Abstract

The Internet of Things (IoT) plays a key role in real-time monitoring at different stages of the power generation system, assisting to achieve better efficiency, minimize load on the grids by analysing usage patterns, provide faster resolutions to power outages, and so on. In this paper, we present a novel energy monitoring approach employing LoRaWAN-enabled smart energy meters and a oneM2M-based platform for collecting and analysing the data. The energy meters transmit data at 15-minutes intervals, i.e., 96 data points per day. A novel format has been developed for the LoRaWAN Protocol Data Unit (PDU) to transmit the values of phase currents and voltages, and data related to power and energy comsumption. This results in a high-resolution dataset containing more than 10,000 instances per meter, accumulated over the last four months. The data can be visualised in a live dashboard enabling the signal parameters such as Received Signal Strength Index (RSSI) and Signal to Noise Ratio (SNR) to be monitored in addition to the electrical parameters, to ensure proper data transmission. Finally, the trends in power and energy consumption of the load have been analysed, which can result in improved efficiency of building management, and early detection of electrical faults and failures

Abstract

Most of the sensors that measure physical variables such as humidity, strain, motion, temperature, and pressure are designed based on the variation of capacitance or the resistance of a circuit entity. This paper presents a 12-bit combined resistance and capacitance to digital converter (RCDC) to analyze and process the information from these sensors. The read-out circuit first converts the capacitance or resistance to an analog voltage, followed by conversion to digital data using a successive approximation (SAR) type analog to digital converter (ADC). The circuit has been designed based on the 180 nm CMOS technology. The capacitance to digital converter (CDC) circuit covers a wide range of capacitance from 30 pF to 100 pF with a 17 fF resolution, whereas the resistance to digital converter (RDC) circuit covers a range of resistance from 1 Ω to 30 Ω and has a resolution of 7 mΩ. The 12-bit CDC and RDC have a measurement time of 20 μs and show a non-linearity of 0.66% and 0.57%, respectively. Index Terms—analog to digital converter; capacitance to dig- ital converter; resistance to digital converter; read-out circuit; successive approximation

Abstract

Detection of color change is an important tool used in several analytical chemistry processes in various industries such as biomedical, diagnostics, agriculture, food processing, and so on. In this study, we present an automated color detection system to perform qualitative analysis of liquid reagents. Many of the available techniques for colorimetric applications make use of expensive, high-resolution digital cameras. We have developed a simple low-cost system based on a light emitting diode (LED) and light-dependent resistor (LDR) pair to obtain information about the color of a liquid reagent. This system has been compared with a camera-based system for color detection. The LED-LDR system provides information about the color of an object by obtaining relative intensities of red, green, and blue (RGB) light reflected by the object in a sequential process. The system was tested by obtaining color information for different concentrations of potassium permanganate solution and by determining the endpoint of a titration experiment. This versatile system can be used for many other colorimetric measurements, for example, the amount of dissolved ammonia in water using Nesslers reagent (potassium tetraiodomercurate).

Abstract

We address a distributed adaptive synchronization problem for complex networks composed of nonlinear nodes under state-dependent a priori interconnections, i.e. interconnection terms acting before control design. The interconnection terms are uncertain and the heterogeneous dynamics of the network nodes further contain state-dependent uncertainty and uncertain input matrix gain. Adaptive distributed control laws are proposed to tackle such an unsolved design. The proposed controller is verified in simulation via a multi-area load frequency control for power systems.

Abstract

The focus of this paper is (i) to derive a suitable dynamic model for a self-reconfigurable pavement sweeping robot PANTHERA and (ii) to design a robust controller for the same to tackle uncertainties stemming from the reconfiguration process, external disturbances and from actuator saturation. To meet the first objective, an Euler-Lagrangian dynamic model is proposed to incorporate the effects of configuration changes on the system dynamics. Based on this model, the second objective is met via designing a singular perturbation based robust controller which can tackle the aforementioned uncertainties without violating the actuation limits. To circumvent the vulnerability toward actuator saturation, the proposed controller is built on contraction theory, which, compared to a conventional Lyapunov theory based design, allows to improve closed-loop tracking performance without reducing the singular perturbation parameter. Experimental results on the PANTHERA reconfigurable robot validate the effectiveness of the proposed controller over the state of the art.

