The reliability and safety of retired battery packs require detailed characterization to benchmark their performance and safety. Electrochemical impedance spectroscopy (EIS) is a widely recognized powerful technique in this regard. However, EIS instruments have inherent voltage and current limitations when characterizing battery packs, making direct pack-level EIS measurement challenging compared to single-cell analysis. Furthermore, estimating pack-level EIS from the impedance data of single lithium-ion cell is not straightforward. Since the terminals connecting the cells introduce non-negligible impedance, an efficient computational approach is required to compensate for these effects and accurately estimate pack-level EIS from individual cell data. This paper addresses these limitations in pack-level EIS testing and proposes a new computational method for estimating lithium-ion pack-level impedance using cell-level measurements. A series-connected battery module was analyzed, and EIS was computed with and without modeling terminal impedance. The results demonstrate that accounting for terminal effects improves the accuracy of pack-level EIS estimation by 9.8% compared to when they are neglected.

This paper investigates the impact of Reconfigurable Intelligent Surface (RIS) orientation on the throughput performance of Unmanned Aerial Vehicle (UAV)-assisted wireless communication systems. Specifically, we study how physical rotation of the RIS, through controlled azimuth and elevation adjustments, influences the effective channel and data rate. A UAV-mounted RIS enables directional alignment to serve ground users in scenarios where the direct Base Station (BS)-to-user path is blocked. Using the SimRIS channel simulator, we analyze the system under various rotation angles and present performance heatmaps that highlight optimal RIS orientations. The study shows that RIS alignment has a substantial effect on achievable rates, thereby motivating orientation-aware optimization in practical deployments.

The Flyback Converter (FBC) is widely utilized in low-power applications. A Proportional Integral Derivative (PID) controller is typically employed to ensure output voltage regulation and stability. However, determining the optimal PID controller gains is challenging due to the bilinear nature of the FBC model, which classifies it as a non-convex optimization problem. Metaheuristic algorithms are often applied to optimize PID gains as their gradient-independent nature eliminates the need for complex analytical formulations and trial-and-error methods. They can provide an excellent workable approximate global solution. The global optimality of metaheuristics cannot be guaranteed as they may or may not provide a globally optimal solution, and there is no deterministic way to determine whether the final solution is global or not because of the inherent randomness involved in their structure. This inherent randomness in their algorithmic structure necessitates standard Statistical Performance Evaluation (SPE) to establish the superiority of one algorithm over another in finding controller gains. In this paper, the Improved Accelerated Particle Swarm Optimization (IAPSO) and Dragonfly (DF) algorithms are implemented for the first time to optimize PID gains for FBC. An independent samples t-test is conducted to underscore the importance of performing standard statistical tests while optimizing controller gains with metaheuristic algorithms. The results demonstrate excellent FBC response and stability for a given reference voltage and the statistical superiority of IAPSO over the DF algorithm in finding FBC PID gains.

A novel framework is introduced in this paper, wherein Software-Defined Radio (SDR) technology is utilized to deploy a Frequency Modulation Continuous Wave (FMCW) radar system in conjunction with Long Range (LoRa) modulation. The system acquires reflected signals and detects micro-Doppler signatures using SDR-based FMCW radar, enabling the identification of individual profiles even when obstructed by visual barriers. Reliable communication among various stakeholders is ensured by incorporating the LoRa modulation scheme for data transmission within the framework. Improved collaboration between rescue teams and surveillance units is fa-cilitated by this integration, thereby enhancing overall response efficiency. The efficacy of the approach is validated through experimental implementation utilizing USRP N210 with GNU Radio software and the LoRa AB01 module.

Future communication systems are expected to employ Reconfigurable intelligent surfaces (RIS) for real-time manipulation of channel to achieve unprecedented efficiency and quality of service (QoS) improvement. Here, we have designed a practical RIS-enabled communication setup to demonstrate beam steering and multi-beam forming in near-to far-field trials. The design of RIS as a component of wireless communication system has many variables that dictate path loss compensation capability of RIS. We investigate RIS path loss compensation by varying RIS size with point source and plane wave source using a 1-bit RIS of size$27\times 28$unit-cells. It is observed that RIS size has less significance compared to phase quantization, in the near field while the effect of RIS size is dominant in the far field. The reported results provide useful insights for design of RIS-enabled wireless communication systems.

