Hypersonic flight vehicle trajectory optimization using pattern search algorithm

In this work, trajectory optimization of an aerodynamically controlled hypersonic boost glide class of flight vehicle is presented. In order to meet the mission constraints such as controllability, skin temperature, and terminal conditions etc., the trajectory is optimized using a pattern search algorithm with the lift to drag (L/D) ratio as a control parameter. It is brought out that the approach offers a viable tool for optimizing trajectories for the considered class of vehicles. Further, the effects of the constraints on trajectory shape and performance are studied and the analysis is used to bring out an optimal vehicle configuration at the initial stage of the design process itself. The research also reveals that the pattern search algorithm offers superior performance in comparison with the genetic algorithm for this class of optimization problem.     

Cryptanalysis of RSA with two decryption exponents

In this paper, we consider RSA with N=pq, where p,q are of same bit size, i.e., q<p<2q. We study the weaknesses of RSA when multiple encryption and decryption exponents are considered with same RSA modulus N. A decade back, Howgrave-Graham and Seifert (CQRE 1999) studied this problem in detail and presented the bounds on the decryption exponents for which RSA is weak. For the case of two decryption exponents, the bound was N^0.357. We have exploited a different lattice based technique to show that RSA is weak beyond this bound. Our analysis provides improved results and it shows that for two exponents, RSA is weak when the RSA decryption exponents are less than N^0.416. Moreover, we get further improvement in the bound when some of the most significant bits (MSBs) of the decryption exponents are same (but unknown).     

Handling Movement Epenthesis and Hand Segmentation Ambiguities in Continuous Sign Language Recognition Using Nested Dynamic Programming

We consider two crucial problems in continuous sign language recognition from unaided video sequences. At the sentence level, we consider the movement epenthesis (me) problem and at the feature level, we consider the problem of hand segmentation and grouping. We construct a framework that can handle both of these problems based on an enhanced, nested version of the dynamic programming approach. To address movement epenthesis, a dynamic programming (DP) process employs a virtual me option that does not need explicit models. We call this the enhanced level building (eLB) algorithm. This formulation also allows the incorporation of grammar models. Nested within this eLB is another DP that handles the problem of selecting among multiple hand candidates. We demonstrate our ideas on four American Sign Language data sets with simple background, with the signer wearing short sleeves, with complex background, and across signers. We compared the performance with Conditional Random Fields (CRF) and Latent Dynamic-CRF-based approaches. The experiments show more than 40 percent improvement over CRF or LDCRF approaches in terms of the frame labeling rate. We show the flexibility of our approach when handling a changing context. We also find a 70 percent improvement in sign recognition rate over the unenhanced DP matching algorithm that does not accommodate the me effect.     

Partition oriented frame based fair scheduler

Proportionate fair schedulers provide an effective methodology for scheduling recurrent real-time tasks on multiprocessors. However, a drawback in these schedulers is that they ignore a task’s affinity towards the processor where it was executed last, causing frequent inter-processor task migrations which ultimately results in increased execution times. This paper presents Partition Oriented Frame Based Fair Scheduler (POFBFS), an efficient proportional fair scheduler for periodic firm and soft real-time tasks that ensures a bounded number of task migrations. Experimental results reveal that POFBFS can achieve 3 to 100 times reduction in the number of migrations suffered with respect to the General-ERfair algorithm (for a set of 25 to 100 tasks running on 2 to 8 processors) while simultaneously maintaining high fairness accuracy.     

Fairness of Transitions in Diagnosability of Discrete Event Systems

Failure diagnosability has been widely studied for discrete event system (DES) models because of modeling simplicity and computational efficiency due to abstraction. In the literature it is often held that for diagnosability, such models can be used not only for systems that fall naturally in the class of DES but also for the ones traditionally treated as continuous variable dynamic systems. A class of algorithms for failure diagnosability of DES models has been successfully developed for systems where fairness is not a part of the model. These algorithms are based on detecting cycles in the normal and the failure model that look identical. However, there exist systems with all transitions fair where the diagnosability condition that hinges upon this feature renders many failures non-diagnosable although they may actually be diagnosable by transitions out of a cycle. Hence, the diagnosability conditions based on cycle detection need to be modified to hold for many real-world systems where all transitions are fair. In this work, however, it is shown by means of an example that a system may have some transitions fair and some unfair. A new failure diagnosability mechanism is proposed for DES models with both fair and unfair transitions. Time complexity for deciding diagnosability of DES models with fair and unfair transitions is analyzed and compared with the time complexities of other DES diagnosability analysis methods reported in the literature.     

Spectral entropy and spectral shape based pre-quantization for real time speaker identification system

Pre-processing is one of the vital steps for developing robust and efficient recognition system. Better pre-processing not only aid in better data selection but also in significant reduction of computational complexity. Further an efficient frame selection technique can improve the overall performance of the system. Pre-quantization (PQ) is the technique of selecting less number of frames in the pre-processing stage to reduce the computational burden in the post processing stages of speaker identification (SI). In this paper, we develop PQ techniques based on spectral entropy and spectral shape to pick suitable frames containing speaker specific information that varies from frame to frame depending on spoken text and environmental conditions. The attempt is to exploit the statistical properties of distributions of speech frames at the pre-processing stage of speaker recognition. Our aim is not only to reduce the frame rate but also to maintain identification accuracy reasonably high. Further we have also analyzed the robustness of our proposed techniques on noisy utterances. To establish the efficacy of our proposed methods, we used two different databases, POLYCOST (telephone speech) and YOHO (microphone speech).     

An imperfect production process for time varying demand with inflation and time value of money – An EMQ model

The paper deals with an economic manufacturing quantity (EMQ) model for time-dependent (quadratic) demand pattern. Every manufacturing sector wants to produce perfect quality items. But in long run process, there may arise different types of difficulties like labor problem, machinery capabilities problems, etc., due to that the machinery systems shift from in-control state to out-of-control state as a result the manufacturing systems produce imperfect quality items. The imperfect items are reworked at a cost to become the perfect one. The rework cost may be reduced by improvements in product reliability i.e., the production process depend on time and also the reliability parameter. We want to determine the optimal product reliability and production rate that achieves the biggest total integrated profit for an imperfect manufacturing process using Euler–Lagrange theory to build up the necessary and sufficient conditions for optimality of the dynamic variables. Finally, a numerical example is discussed to test the model which is illustrated graphically also.     

Design of 5.5 GHz linear low noise amplifier using post distortion technique with body biasing

A highly linear 5.5 GHz low noise amplifier (LNA) has been designed exploiting source inductive degeneration topology by using post distortion linearization techniques in 0.18 m CMOS technology. This technique improves the input third order intercept point \((IIP_{3})\) of a low noise amplifier. For enhancing the linearity, this technique used a diode connected MOSFET as IMD sinker and forward body biased which is done in cadence tool. The proposed low noise amplifier achieves high \(IIP_{3}\) by using two transistors, main and auxiliary transistors. Also source inductive degeneration topology is employed in the proposed LNA to optimize the noise figure (NF) and \(S_{11}\) at high frequency. In order to reduce power consumption and threshold voltage, Forward Body Biased technique was implemented. In this paper, the first section discusses the most widely used eight linearization techniques and in the second section, the proposed circuit is represented along with its employed topology, techniques and the simulated results. The proposed LNA achieves a simulated third order input intercept \((IIP_{3})\) of 9.20 dBm while consuming 10.8 mW from a power supply of 1.8 V. it also exhibits a measured gain of 11.34 dB and NF, NF of 2.33 dB.