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This article proposes a hybrid analytical modeling procedure for the performance parameter estimation of synchronous reluctance machine, including its operation in the region of magnetic saturation. The proposed model better estimated the torque profile through improved translation and stepped transition of rotor flux barriers. The non-linear operation is simulated by considering the global saturation and estimating the machine’s operating point on the magnetization curve of the core material. The procedure formulation focuses on improving the accuracy and rapidness of the iterative design process. The obtained results are validated against an already established design analysis technique, finite-element analysis, and the frequency spectrum analysis of practical machine results. With the improved accuracy and the least requirement of simulation runtime and computational resources, the modeling procedure can be utilized as an initial design iteration tool. [ABSTRACT FROM AUTHOR]

This work builds on our earlier work on designing demodulators for diffusion-based molecular communications using a Markovian approach. The demodulation filters take the form of an ordinary differential equation (ODE) which computes the log-posteriori probability of observing a transmission symbol given the continuous history of receptor activities. A limitation of our earlier work is that the receiver is assumed to be a small cubic volume called a voxel. In this work, we extend the maximum a-posteriori demodulation to the case where the receiver may consist of multiple voxels and derive the ODE for log-posteriori probability calculation. This extension allows us to study receiver behaviour of different volumes and shapes. In particular, it also allows us to consider spatially partitioned receivers where the chemicals in the receiver are not allowed to mix. The key result of this paper is that spatial partitioning can be used to reduce bit-error rate in diffusion-based molecular communications. [ABSTRACT FROM AUTHOR]

This paper proposes a metamaterial inspired radio frequency touchpad sensor system based on two split ring resonators (SRRs) realized on a Rogers/Duroid 5870 with 2.33 dielectric constant = 2.33, loss tangent = 0.0012, and thickness = 0.8 mm. The touchpad consists of two SRRs resonating at $f_{1} = 2.74$ GHz and $f_{2} = 3.86$ GHz. When a finger is placed on SRR 1, $f_{1}$ changes from 2.74 to 2.57 GHz due to electromagnetic coupling between the SRR and the finger, and similarly, $f_{2}$ changes from 3.86 to 3.76 GHz. The proposed touchpad system can detect a finger wearing a glove, in contrast to capacitive touchpads utilized in contemporary smart phones. We demonstrated 56- and 49-MHz frequency shifts for $f_{1}$ and $f_{2}$ , respectively, with 1.66-mm total thickness (0.69-mm-thick photo paper and 0.97-mm-thick glass slide). [ABSTRACT FROM AUTHOR]

A system to perform video analytics is proposed using a dynamically tuned convolutional network. Videos are fetched from cloud storage, preprocessed, and a model for supporting classification is developed on these video streams using cloud-based infrastructure. A key focus in this paper is on tuning hyper-parameters associated with the deep learning algorithm used to construct the model. We further propose an automatic video object classification pipeline to validate the system. The mathematical model used to support hyper-parameter tuning improves performance of the proposed pipeline, and outcomes of various parameters on system’s performance is compared. Subsequently, the parameters that contribute toward the most optimal performance are selected for the video object classification pipeline. Our experiment-based validation reveals an accuracy and precision of 97% and 96%, respectively. The system proved to be scalable, robust, and customizable for a variety of different applications. [ABSTRACT FROM AUTHOR]

In many image segmentation problems involving limited and low-quality data, employing statistical prior information about the shapes of the objects to be segmented can significantly improve the segmentation result. However, defining probability densities in the space of shapes is an open and challenging problem, especially if the object to be segmented comes from a shape density involving multiple modes (classes). Existing techniques in the literature estimate the underlying shape distribution by extending Parzen density estimator to the space of shapes. In these methods, the evolving curve may converge to a shape from a wrong mode of the posterior density when the observed intensities provide very little information about the object boundaries. In such scenarios, employing both shape- and class-dependent discriminative feature priors can aid the segmentation process. Such features may involve, e.g., intensity-based, textural, or geometric information about the objects to be segmented. In this paper, we propose a segmentation algorithm that uses nonparametric joint shape and feature priors constructed by Parzen density estimation. We incorporate the learned joint shape and feature prior distribution into a maximum a posteriori estimation framework for segmentation. The resulting optimization problem is solved using active contours. We present experimental results on a variety of synthetic and real data sets from several fields involving multimodal shape densities. Experimental results demonstrate the potential of the proposed method. [ABSTRACT FROM PUBLISHER]