Reconfigurable Intelligent Surfaces (RIS) have emerged as a promising technology to enhance wireless communication systems by leveraging the propagation environment. This paper introduces a novel approach to optimize the beamwidth of RIS for mitigating intra-beam interference. The proposed framework aims at achieving efficient beamforming while maintaining low interference levels. The optimization problem is formulated as a convex task, seeking to minimize the RIS beam pattern’s beamwidth while ensuring precise alignment with the desired signal. To prevent interference with other beams, a constraint is imposed on sidelobe levels outside the main beam. Simulation results demonstrate the efficacy of our approach in beamwidth optimization and interference mitigation for both indoor and outdoor communication scenarios. The optimized RIS showcases its ability to improve signal quality and minimize interference in real-world wireless environments, highlighting its potential as a key technology for future wireless communication systems. This advancement holds the promise of increased spectral efficiency and improved network performance.

Reconfigurable intelligent surfaces (RIS) can be mounted on aerial platforms to have a complete 360 degree reflection span. Thereafter, the placement of the RIS can be optimized to maximize the received SNR. The rotation of the RIS is another available degree of freedom (DoF). In this paper, we demonstrate the usefulness of the RIS rotation when mounted on aerial platforms. The results demonstrate that if the RIS is rotated at an optimal angle, the received SNR can be improved by 3–4 dB. Furthermore, if we increase the number of base station antennas, an exciting trade-off between BS antennas and bit-resolution with rotation is observed.

This paper presents a novel deep learning-aided scheme dubbed$PR\rho $-net for improving the bit error rate (BER) of the Time Reversal (TR) Ultra-Wideband (UWB) Multiple Input Multiple Output (MIMO) system with imperfect Channel State Information (CSI). The designed system employs Frequency Division Duplexing (FDD) with explicit feedback in a scenario where the CSI is subject to estimation and quantization errors. Imperfect CSI causes a drastic increase in BER of the FDD-based TR-UWB MIMO system, and we tackle this problem by proposing a novel neural network-aided design for the conventional precoder at the transmitter and equalizer at the receiver. A closed-form expression for the initial estimation of the channel correlation is derived by utilizing transmitted data in time-varying channel conditions modeled as a Markov process. Subsequently, a neural network-aided design is proposed to improve the initial estimate of channel correlation. An adaptive pilot transmission strategy for a more efficient data transmission is proposed that uses channel correlation information. The theoretical analysis of the model under the Gaussian assumptions is presented, and the results agree with the Monte-Carlo simulations. The simulation results indicate high performance gains when the suggested neural networks are used to combat the effect of channel imperfections.

Reconfigurable Intelligent Surfaces (RIS), which comprise of large number of unit cells (meta-atoms), reflect the incoming wave according to desired radiation patterns. As the number of unit cells increase, complex radiation patterns can be generated. Similarly, a unit cell allowing more phase control and larger phase difference helps in the generation of complex radiation patterns at the surface level. However, the control circuit complexity and energy requirements also increase. In many practical applications, only a handful of radiation patterns may suffice. Additionally, the overall problem of finding the appropriate unit cell control state to generate any desired radiation patterns has combinatorial complexity. In this paper, we propose a benchmarking framework and suitable performance metrics that enable us to determine and compare the capabilities of RISs made from different unit cells. The proposed framework is numerically tested on three 40x40 RISs made from optimized and unoptimized unit cells of various resolutions reported in the literature. While the performance of RIS made from 1-bit unoptimized unit cell was overall poor, it was able to successfully generate some simple but useful benchmarking patterns. Thus, our framework provides a mechanism to identify the best candidate unit cell for a given set of requirements. The proposed framework has a potential to revolutionize future research and development on unit cell and RIS design.

MIMO radar technology has recently attracted attention of large number of research scientists and engineers. Over the last decade, phased array radars have been quite popular because of their beam forming capabilities. In this paper, we propose a novel scheme for an improved performance of radar systems by transmitting orthogonal waveforms from different antennas. We first investigate MIMO radar systems, with orthogonal transmitted waveforms, with regards to their utility for low SNR fluctuating targets. The results show that the conventional mono-static radars are incapable of dealing with low observable fluctuating targets. The highly sophisticated vehicular targets pose great challenge to the ground based radar systems. Thereafter, we propose an algorithm that exploits the time diversity in addition to inherent spatial diversity order of MIMO systems, thereby exploiting the target scintillations and improving the detection performance. The proposed scheme transmits waveforms separated in time such that they experience un-correlated channels. The proposed scheme outperforms mono-static radar and gives a gain of 12 dB with a 2 X 2 MIMO radar system.

Ultra Wide-band (UWB) indoor positioning provides very accurate location estimates because of extremely narrow time resolution of UWB signals. The most critical part is the estimation of the time of arrival (ToA). In this paper, we propose a novel method for ToA estimation that achieves high accuracy with sub-Nyquist sampling. The proposed method first determines a coarse estimate of ToA by using an energy detection algorithm. Thereafter the power delay profile (PDP) parameters are used to find out the final estimate of ToA. A Cramer-Rao lower bound is analytically derived for the proposed scheme. The simulation results, using the UWB channel models and measured channel responses in an indoor channel environment, show the effectiveness of the scheme in both LOS and NLOS scenarios.

