A document by Hari T S Narayanan . Click on the document to view its contents.

A document by Serhat Özküçük . Click on the document to view its contents.

A novel, general circuit-level description of coupled-resonator microwave filters is introduced in this paper. Unlike well-established coupling-matrix models based on frequency-invariant couplings or linear frequency-variant couplings (LFVCs), a model with arbitrary frequency-variant coupling (AFVC) coefficients is proposed. The engineered formulation is more general than prior-art ones and can be treated as an extension of previous synthesis models, since constant or linear couplings are special cases of arbitrary frequency dependence. The suggested model is fully general, allows for AFVCs with highly nonlinear (even singular) characteristics, loaded or unloaded non-resonating nodes (NRNs), frequency-dependent source-load coupling, multiple frequency-variant cross-couplings, and{/}or multiple dispersive couplings for connecting the source and load to the filter network. The model is accompanied by a powerful synthesis technique that is based on the zeros and poles of the admittance or scattering parameters and the eigenvalues of properly defined eigenproblems. In the most general case, the zeros and poles of the admittance or scattering parameters are related to solutions of nonlinear eigenvalue problems. The synthesis is defined as an inverse nonlinear eigenvalue problem (INEVP) where the matrix is constructed from three sets of eigenvalues. This is accomplished by optimization using an iterative nonlinear least-squares solver with excellent convergence property. Finally, third- and fifth-order examples of bandpass filter topologies involving AFVCs are shown, and the experimental validation of the proposed theory is presented through the manufacturing and characterization of a microstrip filter prototype with transmission zeros (TZs)

For several years people have realized the importance of archiving and finding information. With the need of computers, finding useful information from such collections has become a necessity. Information retrieval has become an important research area in the field of computer science and gained importance in several fields like business, healthcare, agriculture, medicine, law and many other fields. Information retrieval is finding material that could be in the form of a document consisting of unstructured nature that provides the required information. This research paper focuses on the need, models and the processes involved in information retrieval. A case study on INSYDER system has been proposed to gain holistic knowledge of information retrieval in the field of business.

The paper introduces new fractal families with annular and checkerboard structures that include the Sierpinski carpet and the Menger sponge as special cases. The complementary mapping is defined and a notation to represent the families is proposed.

Energy harvesting from human motion can reduce reliance on battery recharging in wearable devices and lead to improved adherence. However, to date, studies estimating energy harvesting potential have largely focused on small scale, healthy, population groups in laboratory settings rather than free-living environments with population level participant numbers. Here, we present the largest scale investigation into energy harvesting potential by utilising the activity data collected in the UK Biobank from over 67,000 participants. This paper presents detailed stratification into how the day of the week and participant age affect harvesting potential, as well as how the presence of conditions (such as diabetes, which we investigate here), may affect the expected energy harvester output. We process accelerometery data using a kinetic energy harvester model to investigate power output at a high temporal resolution. Our results identify key differences between the times of day when the power is available and an inverse relationship between power output and participant age. We also identify that the presence of diabetes substantially reduces energy harvesting output, by over 21%. The results presented highlight a key challenge in wearable energy harvesting: that wearable devices aim to monitor health and wellness, and energy harvesting aims to make devices more energy autonomous, but the presence of medical conditions may lead to substantially lower energy harvesting potential. The findings indicate how it is challenging to meet the required power budget to monitor diseases when energy autonomy is a goal.

This paper proposes an image-based visibility estimation method with deep label distribution learning. To train an accurate model for visibility estimation, it is important to obtain the precise ground truth for every image. However, the ground-truth visibility is difficult to be labeled due to its high ambiguity. To solve this problem, we associate a label distribution to each image. The label distribution contains all the possible visibilities with their probabilities. To learn from such annotation, we employ a CNN-RNN model for visibility-aware feature extraction and a conditional probability neural network for distribution prediction. Our experiment shows that labeling the image with visibility distribution can not only overcome the inaccurate annotation problem, but also boost the learning performance without the increase of training examples.

Reproducing the capabilities of the human sense of touch in machines is an important step in enabling robot manipulation to have the ease of human dexterity. A combination of robotic technologies will be needed, including soft robotics, biomimetics and the high-resolution sensing offered by optical tactile sensors. This combination is considered here as a SoftBOT (Soft Biomimetic Optical Tactile) sensor. This article reviews the BRL TacTip as a prototypical example of such a sensor. Topics include the relation between artificial skin morphology and the transduction principles of human touch, the nature and benefits of tactile shear sensing, 3D printing for fabrication and integration into robot hands, the application of AI to tactile perception and control, and the recent step-change in capabilities due to deep learning. This review consolidates those advances from the past decade to indicate a path for robots to reach human-like dexterity.

In the next wave of insurgence, humans may endeavour self-reflection which can lead to an effortless talk and to find out if an event will fructify. Training the system on how to make accurate prognostication with the help of machine learning and statistical models can lead to an intelligent personalized conversational system. The Chatbot industry is ever-growing and after the COVID-19 pandemic and rigorous lockdowns all around the world, people have realized the importance of human interaction in their lives. We are developing this model to create a more intimate relationship between the system and humans. For this purpose, many open-source platforms are available. Artificial Intelligence Markup Language (AIML) is derived from Extensible Markup Language (XML) which is used to build up a conversational agent artificially. The success of this project will help the model in observing and understanding human emotions which will ultimately help it to form a more personalized relationship to delineate the future course of events.

A document by Melih Can . Click on the document to view its contents.