Aging through Negative Bias Temperature Instability (NBTI) significantly jeopardizes reliability of SRAM-based memories. We propose a content-aware microarchitectural-level technique for mitigating aging of these SRAM-based memories, by altering the input and output data. The goal is to achieve cost-effective lifetime improvement through low-power aging balancing of all memory cells. For a configurable design, we perform power, area, and aging analysis of different aging balancing circuits. This analysis is leveraged to design a novel aging resilient memory architecture. To curtail the power overhead while still achieving a balanced aging, our architecture employs an anti-aging controller that leverages the data characteristics to take spatio-temporal aging balancing decisions. It dynamically selects: (1) which aging balancing circuit to activate, (2) at what time instant the circuit should be activated, and (3) on which SRAM cells aging balancing should be applied. This is achieved by identifying different configuration parameters, which can be adjusted at run time to balance the aging of SRAM memories. Our experiments demonstrate significant aging improvements at a low power overhead. In addition, we perform sensitivity analysis of different parameters of our architecture to demonstrate power vs. reliability tradeoffs under different run-time scenarios. [ABSTRACT FROM PUBLISHER]

In this communication, a microfluidic antenna is proposed for chemical sensor applications. An eighth-mode substrate-integrated-waveguide (EMSIW) antenna with a microfluidic channel is introduced in order to devise a compact and nondestructive chemical sensor. The frequency response of the proposed antenna is controlled by changing the nanoliter liquid in the microfluidic channel, which further results in a change in the effective dielectric constant. First, the EMSIW antenna is designed, and then the microfluidic channel is designed for nondestructive chemical sensor applications. The S-parameters of the proposed microfluidic EMSIW antenna are simulated and measured. The resonant frequency is successfully switched from 4.2 to 4.6 GHz when the concentration of ethanol is changed from 0% to 100%. The possibility of the proposed antenna to be used as an ethanol chemical sensor is demonstrated from the relationship between the resonant frequency and the concentration of ethanol. [ABSTRACT FROM AUTHOR]

High throughput demands have resulted in enormous increase in complexity of multicast video applications, which require multiple video encoders to simultaneously compress individual views. In this paper, we present an approach to encode independent videos using H.264 intra encoder on a single hardware platform, where the hardware resources are shared by independent encoders in a time-multiplexed manner. In addition to lowering the latency introduced by multicasting, we address the strong sequential data dependencies within the encoder. At 25 frames/s, 150 MHz prototype of the proposed encoder and multiple video capture/display on a mid-range field programmable gate array is also presented. [ABSTRACT FROM PUBLISHER]

Heterogeneous systems on a single chip composed of a central processing unit, graphics processing unit (GPU), and field-programmable gate array (FPGA) are expected to emerge in the near future. In this context, the system on chip can be dynamically adapted to employ different architectures for execution of data-intensive applications. Motion estimation (ME) is one such task that can be accelerated using FPGA and GPU for high-performance H.264/Advanced Video Coding encoder implementation. This paper presents an inherent parallel low-complexity rate-distortion (RD) optimized fast ME algorithm well suited for parallel implementations, eliminating various data dependencies caused by a reliance on spatial predictions. In addition, this paper provides details of the GPU and FPGA implementations of the parallel algorithm by using OpenCL and Very High Speed Integrated Circuits (VHSIC) Hardware Descriptive Language (VHDL), respectively, and presents a practical performance comparison between the two implementations. The experimental results show that the proposed scheme achieves significant speedup on GPU and FPGA, and has comparable RD performance with respect to sequential fast ME algorithm. [ABSTRACT FROM AUTHOR]

This article discusses privacy issues related to automated global positioning system (GPS)-based surveillance. It offers information on an experiment done on collecting locational data from a number of volunteers and generating automated profiles about the individual, including home addresses, and social and work activities. It discusses legal precedents on the installation and monitoring of GPS tracking devices, mobility-pricing of insurance, and public surveillance.

Retinopathy represents a group of retinal diseases that, if not treated timely, can cause severe visual impairments or even blindness. Many researchers have developed autonomous systems to recognize retinopathy via fundus and optical coherence tomography (OCT) imagery. However, most of these frameworks employ conventional transfer learning and fine-tuning approaches, requiring a decent amount of well-annotated training data to produce accurate diagnostic performance. This article presents a novel incremental cross-domain adaptation instrument that allows any deep classification model to progressively learn abnormal retinal pathologies in OCT and fundus imagery via few-shot training. Furthermore, unlike its competitors, the proposed instrument is driven via a Bayesian multiobjective function that not only enforces the candidate classification network to retain its prior learned knowledge during incremental training, but also ensures that the network understands the structural and semantic relationships between previously learned pathologies and newly added disease categories to effectively recognize them at the inference stage. The proposed framework, evaluated on six public datasets acquired with three different scanners to screen 13 retinal pathologies, outperforms the state-of-the-art competitors by achieving an overall accuracy and F1 score of 0.9826 and 0.9846, respectively. [ABSTRACT FROM AUTHOR]

