Year-2017
http://repository.iiitd.edu.in/xmlui/handle/123456789/503
2024-01-30T06:23:25ZWavelet transform learning and applications
http://repository.iiitd.edu.in/xmlui/handle/123456789/616
Wavelet transform learning and applications
Ansari, Naushad; Gupta, Anubha (Advisor)
Transform learning (TL) is currently an active research area. It has been explored in several applications including image/video denoising, compressed sensing (CS) of magnetic resonance images (MRI), etc. and is observed to perform better than the existing transforms. However, TL involves non-convex optimization problem with no closed form solution and hence, is solved using greedy algorithms. A large number of variables (transform basis as well as transform coefficients) along with the greedy-based solution makes TL computationally expensive. Also, TL requires a large amount of training data for learning. Hence, it may run with challenges in applications where only single snapshots of short-duration signals such as speech, music or electrocardiogram (ECG) signal are available. Thus, one uses existing transforms that are signal independent. This motivates us to look for a strategy to learn transform in such applications.
Among existing transforms, discrete wavelet transform provides an efficient representation for a variety of multi-dimensional signals. Owing to this, wavelets have been applied successfully in many applications. In addition, wavelet analysis provides an option to choose among existing basis or to learn new basis. This motivates us to learn wavelet transform from a given signal of interest that may perform better than the fixed transforms in an application. The learned wavelet transform is, hereby, called signal-matched wavelet transform. Since the translates of the wavelet filters associated with discrete wavelet transform form the basis in l2-space, wavelet transform learning implies learning wavelet filter coefficients. This reduces the number of parameters required to be learned with wavelet learning compared to the traditional transform learning. Also, the requirement of learning fewer coefficients allows one to learn basis from a short single snapshot of signal or from the small training data. We also show that closed form solution exists for learning the wavelet transform unlike traditional transform learning.
Although the problem of signal-matched wavelet design/learning has been explored in the literature, there are a number of limitations. Firstly, existing methods require full original signal to learn wavelet transforms and hence, these methods cannot be used in inverse problems, where one has access to only the degraded signal and not to the original signal. Secondly, signal-matched wavelet transform learning is not explored for rational wavelets, although rational wavelets are observed to be more effective than dyadic wavelets in audio and speech signal processing. Thus, we note that there is a need for methods to learn signal-matched wavelets that are modular, have compactly supported filters for dyadic or rational wavelet systems, are easily implementable in DSP hardware, and can also be learned from degraded signals. This thesis is motivated to address these limitations and proposes a number of methods along with their utility in applications.
Specifically, we propose methods to learn dyadic as well as rational wavelet transform using the lifting framework. The proposed method inherits all the advantages of lifting, i.e., the learned wavelet transform is always invertible, method is modular, learned transform has compactly supported filters and hence, is DSP hardware friendly, and the corresponding wavelet system can also incorporate nonlinear filters, if required. We show that closed form solution exists for learning the wavelet transform with the proposed method. Also, wavelet transform can be learned using the proposed method even when a small amount of data is present. Since the wavelet transform is being learned from the signal itself, one may use the learned wavelet transform in applications instead of struggling to choose from the existing wavelet bases.
For dyadic wavelet transform learning (DWTL), we propose three methods in different scenarios. Particularly, we propose methods to learn dyadic wavelet transform (DWT) from 1) original signal, 2) degraded signal in inverse problems, and 3) a class of signals. We use the learned DWT as the sparsifying transform in the application of 1) Gaussian denoising of speech and music signals, 2) CS based reconstruction of speech, music, and ECG signals, 3) impulse denoising of images, and 4) CS based reconstruction of images. Extensive simulations have been carried out that demonstrate that the learned transforms outperform the standard dyadic wavelet transforms.
We also extend the existing theory of lifting framework from dyadic to rational wavelets and use the extended lifting theory to learn critically sampled signal-matched rational wavelet transform (RWT) with generic decimation ratios from a given signal of interest. We introduce the concept of rate converters in predict and update stages to handle variable subband sample rates. So far, signal-matched rational wavelet learning have remained limited in use because design methods are in general cumbersome. Since our proposed methodology exploits lifting framework, we provide modular, compactly supported, DSP hardware friendly rational wavelet transform learning (RWTL) methods. This may enhance the use of RWT in applications which is so far restricted. We use the learned RWT as the sparsifying transform in CS based reconstruction of 1-D and 2-D signals. The learned RWT is observed to perform better than the existing dyadic as well as rational wavelet transforms.