Abstract

Slide presentations are an effective and efficient tool used by the teaching community for classroom communication. However, this teaching model can be challenging for the blind and visually impaired (vi) students. The vi student required a personal human assistance for understand the presented slide. This shortcoming motivates us to de- sign a Classroom Slide Narration System (csns) that generates audio descriptions corresponding to the slide content. This problem poses as an image-to-markup language generation task. The initial step is to ex- tract logical regions such as title, text, equation, figure, and table from the slide image. In the classroom slide images, the logical regions are distributed based on the location of the image. To utilize the location of the logical regions for slide image segmentation, we propose the ar- chitecture, Classroom Slide Segmentation Network (cssn). The unique attributes of this architecture differs from most other semantic segmenta- tion networks. Publicly available benchmark datasets such as WiSe and SPaSe are used to validate the performance of our segmentation architec- ture. We obtained 9.54% segmentation accuracy improvement in WiSe dataset. We extract content (information) from the slide using four well- established modules such as optical character recognition (ocr), figure classification, equation description, and table structure recognizer. With this information, we build a Classroom Slide Narration System (csns) to help vi students understand the slide content. The users have given better feedback on the quality output of the proposed csns in compar- ison to existing systems like Facebook’s Automatic Alt-Text (aat) and Tesseract.

Abstract

Very small electromechanical coupling coefficient in micro-electromechanical systems (MEMS) or acoustic resonators is quite a concern for oscillator performance, specially at mmWave frequencies. This small coefficient is the manifestation of the small ratio of motional capacitance to static capacitance in the resonators. This work provides a general solution to overcome the problem of relatively high static capacitance at mmWave frequencies and presents analysis and design techniques for achieving extremely low phase noise and a very high figureof-merit (FoM) in an on-chip MEMS resonator based mmWave oscillator. The proposed analysis and techniques are validated with design and simulation of a 30 GHz oscillator with MEMS resonator having quality factor of 10,000 in 14 nm GF technology. Post layout simulation results show that it achieves a phase noise of −132 dBc/Hz and FoM of 217 dBc/Hz at offset of 1 MHz.

Abstract

Is painting space fundamentally perspectival? In the European Renaissance (14th to the 17th century), the painting space was thought of as having an interior of perspective where one could place an object. It took many years after the Renaissance for European art to come out of this optical or geometrical perspective and realise that the space of painting is fundamentally non-perspectival. Historically in Europe, impressionists (1860) painters are the ones who tried to break away from this optical or single-point perspective and create paintings according to 'lived perspective'. Optical perspective is one of the visual dogmas which are believed till today; thus, it is tough to appreciate non-perspectival paintings. This paper aims to give technical reasons why painting space is fundamentally not perspectival; the first section of the paper will deal with the question 'what kind of space is painting space?', and in the second section, we will compare method of photograph and drawing to find the differences between mechanism of camera and human perception . In the last section of the paper we will use Indian folk paintings, to demonstrate how cognitive or alternative/multiple perspectives open new possibilities in painting space.

Abstract

Around 40% of the world’s population is currently without access to the Internet. The digital divide is due to the high cost of provisioning these services and the low return on investment for network operators. We propose using 5G network slicing with multi-tenancy (also known as neutral host networks (NHN)) for macro-cells and small cells in rural areas to reduce the costs. This paper investigates the techno-economic feasibility of using rural 5G NHN to minimise the digital divide. A generic model is developed to analyse the techno-economic analysis of 5G NHN deployment around the world, with a special focus on rural areas where no MNO is interested in providing services. To understand the application, it is applied to the Indian scenario. First, a discussion on existing infrastructure, competition and statistics for Indian telecommunications is presented. Next, the technical requirements are analysed using the key performance indicators (KPI) required for the rural 5G NHN for the Indian scenario. The study also analyses the relationship between coverage, investment in the network, the number of subscribers, investment time, demand, the investment per user and sensitivity analysis to understand the feasibility of the proposed solution for Indian villages with different input conditions. Later, a case study is carried out based on the proposed approach, along with coverage modelling for a few Indian villages having different topologies. The results show that 5G NHN using network slicing can significantly reduce the total investment required for providing 5G services in rural areas. Furthermore, the study shows that rural 5G NHN is a viable investment and a key enabler for Internet connectivity for villages with 10-year investment, having a subscribers’ base as low as 100 with a customer growth rate of 7%.

Abstract

In this paper, we present a novel approach for domain adaptation in Neural Machine Translation which aims to improve the translation quality over a new domain. Adapting new domains is a highly challenging task for Neural Machine Translation on limited data, it becomes even more difficult for technical domains such as Chemistry and Artificial Intelligence due to specific terminology, etc. We propose Domain Specific Back Translation method which uses available monolingual data and generates synthetic data in a different way. This approach uses Out Of Domain words. The approach is very generic and can be applied to any language pair for any domain. We conduct our experiments on Chemistry and Artificial Intelligence domains for Hindi and Telugu in both directions. It has been observed that the usage of synthetic data created by the proposed algorithm improves the BLEU scores significantly.