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1 s (CNNs), which may be embedded in dedicated hardware.
2 g of home cage mice using Raspberry Pi-based hardware.
3 scope using widely available and inexpensive hardware.
4 propriate bench-mark for comparing different hardware.
5 for memristive-based neuromorphic computing hardware.
6 cryptographic algorithms executed on trusted hardware.
7 ulate the network on specialized cytomorphic hardware.
8 stablished, each reaction class uses bespoke hardware.
9 need to come from software, algorithms, and hardware.
10 actuators with a sequence embedded in their hardware.
11 em outperforms all other currently available hardware.
12 capabilities of near-term quantum computing hardware.
13 ng complete immune system models on parallel hardware.
14 tions with present-day and near-term quantum hardware.
15 ing and do not efficiently utilize available hardware.
16 a and silicon integrated quantum computation hardware.
17 E) with CT angiography but require dedicated hardware.
18 oscope, this approach requires no additional hardware.
19 tire PDB in <10 min on modern, multithreaded hardware.
20 al-energy CT, without the need for dedicated hardware.
21 so allows the use of older and less powerful hardware.
22 need for pipelines and software on personal hardware.
23 ly point-and-click interface using commodity hardware.
24 no boards to provide affordable and flexible hardware.
25 ty without physical experimentation with the hardware.
26 t without the need for additional instrument hardware.
27 ing energy-efficient deep learning inference hardware.
28 nsitivities, costs or the use of specialized hardware.
29 ne-based eye tracking without any additional hardware.
30 pse data collected through expensive imaging hardware.
31 of arsenic quadrupolar nuclear spins as its hardware.
32 n to be overcome by running on more powerful hardware.
33 vates the development of novel computational hardware.
34 ols without a need for specialized computing hardware.
35 nd the current limitations of the commercial hardware.
36 IV services that often interact with systems hardware: (1) leadership, management, and governance pro
37 al fluorescence intensity values on graphics hardware, a crucial feature that allows graphics-process
39 o clients and the lack of native support for hardware-accelerated applications in the local browser u
40 e processor (HIP) is a new library providing hardware-accelerated image processing accessible from in
41 utation across system and video RAM allowing hardware-accelerated processing of arbitrarily large ima
43 ffort toward developing application-specific hardware across many different fields of engineering, su
48 mportantly, the algorithm reported herein is hardware agnostic and is capable of post-processing bina
50 the conventional approach without additional hardware and achieves a better sample coverage using the
51 instrument costs, and a lack of open-source hardware and acquisition software have limited smFRET's
57 s in emerging quantum information processing hardware and has expectation to address large and comple
58 uromorphic computing for both algorithms and hardware and highlight the fundamentals of learning and
59 n (AGAIN) method requires minimal additional hardware and is thus general and can be implemented in t
60 tegrated circuits that host both the quantum hardware and its control circuitry on the same chip, pro
63 the same time, rapid development of computer hardware and molecular simulation methodologies has made
64 edGerm system, which combines cost-effective hardware and open-source software for seed germination e
65 ffline, posing significant challenges to the hardware and preventing real-time analysis and feedback.
66 rity in recent years due to a combination of hardware and software advances, leading to the so-called
67 blocks of machine learning programs, but the hardware and software challenges are still considerable.
73 hlights significant advancements in detector hardware and software for multiwavelength analytical ult
76 computational side, ongoing developments of hardware and software have moved computational spectrosc
77 , further emphasizing the need for effective hardware and software interfaces for dual mass spectrome
79 idic colorimetric assay that can exploit the hardware and software on mobile devices, and circumvent
81 fic platforms," from the technology stack of hardware and software through the supporting ecosystem,
82 ECG from humans using commercially available hardware and software to facilitate adoption of this tec
88 ices after extraction of previously infected hardware and that the risk of a second infection is low,
89 atform - including flow cells, reagents, and hardware - and discovered limited performance loss when
90 rally not equipped with state-of-the-art MRI hardware, and are not suitable for demanding imaging tec
92 xternal experimentation and data acquisition hardware, and includes standard modules for implementing
93 The method does not require any additional hardware, and it can be deployed in typical high-through
95 excellent benchmarks for any type of quantum hardware, and show that our system outperforms all other
96 e believe that recent advances in standards, hardware, and software presented in this work manage to
97 tions, thus providing a potentially scalable hardware approach to the difficult problems of optimizat
101 We have adapted Arioc to recent multi-GPU hardware architectures that support high-bandwidth peer-
102 These illustrate how rapid advancements in hardware automation and machine learning continue to tra
103 These illustrate how rapid advancements in hardware automation and machine learning continue to tra
106 a field programmable gate array for strictly hardware-based computation and algorithm optimization.
