Abstract

PP10. 00010: Structural engineering methods to combine the diaphragm and cantilevers for flexible capacitive pressure sensor*

Abstract

The scalability of remote Internet of Things (IoT) deployments into verticals of smart cities relies primarily on the communication protocols and data platforms employed. Accordingly, the adoption of LoRaWAN, Sigfox, NB-IoT, etc. has risen in recent years. These protocols have been created to cater to the low power and long range requirements of IoT. In this paper, we evaluate one such protocol, Wi-SUN (Wireless Smart Ubiquitous Network), that additionally offers excellent redundancy owing to its use of a mesh topology. Wi-SUN is based on the IEEE 802.15.4g standard and uses license-free sub-GHz bands. However, IoT networks using any communication protocol require a knowledge of signal propagation and attenuation in real-world scenarios. Thus, we evaluated the path loss of Wi- SUN transmissions by analysing the RSSI values inside a typical city building and an urban outdoor habitat. Keywords—Path Loss,

Abstract

Plantar pressure or foot pressure analysis is a predominant tool for biomedical assessment of posture, gait, and other activities. The distribution and magnitude of pressure can provide fruitful information to diagnose or predict any risk of foot disorders or impairments. This paper presents the design and fabrication of a low-cost, flexible pressure sensor (FLEPS) mat for assessing plantar pressure variations. We have designed a 32 × 32 velostat-based piezoresistive pressure sensor array with dimensions of 330 mm × 330 mm and an active sensing area of 284 mm × 284 mm. Each sensor pixel is of size 5 mm × 5 mm with a pitch of 4 mm. To enhance the resolution and the visual quality of the heat map obtained, we have used different interpolation techniques like the nearest neighbor, bilinear, and bicubic for the received data and transformed the 32 × 32 array to a 100 × 100-pixel array. It was observed that the bicubic interpolation technique gives a better image quality but takes more computation time than the other two interpolation techniques. Index Terms—Flexible pressure sensor, Interpolation, Piezore- sistive, Plantar pressure, Velostat.

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The continuous monitoring of maternal vital signs, particularly during labor, can be of critical importance for the health of the child and mother. We present a compact wearable electronic system that measures, in real-time, maternal heart rate using electrocardiography (ECG) signals. The heart rate is determined using moving average filters to remove the noises in the signal and using autocorrelation for the detection of periodic signals. We use electromyography (EMG) signals for calculating uterine contractions using the root mean sum of filtered signal values. The resulting system can identify heart rate in a female subject with an accuracy of ± 2.9bpm, and uterine contraction intensity within ± 5 units compared to values obtained from a gold-standard CTG machine. The system is accurate, low-cost, and portable, so that it can be deployed at primary healthcare centers in low-income countries. The system can also be

Abstract

Approximate Computing techniques are finding a central role in modern applications, by optimizing architectures to relax some computation but with a constrained inaccuracy. In many applications, the FFT algorithm is invariably applied and there is a need for approximate low energy hardware solutions to the FFT. The paper thus proposes an approximate, fixed-point, in-place, shared memory architecture for FFT. It is well observed that the energy at FFT I/Os is not strictly contiguous, hence the proposed FFT exploits this window to tune the error. The proposed FFT and its associated butterfly unit is constructed to efficiently incorporate approximate Toom–Cook multiplication. As said, a supporting function in error correction based on the sparsity patterns, is a feature of this design. The design synthesized at 32 n m shows on average, a 48.6% and 52.8% improvement in consumption of area and energy, respectively …

Abstract

Bayesian Optimization (BO) is used to find the global optima of black box functions. In this work, we propose a practical BO method of function compositions where the form of the composition is known but the constituent functions are expensive to evaluate. By assuming an independent Gaussian process (GP) model for each of the constituent black-box function, we propose EI and UCB based BO algorithms and demonstrate their ability to outperform vanilla BO and the current state-of-art algorithms. We demonstrate a novel application of the proposed methods to dynamic pricing in revenue management when the underlying demand function is expensive to evaluate.

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With the Integrated Circuits becoming pervasive in all key industries and applications, PUF’s has gained immense popularity for securing the IC’s by providing unique identification code to each chip. Designing a highly efficient PUF with optimal values of uniqueness and reliability is a significant challenge. Uniqueness depends on process variations during chip fabrication, and reliability depends on the chip’s ability to resist changes to supply voltage and temperature variations. Multiple PUF designs that employ reliability enhancement circuits and security algorithms achieve these design characteristics. Nonetheless, these techniques are design overheads. This paper presents a novel Physical Unclonable Function (PUF) based on the ProHys switch. It deals with hysteresis and proteresis mode of operation, which are complementary to each other. The Prohys PUF befittingly satisfies both uniqueness and reliability

Abstract

In the channel code detection problem, given a sequence of noise-affected codewords generated by an unknown code, the aim is to identify the correct channel code from the given set of potential codes. This problem has many applications in military spectrum surveillance and in cognitive radios. In this paper, we consider the situation when the set of potential channel codes consists of two linear block codes and propose a deep learning based classification approach for the corresponding code detection problem. In this work, we propose data processing strategies suitable for linear block codes that reduce the amount of data required to train the deep neural network classifier. This however comes at the cost of having a lower probability of detection. For the proposed data processing strategies, we analytically obtained the optimal probability of correct detection that one can hope to achieve using any neural …

Abstract

One of the key challenges for a novice engineer in a product company is to comprehend the product sufficiently and quickly. It can take anywhere from six months to several years for them to attain mastery but they need to start delivering results much before. SaaS (Software-as-a-Service) products have sophisticated system architecture which adds to the time and effort of understanding them. On the other hand, time available to new hires for product understanding continues to be short and getting shorter, given the pressure to deliver more in less time. Constructivist theory views learning as a personal process in which the learner constructs new knowledge for themselves. Building and refining a mental model is the key way in which they learn, similar to how the brain operates. This paper presents an approach to improve system comprehension process by using a system model that a) acts as a transitional object to aid and refine the mental model of the learner, and b) captures the current understanding of the dynamics of the software system in a way that can be reasoned with and simulated. We have adapted discrete systems modeling techniques and used a transition system as a lightweight modeling language. Such a model can be used by novice engineers during their product ramp-up phase to build a model of the software system that captures their knowledge of the system and aid their mental model. The paper also presents a learning approach in which the learners create and refine these models iteratively using the available and newly uncovered knowledge about the software system. We hypothesize that by leveraging this modeling

Abstract

Software practitioners use various requirement elicitation methods to produce a well-defined product. These methods impact the software product’s eventual traits and target a particular audience segment. Virtual Reality(VR) products are no different from this influence. With the notable rise in product offerings across various domains, VR has become an essential technology for the future. Nevertheless, the type of methods practiced for requirement elicitation still has not been thoroughly studied. This paper presents a mapping study on requirement elicitation methods practiced by VR practitioners in academia and industry. We consolidated our observations based on their popularity in the practitioner community. Further, we present our insights on the necessary and sufficient conditions to conduct VR requirement elicitation using the identified methods to benefit the VR practitioner community.