This paper presents a comprehensive performance analysis of coded orthogonal frequency division multiplexing (COFDM) over the quasi-static multipath Rayleigh fading channels. We first analyze the pairwise error probability (PEP) of COFDM and then consider the union bound on bit error rate (BER) by introducing the notion of diversity guard (DG) and a novel interleaver class, named almost linear interleaver (ALI). A construction of ALI is also introduced with its parameter selection. Simulation results show that the COFDM with ALI outperforms that with random interleaver (RI) and the block interleaver (BI) adopted in the IEEE industry standards.

This letter presents a methodology to reduce the computational burden of Particle Filters (PF) used to achieve a target state estimation accuracy in Bayesian tracking problems. A strategy, named Multiple Weighting (MW), that exploits information diversity of the input measurements for an efficient weighting of the particles is introduced. This study shows that, by relying on the prior knowledge of the state-observations relationships, it is possible to achieve a significant reduction in the number of samples required to obtain any target accuracy. An experimental assessment is provided with an application to precise positioning estimation in Global Navigation Satellite System (GNSS) receivers.

Thermal Design Power (TDP) as the chip-level power constraint for a specific chip has been exploited in fault-tolerant embedded systems. TDP, as the chip-level power constraint of the system, could be either pessimistic or thermally unsafe. Employing TDP as a pessimistic constraint can increase the rate of missing real-time constraints because of triggering Dynamic Thermal Management (DTM) more frequently. If TDP as a chip-level power constraint is not a pessimistic constraint, TDP can be thermally unsafe and can lead to thermal violations. Employing Thermal Safe Power (TSP) as the core-level power constraint, which is defined as a function of the number of simultaneously operating cores, can result in improving the efficiency and the schedulability. This comment improves the efficiency and the schedulability rate of one of the proposed methods in the literature by employing TSP.

A document by Abdul Rehman Javed . Click on the document to view its contents.

In a recent publication Ansari et al. (2021) [1] claim (see, in particular, the Discussion and Recommendation Section in their article) that the advanced differential SAR interferometry (InSAR) algorithms for surface deformation retrieval, based on the small baseline approach, are affected by systematic biases in the generated InSAR products. Therefore, to avoid such biases, they recommend a strategy primarily focused on excluding “the short temporal baseline interferograms and using long baselines to decrease the overall phase errors”. In particular, among various techniques, Ansari et al. (2021) [1] identify the solution presented by Manunta et al. (2019) [2] as a small baseline advanced InSAR processing approach where the presence of the above-mentioned biases (referred to as a fading signal) compromises the accuracy of the retrieved InSAR deformation products. We show that the claim of Ansari et al. (2021) [1] is not correct (at least) for what concerns the mentioned approach discussed by Manunta et al. (2019) [2]. In particular, by processing the Sentinel-1 dataset relevant to the same area in Sicily (southern Italy) investigated by Ansari et al. (2021) [1], we demonstrate that the generated InSAR products do not show any significant bias.

Finely tuned deep generative adversarial networks (GANs) can generate high-quality (HQ) images. However, the discriminator in GAN is only able to distinguish true or fake images. Moreover, numerous synthesized images from GANs are imperfect, and we can not reconstruct those images via GANs. In this paper, we revisit pre-trained GANs and offer a self-supervised method to quickly transform GAN’s discriminators into encoders. We reuse parameters of the GAN’s discriminator and replace its output layer, so that it can be transformed into an encoder and output reformed latent vectors. The transformation makes the pre-trained GAN a symmetrical architecture and allows for better performance. Based on the method, GANs can be made to reconstruct synthesized images via encoders. Compared to synthesized images, these reconstructions can maintain or even attain higher quality.

We analyze the whole-genome phylogeny and taxonomy of the SARS-CoV-2 virus, causing the COVID-19 disease, using compression in the form of the alignment-free NCD (Normalized Compression Distance) method to assess similarity. We compare the SARS-CoV-2 virus with a database of over 6,500 viruses. The results comprise that the SARS-CoV-2 virus is closest in that database to the RaTG13 virus and rather close to the bat SARS-like corona viruses bat-SL-CoVZXC21 and bat-SL-CoVZC45. Over 6,500 viruses are identified (given by their registration code) with larger NCD’s. The NCD’s are compared with the NCD’s between the mtDNA’s of familiar species. We treat the question whether Pangolins are involved in the SARS-CoV-2 virus. The NCD method or shortly the {\em compression method} is simpler and possibly faster than any other whole-genome method, which makes it the ideal tool to explore phylogeny. Here we use it for the complex case of determining this similarity between the COVID-19 virus SARS-CoV-2 and many other viruses. The resulting phylogeny and taxonomy closely matches earlier efforts by alignment-based methods and a machine-learning method, providing the most compelling evidence to date for the compression method showing that one can achieve equivalent results both simply and fast.

This study proposes a method of robust regrasping an object using a dual-arm robot with general-purpose hands, which is robust against the error of grasping. In this paper, one arm is assigned to hand over the object to the other arm that is named a receiver arm. The grasping error must be considered to increase the success rate of the regrasping since a hand-over arm first picks up the object with the general-purpose hand. In an online phase, the proposed method performs object positioning at an optimal pose at the time of regrasping using an image-based visual servoing (IBVS) approach to reduce the effect of the grasping error. In the planning phase, the proposed method computes the optimal pose for regrasping by maximizing the minimum singular values of the image Jacobian of IBVS to achieve a high positioning accuracy using a 3D model of the target object. To achieve the regrasping objects with various shapes robustly against image noises and changes in light environments, the image Jacobian of IBVS is computed by numerical differential using an actual data set. A large number of data sets corresponding to each candidate grasp are usually required for computing the image Jacobian. To reduce the number of data sets, we propose a conversion method of the image Jacobian requiring only one data set corresponding to one representative grasp. The experimental results show that the proposed method achieves regrasping of target objects with the general-purpose hands with high success rates and performs target object positioning with less than 0.7[mm] positioning error.