Abstract

Standard Model Predictive Control (MPC) or trajectory optimization approaches perform only a local search to solve a complex non-convex optimization problem. As a result, they cannot capture the multi-modal characteristic of human driving. A global optimizer can be a potential solution but is computationally intractable in a real-time setting. In this paper, we present a real-time MPC capable of searching over different driving modalities. Our basic idea is simple: we run several goaldirected parallel trajectory optimizations and score the resulting trajectories based on user-defined meta cost functions. This allows us to perform a search over several locally optimal motion plans. Although conceptually straightforward, realizing this idea in real-time with existing optimizers is highly challenging from technical and computational standpoints. With this motivation, we present a novel batch non-holonomic trajectory optimization whose underlying matrix algebra is easily parallelizable across problem instances and reduces to computing large batch matrix-vector products. This structure, in turn, is achieved by deriving a linearization-free multi-convex reformulation of the non-holonomic kinematics and collision avoidance constraints. We extensively validate our approach using both synthetic and real data sets (NGSIM) of traffic scenarios. We highlight how our algorithm automatically takes lane-change and overtaking decisions based on the defined meta cost function. Our batch optimizer achieves trajectories with lower meta cost, up to 6x faster than competing baselines.

Abstract

Transfer learning is a popular method for obtaining deep trained models for data-scarce face tasks such as head pose and emotion. However, current transfer learning methods are inefficient and time-consuming as they do not fully account for the relationships between related tasks. Moreover, the transferred model is large and computationally expensive. As an alternative, we propose ETL: a technique that efficiently transfers a pre-trained model to a new task by retaining only cross-task aware filters, resulting in a sparse transferred model. We demonstrate the effectiveness of ETL by transferring VGGFace, a popular face recognition model to four diverse face tasks. Our experiments show that we attain a size reduction up to 97% and an inference time reduction up to 94% while retaining 99.5% of the baseline transfer learning accuracy

Abstract

Quantum sensing applications such as nitrogen vacancy based magnetometry require phase locked loops (PLL), which can synthesize microwave frequencies in a narrow band near 2.87 GHz. Moreover, it is also required that the PLLs should have low noise and low jitter for high stability and fast settling time. These requirements seek low phase noise voltage controlled oscillator (VCO) with small variation in its gain (KVCO) within the desired tuning range. In this paper, we present a technique for designing a low phase noise VCO with low KVCO and small KVCO variation. To validate the proposed technique, an LC VCO has been designed and implemented in 0.18 µm CMOS process and post layout simulation results are presented. The simulation results show that the proposed LC VCO achieves KVCO variation of ±3.82% in the frequency range of 2.75 - 2.94 GHz and exhibits a phase noise of -118.64 dBc/Hz at an offset of 1 MHz, while consuming 9 mW of power from a 1.8 V supply.

Abstract

Precision farming has a positive potential in the agricultural industry regarding water conservation, increased productivity, better development of rural areas, and increased income. Blockchain technology is a better alternative for storing and sharing farm data as it is reliable, transparent, immutable, and decentralized. Remote monitoring of an agricultural field requires security systems to ensure that any sensitive information is exchanged only among authenticated entities in the network. To this end, we design an efficient blockchain-enabled authenticated key agreement scheme for mobile vehicles-assisted precision agricultural Internet of Things (IoT) networks called AgroMobiBlock . The limited existing work on authentication in agricultural networks shows passive usage of blockchains with very high costs. AgroMobiBlock proposes a novel idea using the elliptic curve operations on an active hybrid blockchain over mobile farming vehicles with low computation and communication costs. Formal and informal security analysis along with the formal security verification using the Automated Validation of Internet Security Protocols and Applications (AVISPA) software tool have shown the robustness of AgroMobiBlock against man-in-the-middle, impersonation, replay, physical capture, and ephemeral secret leakage attacks among other potential attacks. The blockchain-based simulation on large-scale nodes shows the computational time for an increase in the network and block sizes. Moreover, the real-time testbed experiments have been performed to show the practical usefulness of the proposed scheme.

Abstract

Coral Reefs are vital ecosystems as they support a vast population of aquatic life and help in maintaining biodiversity. Currently, these ecosystems are under severe threat due to rising ocean temperatures and contaminated surroundings. Hence, Coral Reefs need to be monitored regularly to study their health and protect them in a better way. Remote detection and assessment of Coral Reefs using satellite data would significantly enhance the reach and ensure continuous Coral Reef monitoring. The spectral signature of the Coral Reefs captured by the satellite sensor could be a great asset in this process. Still, these signatures are heavily dopped with several unwanted noise signals coming from various surface and atmospheric features. It is essential to remove these noise signals from the satellite data. This paper studied the effectiveness of different available atmospheric correction methods such as QUAC, 6S, and FLAASH Atmospheric correction tools (ACT) in removing atmospheric noise and studied the impact of sun glint and water column correction on improving remote detection of Coral Reefs. As a case study, these processes were applied on Landsat ETM+ to study the abundant Coral habitat near the North Reef Island at the Andaman Islands. The results indicated that FLAASH is the best atmospheric correction tool among the selected algorithms as FLAASH corrected image facilitated better identification of Coral Reefs. Hence this image was further enhanced using the Hedley sun glint and Lyzenga water column correction methods which improved Coral Reef detectability significantly. The detection of Corals from the image was done using ISO-DATA unsupervised classification to evaluate the role of each process in enhancing the Coral detection. Visual inspection was used to assess the classifier accuracy towards Coral reef detection. The results showed that a combination of FLAASH, sun glint removal, and water column correction significantly improved Coral Reef identification from the remote sensing data.