Objective: Glaucoma is the second leading cause of blindness worldwide. Glaucomatous progression can be easily monitored by analyzing the degeneration of retinal ganglion cells (RGCs). Many researchers have screened glaucoma by measuring cup-to-disc ratios from fundus and optical coherence tomography scans. However, this paper presents a novel strategy that pays attention to the RGC atrophy for screening glaucomatous pathologies and grading their severity. Methods: The proposed framework encompasses a hybrid convolutional network that extracts the retinal nerve fiber layer, ganglion cell with the inner plexiform layer and ganglion cell complex regions, allowing thus a quantitative screening of glaucomatous subjects. Furthermore, the severity of glaucoma in screened cases is objectively graded by analyzing the thickness of these regions. Results: The proposed framework is rigorously tested on publicly available Armed Forces Institute of Ophthalmology (AFIO) dataset, where it achieved the F1 score of 0.9577 for diagnosing glaucoma, a mean dice coefficient score of 0.8697 for extracting the RGC regions and an accuracy of 0.9117 for grading glaucomatous progression. Furthermore, the performance of the proposed framework is clinically verified with the markings of four expert ophthalmologists, achieving a statistically significant Pearson correlation coefficient of 0.9236. Conclusion: An automated assessment of RGC degeneration yields better glaucomatous screening and grading as compared to the state-of-the-art solutions. Significance: An RGC-aware system not only screens glaucoma but can also grade its severity and here we present an end-to-end solution that is thoroughly evaluated on a standardized dataset and is clinically validated for analyzing glaucomatous pathologies. [ABSTRACT FROM AUTHOR]

An electric arc furnace exhibits highly nonlinear characteristics and can cause power quality issues. Its performance is a vital issue for electric utilities. In this paper, a novel multiobjective optimization technique has been proposed for the estimation of the electric arc furnace parameters to predict the voltage and current waveforms and evaluate the performance of the furnace under various conditions. The multiobjective optimization technique minimizes the extinction voltage error and arc resistance error simultaneously to enhance the performance of the electric arc furnace model. The proposed optimization model employs a genetic algorithm and the stochastic variation of the arc length to estimate the parameters of a nonlinear time-variant resistance model of the electric arc furnace by using actual current and voltage data. These data have been obtained from a steel plant in Saudi Arabia. Optimization has been carried out in MATLAB/SIMULINK environment and the model of the electric arc furnace with the estimated parameters has been developed in PSCAD/EMTDC. The results obtained from the PSCAD model have been validated with actual data from the steel industry. The validation shows that the proposed method is efficient and accurate in the estimation of the electric arc furnace parameters. [ABSTRACT FROM PUBLISHER]

The widespread applications of massive MTC are limited by energy availability, spectrum congestion, and device costs. The emerging AmBC not only addresses these bottlenecks but also opens opportunities for new applications. This article aims to explore AmBC-enhanced future ultra-low-power MTC. In this context, we present the development trends in AmBC prototype designs and discuss potential applications, highlight the specific features of the AmBC technology, and review AmBC receiver designs. Finally, we investigate and outline the future research challenges and trends from the practical aspects of AmBC systems. [ABSTRACT FROM AUTHOR]

This paper presents a fault-tolerance technique for H.264's Context-Adaptive Variable Length Coding (CAVLC) on unreliable computing hardware. The application-specific knowledge is leveraged at both algorithm and architecture levels to protect the CAVLC process (especially context adaptation and coding tables) in a reliable yet power-efficient manner. Specifically, the statistical analysis of coding syntax and video content properties are exploited for: (1) selective redundancy of coefficient/header data of video bitstreams; (2) partitioning the coding tables into various sub-tables to reduce the power overhead of fault tolerance; and (3) run-time power management of memory parts storing the sub-tables and their parity computations. Experimental results demonstrate that leveraging application-specific knowledge reduces area and performance overhead by 2x compared to a double-parity table protection technique. For functional verification and area comparison, the complete H.264 CAVLC architecture is prototyped on a Xilinx Virtex-5 FPGA (though not limited to it). [ABSTRACT FROM PUBLISHER]