2) normalized min-sum algorithm 归一化最小和算法3) NLMS 归一化最小均方 1. Normalized least mean square(NLMS)adapting filtering technology has been applieread spectrum c mmunication system to lim t narrowband interferences in the situationd tosp o i of Gaun ises. 为了提高直扩通信系统在...
Normalized Min-Sum algorithmiterative decodingIn this letter an improvement is proposed for the Normalized Min-Sum (NMS) algorithm to decode LowDensity Parity Check codes. The new algorithm introduces an efficient adjustment for check node update in view of several common error conditions in decoding...
2) layered revised min-sum decoding algorithm 分层修正最小和译码算法 1. And a new layered revised min-sum decoding algorithm is proposed,then fixed-point simulation result shows that this algorithm can improve decoding throughout and reduce iteration. 提出了分层修正最小和译码算法并对该算法进行...
Recalling \widetilde{\varphi }_{\Phi ^{\widetilde{P}}_{m,n}}(t) =e^{-t^2/2}\big (1+\sum _{r=1}^{m-2} \widetilde{U}_{r,n}(it)\big ), we now have to show that R_n(t,z) and its derivatives are small for z\rightarrow 1.First...
The sum of the Euclidean distance between the uncorrected color and the standard color was 1296.345, whereas the sum between the corrected color and the standard color was 403.527. The maximum distances before and after correction were 163.68 and 44.69, respectively. The minimum distances before and...
imagesc() does (data-min)/(max-min) but your manual conversion does data/max
MPSNNReduceFeatureChannelsAndWeightsSum MPSNNReduceFeatureChannelsArgumentMax MPSNNReduceFeatureChannelsArgumentMin MPSNNReduceFeatureChannelsMax MPSNNReduceFeatureChannelsMean MPSNNReduceFeatureChannelsMin MPSNNReduceFeatureChannelsSum MPSNNReduceRowMax MPSNNReduceRowMean MPSNNReduceRowMin MPSNNReduceRowSum MPSNNRed...
The Schwinger–Dyson functional (1.4) can be written as Z SD[B] = exp(i WSD[B]) where i WSD[B] is given by the sum of the connected diagrams iWSD[B] = in n n! d4x1 ··· d4xn B(x1) ··· B(xn) Eφ(x1) ··· Eφ(xn) ei SI [φ] c0. (2.8) Note that the...
The ksdensity produces a Probability density function, no need to divide by the length of the x vector : 테마복사 x=randn(200,1); y=[min(x):0.1:max(x)]; p=ksdensity(x,y); sum(p) % plot(y,p) 댓글 수: 0 댓글을 달려면 로그인하십시...
clamp(min=1) ) normalized_prompt_logprobs = sum_logp / (pruned_lens - 1).clamp(min=1) return normalized_prompt_logprobs @staticmethod def get_top_logprobs(all_logprobs: torch.Tensor, logits_metadata: LogitsMetadata): max_k = max(logits_metadata.top_logprobs_nums) ret = all_logpr...