Matrix recovery using Split Bregman

In this paper we address the problem of recovering a matrix, with inherent low rank structure, from its lower dimensional projections. This problem is frequently encountered in wide range of areas including pattern recognition, wireless sensor networks, control systems, recommender systems, image/video reconstruction etc. Both in theory and practice, the most optimal way to solve the low rank matrix recovery problem is via nuclear norm minimization. In this paper, we propose a Split Bregman algorithm for nuclear norm minimization. The use of Bregman technique improves the convergence speed of our algorithm and gives a higher success rate. Also, the accuracy of reconstruction is much better even for cases where small number of linear measurements are available. Our claim is supported by empirical results obtained using our algorithm and its comparison to other existing methods for matrix recovery. The algorithms are compared on the basis of NMSE, execution time and success rate for varying ranks and sampling ratios

Low-threshold photonic crystal laser

We have fabricated photonic crystal nanocavity lasers, based on a high-quality factor design that incorporates fractional edge dislocations. Lasers with InGaAsP quantum well active material emitting at 1550 nm were optically pumped with 10 ns pulses, and lased at threshold pumping powers below 220 µW, the lowest reported for quantum-well based photonic crystal lasers, to our knowledge. Polarization characteristics and lithographic tuning properties were found to be in excellent agreement with theoretical predictions

Study of resonance hairpin probe for electron density measurements in low temperature plasmas

The thesis deals with a plasma diagnostic device, the Hairpin Probe, popularly used for measuring electron density in rarefied gaseous plasma. Electron density, n_e, is an important plasma parameter as electrons are mainly responsible for inelastic collision with background neutrals resulting in ionization, excitation, and various chemical processes in plasma. Besides, the basic plasma parameters such as plasma frequency, Debye length, plasma permittivity, and plasma conductivity are all based on n_e. Therefore accurate measurement of n_e is fundamentally desirable for quantifying the state of plasma. The underlying principle relies on measuring the effective permittivity of medium surrounding the hairpin. If length of hairpin is chosen equal to a quarter-wavelength of an incident microwave signal, a standing wave is set-up along its length. Under this condition, a strong absorbance of incident em signal is observed as hairpin is driven to resonance. When hairpin is immersed in plasma, the cold plasma permittivity is related to ne. However if adjacent dielectrics are present in the vicinity of probe, it can adversely affects the measurement. As one of the practical applications of hairpin, high refractory material is coated on the probe surface when applied to reactive etch plasmas. However, the contribution of external dielectric on probe resonances in plasma is an outstanding problem. In this thesis, we have primarily addressed the above issues. A comprehensive study is also devoted towards application of probe in strongly magnetized plasmas. The electrons gyro motion modifies the plasma permittivity and results in the observance of dual resonances as compared with non-magnetized plasmas. The other important issues addressed are different loss mechanisms causing dispersion of resonance signal in plasma. This is particular topic of interest in order to broaden the range of n_e measurement by probe

Securing in-memory processors against Row Hammering Attacks

Modern applications on general purpose processors require both rapid and power-efficient computing and memory components. As applications continue to improve, the demand for high speed computation, fast-access memory, and a secure platform increases. Traditional Von Neumann Architectures split the computing and memory units, causing both latency and high power-consumption issues; henceforth, a hybrid memory processing system is proposed, known as in-memory processing. In-memory processing alleviates the delay of computation and minimizes power-consumption; such improvements saw a 14x speedup improvement, 87\% fewer power consumption, and appropriate linear scalability versus performance. Several applications of in-memory processing include data-driven applications such as Artificial Intelligence (AI), Convolutional and Deep Neural Networks (CNNs/DNNs). However, processing-in-memory can also suffer from a security and reliability issue known as the Row Hammer Security Bug; this security exploit flips bits within memory without access, leading to error injection, system crashes, privilege separation, and total hijack of a system; the novel Row Hammer security bug can negatively impact the accuracies of CNNs and DNNs via flipping the bits of stored weight values without direct access. Weights of neural networks are stored in a variety of data patterns, resulting in either a solid (all 1s or all 0s), checkered (alternating 1s and 0s in both rows and columns), row-stripe (alternating 1s and 0s in rows), or column-striped (alternating 1s and 0s in columns) manner; the row-stripe data pattern exhibits the largest likelihood of a Row Hammer attack, resulting in the accuracies of neural networks dropping over 30\%. A row-stripe avoidance coding scheme is proposed to reduce the probability of the Row Hammer Attack occurring within neural networks. The coding scheme encodes the binary portion of a weight in a CNN or DNN to reduce the chance of row-stripe data patterns, overall reducing the likelihood of a Row Hammer attack occurring while improving the overall security of the in-memory processing system