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Low-Earth-orbit (LEO) satellites are widely acknowledged as a promising infrastructure solution for global Internet of Things (IoT) services. However, the Doppler effect presents a significant challenge in the context of long-range (LoRa) modulation uplink connectivity. This study comprehensively examines the operational efficiency of LEO satellites concerning the Doppler weather effect, with state-of-the-art artificial intelligence techniques. Two LEO satellite constellations-Globalstar and the International Space Station (ISS)-were detected and tracked using ground radars in Perth and Brisbane, Australia, for 24 h starting 1 January 2024. The study involves modelling the constellation, calculating latency, and frequency offset and designing a hybrid Iterative Input Selection-Long Short-Term Memory Network (IIS-LSTM) integrated model to predict the Doppler weather profile for LEO satellites. The IIS algorithm selects relevant input variables for the model, while the LSTM algorithm learns and predicts patterns. This model is compared with Convolutional Neural Network and Extreme Gradient Boosting (XGBoost) models. The results show that the packet delivery rate is above 91% for the sensitive spread factor 12 with a bandwidth of 11.5 MHz for Globalstar and 145.8 MHz for ISS NAUKA. The carrier frequency for ISS orbiting at 402.3 km is 631 MHz and 500 MHz for Globalstar at 1414 km altitude, aiding in combating packet losses. The ISS-LSTM model achieved an accuracy of 97.51% and a loss of 1.17% with signal-to-noise ratios (SNRs) ranging from 0-30 dB. The XGB model has the fastest testing time, attaining ≈0.0997 s for higher SNRs and an accuracy of 87%. However, in lower SNR, it proves to be computationally expensive. IIS-LSTM attains a better computation time for lower SNRs at ≈0.4651 s, followed by XGB at ≈0.5990 and CNN at ≈0.6120 s. The study calls for further research on LoRa Doppler analysis, considering atmospheric attenuation, and relevant space parameters for future work.

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Short-range Internet of Things (IoT) sensor nodes operating at 2.4 GHz must provide ubiquitous wireless sensor networks (WSNs) with energy-efficient, wide-range output power (POUT). They must also be fully integrated on a single chip for wireless body area networks (WBANs) and wireless personal area networks (WPANs) using low-power Bluetooth (BLE) and Zigbee standards. The proposed fully integrated transmitter (TX) utilizes a digitally controllable current-mode class-D (CMCD) power amplifier (PA) with a second harmonic distortion (HD2) suppression to reduce VCO pulling in an integrated system while meeting harmonic limit regulations. The CMCD PA is divided into 7-bit slices that can be reconfigured between differential and single-ended topologies. Duty cycle distortion compensation is performed for HD2 suppression, and an HD2 rejection filter and a modified C-L-C low-pass filter (LPF) reduce HD2 further. Implemented in a 28 nm CMOS process, the TX achieves a wide POUT range of from 12.1 to -31 dBm and provides a maximum efficiency of 39.8% while consuming 41.1 mW at 12.1 dBm POUT. The calibrated HD2 level is -82.2 dBc at 9.93 dBm POUT, resulting in a transmitter figure of merit (TX_FoM) of -97.52 dB. Higher-order harmonic levels remain below -41.2 dBm even at 12.1 dBm POUT, meeting regulatory requirements.

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Toxic gases have surreptitiously influenced the health and environment of contemporary society with their odorless/colorless characteristics. As a result, a pressing need for reliable and portable gas-sensing devices has continuously increased. However, with their negligence to efficiently microstructure their bulky supportive layer on which the sensing and heating materials are located, previous semiconductor metal-oxide gas sensors have been unable to fully enhance their power efficiency, a critical factor in power-stringent portable devices. Herein, an ultrathin insulation layer with a unique serpentine architecture is proposed for the development of a power-efficient gas sensor, consuming only 2.3 mW with an operating temperature of 300 °C (≈6% of the leading commercial product). Utilizing a mechanically robust serpentine design, this work presents a fully suspended standalone device with a supportive layer thickness of only ≈50 nm. The developed gas sensor shows excellent mechanical durability, operating over 10 000 on/off cycles and ≈2 years of life expectancy under continuous operation. The gas sensor detected carbon monoxide concentrations from 30 to 1 ppm with an average response time of ≈15 s and distinguishable sensitivity to 1 ppm (ΔR/R0 = 5%). The mass-producible fabrication and heating efficiency presented here provide an exemplary platform for diverse power-efficient-related devices.

