ALBERT

Description: This paper reports the synthesis and fabrication of aligned electrospun nanofibers derived out of multiwalled carbon nanotubes (MWCNTs) embedded SU-8 photoresist, which are targeted towards ultrasensitive biosensor applications. The ultrasensitivity (detection in the range of fg/mL) and the specificity of these biosensors were achieved by complementing the inherent advantages of MWCNTs such as high surface to volume ratio and excellent electrical and transduction properties with the ease of surface functionalization of SU-8. The electrospinning process was optimized to precisely align nanofibers in between two electrodes of a copper microelectrode array. MWCNTs not only enhance the conductivity of SU-8 nanofibers but also act as transduction elements. In this paper, MWCNTs were embedded way beyond the percolation threshold and the optimum percentage loading of MWCNTs for maximizing the conductivity of nanofibers was figured out experimentally. As a proof of concept, the detection of myoglobin, an important biomarker for on-set of Acute Myocardial Infection (AMI) has been demonstrated by functionalizing the nanofibers with anti-myoglobin antibodies and carrying out detection using a chemiresistive method. This simple and robust device yielded a detection limit of 6 fg/mL.

Description: Droplet microfluidics (DMF) is a fluidic handling technology that enables precision control over dispensing and subsequent manipulation of droplets in the volume range of microliters to picoliters, on a micro-fabricated device. There are several different droplet actuation methods, all of which can generate external stimuli, to either actively or passively control the shape and positioning of fluidic droplets over patterned substrates. In this review article, we focus on the operation and utility of electro-actuation-based DMF devices, which utilize one or more micro-/nano-patterned substrates to facilitate electric field-based handling of chemical and/or biological samples. The underlying theory of DMF actuations, device fabrication methods and integration of optical and opto-electronic detectors is discussed in this review. Example applications of such electro-actuation-based DMF devices have also been included, illustrating the various actuation methods and their utility in conducting chip-based laboratory and clinical diagnostic assays.

Description: A case study from India identifies optimal dosages and pH

Description: Joint replacement surgeries have enabled motion for millions of people suffering from arthritis or grave injuries. However, over 10% of these surgeries are revision surgeries. We have first analyzed the data from the worldwide orthopedic registers and concluded that the micromotion of orthopedic implants is the major reason for revisions. Then, we propose the use of inductive eddy current sensors for in vivo micromotion detection of the order of tens of μ m. To design and evaluate its characteristics, we have developed efficient strategies for the accurate numerical simulation of eddy current sensors implanted in the human body. We present the response of the eddy current sensor as a function of its frequency and position based on the robust curve fit analysis. Sensitivity and Sensitivity Range parameters are defined for the present context and are evaluated. The proposed sensors are fabricated and tested in the bovine leg.

Description: Unobtrusive indoor location systems must rely on methods that avoid the deployment of large hardware infrastructures or require information owned by network administrators. Fingerprinting methods can work under these circumstances by comparing the real-time received RSSI values of a smartphone coming from existing Wi-Fi access points with a previous database of stored values with known locations. Under the fingerprinting approach, conventional methods suffer from large indoor scenarios since the number of fingerprints grows with the localization area. To that aim, fingerprinting-based localization systems require fast machine learning algorithms that reduce the computational complexity when comparing real-time and stored values. In this paper, popular machine learning (ML) algorithms have been implemented for the classification of real time RSSI values to predict the user location and propose an intelligent indoor positioning system (I-IPS). The proposed I-IPS has been integrated with multi-agent framework for betterment of context-aware service (CAS). The obtained results have been analyzed and validated through established statistical measurements and superior performance achieved.

Description: Home monitoring and remote care systems aim to ultimately provide independent living care scenarios through non-intrusive, privacy-protecting means. Their main aim is to provide care through appreciating normal habits, remotely recognizing changes and acting upon those changes either through informing the person themselves, care providers, family members, medical practitioners, or emergency services, depending on need. Care giving can be required at any age, encompassing young to the globally growing aging population. A non-wearable and unobtrusive architecture has been developed and tested here to provide a fruitful health and wellbeing-monitoring framework without interfering in a user’s regular daily habits and maintaining privacy. This work focuses on tracking locations in an unobtrusive way, recognizing daily activities, which are part of maintaining a healthy/regular lifestyle. This study shows an intelligent and locally based edge care system (ECS) solution to identify the location of an occupant’s movement from daily activities using impulse radio-ultra wide band (IR-UWB) radar. A new method is proposed calculating the azimuth angle of a movement from the received pulse and employing radar principles to determine the range of that movement. Moreover, short-term fourier transform (STFT) has been performed to determine the frequency distribution of the occupant’s action. Therefore, STFT, azimuth angle, and range calculation together provide the information to understand how occupants engage with their environment. An experiment has been carried out for an occupant at different times of the day during daily household activities and recorded with time and room position. Subsequently, these time-frequency outcomes, along with the range and azimuth information, have been employed to train a support vector machine (SVM) learning algorithm for recognizing indoor locations when the person is moving around the house, where little or no movement indicates the occurrence of abnormalities. The implemented framework is connected with a cloud server architecture, which enables to act against any abnormality remotely. The proposed methodology shows very promising results through statistical validation and achieved over 90% testing accuracy in a real-time scenario.