This paper presents novel results for Massive MIMO time division duplex (TDD) system where the beam- forming vector in the down-link makes use of MMSE channel estimates with quasi-orthogonal pilot sequences as opposed to orthogonal pilot sequences used in the prior art. The proposed method tackles the issue of pilot contamination by adapting the length of quasi-orthogonal pilot sequences according to the channel conditions or coherence time, leading to maximization of the total throughput. We compute the optimal length of the pilot sequences, for both orthogonal and quasi- orthogonal pilot sequences, that maximizes the achievable rates. We formulate the design criteria of the proposed scheme by using a classical theorem of estimation theory for multi- variate Gaussian distribution. Then we solve the criteria using heuristic algorithms and carry out simulations for different coherence times in a multi-cell system. It is shown that our proposed scheme requires 17\% less pilot resources as compared to schemes that employ orthogonal pilots. Furthermore, an interesting trade-off between the choice of pilots and the sum rate is observed for variable length of coherence interval.

Pedestrian dead reckoning (PDR) schemes typically rely on inertial measurement unit (IMU) of smart- phones to estimate position of the user. However, the performance of IMU based schemes depends on the placement of the IMU on the user and is affected by error accumulation over time. In this paper, we propose and implement PILoT, a Precise IMU based Localization Technique to overcome these shortcomings without compromising the accuracy of the system. It employs a novel wavelet based filtering technique to i) remove noise from the accelerometer readings and ii) deal with the positional changes of the device on the user. Heading is estimated using magnetometer and gyroscope. These techniques are then used in conjunction with a novel map awareness algorithm which eliminates the accumulated error based on the turn events. The proposed scheme achieves sub meter accuracy with a mean error of 0.52 meters.

This paper introduces EveTrack, an online and distributed method for localization and tracking of global and composite events. Based on hyper-ellipsoid clustering model, we compute the percentage contributions of the individual attributes in multi-attribute and correlated events. In addition, EveTrack utilizes spatio-temporal correlations between multiple events during its event identification phase. Finally, EveTrack estimates the event location using an iterative Linear Least Square (LLS) approach based on the event intensities estimated at different nodes. The results of our localization algorithm show 4–10 fold improvement in localization accuracy with significantly less computational complexity when compared to previously proposed event localization algorithms.

This paper proposes a novel scheme to estimate the percentage contribution of different attributes in a detected event (a process termed as event identification) for streaming multi-attribute data in WSNs. The proposed event detection and identification algorithm takes into account correlation among sensed attributes as well as the spatio-temporal correlations with similar attributes measured by neighboring nodes. Moreover we update our statistical parameters in an iterative manner such that the dynamics of non- stationary environments are taken into account. We test our leave one out (LOO) event identification approach with simulations on both synthetic and real data sets and an implementation on off-the- shelf WizziMotes. The experimental results show that our detection scheme outperforms state of the art schemes by showing detection rates (DRs) of more than 98\% and false positive rates (FPRs) of less that 2\%. Moreover, our event identification approach effectively determines the contribution of both correlated and uncorrelated attributes in an event of interest. The identification has also been shown to be in strong agreement with previous computationally complex benchmark PCA based event identification approaches.

In this paper, we propose an on-line technique for in-network, distributed and composite event detection and identification for streaming sensor data in resource constrained Wireless Sensor Networks (WSNs). We use General Hebbian Algorithm (GHA) to find out principal components of a multi-attribute input data which has a linear complexity as opposed to quadratic complexity with eigen value decomposition (EVD). This allows for on-line computation of percentage contributions of individual attributes towards detected event. Comparison with other event detection techniques shows that our scheme incurs low communication overhead as compared to some state-of-the-art schemes. Moreover, our hyper-ellipsoidal clustering based event detection algorithm is shown to achieve high detection rates (DRs) of over 98.88% and very low false positive rates (FPRs) of below 0.01%. Our simulation results and the hardware implementation also show that the accuracy of proposed identification scheme is in strong agreement with EVD based techniques, proving it to be a successful event identification method for WSNs.

With the discovery of Graphene, different nano-scale components can be integrated to form a nano-sensor capable of transmitting in the tera-hertz range (0.1–10 THz). The transmitted signal gets attenuated when it passes through different types of molecules. Thus, channel models for wireless nano sensor networks (WNSN) are environment specific. For most of the applications, the nano-sensors are placed inside the human body, which may interact with other nano-sensors (inside the body) and other non nano-scale devices (outside the body). We present SimpleNano, a simple and novel channel model for WNSN applications in the tera-hertz range. SimpleNano is an approximation to log-distance path loss model along with the random (log-normally distributed) attenuation caused by the molecular absorption. For certain band windows within terahertz range SimpleNano experiences low molecular noise. These different band windows have been compared for a novel capacity performance metric i.e. spatial capacity per Hertz and the results prove that the proposed band windows achieve better throughput than the ones with larger molecular noise.

