy of sub-load forecasting. We conducted evaluations of thermal load disaggregation and forecasting across a range of realistic building scenarios. The findings indicate that the integrated approach predicts sub-loads with a MAE that is up to 34.9% lower than that of the two-step approach. Additionally, it identifies occupants presence with an 18.5% higher F-score. This demonstrates its enhanced suitability for accurately predicting sub-loads and estimating future occupancy schedules. • Focusing on a novel NITLM framework targeting future sub-loads. • Proposing two approaches for thermal sub-load forecasting. • Evaluation in multiple buildings based on on-site measurement. • Integrated approach forecasts sub-loads up to 34.9% smaller error than Two-step. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a gate-delay model suitable for timing analysis that takes into consideration wide-ranging process–voltage–temperature (PVT) variations. The proposed model translates an output-current fluctuation due to PVT variations into modifications of the output load and input waveform. After translation, any conventional model can compute delay taking into account PVT variations by using the modified output load and reshaped input waveform. Experimental results with 90- and 45-nm technologies demonstrate that the average error of the fall and rise delay estimation in single- and multi-stage gates was approximately 5% on average over a wide range of input slews, output loads, and PVT variations. The proposed model can be used in Monte Carlo STA (static timing analysis) in addition to corner-based timing analysis. It can be also used in statistical STA to calculate the sensitivities of delays to variation parameters on-the-fly even when the nominal operating condition changes as well. [Copyright &y& Elsevier]

In this study, we clarify the effectiveness of the energy‐effective techniques for the occupancy lighting control that is based on an occupancy sensor. The time delay from detecting no occupant to turning off the light is used for performing the occupancy control to avoid erroneously turning off the light (hereafter referred to as false‐off). Our experiment was conducted at an actual home environment over 8 days. We evaluated the performance of an image‐based sensor, which detects the occupants using image‐processing techniques and time‐delay adaptation, which decreases the time delay without false‐off. Compared with a passive infrared sensor, the image‐based sensor increased the average sensing accuracy for occupants with small movements by 21.58% or more. In addition, compared with the no‐adaptation case, the time‐delay adaptation increased the rate of average lighting energy saving by 3.94% or more. However, the experimental results suggested that the parameter of the time‐delay adaptation should be carefully set in terms of the energy‐saving effect and false‐off. The analysis and its practical discussions are included in this paper to achieve further energy efficiency. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The optimization of the operation of heating, ventilation, and air conditioning (HVAC) systems has gained considerable attention due to its energy conservation potential. Collecting various building operation data presents an opportunity to improve the efficiency of energy management and reduce consumption. In this paper, a data-driven online energy management framework is proposed for enhancing the effectiveness of HVAC systems and minimizing energy consumption. To streamline the modeling process and improve the accuracy of temperature estimation, our proposed framework adopts a symbolic regression approach to estimate building thermodynamics based on the collected data. It also formulates a model predictive control (MPC)-based HVAC scheduling strategy to optimize HVAC operations while maximizing thermal comfort and reducing energy consumption and peak power demand. We conducted on-site experiments to evaluate the proposed framework and demonstrated a notable reduction in total energy consumption in both HVAC cooling and heating operations: average reductions of 4.9% and 30.2%, and 49.3% and 73.9% for best cases in the on-site experiment under certain conditions. In the heating operation, the proposed data-driven HVAC management framework efficiently utilized indoor/outdoor heat generation to maintain comfortable temperature levels without activating the HVAC. Such effective utilization of heat sources shortened the HVAC runtimes and further reduced the total energy consumption. With the proposed framework, the average HVAC peak power demand fell by 25.8% and 35.1% during cooling and heating operations in the on-site experiment with certain scenarios. On-site experiment results demonstrated the proposed framework can efficiently suppress peak power demand, reduce energy consumption in both cooling and heating operation under certain conditions. The framework's data-driven approach provides an effective means of optimizing HVAC operations, reducing energy consumption, and improving the thermal comfort of the building's occupants. • Propose a data-driven online energy management framework for HVAC systems. • Employ a symbolic regression approach for estimating building thermodynamics. • Model predictive control approach is employed to achieve real-time energy management. • First on-site expeiment for data-driven HVAC management in office environment. • The proposed framework reduce the energy consumption up to 73.9%. [ABSTRACT FROM AUTHOR]