Apart from the wavelet transform learning methods, we propose a new multilevel wavelet decomposition strategy for images, named as L-Pyramid wavelet decomposition. L-Pyramid wavelet decomposition is observed to perform better in
CS based image reconstruction. In addition, we also propose weighted non-convex minimization for CS based recovery. Detailed experiments are provided using the weighted non-convex minimization and the learned wavelet transform for CS based
ECG signal recovery with various sensing matrices. The learned wavelet transform along with the proposed weighted non-convex minimization method is observed to provide much better ECG signal reconstruction as compared to existing wavelet transforms as well as existing methods.
2017-12-01T00:00:00ZCognitive subcarrier sharing schemes for cooperative D2D communication frameworks
http://repository.iiitd.edu.in/xmlui/handle/123456789/599
Cognitive subcarrier sharing schemes for cooperative D2D communication frameworks
Gupta, Naveen; Bohara, Vivek Ashok (Advisor)
Past few decades have seen a phenomenal growth in wireless multimedia and data applications. Recent forecast (via Cisco) suggested that the overall mobile data traffic could reach 24.3 exabytes per month by 2019. As a consequence, there is a perpetual need for novel spectrum access techniques to alleviate the problem of spectrum scarcity. This dissertation investigates two solutions to enhance the capacity of the wireless/cellular networks: cooperative spectrum sharing (CSS) and device-to-device (D2D) communication.
In a conventional CSS protocol, the secondary system (a cognitive user) acts as an amplify-and-forward (AF) or decode-and-forward (DF) relay for the primary system to achieve the target quality-of-services (QoS) of the primary system in exchange for spectrum access by the secondary system. However, most of the existing CSS protocols are interference limited and performance of the system may degrade due to interference from one system to another. To mitigate the interference, we propose orthogonal frequency division multiplexing (OFDM) based opportunistic spectrum sharing (OSS) for cooperative cognitive radios. According to the scheme, secondary system helps the primary system via two phase DF relaying in exchange of OSS. Both primary and secondary systems employ OFDM modulator and demodulator at transmitter and receiver, respectively. If the primary system is unable to achieve its target rate, then the secondary transmitter (ST) forwards a few subcarriers to the primary receiver (PR) to ful_ll the quality-of-services (QoS) requirement of the primary system and the remaining subcarriers can be used by the secondary system for its own data transmission. Thus, OSS can be achieved by the secondary system without interfering to the primary system, since primary and secondary subcarriers are orthogonal to each other.
To further boost the performance of both primary and secondary systems, we propose an adaptive subcarrier sharing scheme for OFDM-based cooperative cognitive radios. According to the scheme, ST uses adaptive mode of transmission to relay the primary signal with higher throughput while maintaining the bit-error rate (BER) constraint of the primary system. At PR, a BER based selection combining (BER-SC) scheme is employed to combine the signals received in two phases. Closed-form analytical expressions for BER and outage probability of primary and secondary system for a Rayleigh at fading channel have been derived. Results show that the outage probability with the proposed scheme (for dissimilar modulation) outperforms direct transmission and conventional maximal ratio combining scheme (for similar modulation).
Recently, D2D communication has been incorporated as a part of long-term-evolution advanced (LTE-A) Release 12 and 13 to avail the high capacity benefits to the cellular users with minimal constraints on infrastructure maintenance. In a generic D2D framework, two cellular users living in proximity can form a direct link for data transmission without routing it through the evolved node B (eNB) / base station (BS). Utilization of available resource allocation frameworks to facilitate D2D communication while maximizing the throughput of both cellular and D2D users is an open issue. In this dissertation, we propose the rate and outage trade-offs for orthogonal frequency division multiple access (OFDMA) based D2D communication frameworks where multiple D2D users coexist with the cellular users in the same cell. Analytical expressions of outage probability for three D2D frameworks namely underlay, overlay and cooperative D2D (C-D2D) have been derived. Specifically, for underlay framework, a minimum value of the angle (an angle between a cellular link and D2D interference link) is derived for which the target rate and outage probability constraint of both cellular and D2D users are satisfied. For overlay and C-D2D frameworks, an optimal subcarrier sharing scheme is proposed which not only helps the cellular users to achieve the target
QoS, but also helps the D2D users to communicate with each other. In addition to above, benefits involved in employing one framework over other have also been investigated. Our results show that for a higher outage probability constraint of the cellular user, the C-D2D framework outperforms the underlay and overlay frameworks.