Abstract

Speakers exhibit dialectal traits in speech at sub-segmental, segmental, and supra-segmental levels. Any feature representation for dialect classification should appropriately represent these dialectal traits. Traditional segmental features such as mel-frequency cepstral coefficients (MFCCs) fail to represent sub-segmental and supra-segmental dialectal traits. This study proposes to use frequency domain linear prediction cepstral coefficients (FDLPCCs) for dialect classification inspired by its long temporal summarization during pole estimation. The i-vectors and x-vectors derived from both baseline (MFCCs, linear prediction cepstral coefficients (LPCCs), perceptual LPCCs (PLPCCs), RASTA filtered PLPCCs (PLPCC-R) and proposed (FDLPCC) features are used for identifying the dialects with support vector machine (SVM) and feed-forward neural network (FFNN) as classifiers. Proposed FDLPCC features have shown to perform better than baseline features such as MFCCs and PLPCC-Rs (best among LPCCs variants) by an absolute improvement of 3.4% and 3.9% (in unweighted average recall (UAR)), with i-vector + SVM system and 1.6% and 4.6% (in UAR), i-vector + FFNN system respectively. It is also found that there exists a complementary information between the proposed and baseline features. Furthermore current studies are compared with previous studies and it is found that performances of current studies are better than previous studies.

Abstract

We study lepton number violating and lepton number conserving semileptonic decays of heavy baryons Λ0 b to three charged leptons and a neutrino. The decays occur through two intermediate quasidegenerate GeV-scale sterile neutrinos of either Majorana or Dirac type that can be on-shell. Interference between the intermediate heavy neutrinos leads to CP violation in the final states. Effect of neutrino oscillations between the heavy states are also considered in observables of interests, i.e., branching ratio, and the CP-asymmetry. Given the present constraints on the heavy-to-light mixing elements jVeNj and jVμNj, CP-averaged branching ratio of Λ0 b → ðΛc; pÞμμeν with intermediate Majorana neutrinos is almost two orders of magnitude larger than for the same with Dirac neutrinos, whereas, CP-averaged branching ratio of Λ0 b → ðΛc; pÞeeμν is of the same order of magnitude for both Majorana and Dirac neutrino induced decays. CP-violation is found to be appreciable when the neutrino mass difference is comparable with the average decay widths.

Abstract

The acoustic responses of a dynamical system reflect the characteristics of the external excitation and vibrating system. The objective in an identification problem is to characterise the system and excitation from the measured responses. This becomes challenging if the characteristics of the dynamical system are time-varying. The analysis of the acoustic signal generated as a result of the tyre-pavement interaction (TPI) is one such situation, where in order to study the time-varying nature there is a need to obtain the characteristics of the instantaneous response. This study explores the single frequency filtering analysis of signals to examine the time-varying characteristics of tyre-pavement interaction noise (TPIN). Field measurements of TPI acoustic signals were obtained for a two-wheeler driven at different speeds on asphalt and cement concrete pavements. The time-frequency characteristics of the tyre tread impact and of the air cavity resonances are analysed by investigating the effect of vehicular speed and pavement type on TPIN. The specific features of TPIN such as the harmonics due to periodic tread impact and the frequency response due to resonances of the air cavities are examined in detail. The analysis presented in this paper will be useful in developing methods for health monitoring of tyre and pavement systems.

Abstract

Stress, either physical or mental, is experienced by almost every person at some point in his lifetime. Stress is one of the leading causes of various diseases and burdens society globally. Stress badly affects an individual's well-being. Thus, stress-related study is an emerging field, and in the past decade, a lot of attention has been given to the detection and classification of stress. The estimation of stress in the individual helps in stress management before it invades the human mind and body. In this paper, we proposed a system for the detection and classification of stress. We compared the various machine learning algorithms for stress classification using EEG signal recordings. Interaxon Muse device having four dry electrodes

Abstract

In recent years, Deep Reinforcement Learning (DRL) has been extensively used to solve problems in various domains like traffic control, healthcare, and simulation-based training. Proximal Policy Optimization (PPO) and Soft-Actor Critic (SAC) methods are DRL’s latest state of art on-policy and off-policy algorithms. Though previous studies have shown that SAC generally performs better than PPO, hyperparameter tuning can significantly impact the performance of these algorithms. Also, a systematic evaluation of the efficacy of these algorithms after hyperparameter tuning in dynamic and complex environments is missing and much needed in literature. This research aims to evaluate the effect of the number of layers