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A capacitance-to-voltage converter (CVC) is proposed in this paper and applied to a readout circuit for a micro-electro-mechanical system (MEMS) accelerometer to improve the power efficiency. In a traditional readout circuit, the front-end CVC has to operate at a high sampling frequency to resist thermal noise deterioration due to the large parasitic capacitance introduced by the mechanical sensing element. Thus, the back-end analog-to-digital converter (ADC) also has to operate at a high sampling frequency to avoid noise aliasing when sampling the output signal of the CVC, which leads to high power consumption. The average CVC technique is proposed in this paper to reduce the sampling frequency requirement of the back-end ADC and thus reduce the power consumption. Both the traditional readout circuit and the proposed readout circuit are simulated with a commercial 0.18 µm BCD process. The simulation results show that noise aliasing occurs, and the noise power spectral density (PSD) of the traditional readout circuit increases by 12 dB when the sampling frequency of back-end ADC is reduced by 24 dB. However, in the proposed readout circuit, a noise aliasing effect does not occur. Moreover, the proposed readout circuit reduces the power consumption by 53% without thermal noise deterioration. In addition, the proposed CVC circuits are fabricated in an 0.18 µm BCD process, and the test results show that the presented readout circuit based on the average CVC technique can obtain better performance than the traditional CVC-based readout circuit.

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This paper presents a comprehensive timing optimization methodology for power-efficient high-resolution image sensors with column-parallel single-slope analog-to-digital converters (ADCs). The aim of the method is to optimize the read-out timing for each period in the image sensor's operation, while considering various factors such as ADC decision time, slew rate, and settling time. By adjusting the ramp reference offset and optimizing the amplifier bandwidth of the comparator, the proposed methodology minimizes the power consumption of the amplifier array, which is one of the most power-hungry circuits in the system, while maintaining a small color linearity error and ensuring optimal performance. To demonstrate the effectiveness of the proposed method, a power-efficient 108 MP 3-D stacked CMOS image sensor with a 10-bit column-parallel single-slope ADC array was implemented and verified. The image sensor achieved a random noise of 1.4 e-rms, a column fixed-pattern noise of 66 ppm at an analog gain of 16, and a remarkable figure-of-merit (FoM) of 0.71 e-·nJ. The sensor utilized a one-row read-out time of 6.9 µs, an amplifier bandwidth of 1.1 MHz, and a reference digital-to-analog converter (DAC) offset of 512 LSB. This timing optimization methodology enhances energy efficiency in high-resolution image sensors, enabling higher frame rates and improved system performance. It could be adapted for various imaging applications requiring optimized performance and reduced power consumption, making it a valuable tool for designers aiming to achieve optimal performance in power-sensitive applications.

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A continuing trend in precision apiculture is to use computer vision methods to quantify characteristics of bee traffic in managed colonies at the hive's entrance. Since traffic at the hive's entrance is a contributing factor to the hive's productivity and health, we assessed the potential of three open-source convolutional network models, YOLOv3, YOLOv4-tiny, and YOLOv7-tiny, to quantify omnidirectional traffic in videos from on-hive video loggers on regular, unmodified one- and two-super Langstroth hives and compared their accuracies, energy efficacies, and operational energy footprints. We trained and tested the models with a 70/30 split on a dataset of 23,173 flying bees manually labeled in 5819 images from 10 randomly selected videos and manually evaluated the trained models on 3600 images from 120 randomly selected videos from different apiaries, years, and queen races. We designed a new energy efficacy metric as a ratio of performance units per energy unit required to make a model operational in a continuous hive monitoring data pipeline. In terms of accuracy, YOLOv3 was first, YOLOv7-tiny-second, and YOLOv4-tiny-third. All models underestimated the true amount of traffic due to false negatives. YOLOv3 was the only model with no false positives, but had the lowest energy efficacy and highest operational energy footprint in a deployed hive monitoring data pipeline. YOLOv7-tiny had the highest energy efficacy and the lowest operational energy footprint in the same pipeline. Consequently, YOLOv7-tiny is a model worth considering for training on larger bee datasets if a primary objective is the discovery of non-invasive computer vision models of traffic quantification with higher energy efficacies and lower operational energy footprints.