We augment the development of C-D2D framework by incorporating the best
D2D user selection scheme. As per the proposed scheme, among M available D2D pairs, a D2D transmitter (DT) that can achieve the largest QoS improvement for uplink cellular transmission is selected to serve as a user relay for the cellular transmission. In addition, a novel round robin process with priority cube method is proposed to facilitate the fair distribution of available resources among M DTs. In particular, closed-form expressions of the outage probability for two different cases: with and without direct cellular link are derived. Results show that the proposed best D2D user selection improves the QoS of the cellular user as compared to the conventional C-D2D framework.
Another issue related to D2D communication is the lack of measurement results for proof of concept demonstration and performance assessment in a realistic scenario. We try to resolve this issue by designing and developing a software defined radio (SDR) based test-bed implemented on National Instruments (NI) Universal Software Radio Peripheral (USRP) platform. The performance of the testbed has been validated by obtaining received signal to noise ratio (SNR) and symbol error rate (SER) for both cellular and D2D users. The measurement results show that the proposed frameworks significantly improves the performance of cellular network in both line of sight (LOS) and non-line of sight (N-LOS) scenarios.
2017-09-01T00:00:00ZPower- and performance-aware on-chip interconnection architectures for many-core systems
http://repository.iiitd.edu.in/xmlui/handle/123456789/511
Power- and performance-aware on-chip interconnection architectures for many-core systems
Mondal, Hemanta Kumar; Deb, Sujay (Advisor)
Networks-on-Chip (NoCs) are fast becoming the de-facto communication infrastructures in Chip Multi-Processors (CMPs) for large-scale applications. The traditional approaches of implementing a NoC with planar metal interconnects have high latency and significant power consumption overhead. This is mainly due to the multi-hop data exchange using wired links, specifically when the number of cores is significantly high. To address these problems, Wireless NoCs (WNoCs) that augment multi-hop wired interconnects in a NoC with high-bandwidth, single-hop, long-range wireless links are being explored. Although multi-hop communication is replaced by WNoC, still, NoC components including wireless transceivers consume a significant portion of chip power, which is one of the major bottlenecks in NoC architectures for CMPs. With progressing generations and system sizes, this proportion increases exponentially. Another important concern with the existing WNoCs is the performance limitations due to single frequency channel communication with omnidirectional antenna setups. These bottlenecks open up new opportunities for detailed investigations into the power and performance efficiency of WNoCs and design low-energy, high-performance communication infrastructures for CMPs.
Analysis of network resources for several benchmarks shows that, utilization and hence energy consumption is application dependent and the desired performance can be achieved even without operating all resources at maximum specifications. To reduce the power consumption, we propose a leakage power-aware NoC architecture using power gated router based on the router utilization. To compute the utilization of routers, we propose an adaptive two-step estimation method that computes utilization at both global and local router levels. This hybrid estimation method provides an accurate prediction of router utilization with low run-time overheads. Using the utilization estimates, we reduce the switching and idle-state power consumption of WNoC architecture. To eliminate power-gating impacts and maintain the performance, we implement a deadlock-free Seamless Bypass Routing (SBR) strategy that bypasses a power-gated router.
Based on the utilization of routers, we propose a switching (dynamic) power-aware NoC architecture using Adaptive Multi-Voltage Scaling (AMS) mechanism to achieve significant energy saving. To implement the AMS based WNoC architectures, we also propose a multi-level voltage shifter along with efficient control mechanism that allows switching between two voltage levels from a given fixed set of voltage levels. But most wireless interconnects are implemented using a token passing protocol in which only a single paired is actively involved in data transmission at any given time. Hence, the wireless transceivers can be selectively switched on and off depending on the workload. This will improve the power efficiency of the network without affecting the overall network performance, especially when all the wireless transceivers are designed to operate at the same frequency and only one pair can use the channel at a time.
Since all these wireless links are not required all time, power-gated wireless transceivers can provide an effective solution for power efficient WNoC design. In this dissertation, to increase the power efficiency, we also propose the partially power gated transceiver for wireless interfaces (WIs) using AMS to reduce the idle-state power consumption based on the utilization of WIs. For packets transmitted over wireless links, receiver-end control strategy is proposed with WNoC. This enables effective power gating strategy for WIs as it eliminates periodic waking up of complete receiver chain. The proposed technique also reduces routing overhead and need of control signals significantly.
However, most existing WNoC architectures generally use omnidirectional antenna along with token passing protocol to access wireless medium. That limits the achievable performance benefits since only one wireless pair can communicate at a time. It is also not practical in the immediate future to arbitrarily scale up the number of non-overlapping channels by designing mm-wave transceivers operating in disjoint frequency bands. Consequently, we explore the use of directional antennas where multiple simultaneous wireless interconnect pairs can communicate. Concurrent wireless communications can result in interference. This can be minimized by optimal placement of wireless nodes. To address this, we propose an interference-aware Directional Wireless NoC (DWNoC) topology with optimal placement of WIs by incorporating planner log-periodic antennas (PLPAs). This DWNoC architecture enables the directional point-to-point links between transceivers and hence multiple wireless links can operate at the same time without interference. It also increases the energy efficiency of DWNoC as well as utilization of WIs significantly as compared to existing NoC architectures.
