Abstract

Individual device loads and energy consumption feedback is one of the important approaches for pursuing users to save energy in residences. This can help in identifying faulty devices and wasted energy by devices when left On unused. The main challenge is to identity and estimate the energy consumption of individual devices without intrusive sensors on each device. Non-intrusive load monitoring (NILM) or energy disaggregation, is a blind source separation problem which requires a system to estimate the electricity usage of individual appliances from the aggregated household energy consumption. In this paper, we propose a novel deep neural network-based approach for performing load disaggregation on low frequency power data obtained from residential households. We combine a series of one-dimensional Convolutional Neural Networks and Long Short Term Memory (1D CNN-LSTM) to extract features that can identify active appliances and retrieve their power consumption given the aggregated household power value. We used CNNs to extract features from main readings in a given time frame and then used those features to classify if a given appliance is active at that time period or not. Following that, the extracted features are used to model a generation problem using LSTM. We train the LSTM to generate the disaggregated energy consumption of a particular appliance. Our neural network is capable of generating detailed feedback of demandside, providing vital insights to the end-user about their electricity consumption. The algorithm was designed for low power offline devices such as ESP32. Empirical calculations show that our model outperforms the state-of-the-art on the Reference Energy Disaggregation Dataset (REDD)

Abstract

The radiant systems consume less energy and provide better thermal comfort as compared to the conventional system, but the main drawback of radiant technology is inability to cater the latent load and condensation. This problem has engaged researchers to push their emphasis on Personalized Conditioning System (PCS). It forms a micro-climate region near the occupant. This study is aimed at PCS combined with a radiant conditioning system, which evaluates the performance of the PCS and compares it with different radiant cooling systems. A radiant cubicle was used to investigate the performance of the PCS for both heating & cooling modes. For heating mode, the Standard Effective Temperature (SET) was calculated with the help of thermal comfort surveys which were in a range of 23.4°C to 27.3°C. For cooling mode, four different cases compared the performance of the system which are: Conventional Air Conditioning System (CACS), Radiant Cooled Ceiling System (RCCS), Personalized Radiant Conditioning System (PRCS), and both PRCS-RCCS coupled. It was observed that PRCS alone reaches the thermal comfort criteria with low energy consumption. The energy-saving of RCCS-PRCS was achieved 38% when compared with CACS. It can be concluded that PRCS is a new step in the field of thermal comfort.

Abstract

The growing need for localization has created an array of alternative approaches to GNSS, based on Wi-Fi, Bluetooth, Ultra-Wideband, etc. Long Range Wide Area Network (LoRaWAN) is one such technology that has garnered tremendous attention due to its low power and long-range capabilities. Many attempts to achieve localization using LoRaWAN have been made till now, based on a variety of techniques such as Angle of Arrival (AoA), Time Difference of Arrival (TDoA), Received Signal Strength Index (RSSI). In this paper, we present a novel, standalone localization approach by developing a collaborative, TDoA-based methodology using LoRaWAN. The server determines the target node location by means of TDoA measurements from the target node to the gateways. The introduction of an additional stationary node allows the synchronization of the gateways without utilizing GNSS, either inbuilt or external. The cooperation between target node, synchronization node and server, is the innovative feature which makes this approach attractive for GNSS-free localization. Further, we explore the effects of timing resolution, time-on-air, and duty cycle constraints on the localization error. Finally, the distribution of error in a triangle of gateways situated approximately 8.6 kilometers apart is simulated in ideal Line of Sight (LoS) conditions, showing the maximum error to be around 23 meters

Abstract

Methods and apparatus for modulating light using a tunable light modulation device . The tunable light modulation devices comprises an elastomer structure including at least one elastomer layer , a compliant electrode network of con ducting fibers arranged on a first surface of the at least one elastomer layer , a patterned electric conductor arranged on a second surface of the at least one elastomer layer opposite the first surface . The patterned electric conductor includes a plurality of individually - addressable sections , and the com pliant electrode network is configured to compress the at least one elastomer layer in the presence of an electric field between the compliant electrode network and one or more of the individually - addressable sections of the patterned elec tric conductor to produce a voltage - dependent roughening of the at least one elastomer layer .