108 models, simulation algorithms, and computing hardware, biomolecular simulations with advanced force f
109 is to realise artificial neural networks in hardware by implementing their synaptic weights using me
114 pensive, commercially available reagents and hardware commonly found in a routine pathology laborator
116 leteness', which relaxes the requirement for hardware completeness, and a corresponding system hierar
117 cation of such integration by offloading the hardware complexity into advanced signal processing tech
118 equire ~12 months from the start of ordering hardware components to acquiring high-quality biological
119 eing made to mimic neurons and synapses with hardware components, an approach known as neuromorphic c
121 chemical reaction networks and circuits into hardware configurations that can be used to simulate the
123 NanoJ-Fluidics is based on low-cost Lego hardware controlled by ImageJ-based software, making hig
124 ting, and deep learning on a hybrid software-hardware data management infrastructure, enabling real-t
125 sis, the large number of associated software/hardware dependencies, and the detailed expertise requir
127 communication with control systems, and its hardware design is customizable, enabling reduction in i
129 rovides an extensive set of slurry solvents, hardware designs, and a flowchart, a logical approach to
130 ly a single lens and output port, making the hardware development much simpler and cost-effective com
132 sed on a power-of-two encoding scheme and is hardware-dominant relying on only one classically optimi
133 ve to gating approaches requiring additional hardware (e.g., pressure-sensitive belt gating [BG]).
135 tesman, Kitaev and Preskill (GKP) proposed a hardware-efficient instance of such a non-local qubit: a
136 a harmonic oscillator-can take advantage of hardware-efficient quantum error correction protocols th
137 osed by the non-integration of health system hardware elements (eg, financing, guidelines, and commod
139 is area has been hindered due to the lack of hardware elements that can mimic neuronal/synaptic behav
140 Improved software, even within constrained hardware (especially in low-income and middle-income cou
141 le contraction, but these require customized hardware, expensive apparatus, and advanced informatics
142 try is substantially cheaper than commercial hardware filling the same role, and we anticipate, as an
143 s revived application-specific computational hardware for continuing speed and power improvements, fr
144 nd optimization of new intersectional tools, hardware for in vivo monitoring of expression dynamics,
149 n made in developing algorithms and physical hardware for quantum computing, heralding a revolution i
154 Free and Open Source scientific and medical Hardware (FOSH) as well as personal protective equipment
157 spects, covering documentation of instrument hardware functionality, data handling and software for d
158 differ from ground controls housed in flight hardware (GC), while thymus weights were 35% greater in
160 , and evolution of high-performance computer hardware has extended these simulations to very large mo
161 ecently, inherent limitations with available hardware has resulted in a modest operational figure of
162 gorithm for molecular simulations on quantum hardware, has a serious limitation in that it typically
163 oped cell lines and calcium release analysis hardware, has created a new and faster method for determ
164 s multiple NMR consoles without adding extra hardware; ii) acquires signals from multiple NMR channel
166 fects the compatibility between software and hardware, impairing the programming flexibility and deve
167 rately, of stochastic neurons, the efficient hardware implementation combining both functionalities i
168 complexity of TC neuron model restrains its hardware implementation in parallel structure, a cost ef
169 l routines, but has evolved to encompass the hardware implementation of algorithms with spike-based e
170 nalog switching performance is leveraged for hardware implementation of an SNN with the capability of
172 d probabilistic computational primitives for hardware implementation of statistical neural networks.
174 of-the-art memristors is a concern for their hardware implementation since trained weights must be ro
178 Furthermore, our approach relies on simple hardware implementations using off-the-shelf components.
181 grammable photonic processor, where a common hardware implemented by a two-dimensional photonic waveg
182 image recognition(5)-have not yet been fully hardware-implemented using memristor crossbars, which ar
186 development of massively parallel computing hardware including inexpensive graphics processing units
188 ditionally discuss any new software, the new hardware infrastructure, our webservices and web site.