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A fully integrated and high-efficiency low-dropout regulator (LDO) with 100 mV dropout voltage and nA-level quiescent current for energy harvesting has been proposed and simulated in the 180 nm CMOS process in this paper. A bulk modulation without an extra amplifier is proposed, which decreases the threshold voltage, lowering the dropout voltage and supply voltage to 100 mV and 0.6 V, respectively. To ensure stability and realize low current consumption, adaptive power transistors are proposed to enable system tropology to alter between 2-stage and 3-stage. In addition, an adaptive bias with bounds is utilized in an attempt to improve the transient response. Simulation results demonstrate that the quiescent current is as low as 220 nA and the current efficiency reaches 99.958% in the full load condition, load regulation is 0.0059 mV/mA, line regulation is 0.4879 mV/V, and the optimal PSR is -51 dB.

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Fused Filament Fabrication (FFF) 3D printing is an additive technology used to manufacture parts. Used in the engineering industry for prototyping polymetric parts, this disruptive technology has been adopted commercially and there are affordable printers on the market that allow for at-home printing. This paper examines six methods of reducing the energy and material consumption of 3D printing. Using different commercial printers, each approach was investigated experimentally, and the potential savings were quantified. The modification most effective at reducing energy consumption was the hot-end insulation, with savings of 33.8-30.63%, followed by the sealed enclosure, yielding an average power reduction of 18%. For material, the most influential change was noted using 'lightning infill', reducing material consumption by 51%. The methodology includes a combined energy- and material-saving approach in the production of a referenceable 'Utah Teapot' sample object. Using combined techniques on the Utah Teapot print, the material consumption was reduced by values between 55.8% and 56.4%, and power consumption was reduced by 29% to 38%. The implementation of a data-logging system allowed us to identify significant thermal management and material usage opportunities to minimise power consumption, providing solutions for a more positive impact on the sustainable manufacturing of 3D printed parts.

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Computing has undergone a significant transformation over the past two decades, shifting from a machine-based approach to a human-centric, virtually invisible service known as ubiquitous or pervasive computing. This change has been achieved by incorporating small embedded devices into a larger computational system, connected through networking and referred to as edge devices. When these devices are also connected to the Internet, they are generally named Internet-of-Thing (IoT) devices. Developing Machine Learning (ML) algorithms on these types of devices allows them to provide Artificial Intelligence (AI) inference functions such as computer vision, pattern recognition, etc. However, this capability is severely limited by the device's resource scarcity. Embedded devices have limited computational and power resources available while they must maintain a high degree of autonomy. While there are several published studies that address the computational weakness of these small systems-mostly through optimization and compression of neural networks- they often neglect the power consumption and efficiency implications of these techniques. This study presents power efficiency experimental results from the application of well-known and proven optimization methods using a set of well-known ML models. The results are presented in a meaningful manner considering the "real world" functionality of devices and the provided results are compared with the basic "idle" power consumption of each of the selected systems. Two different systems with completely different architectures and capabilities were used providing us with results that led to interesting conclusions related to the power efficiency of each architecture.