Description: Chemiresistors (conductometric sensor) were fabricated on the basis of novel nanomaterials—silica nanosprings ALD coated with ZnO. The effects of high temperature and UV illumination on the electronic and gas sensing properties of chemiresistors are reported. For the thermally activated chemiresistors, a discrimination mechanism was developed and an integrated sensor-array for simultaneous real-time resistance scans was built. The integrated sensor response was tested using linear discriminant analysis (LDA). The distinguished electronic signatures of various chemical vapors were obtained at ppm level. It was found that the recovery rate at high temperature drastically increases upon UV illumination. The feasibility study of the activation method by UV illumination at room temperature was conducted.

Description: As IoT grows at a staggering pace, the need for contextual intelligence is a fundamental and critical factor for IoT intelligence, efficiency, effectiveness, performance, and sustainability. As the standardisation efforts for IoT are fast progressing, efforts in standardising context management platforms led by the European Telecommunications Standards Institute (ETSI) are gaining more attention from both academic and industrial research organizations. These standardisation endeavours will enable intelligent interactions between ‘things’, where things could be devices, software components, web-services, or sensing/actuating systems. Therefore, having a generic platform to describe and query context is crucial for the future of IoT applications. In this paper, we propose Context Definition and Query Language (CDQL), an advanced approach that enables things to exchange, reuse and share context between each other. CDQL consists of two main parts, namely: context definition model, which is designed to describe situations and high-level context; and Context Query Language (CQL), which is a powerful and flexible query language to express contextual information requirements without considering details of the underlying data structures. An important feature of the proposed query language is its ability to query entities in IoT environments based on their situation in a fully dynamic manner where users can define situations and context entities as part of the query. We exemplify the usage of CDQL on three different smart city use cases to highlight how CDQL can be utilised to deliver contextual information to IoT applications. Performance evaluation has demonstrated scalability and efficiency of CDQL in handling a fairly large number of concurrent context queries.

Description: We present a new experimental technique to characterise the crosstalk of a thermopile-based thermal imager, based on bi-directional electrical heating of thermopile elements. The new technique provides a significantly simpler and more reliable method to determine the crosstalk, compared to a more complex experimental setup with a laser source. The technique is used to characterise a novel single-chip array, fabricated on a single dielectric membrane. We propose a theoretical model to simulate the crosstalk, which shows good agreement with the experimental results. Our results allow a better understanding of the thermal effects in these devices, which are at the center of a rising market of industrial and consumer applications.

Description: Autonomous vehicles are gaining popularity throughout the world among researchers and consumers. However, their popularity has not yet reached the level where it is widely accepted as a fully developed technology as a large portion of the consumer base feels skeptical about it. Proving the correctness of this technology will help in establishing faith in it. That is easier said than done because of the fact that the formal verification techniques has not attained the level of development and application that it is ought to. In this work, we present Statistical Model Checking (SMC) as a possible solution for verifying the safety of autonomous systems and algorithms. We apply it on Heuristic Autonomous Intersection Management (HAIM) algorithm. The presented verification routine can be adopted for other conflict point based autonomous intersection management algorithms as well. Along with verifying the HAIM, we also demonstrate the modeling and verification applied at each stage of development to verify the inherent behavior of the algorithm. The HAIM scheme is formally modeled using a variant of the language of Timed Automata. The model consists of automata that encode the behavior of vehicles, intersection manager (IM) and collision checkers. To verify the complete nature of the heuristic and ensure correct modeling of the system, we model it in layers and verify each layer separately for their expected behavior. Along with that, we perform implementation verification and error injection testing to ensure faithful modeling of the system. Results show with high confidence the freedom from collisions of the intersection controlled by the HAIM algorithm.