Abstract

Cognitive radio is an innovative technology that possesses profound potential to mitigate spectrum scarcity in the next-generation wireless networks. In this technology, unlicensed users are capable of accessing unused portions of the spectrum, popularly known as white spaces, for communication that enhances that spectrum usage. Each of these unlicensed user nodes must incorporate spectrum sensor to detect white space for transmitting and receiving the information. Such spectrum sensors must be portable and it needs to support wide bandwidth suitable for contemporary wireless networks. This paper presents a new frequency-domain energy-detector for spectrum sensing that is hardware efficient. We propose VLSI architecture for this detector where the area and power consuming FFT unit has been replaced by an iterative FFT- computation unit with simple architecture. This area-efficient design also improves the performance of detector in real-world noisy environment. In addition, we extend the conventional single-band detection-approach to a wide-band detection ap- proach and present its VLSI architecture. Performance analysis of such detection in AWGN channel environment showed that the detection probability of 0.16 is achieved at a SNR of -20 dB. Hardware implementation of the proposed energy detector is carried out using FPGA and its real world testing plus verification is performed using USRP software-defined radio- platform. It could operate with the bandwidth as high as 150 MHz with a sensing time of 7 ms. Compared to the state-of- the-art implementation, our prototype consumes 46% lesser hardware resources and does not require any memory in its architecture. Keywords-Cognitive radio; spectrum sensing; energy det

Abstract

The current procedure for assessing emergency situational awareness in the USA requires Police, Paramedic and Fire First Responders to go on site. This observation is termed “size up”. It is effective, but not efficient. Using advanced information and communication for size-up would save Responders time and allow them to begin actual response faster- possibly saving lives. The commercial software that exists for size up is not in wide use. We have developed a software application called an Indoor-Outdoor Viewer for size-up for emergencies so that Responders can get information about the incident before they arrive at the site. It is an easy-to-use Viewer that shows building surroundings and their interiors, and allows Responders to mark the map to organize their approach. It will be compatible with real-time emergency data streams.

Abstract

The problem of estimating scene-depth from a single im- age has seen great progress lately. Recent unsupervised methods are based on view-synthesis and learn depth by minimizing photometric reconstruction error. In this pa- per, we introduce Structured Adversarial Training (StrAT) to this problem. We generate multiple novel views using depth (or disparity), with the stereo-baseline changing in an increasing order. Adversarial training that goes from easy examples to harder ones produces richer losses and better models. The impact of StrAT is shown to exceed tra- ditional data augmentation using random new views. The combination of an adversarial framework, multiview learn- ing, and structured adversarial training produces state-of- the-art performance on unsupervised depth estimation for monocular images. The StrAT framework can benefit sev- eral problems that use adversarial training

Abstract

In this work, the Switched-capacitor Power Amplifier (SCPA) impairments are modelled and analysed along with their effect on the efficiency, the linearity and the output power. The proposed model considers the mismatch between the switch On-resistances, the variation of switch On-resistance with VDS and its effects on the SCPA performance. Additionally, the influence of supply non-idealities are modelled. The proposed model provides expected Integral Non-Linearity for the SCPA as well as some insight on possible design improvements and SCPA limitations.

Abstract

The inter-cell interference coordination techniques which include frequency reuse and Dynamic Channel Allocation (DCA) play a vital role in improving the spectrum efficiency. The techniques of frequency reuse are very sophisticated. Depending on the traffic demand, channels allocated to each cell is varied in DCA. In OFDM based cellular systems, the channel information and interference signals can be measured easily whose knowledge is essential for DCA. Hence, in OFDM systems, DCA can improve the spectral efficiency to a great extent. The next generation cellular systems would incorporate small cell (Micro, Pico and Femto) base stations for extensive frequency reuse and spectrum utility. They would also include millimeter wave (mmWave) for communication i.e. 5G cellular networks. DCA techniques in such a HetNet would improve the efficiency to a great extent. With the development of Software Defined Mobile Networking (SDMN), the protocol-specific features of mobile networks for various radio access technologies can be carried out with software improving the functionality of network. Thus, the implementation of DCA techniques becomes less complex. In this paper, DCA in small cells with respect to three cases namely spatial, temporal and spatio-temporal allocation is proposed. The problem formulated for DCA is solved using integer linear programming (ILP) technique and the solution is an effective prediction based model to optimize the channel allocation problem. Simulation results show the performance for various number of available resources and number of cells for allocation.