190 Here, a new approach is described to embed hardware intelligence in soft robots where multiple actu
191 ers must pay close attention to the software-hardware interface to ensure that the three main safety
192 ntegrating precise measurement and actuation hardware into a single low-cost platform, Chi.Bio facili
197 ing particular quantum applications with the hardware itself will be paramount in successfully using
198 t integrates local unitary operations on its hardware level for the optimization of the readout proce
199 stems may be problematic because of sampling hardware limitations, and many relevant analytes (neutra
201 theoretical possibility, recent advances in hardware mean that quantum computing devices now exist t
203 omparable to literature reports, and because hardware modification was unnecessary, it was convenient
208 then be combined with a graph describing the hardware modules and compiled into platform-specific, lo
209 Because of its flexible design (software and hardware), more complex array scan patterns, only found
211 e feasibility of our hybrid synapse toward a hardware neural-network and also delivered high recognit
212 acteristics of artificial synapses prevent a hardware neural-network from delivering the same high-le
213 puting, which is typically performed using a hardware neural-network platform consisting of numerous
214 nic surface technology incorporated into the hardware of the column not only improved the mass spectr
216 -fold without any axial scanning, additional hardware or a trade-off of imaging resolution and speed.
219 able illumination without requiring invasive hardware or custom test objects; hence, it provides subs
224 ate how the low communication latency of our hardware platform can reduce image intensity and reconst
227 ifferent types of program on various typical hardware platforms, demonstrating the advantage of our s
230 p of the pipette and appropriate positioning hardware provided a route to recording micro- and nanosc
231 This opens up tantalizing prospects for hardware realization of a low-power brain-inspired compu
232 with active DBS systems and characterize the hardware-related artifacts on images from functional MRI
238 he use of BRET simultaneously simplifies the hardware required for sensing and offers improved detect
240 ingle fibre levels, experimental demands and hardware requirements increase, limiting biomechanics re
242 n a significant simplification in the system hardware, requiring only a single antenna to achieve DoA
245 ifetime, are of interest for applications in hardware secure technologies, temporary biomedical impla
249 at 0.55 T while maintaining high-performance hardware, shielded gradients (45 mT/m; 200 T/m/sec), and
250 A gap still exists, however, between the hardware size and reliability requirements of quantum co
251 the design, construction and control of MRI hardware so that the magnetic field is switched within 1
252 This blending of advanced visualization hardware, software development, and neuroanatomy data en
253 aneous control of them demands funds for new hardware, software, and licenses, in addition to very sk
256 ble a viable memristor-based non-von Neumann hardware solution for deep neural networks and edge comp
258 w microscopes with capabilities beyond their hardware specifications in terms of sensitivity and reso
261 e been shown useful, background reduction by hardware subtraction has not yet been explored for these
263 fficiency with a prototype of a neuromorphic hardware system and provide testable predictions on the
265 al domain, and demonstrate that even a small hardware system with only 88 memristors can already be u
267 ing such larger scale systems using existing hardware technologies and could pave the way towards an
268 demonstrated, paving the way for intelligent hardware technology with up-scaled memristive neural net
269 between isotopes was more likely mediated by hardware than by source scatter and was strongly depende
271 Here, we introduce new electrochemistry hardware that considerably suppresses nonfaradaic curren
272 This work can potentially pave the way for hardware that directly mimics the computational units of
273 paper demonstrates important improvements in hardware that has brought the technology out of the labo
275 ids the use of the complex and sophisticated hardware that is required for precise maintenance of tem
277 an emergence of research in machine learning hardware that strives to bring memory and computing clos
278 lent executable on any neuromorphic complete hardware-that is, it ensures programming-language portab
280 med in a way that is blind to the underlying hardware, thus allowing a comparison of identical quantu
281 and dynamic environments may require neural hardware to adapt to different computational tasks, each
282 ls to reduce false negatives and specialized hardware to enable real-time fluorescence detection.
283 ignment software to exploit high-concurrency hardware to generate short-read alignments at high speed
284 This approach could enable neuromorphic hardware to take full advantage of the rapid advances in
285 obscure the contribution of the neuromorphic hardware to the overall speech recognition performance.
287 hallenges the current limitations of robotic hardware toward future intelligent systems that replicat
288 tion, multiplexed analog outputs, and native hardware triggers into a single central hub provides a v
289 features automatically work on all supported hardware types (including both CPUs and GPUs) and perfor
290 ithout necessarily having to involve complex hardware upgrades or other printing parameter alteration
291 fore, the implementation of PPNs and SRNs in hardware using emerging nanoscale devices can greatly im
292 rmance, sensitivity, collimator penetration, hardware versus object scatter, spectral crosstalk, spat
293 onditional probability tables to the circuit hardware, we demonstrate that any two node Bayesian netw
295 ee dimensional (3D)-printed parts and common hardware, which is amenable to deployment with microflui
297 le to successfully operate even on commodity hardware with a system memory of just a few gigabytes.
298 nto our native gates and execute them on the hardware with average success rates of 78[Formula: see t
300 The polarimeter is released here as open hardware, with technical diagrams, a full parts list, an