In addition, we also address the on-chip communication bottlenecks between Last Level Caches (LLCs) and Memory Controllers (MCs) to access off-chip memory. Communication between LLCs and memory controllers faces significant challenge due to the placement of memory controllers, high network latency, and switching strategy. Especially, as system size increases, the latency between caches and limited number of memory controllers increases, thereby degrading the memory performance. To overcome this, we propose an adaptive hybrid switching strategy with dual crossbar router to provide low latency paths between caches and memory controllers. The performance is further improved by finding the optimal number and placement of memory controllers with low overheads. To reduce the energy overhead of dual crossbar routers, we introduce partially drowsy and power gated techniques with routers in the proposed architecture.
2017-04-01T00:00:00ZLatent factor models for collaborative filtering
http://repository.iiitd.edu.in/xmlui/handle/123456789/501
Latent factor models for collaborative filtering
Gogna, Anuprriya; Majumdar, Angshul (Advisor)
The enormous growth in online availability of information content has made Recommender Systems (RS) an integral part of most online portals and e-commerce sites. Most websites and service portals, be it movie rental services, online shopping or travel package providers, offer some form of recommendations to users. These recommendations provide the users more clarity, that too expeditiously and accurately in limiting (shortlisting) the items/information they need to search through, thereby improving the customer's experience. The direct link between customer's satisfaction and revenue of e-commerce sites induce widespread interest of both, academia and industry, in the design of efficient recommender systems.
The current de-facto approach for RS design is Collaborative Filtering (CF). CF techniques use the ratings provided by users, to a subset of the items in the repository, to make future recommendations. However, the rating information is hard to acquire; often a user has rated less than 5% of the items. Thus, the biggest challenge in recommender system design is to infer users’ preference from this extremely limited predilection information. The lack of adequate (explicit) preference information has motivated several works to augment the rating data with auxiliary information such as user’s demographics, trust networks, and item tags. Further, the scale of the problem, i.e. the amount of the data to be processed (selecting few items out of hundreds and thousands of items for an equally large number of users) adds another dimension to the concerns surrounding the design of a good RS. There have been several developments in the field of RS design over the past decades. However, the difficulty in achieving the desired accuracy and effectiveness in recommendations leaves considerable scope for improvement.
In this work, we model effective recommendation strategies, using optimization centric frameworks, by exploiting reliable and readily available information, to address several pertinent issues concerning RS design. Our proposed recommendation strategies are built on the principals of latent factor models (LFM). LFM are constructed on the belief that a user’s choice for an item is governed by a handful of factors – the latent factors. For
example, in the case of movies, these factors may be genre, director, language while for hotels it can be price and location.
Our first contribution targets improvement in prediction accuracy as well the speed of processing by suggesting modifications to the standard LFM frameworks. We develop a more intuitive model, supported by effective algorithm design, which better captures the underlying structure of the rating database while ensuring a reduction in run time compared to standard CF techniques. In the next step, we build upon these proposed frameworks to address the problem of lack of collaborative data, especially for cold start (new) users and items, by making use of readily available user and item metadata - item category and user demographics. Our suggested frameworks make use of available metadata to add additional constraints in the standard models; thereby presenting a comprehensive strategy to improve prediction accuracy in both warm (existing users/items for which rating data is available) and cold start scenario.
Although, high recommendation accuracy is the hallmark of a good RS, over-emphasis on accuracy compromises on variety and leads to monotony. Our next set of models aims to address this concern and promote diversity and novelty in recommendations. Most existing works, targeting diversity, build ad-hoc exploratory models relying heavily on heuristic formulations. In the proposed work, we modify the latent factor model to formulate a joint optimization strategy to establish accuracy-diversity balance; our models yield superior results than existing works.
The last contribution of this work is to explore the use of another representation learning tool for collaborative filtering – Autoencoder (AE). Conventional AE based designs, use only the rating information; lack of adequate data hampers the performance of these structures, thus, they do not perform as well as conventional LFM based designs. In this work, we propose a modification of the standard autoencoder – the Supervised Autoencoder – which can jointly accommodate information from multiple sources resulting in better performance than existing architectures.
2017-04-01T00:00:00Z