Sébastien Gaboury

Accurate passive RFID localization system for smart homes

The smart home paradigm is a promising new trend of research aiming to propose an alternative to postpone the institutionalization of cognitively-impaired silver-aged people. These habitats are intended to provide security, guidance and direct support services to its resident. To be able to fulfill this important mission, a smart home system first has to identify the ongoing activities of its user by tracking, in real time, the position of the main daily living objects. Many researchers addressed this issue by proposing systems based on ultrasonic wave sensors, video cameras, and radio-frequency identification (RFID). However, because of its robustness and its low price, RFID constitutes the most viable technology for smart homes. Recently, several RFID localization algorithms have been developed, mainly for commercial and industrial uses, but they are not precise enough to be used in an assistive recognition context or they focus on active tags, which need batteries and are much more expensive. We present, in this paper, a new algorithmic approach for passive RFID localization in smart homes based on elliptical trilateration and fuzzy logic. This new algorithm has been implemented in a real smart home infrastructure and has been rigorously tested. We also analyze and compare the obtained results with the main existing approaches.

An efficient and inexpensive method for activity recognition within a smart home based on load signatures of appliances

Abstract With the increasing demand in terms of non-intrusive appliance load monitoring (NIALM), more and more smart meters and smart analyzers were released on the market to extract well-defined load signatures and/or for performing autonomously the various monitoring operations as needed. Nevertheless, this hardware proves to be very expensive and not necessarily accessible to all. Moreover, most applications resulting from the use of these smart devices simply refer to energy saving and costs reducing of energy consumption. Thus, this paper proposes a new algorithmic method for an application field that is still very lightly exploited, i.e. the activity recognition of reduced-autonomy residents living in a smart habitat through load signatures. This one is based on steady-state operations and signatures and its extraction process of load signatures of appliances is carried out in a three-dimensional space through a single power analyzer which is non-intrusive (NIALM). This approach has been tested and verified rigorously through daily scenarios reproduced in the smart home prototype in a laboratory. Hence, we can affirm that, with an exceptionally minimal investment and the exploitation of especially limited data, our method can recognize the use of appliances with high precision and low-cost allowing us to compete with other approaches which are much more expensive and require supplementary equipment.

Nonintrusive system for assistance and guidance in smart homes based on electrical devices identification

We present an assistive system for guiding cognitively-impaired people in performing daily activities.The system is based on electrical signal analysis with a single sensor.The system is able to detect cognitive errors and send prompts accordingly.The system has been implemented, deployed and tested in real home environment.The simulation of real-case scenarios has shown promising results. Recently, sensors and actuators have quickly spread throughout our everyday life. These devices are robust, cheap, accessible, connected to the Internet, etc. With the growing needs in terms of human and medical resources to help cognitively-impaired people to remain at home, researchers are investing in new ways to exploit this technology with artificial intelligence, in order to build expert systems to assist the residents in their daily activities. Several systems have been proposed in the last few years, mostly based on binary sensors, cameras and other sensors such as Radio-frequency identification (RFID) tags. Cameras are very intrusive, binary sensors (such as movement detectors) give only basic information, and other types of sensors (such as RFID) need complex deployment. In this context, this paper presents a new assistive expert system based on electric device identification to address the problem of guidance and supervision in the performance of activities for people with cognitive disorders living in a smart home. This system is solely based on a single power analyzer placed in the electric panel. We propose an algorithmic approach used to recognize erratic behaviors related to cognitive deficits and provides cues to guide the person in the completion of an ongoing task. This is achieved through load signatures study of appliances represented by three features (active power (P), reactive power (Q) and line-to-neutral), which allows to determine the errors committed by the resident. We implemented this system within a genuine smart-home prototype equipped with household appliances used by the patient during his morning routines. Different multimedia prompting devices (iPad, screen, speakers, etc.) were used. We tested the system with real-case scenarios modeled from former clinical trials, allowing demonstration of accuracy and effectiveness of our system in assisting a cognitively-impaired resident in the completion of daily activities.

Certain subclasses of meromorphically univalent functions defined by a linear operator associated with the λ-generalized Hurwitz–Lerch zeta function

By using a linear operator associated with the λ-generalized Hurwitz–Lerch zeta function, which is defined here by means of the Hadamard product (or convolution), the authors introduce and investigate various properties of certain subclasses of meromorphically univalent functions in the punctured unit disk. Some closely related (known or new) corollaries and consequences of the main results presented in this paper are also considered.

