Abstract

In this paper, we propose beamformed energy detection (BFED) spectrum sensing schemes for a single secondary user (SU) or a cognitive radio to detect a primary user (PU) transmission in the presence of an interferer. In the millimeter wave (mmWave) band, due to high attenuation, there are fewer multipaths, and the channel is sparse, giving rise to fewer directions of arrivals (DoAs). Sensing in only these paths instead of blind energy detection can reap significant benefits. An analog beamforming weight vector is designed such that the beamforming gain in the true DoAs of the PU signal is maximized while minimizing interference from the interferer. To demonstrate the bound on the system performance, the proposed sensing scheme is designed under the knowledge of full channel state information (CSI) at the SU for the PU-SU and Interferer-SU channels. However, as the CSI may not be available at the SU, another BFED sensing scheme is proposed, which only utilizes the estimate the DoAs. To model the estimates of DoAs, perturbations are added to the true DoAs. The distribution of the test statistic for BFED with full CSI schemes is derived under the null hypothesis so that the threshold of the NeymanPearson detector can be found analytically. The performance of both schemes is also compared with the traditional energy detector for multi-antenna systems. Index Terms—Beamforming, direction of arrival (DoA), energy detection, mmWave, spectrum sensing.

Abstract

— Centrality measures on graphs have found applications in a large number of domains including modeling the spread of an infection/disease, social network analysis, and transportation networks. As a result, parallel algorithms for computing various centrality metrics on graphs are gaining significant research attention in recent years. In this paper, we study parallel algorithms for the percolation centrality measure which extends the betweenness-centrality measure by incorporating a time dependent state variable with every node. We present parallel algorithms that compute the source-based and source-destination variants of the percolation centrality values of nodes in a network. Our algorithms extend the algorithm of Brandes, introduce optimizations aimed at exploiting the structural properties of graphs, and extend the algorithmic techniques introduced by Sariyuce et al. [26] in the context of centrality computation. Experimental studies of our algorithms on an Intel Xeon(R) Silver 4116 CPU and an Nvidia Tesla V100 GPU on a collection of 12 real-world graphs indicate that our algorithmic techniques offer a significant speedup.

Abstract

Current aerial robots are increasingly adaptive; they can morph to enable operation in changing conditions to complete diverse missions. Each mission may require the robot to conduct a different task. A conventional learning approach can handle these variations when the system is trained for similar tasks in a representative environment. However, it may result in overfitting to the new data stream or the failure to adapt, leading to degradation or a potential crash. These problems can be mitigated with an excessive amount of data and embedded model, but the computational power and the memory of the aerial robots are limited. In order to address the variations in the model, environment as well as the tasks within onboard computation limitations, we propose a deep neuromorphic controller approach with variable topologies to handle each different condition and the data stream with a feasible computation and memory allocation. The proposed approach is based on a deep neuromorphic (multi and variable layered neural network) controller with dynamic depth and progressive layer adaptation for each new data stream. This adaptive structure is combined with a switching function to form a sliding mode controller. The network parameter update rule guarantees the stability of the closed loop system by the convergence of the error dynamics to the sliding surface. Being the first implementation on an aerial robot in this context, the results illustrate the adaptation capability, stability, computational efficiency as well as the real-time validation.

Abstract

An Internet of Things (IoT)-based healthcare monitoring system is an amalgamation of smart healthcare devices and applications which connect to the healthcare information system through the internet. It has opened up a world of possibilities in the medical field. Connected smart healthcare devices can collect important healthcare data and provide extra details about the symptoms. That further enables remote care as the patient can consult the appropriate healthcare expert. It generally provides patients more control over their lives and treatment. Moreover, a blockchain-enabled IoT-based healthcare monitoring system (BIoT-HMS) is one step ahead as it can reduce unnecessary hospital visits and the burden on healthcare systems by connecting patients to their healthcare experts and allowing the secure transfer and storage of healthcare data by making use of the blockchain mechanism. Some of the possible applications of BIoT-HMS are “remote health monitoring,” “management of healthcare operations,” “detection and prevention of diseases” and many more. However, IoT-based healthcare monitoring systems have issues related to security breaches and privacy of healthcare data. Therefore, it is essential to design some security protocols for IoT-based healthcare monitoring systems. From time to time researchers propose different protocols to secure the data of IoT-based healthcare monitoring systems. However, most of them are vulnerable to various types of attack like, replay, MiTM, impersonation, secret credentials leakage, unauthorized data update, etc., Therefore, researchers came up with the idea of inclusion of blockchain-enabled security in the IoT-based healthcare monitoring system, which further can be considered as a blockchain-enabled IoT-based healthcare monitoring system (BIoT-HMS). In this chapter, we provide the details of various possible architectures of BIoT-HMS. We also provide the details of various threats and attacks along with the threat model in BIoT-HMS. The security requirements and applications of BIoT-HMS are also discussed. We then provide the summary of various existing security protocols related to BIoT-HMS. The comparison of “security and functionality features” of security protocols related to BIoT-HMS is also provided.