Distributed clustering based techniques have been increasingly employed for outlier detection in Wireless Sensor Networks (WSNs). But despite its numerous advantages such as online and efficient computations and incorporation of spatiotemporal & attribute correlations, clustering has not been studied for event detection & identification, which is essential for smooth and reliable operations of large scale WSNs. This paper introduces the significance of clustering based event detection & identification to the research community. Further, it presents an online technique for joint event detection and identification that achieves a very high performance for synthetic and real data sets with a significant reduction in computational complexity as compared to the state-of-the-art techniques. A remarkable advantage of the proposed technique is that it can identify the key attributes in the ascending order of their contribution towards an event without incurring any additional complexity.

The robustness of the time reversal (TR) scheme in a non stationary channel environment has been previously studied for ultra-wide band (UWB) channels. However, its robustness with different bandwidth specially for bandwidths significantly lower than ultra-wide bandwidths has not been established. In this paper, the performance of a TR communication system has been studied for large number of bandwidths for both UWB and narrow band systems in a non-stationary channel environment. The results show that TR scheme gives a robust performance for both wide and narrow bandwidths. The performance of the TR scheme also gets better for higher bandwidths. The analysis of the correlation of the channel and the TR responses for different bandwidths with respect to the responses having bandwidths that equal the coherence bandwidth of the channel has also been studied.

Support-Vector Machines (SVM) have received a great interest in the machine learning community since their introduction, especially in Outlier Detection in Wireless Sensor Networks (WSN). The Quarter-Sphere formulation of One-Class SVM (QS-SVM), extends the main SVM ideas from supervised to unsupervised learning algorithms. The QS-SVM formulation is based only on Spatio-Temporal correlations between the sensor nodes (hence the name Spatio-Temporal Quarter-Sphere SVM, ST-QS-SVM). Thus, it has a non-ideal performance. This work presents a new One-Class Quarter-Sphere SVM formulation based on the novel concept of Attribute Correlations between the sensor nodes, hence the name, Spatio-Temporal-Attribute Quarter-sphere SVM (STA-QS-SVM) formulation. Online and partially online approaches to Outlier Detection in WSNs have been presented using this formulation. The results indicate a significant increase in the Outlier Detection rates and a remarkable reduction in the False Positive rates over the previous formulation (ST-QS-SVM). The results of this novel technique also suggest that the partially online approach is as efficient as the online approach, thereby conserving significant computational and communication complexity. Moreover very high Event Detection rates have been reported for STA-QS-SVM, which have not been reported by ST-QS-SVM.

Time reversal (TR) is gaining more and more attention due to its potential to simplify the receiver for an ultra-wideband communication system. The temporal and spatial focusing properties of a TR communication system allow the detection of the received signal with simple receivers. However, the transmitter system becomes complicated as perfect channel estimation is required on regular basis. However, if the TR system is robust and can resist the changes in the channel, frequency of the estimation cycles will be less resulting in an increased transmission throughput. In this paper, we study the robustness of a TR system in a time varying channel environment. Different TR characteristics are compared and the ability of a TR system to resist the varying channel is shown.

In this paper, ultra-wideband (UWB), time reversal (TR) communication is investigated by modifying the transmission prefilter. Mathematical expressions for received signal and the interference in the modified transmission scheme are derived. It is shown that the modified transmission approach reduces multi-user interference which eventually translates into a better bit error rate (BER) performance than simple TR multiuser scheme. Channel impulse responses (CIR) of a typical indoor channel are measured. In a multi-user scenario, both TR and the modified TR schemes are studied using the measured CIRs. It is shown that the proposed modified TR scheme outperforms the original TR scheme.

Existing transport protocols, be it TCP, SCTP or DCCP, do not provide an efficient congestion control mechanism for heterogeneous wired-cum-wireless networks. Solutions involving implicit loss discrimination schemes have been proposed but were never implemented. Appropriate mechanisms can dramatically improve bandwidth usage over the Internet, especially for multimedia transport based on partial reliability. In this paper we have implemented and evaluated a congestion control mechanism that implicitly discriminates congestion and wireless losses in the datagram congestion control protocol (DCCP) congestion control identification (CCID) framework. The new CCID was implemented as a NS-2 module. Comparisons were made with the TCP-like CCID and showed that the bandwidth utilization was improved by more than 30% and up to 50% in significant setups.