Abstract

In this paper, we tackle the problem of fake news detection from social media by exploiting the presence of echo chamber communities (communities sharing same beliefs) that exist within the social network of the users. By modeling the echo-chambers as closely-connected communities within the social network, we represent a news article as a 3-mode tensor of the structure - <;News, User, Community> and propose a tensor factorization based method to encode the news article in a latent embedding space preserving the community structure. We also propose an extension of the above method, which jointly models the community and content information of the news article through a coupled matrix-tensor factorization framework. We empirically demonstrate the efficacy of our method for the task of Fake News Detection over two real-world datasets.

Abstract

This paper describes the systems submitted by IIT (BHU), Varanasi/IIIT Hyderabad (IITBHU–IIITH) for Task 1 of CoNLL– SIGMORPHON 2018 Shared Task on Universal Morphological Reinflection (Cotterell et al., 2018). The task is to generate the inflected form given a lemma and set of morphological features. The systems are evaluated on over 100 distinct languages and three different resource settings (low, medium and high). We formulate the task as a sequence to sequence learning problem. As most of the characters in inflected form are copied from the lemma, we use Pointer-Generator Network (See et al., 2017) which makes it easier for the system to copy characters from the lemma. PointerGenerator Network also helps in dealing with out-of-vocabulary characters during inference. Our best performing system stood 4th among 28 systems, 3rd among 23 systems and 4th among 23 systems for the low, medium and high resource setting respectively.

Abstract

With neural networks rapidly becoming deeper, there emerges a need for compact models. One popular approach for this is to train small student networks to mimic larger and deeper teacher models, rather than directly learn from the training data. We propose a novel technique to train studentteacher networks without directly providing label information to the student. However, our main contribution is to learn how to learn from the teacher by a unique strategy - having the student compete with a discriminator.

Abstract

Graph grammars, being a natural extension of string grammars as well as tree grammars, are highly expressive as well as powerful enough to capture the generative structure of a wide variety of scenarios - both simple and complex. But, unfortunately, we do not find many instances which leverage this power graph grammars provide us. Most of the graph grammars available so far are either toy grammars or limited to addressing highly specialized problems. In this paper, we present Vaiśeṣika Graph Grammar (VGG) system, which is as wide as any graph grammar can get i.e., a grammar for reality as theorized in Vaiśeṣika - the foundational ontology found in Indian analytic tradition. This paper extends the work presented in [1]. In [1], the generative as well as the interpretation rules of this system were presented. Here, apart from briefly discussing some of these rules, we also present a parser for such a system which makes it extremely powerful in terms of its ability to classify an input graph as a valid Vaiśeṣika graph or an invalid one which, in itself, is an immensely significant breakthrough for any ontological application. Apart from that, we also present a verification mechanism to verify the interpretations of graphs generated by the generative rules. This is done, at least statistically, if not formally, to give a statistical proof of the soundness of the system (that every generated graph has at least one Vaiśeṣika

Abstract

We show an effective way of adding context information to shallow neural language models. We propose to use Subspace Multinomial Model (SMM) for context modeling and we add the extracted i-vectors in a computationally efficient way. By adding this information, we shrink the gap between shallow feed-forward network and an LSTM from 65 to 31 points of perplexity on the Wikitext-2 corpus (in the case of neural 5-gram model). Furthermore, we show that SMM i-vectors are suitable for domain adaptation and a very small amount of adaptation data (e.g. endmost 5 % of a Wikipedia article) brings a substantial improvement. Our proposed changes are compatible with most optimization techniques used for shallow feedforward LMs.