Herein, a picture control method designed to facilitate power management of large active‐matrix organic‐light‐emitting‐diode displays is proposed. In this method, Hybrid Log‐Gamma (HLG) pictures are presented simply by multiplying the upper half of the picture level by the gain corresponding to the power suppression. HLG is a standard for high‐dynamic‐range images. The controlled picture is processed by nominal luminance transformation, as well as by color conversion from red–green–blue into hue–saturation–value (HSV) imagery. Results obtained via simulations show that high‐contrast HLG pictures can be better simulated by our proposed method than by currently used conventional methods. The simulation results also demonstrate that when the picture is presented using the proposed power consumption management method, both the dark and bright regions show less deterioration. In addition, through an examination of image histograms created to elucidate detail descriptions of simulated pictures in the red–green–blue and HSV color spaces, we found that using HSV color conversion in conjunction with the proposed method mitigates chromaticity shift and that the feasibility of HSV color conversions could be quantitatively clarified by the evaluation of color differences defined by the Euclidean distance in a uniform color space. A picture level control method designed for power management of large active‐matrix organic‐light‐emitting‐diode displays is demonstrated. Bright regions of Hybrid Log‐Gamma pictures are partly suppressed to efficiently control the power consumption, and hue–saturation–value color conversion is employed to reduce chromaticity shift. Simulation results show that the proposed method can provide Hybrid Log‐Gamma pictures with less deterioration of both the dark and bright regions. [ABSTRACT FROM AUTHOR]

We experimentally characterized and compared the soft error rates of 65-nm bulk and silicon on thin buried oxide (SOTB) SRAMs by conducting accelerated alpha and neutron irradiation tests. Measurement results show that an SOTB SRAM has better soft error immunity than a bulk SRAM. In particular, the number of 2-bit multiple cell upsets (MCUs) of SOTB SRAM was smaller by two orders of magnitude than that of bulk SRAM, and the number of 3-bit or larger MCUs decreased further. In addition, the reverse body bias (RBB) reduced the soft error rate of SOTB SRAM to two-thirds of zero body bias (ZBB). To investigate this dependence on body bias, we evaluated the sensitive cross sectional area for ZBB and RBB with 3D technology computer aided design device simulations. The simulation results show that the RBB decreases the sensitive cross-sectional area of an SOTB device for small linear energy transfer (LET) ions, which is consistent with the measured dependence on body bias. [ABSTRACT FROM PUBLISHER]

This paper gives an explanation that SET pulse-width modulation in bulk CMOS devices happens due to negative bias temperature instability (NBTI). To investigate this, we propose and implement a stress adjustable pulse-width measurement circuit. Measurement results of test chips fabricated in a 65nm bulk CMOS process clearly show that pulse-width broadening and shrinking depend on the condition of static and dynamic stress before the pulse propagation. The measured dependency of pulse-width modulation on supply voltage is well correlated with that of NBTI model. We also point out that soft error rate computed from SET pulse-width distribution measured under static stress is pessimistic. [ABSTRACT FROM AUTHOR]

We propose a multi-time scale energy management framework for a smart photovoltaic (PV) system that can calculate optimized schedules for battery operation, power purchases, and appliance usage. A smart PV system is a local energy community that includes several buildings and households equipped with PV panels and batteries. However, due to the unpredictability and fast variation of PV generation, maintaining energy balance and reducing electricity costs in the system is challenging. Our proposed framework employs a model predictive control approach with a physics-based PV forecasting model and an accurately parameterized battery model. We also introduce a multi-time scale structure composed of two-time scales: a longer coarse-grained time scale for daily horizon with 15-minutes resolution and a shorter fine-grained time scale for 15-minutes horizon with 1-second resolution. In contrast to the current single-time scale approaches, this alternative structure enables the management of a necessary mix of fast and slow system dynamics with reasonable computational times while maintaining high accuracy. Simulation results show that the proposed framework reduces electricity costs up 48.1% compared with baseline methods. The necessity of a multi-time scale and the impact on accurate system modeling in terms of PV forecasting and batteries are also demonstrated. • Propose a multi-time scale energy management framework for smart PV systems. • Fast and slow dynamics are considered in fine-grained and coarse-grained time scale. • Model predictive control approach is employed to achieve real-time energy management. • Real-time PV forecasting and accurate parameterized battery model are integrated. • The proposed method reduces the electricity costs up to 48.1% compared to baselines. [ABSTRACT FROM AUTHOR]

This paper presents a real-time image enhancement technique for gastric endoscopy, which is based on the variational approach of the Retinex theory. In order to efficiently reduce the computational cost required for image enhancement, processing layers and repeat counts of iterations are determined in accordance with software evaluation result, and as for processing architecture, the pipelining architecture can handle high resolution pictures in real-time. To show its potential, performance comparison between with and without the proposed image enhancement technique is shown using several video images obtained by endoscopy for different parts of digestive organ.