Human activity recognition in smart homes based on passive RFID localization

Modern societies are facing an important ageing of their population leading to arising economical and sociological challenges such as the pressure on health support services for semi-autonomous persons. Smart home technology is considered by many researchers as a promising potential solution to help supporting the needs of elders. It aims to provide cognitive assistance by taking decisions, such as giving hints, suggestions and reminders, with different kinds of effectors (light, sound, screen, etc.) to a resident suffering from cognitive deficits in order to foster their autonomy. To implement such technology, the first challenge we need to overcome is the recognition of the ongoing inhabitant activity of daily living (ADL). Moreover, to assist him correctly, we also need to be able to detect the cognitive errors he performs. Therefore, we present in this paper a new affordable activity recognition system, based on passive RFID technology, able to detect errors related to cognitive impairment in morning routines. The entire system relies on an innovative model of elliptical trilateration with several filters, and on an ingenious representation of activities with spatial zones. This system has been implemented and deployed in a real smart home prototype. We also present the promising results of a first experiment conducted on this new activity recognition system with real cases scenarios about morning routines.

A unified class of analytic functions involving a generalization of the Srivastava-Attiya operator

In this paper, we present a unified class of analytic functions defined by a new convolution operator J ( λ p ) , ( µ q ) , b s , a , λ introduced recently by Srivastava and Gaboury (2014) which generalizes the well-known Srivastava-Attiya operator investigated by Srivastava and Attiya (2007). We derive coefficient inequalities, growth and distortion theorems, extreme points and Fekete-Szego problem for this new function class.

Accurate RFID Trilateration to Learn and Recognize Spatial Activities in Smart Environment

The rapid adoption of wireless communication and sensors technology has raised the awareness of many laboratories about the field of network embedded system. Most researchers aim to exploit these advances to enable technological assistance of frail persons in smart homes. However, to reach the full potential of applications using network embedded systems such as assistive smart home, scientists need to work toward the creation of support services. In this paper, we present an accurate passive RFID localization technique, which can easily be implemented and deployed in various environments, coupled to a complete human activity recognition model. The goal of this paper is to demonstrate, through concrete experiments, that support services can enable powerful solution to long-lived challenges of the network embedded system community. Particularly, the model exploits qualitative spatial reasoning from RFID localization of objects in the smart home to learn and recognize the basic and instrumental activities of daily living of a resident. Our system was deployed in a real smart home, and the results obtained were quite encouraging. The developed RFID technique gives an average precision of ±14.12 cm, and the recognition algorithm recognizes up to 92% activities.

Some relations involving a generalized fractional derivative operator

Recently, Katugampola (Appl. Math. Comput. 218:860-865, 2011) studied a special case of the Erdélyi-Kober generalized fractional derivative. This special case generalized the well-known Riemann-Liouville and the Hadamard fractional integrals into a single form. Katugampola denoted this special case by the operator Dxα0ρ. Some properties and examples for this fractional derivative operator was given. In this paper, we present some additional properties for this operator defined, this time, in the complex plane. In particular, we express this fractional derivative operator in terms of the classical Riemann-Liouville fractional derivative operator. A generalized Leibniz rule is obtained.MSC:26A33, 33C45.

Some further properties of a linear operator associated with the λ -generalized Hurwitz-Lerch zeta function related to the class of meromorphically univalent functions

In this sequel to the recent work (see Srivastava, 2015), we investigate some further properties of a linear operator associated with Srivastavas λ -generalized Hurwitz-Lerch zeta function, which are related to a certain class of meromorphically univalent functions in the punctured unit disk defined here by means of the Hadamard product (or convolution). We also give several interesting corollaries and consequences of the main results presented in this paper.

Expansion formulas for an extended Hurwitz-Lerch zeta function obtained via fractional calculus

Motivated by the recent investigations of several authors, in this paper, we derive several new expansion formulas involving a generalized Hurwitz-Lerch zeta function introduced and studied recently by Srivastava et al. (Integral Transforms Spec. Funct. 22:487-506, 2011). These expansions are obtained by using some fractional calculus theorems such as the generalized Leibniz rules for the fractional derivatives and the Taylor-like expansions in terms of different functions. Several (known or new) special cases are also considered.MSC:11M25, 11M35, 26A33, 33C05, 33C60.