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BACKGROUND: Invasive hemodynamic variables obtained from right heart catheterization have been used for risk-stratifying patients with advanced heart failure (HF). However, there is a paucity of data on the prognostic value of invasive hemodynamic variables in patients with left ventricular assist devices (LVAD). We hypothesized that cardiac power output (CPO), cardiac power efficiency (CPE), and left ventricular stroke work index (LVSWI) can serve as prognostic markers in patients with LVADs. METHODS: Baseline hemodynamic data from patients who had LVAD ramp studies at our institution from 4/2014 to 7/2018 were prospectively collected, from which advanced hemodynamic variables (CPO, CPE, and LVSWI) were retrospectively analyzed. Univariate and multivariable analyses were performed for hemocompatibility-related adverse events (HRAE), HF admissions, and mortality. RESULTS: Ninety-one participants (age 61 ± 11 years, 34% women, 40% Black or African American, and 38% ischemic cardiomyopathy) were analyzed. Low CPE was significantly associated with mortality (HR 2.42, 95% CI 1.02-5.74, p = 0.045) in univariate analysis and Kaplan-Meier analysis (p = 0.04). Low LVSWI was significantly associated with mortality (HR 2.13, 95% CI 1.09-4.17, p = 0.03) in univariate analysis and Kaplan-Meier analysis (p = 0.02). CPO was not associated with mortality. CPO, CPE, and LVSWI were not associated with HRAE or HF admissions. CONCLUSIONS: Advanced hemodynamic variables can serve as prognostic indicators for patients with LVADs. Low CPE and LVSWI are prognostic for higher mortality, but no variables were associated with HF admissions or HRAEs.

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In order to meet the application requirements of radar networks for high efficiency and high second harmonic suppression (SHS) of power amplifiers, this paper proposes a C-band 30 W power amplifier (PA) microwave monolithic integrated circuit (MMIC) based on 0.25 µm gallium nitride (GaN) high electron mobility transistor (HEMT) process. The proposed PA uses a two-stage amplifier structure to achieve high power gain. A topology with SHS is designed in the output-matching network. Besides, the large signal model load pull simulation and the harmonic control technology in the output stage are used to improve efficiency. The high-power additional efficiency (PAE) and high SHS of the PA MMIC are achieved simultaneously. In the 5-6 GHz frequency range, multiple indicator measurements of the proposed PA show that output power is over 45 dBm, the PAE is more than 57%, the SHS exceeds 45 dBc, the power gain is greater than 24 dB, which are conducted under the condition of 100 µs pulse width and 10% duty cycle. In addition, the size of the PA MMIC, including bonding pads, is 3.3 × 3.1 mm2.

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Entropy is one of the most fundamental notions for understanding complexity. Among all the methods to calculate the entropy, sample entropy (SampEn) is a practical and common method to estimate time-series complexity. Unfortunately, SampEn is a time-consuming method growing in quadratic times with the number of elements, which makes this method unviable when processing large data series. In this work, we evaluate hardware SampEn architectures to offload computation weight, using improved SampEn algorithms and exploiting reconfigurable technologies, such as field-programmable gate arrays (FPGAs), a reconfigurable technology well-known for its high performance and power efficiency. In addition to the fundamental disclosed straightforward SampEn (SF) calculation method, this study evaluates optimized strategies, such as bucket-assist (BA) SampEn and lightweight SampEn based on BubbleSort (BS-LW) and MergeSort (MS-LW) on an embedded CPU, a high-performance CPU and on an FPGA using simulated data and real-world electrocardiograms (ECG) as input data. Irregular storage space and memory access of enhanced algorithms is also studied and estimated in this work. These fast SampEn algorithms are evaluated and profiled using metrics such as execution time, resource use, power and energy consumption based on input data length. Finally, although the implementation of fast SampEn is not significantly faster than versions running on a high-performance CPU, FPGA implementations consume one or two orders of magnitude less energy than a high-performance CPU.

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As a potential air control measure, RF-based surveillance is one of the most commonly used unmanned aerial vehicles (UAV) surveillance methods that exploits specific emitter identification (SEI) technology to identify captured RF signal from ground controllers to UAVs. Recently many SEI algorithms based on deep convolution neural network (DCNN) have emerged. However, there is a lack of the implementation of specific hardware. This paper proposes a high-accuracy and power-efficient hardware accelerator using an algorithm-hardware co-design for UAV surveillance. For the algorithm, we propose a scalable SEI neural network with SNR-aware adaptive precision computation. With SNR awareness and precision reconfiguration, it can adaptively switch between DCNN and binary DCNN to cope with low SNR and high SNR tasks, respectively. In addition, a short-time Fourier transform (STFT) reusing DCNN method is proposed to pre-extract feature of UAV signal. For hardware, we designed a SNR sensing engine, denoising engine, and specialized DCNN engine with hybrid-precision convolution and memory access, aiming at SEI acceleration. Finally, we validate the effectiveness of our design on a FPGA, using a public UAV dataset. Compared with a state-of-the-art algorithm, our method can achieve the highest accuracy of 99.3% and an F1 score of 99.3%. Compared with other hardware designs, our accelerator can achieve the highest power efficiency of 40.12 Gops/W and 96.52 Gops/W with INT16 precision and binary precision.