Abstract

We propose a pitch stylization technique in the presence of pitch halving and doubling errors. The technique uses an optimization criterion based on a minimum mean absolute error to make the stylization robust to such pitch estimation errors, particularly under noisy conditions. We obtain segments for the stylization automatically using dynamic programming. Experiments are performed at the frame level and the syllable level. At the frame level, the closeness of stylized pitch is analyzed with the ground truth pitch, which is obtained using a laryngograph signal, considering root mean square error (RMSE) measure. At the syllable level, the effectiveness of perceptual relevant embeddings in the stylized pitch is analyzed by estimating syllabic tones and comparing those with manual tone markings using the Levenshtein distance measure. The proposed approach performs better than a minimum mean squared error criterion based pitch stylization scheme at the frame level and a knowledgebased tone estimation scheme at the syllable level under clean and 20dB, 10dB and 0dB SNR conditions with five noises and four pitch estimation techniques. Among all the combinations of SNR, noise and pitch estimation techniques, the highest absolute RMSE and mean distance improvements are found to be 6.49Hz and 0.23, respectively. Index Terms: Pitch stylization, minimum MAE criterion, dynamic programming based segmentation, noise robustness

Abstract

—Automatic phrase boundary detection could be useful in applications, including computer-assisted pronunciation tutoring, spoken language understanding, and automatic speech recognition. In this work, we consider the problem of phrase boundary detection on English utterances spoken by native American speakers. Most of the existing works on boundary detection use either knowledge-based features or representations learnt from a convolutional neural network (CNN) based architecture, considering word segments. However, we hypothesize that combining knowledge-based features and learned representations could improve the boundary detection task’s performance. For this, we consider a fusion-based model considering deep neural network (DNN) and CNN, where CNNs are used for learning representations and DNN is used to combine knowledge-based features and learned representations. Further, unlike existing data-driven methods, we consider two CNNs for learning representation, one for word segments and another for word-final syllable segments. Experiments on Boston University radio news and Switchboard corpora show the benefit of the proposed fusionbased approach compared to a baseline using knowledge-based features only and another baseline using feature representations from CNN only. Index Terms—Boundary detection, human-computer interaction, computer-assisted pronunciation tutoring, CNN based representation learning

Abstract

Recently, there is an increasing interest in multilingual automatic speech recognition (ASR) where a speech recognition system caters to multiple low resource languages by taking advantage of low amounts of labelled corpora in multiple languages. With multilingualism becoming common in today’s world, there has been increasing interest in code-switching ASR as well. In code-switching, multiple languages are freely interchanged within a single sentence or between sentences. The success of low-resource multilingual and code-switching (MUCS) ASR often depends on the variety of languages in terms of their acoustics, linguistic characteristics as well as the amount of data available and how these are carefully considered in building the ASR system. In this MUCS 2021 challenge, we would like to focus on building MUCS ASR systems through two different subtasks related to a total of seven Indian languages, namely Hindi, Marathi, Odia, Tamil, Telugu, Gujarati and Bengali. For this purpose, we provide a total of ∼600 hours of transcribed speech data, comprising train and test sets, in these languages, including two code-switched language pairs, Hindi-English and Bengali-English. We also provide baseline recipes1 for both the subtasks with 30.73% and 32.45% word error rate on the MUCS test sets, respectively. Index Terms: Multilingual, Code-switching, low-resource

Abstract

In this study, listeners of varied Indian nativities are asked to listen and recognize TIMIT utterances spoken by American speakers. We have three kinds of responses from each listener while they recognize an utterance: 1. Sentence difficulty ratings, 2. Speaker difficulty ratings, and 3. Transcription of the utterance. From these transcriptions, word error rate (WER) is calculated and used as a metric to evaluate the similarity between the recognized and the original sentences. The sentences selected in this study are categorized into three groups: Easy, Medium and Hard, based on the frequency of occurrence of the words in them. We observe that the sentence, speaker difficulty ratings and the WERs increase from easy to hard categories of sentences. We also compare the human speech recognition performance with that using three automatic speech recognition (ASR) under following three combinations of acoustic model (AM) and language model (LM): ASR1) AM trained with recordings from speakers of Indian origin and LM built on TIMIT text, ASR2) AM using recordings from native American speakers and LM built on text from LIBRI speech corpus, and ASR3) AM using recordings from native American speakers and LM build on LIBRI speech and TIMIT text. We observe that HSR performance is similar to that of ASR1 whereas ASR3 achieves the best performance. Speaker nativity wise analysis shows that utterances from speakers of some nativity are more difficult to recognize by Indian listeners compared to few other nativities. Index Terms: human speech recognition, automatic speech recognition

Abstract

Research and fabrication of micro devices hold the key to low power devices, sensors and lab-on-a-chip systems which are in much need today for diagnosis, IOT etc. Cost of setting up a fabrication laboratory and conducting photolithography is high due to need of expensive equipment. We present a design of low-cost UV exposure system which can be used for both high resolution and low resolution photolithography. In first part, we present the study of irradiance distribution patterns of different UV LED arrangements. We use uniformity as a criterion for selecting the arrangement that gives the best illumination uniformity in largest area. The maximum, minimum and average exposure energy and uniformity have been presented. Using these results, we present an optimal system design for UV photolithography. The novelty of our work is simulating the UV exposure system for different parameters for making an optimal choice for a versatile system (high energy & low energy), different uniformity for different resolution to save power and have better control.

Abstract

A TeV scale leptoquark (LQ) is one of the promising explanations of the recent anomalies in the semileptonic decays of 퐵 mesons. Among the various LQs, the vector U1 is capable of explaining the anomalies in both 푅퐷(∗) and 푅퐾 (∗) observables. We use the current LHC data to put bounds on the parameter space of U1 relevant for the anomalies. Precise bounds are drawn by recasting the latest 휏휏 and 휇휇 searches by the ATLAS and CMS collaborations. We find that it is imperative to include the resonant production modes for obtaining limits in the low mass regions. For higher mass points, the non-resonant production modes play a dominant role.