Abstract

This paper presents an ultra-low power CMOS voltage reference (CVR) which is free of amplifier and resistor. A current generator circuit is used to generate a supply independent current to bias the active load in the temperature compensation circuit. Drain-source voltage of two critical MOSFETs is made equal in the current generator circuit by using a feedback arrangement to ensure a high PSRR of -75dB for the reference voltage. Temperature compensation is achieved by using the complementary to absolute temperature (CTAT) nature of gatesource of a MOSFET operating in the subthreshold region and proportional to absolute temperature (PTAT) nature of conventional composite pair architecture. The proposed CVR is designed in TSMC 180nm technology. The circuit works desirably for a supply range of 0.85V to 2.3V while generating a reference voltage of around 0.68V. Maximum temperature coefficient (TC) of 184 ppm/˚C and minimum TC of 69 ppm/˚C are noted for mismatch and process variations in the Monte-Carlo simulation for 1000 samples. The circuit consumes only 46nW of power, which makes it suitable for ultra-low power applications.

Abstract

The present description relates to a method based on artifi cial intelligence to implement a wide range of microelec tronic circuits that can adapt by themselves to the usage conditions ( e.g. loading changes ) , manufacturing variances or defects ( e.g. process variations , device parameter mis matches , device model inaccuracies or changes , etc. ) , as well as environmental conditions ( e.g. voltage , temperature , interference ) in order to negate all or part of their effects on the circuit performance characteristics and achieve a very tight set of specifications over the wide range of conditions . Each microelectronic circuit is represented by a neural network model whose behavior is a function of the actual input signals , the usage and environmental conditions . An attached AI engine will infer from the model , the input signals , the usage conditions and the environmental condi tions and create the adaptive changes required to modify the microelectronic circuit's behavior to negate all or part of their effects on the circuit performance characteristics and to achieve a very tight set of specifications

Abstract

Hysteresis and proteresis are two complementary phenomena used in integrated circuits. Hysteresis is a widely known and applied phenomenon, while proteresis is used less frequently. Hysteresis exhibits a delayed response, whereas proteresis demonstrates an advanced response. This paper presents the design of a single CMOS circuit that exhibits both hysteretic and proteretic properties and has been named as ProHys switch, thus leveraging the benefits of both phenomena. The circuit can switch between hysteretic and proteretic operations based on the control input signal. It maintains the noise immunity of 0.2V in each state. Proof of concept is established by postlayout simulation of the proposed circuit in TSMC 180nm CMOS technology and occupies an area of 41µm X 48µm. It is demonstrated to work in the temperature range of -20°C to 100°C and up to a maximum frequency of 10MHz. This paper also explores the possibility of using the proposed ProHys Switch as a Physical Unclonable Function (PUF) for security applications. Index Terms—Hysteresis, Proteresis, ProHys, Switch

Abstract

Location-based services (LBS), which provide personalized and timely information, entail privacy concerns such as unwanted leak of current user locations to potential stalkers. Existing works have proposed dummy generation techniques by creating a cloaking region (CR) such that the user’s location is at a fixed distance from the center of CR. Hence, if the adversary somehow knows the location of the center of CR, the user’s location would be vulnerable to attack. We propose an improved dummy generation approach for facilitating improved location privacy for mobile users. Our performance study demonstrates that our proposed approach is indeed effective in improving user location privacy.

Abstract

Time series classification is an important model in data mining. It involves assigning a class label to a test instance based on the training data with known class labels. Most previous studies developed time series classifiers by disregarding the fuzzy nature of events (i.e., events with similar values may belong to different classes) within the data. Consequently, these studies suffered from performance issues, including decreased accuracy and increased memory, runtime, and energy requirements. With this motivation, this paper proposes a novel fuzzy nearest neighbor classifier for time series data. The basic idea of our classifier is to transform the very large training data into a relatively small representative training data and use it to label a test instance by employing a new fuzzy distance measure known as Ravi. Experimental results on real world benchmark datasets demonstrate that the proposed classifier …

Abstract

While the problems of fnding the shortest path and k-shortest paths have been extensively researched, the research community has been shifting its focus towards discovering and identifying paths based on user preferences. Since users naturally follow some of the paths more than other paths, the popularity of a given path often refects such user preferences. Given a set of user traversals in a road network and a set of paths between a given source and destination pair, we address the problem of performing top-k ranking of the paths in that set based on path popularity. In this paper, we introduce a new model for computing the popularity scores of paths. Our main contributions are threefold. First, we propose a framework for modeling user traversals in a road network as transactions. Second, we present an approach for efciently computing the popularity score of any path based on the itemsets extracted from the transactions using pattern mining techniques. Third, we conducted an extensive performance evaluation with two real datasets to demonstrate the efectiveness of the proposed scheme.