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The cutting power consumption of milling has direct influence on the economic benefits of manufacturing particle boards. The influence of the milling parameters on the cutting power were investigated in this study. Experiments and data analyses were conducted based on the response surface methodology. The results show that the input parameters had significant effects on the cutting power. The high rake angle reduced the cutting force. Thus, the cutting power decreased with the increase in the rake angle and the cutting energy consumption was also reduced. The cutting power increased with the rotation speed of the main shaft and the depth of milling induced the impact resistance between the milling tool and particle board and the material removal rate. The p-values of the created models and input parameters were less than 0.05, which meant they were significant for cutting power and power efficiency. The depth of milling was the most important factor, followed by the rotation speed of the main shaft and then the rake angle. Due to the high values of R2 of 0.9926 and 0.9946, the quadratic models were chosen for creating the relationship between the input parameters and response parameters. The predicted values of cutting power and power efficiency were close to the actual values, which meant the models could perform good predictions. To minimize the cutting power and maximize the power efficiency for the particle board, the optimized parameters obtained via the response surface methodology were 2°, 6991.7 rpm, 1.36 mm for rake angle, rotation speed of the main shaft and depth of milling, respectively. The model further predicted that the optimized parameters combination would achieve cutting power and power efficiency values of 52.4 W and 11.9%, respectively, with the desirability of 0.732. In this study, the influence of the input parameters on the cutting power and power efficiency are revealed and the created models were useful for selecting the milling parameters for particle boards, to reduce the cutting power.

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Fe-based amorphous alloys have advantages of low iron loss and high effective permeability, which are widely used in sensors and actuators. Power efficiency is one of the most important indicators among power conversion applications. We compared the magnetomechancial power conversion factors of metallic glassy ribbons FeCoSiB (Vitrovac 7600) and FeSiB (Metglas 2605SA1). We investigated the crystallization process under different annealing temperatures and tested the magnetomechancial coupling factors (k) and quality factors (Q) by using resonant and anti-resonant methods. We found that the maximum coupling factor of the annealed Vitrovac ribbons was 23% and the figure of merits k2Q was 4-7; however, the maximum coupling factor of the annealed Metglas ribbons was 73% and the maximum value of k2Q was 16. We can observe that the Metglas 2605SA1 ribbons have higher values of the magnetomechanical power efficiency than those of the Vitrovac 7600 ribbons, which means they are better to be used in subsequent research regarding acoustically driven antennas.

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Power efficiency is becoming a critical aspect of IoT devices. In this paper, we present a compact object-detection coprocessor with multiple cores for multi-scale/type classification. This coprocessor is capable to process scalable block size for multi-shape detection-window and can be compatible with the frame-image sizes up to 2048 × 2048 for multi-scale classification. A memory-reuse strategy that requires only one dual-port SRAM for storing the feature-vector of one-row blocks is developed to save memory usage. Eventually, a prototype platform is implemented on the Intel DE4 development board with the Stratix IV device. The power consumption of each core in FPGA is only 80.98 mW.

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Exciplex-forming hosts with thermally activated delayed fluorescence (TADF) provide a viable opportunity to unlock the full potential of the yet-to-be improved power efficiencies (PEs) and stabilities of all-fluorescent white organic light-emitting diodes (WOLEDs), but this, however, is hindered by the lack of stable blue exciplexes. Here, an advanced exciplex system is proposed by incorporating bipolar charge-transport π-spacers into both the electron-donor (D) and the electron-accepter (A) to increase their distance for hypsochromic-shifted emission while maintaining the superior transporting ability. By using spirofluorene as the π-spacer, 3,3'-bicarbazole as the D-unit, and 2,4,6-triphenyl-1,3,5-triazine as the A-unit, a π-D and π-A exciplex with sky-blue emission and fast reverse intersystem crossing process is thereof constructed. Combining this exciplex-forming host, a blue TADF-sensitizer, and a yellow conventional fluorescent dopant in a single-emissive-layer, the fabricated warm-white-emissive device simultaneously exhibits a low driving voltage of 3.08 V, an external quantum efficiency of 21.4%, and a remarkable T80 (time to 80% of the initial luminance) of >8200 h at 1000 cd m-2 , accompanied by a new benchmark PE of 69.6 lm W-1 among all-fluorescent WOLEDs.

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Magnetoelectric (ME) power efficiency is a more important property than the ME voltage or the current coefficients for power conversion applications. This paper introduces an analytical model that describes the relation between the external magnetic field and the power efficiency in layered ME composites. It is a two-phase model. The first fragment establishes the expression between the magnetic field strength and the temperature increase within an operating period. It uses a magneto-elasto-electric equivalent circuit model that was developed by Dong et al. Following previous investigations; the main loss source is the mechanical power dissipation. The second fragment links the power efficiency and the temperature increase in a heat-balanced system. This method is generally used by researchers in the piezoelectric field. The analytical model and the experimental data shows that the decrease of the power efficiency in a laminated composite is between 5% and 10% for a power density of 10 W/in3 (0.61 W/cm3) to 30 W/in3 (1.83 W/cm3). The failure mechanism/process of ME composites under high power density can be estimated/monitored by the proposed method for ME composites in practical applications.

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Currently, exploring the applications of intermolecular donor-acceptor exciplex couple as host of OLEDs with phosphorescence, thermally activated delayed fluorescence (TADF) or fluorescence emitter as dopant is a hot topic. Compared to other host strategies, interfacial exciplex has the advantage in various aspects, such as barrier-free charge injection, unimpeded charge transport, and the energy-saving direct exciton formation process at the "Well"-like heterojunction interface region. Most importantly, due to a very fast and efficient reverse intersystem-crossing (RISC) process, such a host is capable of regulating singlet/triplet exciton populations in itself as well as in the dopant emitters both under photoluminescent (PL) and electroluminescent (EL) driving conditions. In this mini-review, we briefly summarize and comment on recent applications of this ideal host in OLEDs (including both thermal-evaporation OLEDs and solution-processed OLEDs) with diverse emitters, e.g., fluorescence, phosphorescence, delayed fluorescence, or others. Special attention is given to illustrate the peculiar achievement of high overall EL performance with superiorities of low driving voltages, slow roll-off rate, high power efficiencies and satisfied device lifetime using this host strategy, which is then concluded by personal perspectives on the relevant next-step in this field.

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Introduction: Intradural spinal cord stimulation (SCS) may offer significant therapeutic benefits for those with intractable axial and extremity pain, visceral pain, spasticity, autonomic dysfunction and related disorders. A novel intradural electrical stimulation device, limited by the boundaries of the thecal sac, CSF and spinal cord was developed to test this hypothesis. In order to optimize device function, we have explored finite element modeling (FEM). Methods: COMSOL®Multiphysics Electrical Currents was used to solve for fields and currents over a geometric model of a spinal cord segment. Cathodic and anodic currents are applied to the center and tips of the T-cross component of the electrode array to shape the stimulation field and constrain charge-balanced cathodic pulses to the target area. Results: Currents from the electrode sites can move the effective stimulation zone horizontally across the cord by a linear step method, which can be diversified considerably to gain greater depth of penetration relative to standard epidural SCS. It is also possible to prevent spread of the target area with no off-target action potential. Conclusion: Finite element modeling of a T-shaped intradural spinal cord stimulator predicts significant gains in field depth and current shaping that are beyond the reach of epidural stimulators. Future studies with in vivo models will investigate how this approach should first be tested in humans.